Targeting the neurovascular unit for treatment of neurological disorders.

PubMed

Vangilder, Reyna L; Rosen, Charles L; Barr, Taura L; Huber, Jason D

2011-06-01

Drug discovery for CNS disorders has been restricted by the inability for therapeutic agents to cross the blood-brain barrier (BBB). Moreover, current drugs aim to correct neuron cell signaling, thereby neglecting pathophysiological changes affecting other cell types of the neurovascular unit (NVU). Components of the NVU (pericytes, microglia, astrocytes, and neurons, and basal lamina) act as an intricate network to maintain the neuronal homeostatic microenvironment. Consequently, disruptions to this intricate cell network lead to neuron malfunction and symptoms characteristic of CNS diseases. A lack of understanding in NVU signaling cascades may explain why current treatments for CNS diseases are not curative. Current therapies treat symptoms by maintaining neuron function. Refocusing drug discovery to sustain NVU function may provide a better method of treatment by promoting neuron survival. In this review, we will examine current therapeutics for common CNS diseases, describe the importance of the NVU in cerebral homeostasis and discuss new possible drug targets and technologies that aim to improve treatment and drug delivery to the diseased brain. Copyright © 2011 Elsevier Inc. All rights reserved.

Deep Brain Stimulation

PubMed Central

Lyketsos, Constantine G.; Pendergrass, Jo Cara; Lozano, Andres M.

2012-01-01

Recent studies have identified an association between memory deficits and defects of the integrated neuronal cortical areas known collectively as the default mode network. It is conceivable that the amyloid deposition or other molecular abnormalities seen in patients with Alzheimer’s disease may interfere with this network and disrupt neuronal circuits beyond the localized brain areas. Therefore, Alzheimer’s disease may be both a degenerative disease and a broader system-level disorder affecting integrated neuronal pathways involved in memory. In this paper, we describe the rationale and provide some evidence to support the study of deep brain stimulation of the hippocampal fornix as a novel treatment to improve neuronal circuitry within these integrated networks and thereby sustain memory function in early Alzheimer’s disease. PMID:23346514

Exposure to bisphenol A affects GABAergic neuron differentiation in neurosphere cultures.

PubMed

Fukushima, Nobuyuki; Nagao, Tetsuji

2018-06-13

Endocrine-disrupting chemicals (EDCs) influence not only endocrine functions but also neuronal development and functions. In-vivo studies have suggested the relationship of EDC-induced neurobehavioral disorders with dysfunctions of neurotransmitter mechanisms including γ-aminobutyric acid (GABA)ergic mechanisms. However, whether EDCs affect GABAergic neuron differentiation remains unclear. In the present study, we show that a representative EDC, bisphenol A (BPA), affects GABAergic neuron differentiation. Cortical neurospheres prepared from embryonic mice were exposed to BPA for 7 days, and then neuronal differentiation was induced. We found that BPA exposure resulted in a decrease in the ratio of GABAergic neurons to total neurons. However, the same exposure stimulated the differentiation of neurons expressing calbindin, a calcium-binding protein observed in a subpopulation of GABAergic neurons. These findings suggested that BPA might influence the formation of an inhibitory neuronal network in developing cerebral cortex involved in the occurrence of neurobehavioral disorders.

Multi-channels coupling-induced pattern transition in a tri-layer neuronal network

NASA Astrophysics Data System (ADS)

Wu, Fuqiang; Wang, Ya; Ma, Jun; Jin, Wuyin; Hobiny, Aatef

2018-03-01

Neurons in nerve system show complex electrical behaviors due to complex connection types and diversity in excitability. A tri-layer network is constructed to investigate the signal propagation and pattern formation by selecting different coupling channels between layers. Each layer is set as different states, and the local kinetics is described by Hindmarsh-Rose neuron model. By changing the number of coupling channels between layers and the state of the first layer, the collective behaviors of each layer and synchronization pattern of network are investigated. A statistical factor of synchronization on each layer is calculated. It is found that quiescent state in the second layer can be excited and disordered state in the third layer is suppressed when the first layer is controlled by a pacemaker, and the developed state is dependent on the number of coupling channels. Furthermore, the collapse in the first layer can cause breakdown of other layers in the network, and the mechanism is that disordered state in the third layer is enhanced when sampled signals from the collapsed layer can impose continuous disturbance on the next layer.

Recording axonal conduction to evaluate the integration of pluripotent cell-derived neurons into a neuronal network.

PubMed

Shimba, Kenta; Sakai, Koji; Takayama, Yuzo; Kotani, Kiyoshi; Jimbo, Yasuhiko

2015-10-01

Stem cell transplantation is a promising therapy to treat neurodegenerative disorders, and a number of in vitro models have been developed for studying interactions between grafted neurons and the host neuronal network to promote drug discovery. However, methods capable of evaluating the process by which stem cells integrate into the host neuronal network are lacking. In this study, we applied an axonal conduction-based analysis to a co-culture study of primary and differentiated neurons. Mouse cortical neurons and neuronal cells differentiated from P19 embryonal carcinoma cells, a model for early neural differentiation of pluripotent stem cells, were co-cultured in a microfabricated device. The somata of these cells were separated by the co-culture device, but their axons were able to elongate through microtunnels and then form synaptic contacts. Propagating action potentials were recorded from these axons by microelectrodes embedded at the bottom of the microtunnels and sorted into clusters representing individual axons. While the number of axons of cortical neurons increased until 14 days in vitro and then decreased, those of P19 neurons increased throughout the culture period. Network burst analysis showed that P19 neurons participated in approximately 80% of the bursting activity after 14 days in vitro. Interestingly, the axonal conduction delay of P19 neurons was significantly greater than that of cortical neurons, suggesting that there are some physiological differences in their axons. These results suggest that our method is feasible to evaluate the process by which stem cell-derived neurons integrate into a host neuronal network.

Ambra1 Shapes Hippocampal Inhibition/Excitation Balance: Role in Neurodevelopmental Disorders.

PubMed

Nobili, Annalisa; Krashia, Paraskevi; Cordella, Alberto; La Barbera, Livia; Dell'Acqua, Maria Concetta; Caruso, Angela; Pignataro, Annabella; Marino, Ramona; Sciarra, Francesca; Biamonte, Filippo; Scattoni, Maria Luisa; Ammassari-Teule, Martine; Cecconi, Francesco; Berretta, Nicola; Keller, Flavio; Mercuri, Nicola Biagio; D'Amelio, Marcello

2018-02-27

Imbalances between excitatory and inhibitory synaptic transmission cause brain network dysfunction and are central to the pathogenesis of neurodevelopmental disorders. Parvalbumin interneurons are highly implicated in this imbalance. Here, we probed the social behavior and hippocampal function of mice carrying a haploinsufficiency for Ambra1, a pro-autophagic gene crucial for brain development. We show that heterozygous Ambra1 mice (Ambra +/- ) are characterized by loss of hippocampal parvalbumin interneurons, decreases in the inhibition/excitation ratio, and altered social behaviors that are solely restricted to the female gender. Loss of parvalbumin interneurons in Ambra1 +/- females is further linked to reductions of the inhibitory drive onto principal neurons and alterations in network oscillatory activity, CA1 synaptic plasticity, and pyramidal neuron spine density. Parvalbumin interneuron loss is underlined by increased apoptosis during the embryonic development of progenitor neurons in the medial ganglionic eminence. Together, these findings identify an Ambra1-dependent mechanism that drives inhibition/excitation imbalance in the hippocampus, contributing to abnormal brain activity reminiscent of neurodevelopmental disorders.

Neuronal network models of epileptogenesis

PubMed Central

Abdullahi, Aminu T.; Adamu, Lawan H.

2017-01-01

Epilepsy is a chronic neurological condition, following some trigger, transforming a normal brain to one that produces recurrent unprovoked seizures. In the search for the mechanisms that best explain the epileptogenic process, there is a growing body of evidence suggesting that the epilepsies are network level disorders. In this review, we briefly describe the concept of neuronal networks and highlight 2 methods used to analyse such networks. The first method, graph theory, is used to describe general characteristics of a network to facilitate comparison between normal and abnormal networks. The second, dynamic causal modelling, is useful in the analysis of the pathways of seizure spread. We concluded that the end results of the epileptogenic process are best understood as abnormalities of neuronal circuitry and not simply as molecular or cellular abnormalities. The network approach promises to generate new understanding and more targeted treatment of epilepsy. PMID:28416779

Epigenetic Basis of Neuronal and Synaptic Plasticity.

PubMed

Karpova, Nina N; Sales, Amanda J; Joca, Samia R

2017-01-01

Neuronal network and plasticity change as a function of experience. Altered neural connectivity leads to distinct transcriptional programs of neuronal plasticity-related genes. The environmental challenges throughout life may promote long-lasting reprogramming of gene expression and the development of brain disorders. The modifications in neuronal epigenome mediate gene-environmental interactions and are required for activity-dependent regulation of neuronal differentiation, maturation and plasticity. Here, we highlight the latest advances in understanding the role of the main players of epigenetic machinery (DNA methylation and demethylation, histone modifications, chromatin-remodeling enzymes, transposons, and non-coding RNAs) in activity-dependent and long- term neural and synaptic plasticity. The review focuses on both the transcriptional and post-transcriptional regulation of gene expression levels, including the processes of promoter activation, alternative splicing, regulation of stability of gene transcripts by natural antisense RNAs, and alternative polyadenylation. Further, we discuss the epigenetic aspects of impaired neuronal plasticity and the pathogenesis of neurodevelopmental (Rett syndrome, Fragile X Syndrome, genomic imprinting disorders, schizophrenia, and others), stressrelated (mood disorders) and neurodegenerative Alzheimer's, Parkinson's and Huntington's disorders. The review also highlights the pharmacological compounds that modulate epigenetic programming of gene expression, the potential treatment strategies of discussed brain disorders, and the questions that should be addressed during the development of effective and safe approaches for the treatment of brain disorders.

Dynamical analysis of Parkinsonian state emulated by hybrid Izhikevich neuron models

NASA Astrophysics Data System (ADS)

Liu, Chen; Wang, Jiang; Yu, Haitao; Deng, Bin; Wei, Xile; Li, Huiyan; Loparo, Kenneth A.; Fietkiewicz, Chris

2015-11-01

Computational models play a significant role in exploring novel theories to complement the findings of physiological experiments. Various computational models have been developed to reveal the mechanisms underlying brain functions. Particularly, in the development of therapies to modulate behavioral and pathological abnormalities, computational models provide the basic foundations to exhibit transitions between physiological and pathological conditions. Considering the significant roles of the intrinsic properties of the globus pallidus and the coupling connections between neurons in determining the firing patterns and the dynamical activities of the basal ganglia neuronal network, we propose a hypothesis that pathological behaviors under the Parkinsonian state may originate from combined effects of intrinsic properties of globus pallidus neurons and synaptic conductances in the whole neuronal network. In order to establish a computational efficient network model, hybrid Izhikevich neuron model is used due to its capacity of capturing the dynamical characteristics of the biological neuronal activities. Detailed analysis of the individual Izhikevich neuron model can assist in understanding the roles of model parameters, which then facilitates the establishment of the basal ganglia-thalamic network model, and contributes to a further exploration of the underlying mechanisms of the Parkinsonian state. Simulation results show that the hybrid Izhikevich neuron model is capable of capturing many of the dynamical properties of the basal ganglia-thalamic neuronal network, such as variations of the firing rates and emergence of synchronous oscillations under the Parkinsonian condition, despite the simplicity of the two-dimensional neuronal model. It may suggest that the computational efficient hybrid Izhikevich neuron model can be used to explore basal ganglia normal and abnormal functions. Especially it provides an efficient way of emulating the large-scale neuron network and potentially contributes to development of improved therapy for neurological disorders such as Parkinson's disease.

Engineered 3D vascular and neuronal networks in a microfluidic platform.

PubMed

Osaki, Tatsuya; Sivathanu, Vivek; Kamm, Roger D

2018-03-26

Neurovascular coupling plays a key role in the pathogenesis of neurodegenerative disorders including motor neuron disease (MND). In vitro models provide an opportunity to understand the pathogenesis of MND, and offer the potential for drug screening. Here, we describe a new 3D microvascular and neuronal network model in a microfluidic platform to investigate interactions between these two systems. Both 3D networks were established by co-culturing human embryonic stem (ES)-derived MN spheroids and endothelial cells (ECs) in microfluidic devices. Co-culture with ECs improves neurite elongation and neuronal connectivity as measured by Ca 2+ oscillation. This improvement was regulated not only by paracrine signals such as brain-derived neurotrophic factor secreted by ECs but also through direct cell-cell interactions via the delta-notch pathway, promoting neuron differentiation and neuroprotection. Bi-directional signaling was observed in that the neural networks also affected vascular network formation under perfusion culture. This in vitro model could enable investigations of neuro-vascular coupling, essential to understanding the pathogenesis of neurodegenerative diseases including MNDs such as amyotrophic lateral sclerosis.

Novel transcriptional networks regulated by CLOCK in human neurons.

PubMed

Fontenot, Miles R; Berto, Stefano; Liu, Yuxiang; Werthmann, Gordon; Douglas, Connor; Usui, Noriyoshi; Gleason, Kelly; Tamminga, Carol A; Takahashi, Joseph S; Konopka, Genevieve

2017-11-01

The molecular mechanisms underlying human brain evolution are not fully understood; however, previous work suggested that expression of the transcription factor CLOCK in the human cortex might be relevant to human cognition and disease. In this study, we investigated this novel transcriptional role for CLOCK in human neurons by performing chromatin immunoprecipitation sequencing for endogenous CLOCK in adult neocortices and RNA sequencing following CLOCK knockdown in differentiated human neurons in vitro. These data suggested that CLOCK regulates the expression of genes involved in neuronal migration, and a functional assay showed that CLOCK knockdown increased neuronal migratory distance. Furthermore, dysregulation of CLOCK disrupts coexpressed networks of genes implicated in neuropsychiatric disorders, and the expression of these networks is driven by hub genes with human-specific patterns of expression. These data support a role for CLOCK-regulated transcriptional cascades involved in human brain evolution and function. © 2017 Fontenot et al.; Published by Cold Spring Harbor Laboratory Press.

Phenotypic Checkpoints Regulate Neuronal Development

PubMed Central

Ben-Ari, Yehezkel; Spitzer, Nicholas C.

2010-01-01

Nervous system development proceeds by sequential gene expression mediated by cascades of transcription factors in parallel with sequences of patterned network activity driven by receptors and ion channels. These sequences are cell type- and developmental stage-dependent and modulated by paracrine actions of substances released by neurons and glia. How and to what extent these sequences interact to enable neuronal network development is not understood. Recent evidence demonstrates that CNS development requires intermediate stages of differentiation providing functional feedback that influences gene expression. We suggest that embryonic neuronal functions constitute a series of phenotypic checkpoint signatures; neurons failing to express these functions are delayed or developmentally arrested. Such checkpoints are likely to be a general feature of neuronal development and may constitute presymptomatic signatures of neurological disorders when they go awry. PMID:20864191

Altered neuronal network and rescue in a human MECP2 duplication model

PubMed Central

Nageshappa, Savitha; Carromeu, Cassiano; Trujillo, Cleber A.; Mesci, Pinar; Espuny-Camacho, Ira; Pasciuto, Emanuela; Vanderhaeghen, Pierre; Verfaillie, Catherine; Raitano, Susanna; Kumar, Anujith; Carvalho, Claudia M.B.; Bagni, Claudia; Ramocki, Melissa B.; Araujo, Bruno H. S.; Torres, Laila B.; Lupski, James R.; Van Esch, Hilde; Muotri, Alysson R.

2015-01-01

Increased dosage of MeCP2 results in a dramatic neurodevelopmental phenotype with onset at birth. We generated induced pluripotent stem cells (iPSC) from patients with the MECP2 duplication syndrome (MECP2dup), carrying different duplication sizes, to study the impact of increased MeCP2 dosage in human neurons. We show that cortical neurons derived from these different MECP2dup iPSC lines have increase synaptogenesis and dendritic complexity. Additionally, using multi-electrodes arrays, we show that neuronal network synchronization was altered in MECP2dup-derived neurons. Given MeCP2 function at the epigenetic level, we tested if these alterations were reversible using a library of compounds with defined activity on epigenetic pathways. One histone deacetylase inhibitor, NCH-51, was validated as a potential clinical candidate. Interestingly, this compound has never been considered before as a therapeutic alternative for neurological disorders. Our model recapitulates early stages of the human MECP2 duplication syndrome and represents a promising cellular tool to facilitate therapeutic drug screening for severe neurodevelopmental disorders. PMID:26347316

The neuroendocrine genesis of polycystic ovary syndrome: A role for arcuate nucleus GABA neurons.

PubMed

Moore, Aleisha M; Campbell, Rebecca E

2016-06-01

Polycystic ovary syndrome (PCOS) is a prevalent and distressing endocrine disorder lacking a clearly identified aetiology. Despite its name, PCOS may result from impaired neuronal circuits in the brain that regulate steroid hormone feedback to the hypothalamo-pituitary-gonadal axis. Ovarian function in all mammals is controlled by the gonadotropin-releasing hormone (GnRH) neurons, a small group of neurons that reside in the pre-optic area of the hypothalamus. GnRH neurons drive the secretion of the gonadotropins from the pituitary gland that subsequently control ovarian function, including the production of gonadal steroid hormones. These hormones, in turn, provide important feedback signals to GnRH neurons via a hormone sensitive neuronal network in the brain. In many women with PCOS this feedback pathway is impaired, resulting in the downstream consequences of the syndrome. This review will explore what is currently known from clinical and animal studies about the identity, relative contribution and significance of the individual neuronal components within the GnRH neuronal network that contribute to the pathophysiology of PCOS. We review evidence for the specific neuronal pathways hypothesised to mediate progesterone negative feedback to GnRH neurons, and discuss the potential mechanisms by which androgens may evoke disruptions in these circuits at different developmental time points. Finally, this review discusses data providing compelling support for disordered progesterone-sensitive GABAergic input to GnRH neurons, originating specifically within the arcuate nucleus in prenatal androgen induced forms of PCOS. Copyright © 2015 Elsevier Ltd. All rights reserved.

Stressing out the Social Network.

PubMed

Kirkby, Lowry A; Sohal, Vikaas S

2016-07-20

In this issue of Neuron, Hultman et al. (2016) find that stress-induced abnormal social behavior reflects aberrant prefrontal regulation of downstream limbic networks. This illustrates how linking aberrant network dynamics to neuropsychiatric disorders may lead to new circuit-based therapeutic interventions. Copyright © 2016. Published by Elsevier Inc.

Neural Oscillations and Synchrony in Brain Dysfunction and Neuropsychiatric Disorders: It's About Time.

PubMed

Mathalon, Daniel H; Sohal, Vikaas S

2015-08-01

Neural oscillations are rhythmic fluctuations over time in the activity or excitability of single neurons, local neuronal populations or "assemblies," and/or multiple regionally distributed neuronal assemblies. Synchronized oscillations among large numbers of neurons are evident in electrocorticographic, electroencephalographic, magnetoencephalographic, and local field potential recordings and are generally understood to depend on inhibition that paces assemblies of excitatory neurons to produce alternating temporal windows of reduced and increased excitability. Synchronization of neural oscillations is supported by the extensive networks of local and long-range feedforward and feedback bidirectional connections between neurons. Here, we review some of the major methods and measures used to characterize neural oscillations, with a focus on gamma oscillations. Distinctions are drawn between stimulus-independent oscillations recorded during resting states or intervals between task events, stimulus-induced oscillations that are time locked but not phase locked to stimuli, and stimulus-evoked oscillations that are both time and phase locked to stimuli. Synchrony of oscillations between recording sites, and between the amplitudes and phases of oscillations of different frequencies (cross-frequency coupling), is described and illustrated. Molecular mechanisms underlying gamma oscillations are also reviewed. Ultimately, understanding the temporal organization of neuronal network activity, including interactions between neural oscillations, is critical for elucidating brain dysfunction in neuropsychiatric disorders.

From Neurons to Social Beings: Short Review of the Mirror Neuron System Research and Its Socio-Psychological and Psychiatric Implications

PubMed Central

Jeon, Hyeonjin

2018-01-01

The mirror neuron system (MNS) is a brain network activated when we move our body parts and when we observe the actions of other agent. Since the mirror neuron’s discovery in research on monkeys, several studies have examined its network and properties in both animals and humans. This review discusses MNS studies of animals and human MNS studies related to high-order social cognitions such as emotion and empathy, as well as relations between MNS dysfunction and mental disorders. Finally, these evidences are understood from an evolutionary perspective. PMID:29397663

Functional morphological imaging of autism spectrum disorders: current position and theories proposed.

PubMed

Lauvin, M-A; Martineau, J; Destrieux, C; Andersson, F; Bonnet-Brilhault, F; Gomot, M; El-Hage, W; Cottier, J-P

2012-03-01

Autism is a pervasive disorder of childhood development. Polymorphous clinical profiles combining various degrees of communication and social interaction with restricted and stereotyped behaviour are grouped under the heading of 'autism spectrum disorders' (ASD). Many teams are trying to pick out the underlying cerebral abnormalities in order to understand the neuronal networks involved in relationships with others. Here we review the morphological, spectroscopic and functional abnormalities in the amygdala-hippocampal circuit, the caudate nuclei, the cerebellum, and the frontotemporal regions, which have been described in subjects with ASD. White matter abnormalities have also been described in diffusion tensor imaging, leading to suspected damage to the subjacent neural networks, such as mirror neurones or the social brain. Copyright © 2012 Éditions Françaises de radiologie. Published by Elsevier Masson SAS. All rights reserved.

Synaptic Synthesis, Dephosphorylation, and Degradation

PubMed Central

La Montanara, Paolo; Rusconi, Laura; Locarno, Albina; Forti, Lia; Barbiero, Isabella; Tramarin, Marco; Chandola, Chetan; Kilstrup-Nielsen, Charlotte; Landsberger, Nicoletta

2015-01-01

Mutations in the X-linked CDKL5 (cyclin-dependent kinase-like 5) gene have been associated with several forms of neurodevelopmental disorders, including atypical Rett syndrome, autism spectrum disorders, and early infantile epileptic encephalopathy. Accordingly, loss of CDKL5 in mice results in autistic-like features and impaired neuronal communication. Although the biological functions of CDKL5 remain largely unknown, recent pieces of evidence suggest that CDKL5 is involved in neuronal plasticity. Herein, we show that, at all stages of development, neuronal depolarization induces a rapid increase in CDKL5 levels, mostly mediated by extrasomatic synthesis. In young neurons, this induction is prolonged, whereas in more mature neurons, NMDA receptor stimulation induces a protein phosphatase 1-dependent dephosphorylation of CDKL5 that is mandatory for its proteasome-dependent degradation. As a corollary, neuronal activity leads to a prolonged induction of CDKL5 levels in immature neurons but to a short lasting increase of the kinase in mature neurons. Recent results demonstrate that many genes associated with autism spectrum disorders are crucial components of the activity-dependent signaling networks regulating the composition, shape, and strength of the synapse. Thus, we speculate that CDKL5 deficiency disrupts activity-dependent signaling and the consequent synapse development, maturation, and refinement. PMID:25555910

Identification of Neurodegenerative Factors Using Translatome-Regulatory Network Analysis

PubMed Central

Brichta, Lars; Shin, William; Jackson-Lewis, Vernice; Blesa, Javier; Yap, Ee-Lynn; Walker, Zachary; Zhang, Jack; Roussarie, Jean-Pierre; Alvarez, Mariano J.; Califano, Andrea; Przedborski, Serge; Greengard, Paul

2016-01-01

For degenerative disorders of the central nervous system, the major obstacle to therapeutic advancement has been the challenge of identifying the key molecular mechanisms underlying neuronal loss. We developed a combinatorial approach including translational profiling and brain regulatory network analysis to search for key determinants of neuronal survival or death. Following the generation of transgenic mice for cell type-specific profiling of midbrain dopaminergic neurons, we established and compared translatome libraries reflecting the molecular signature of these cells at baseline or under degenerative stress. Analysis of these libraries by interrogating a context-specific brain regulatory network led to the identification of a repertoire of intrinsic upstream regulators that drive the dopaminergic stress response. The altered activity of these regulators was not associated with changes in their expression levels. This strategy can be generalized for the elucidation of novel molecular determinants involved in the degeneration of other classes of neurons. PMID:26214373

Combined exposure to simulated microgravity and acute or chronic radiation reduces neuronal network integrity and cell survival

NASA Astrophysics Data System (ADS)

Benotmane, Rafi

During orbital or interplanetary space flights, astronauts are exposed to cosmic radiations and microgravity. This study aimed at assessing the effect of these combined conditions on neuronal network density, cell morphology and survival, using well-connected mouse cortical neuron cultures. To this end, neurons were exposed to acute low and high doses of low LET (X-rays) radiation or to chronic low dose-rate of high LET neutron irradiation (Californium-252), under the simulated microgravity generated by the Random Positioning Machine (RPM, Dutch space). High content image analysis of cortical neurons positive for the neuronal marker βIII-tubulin unveiled a reduced neuronal network integrity and connectivity, and an altered cell morphology after exposure to acute/chronic radiation or to simulated microgravity. Additionally, in both conditions, a defect in DNA-repair efficiency was revealed by an increased number of γH2AX-positive foci, as well as an increased number of Annexin V-positive apoptotic neurons. Of interest, when combining both simulated space conditions, we noted a synergistic effect on neuronal network density, neuronal morphology, cell survival and DNA repair. Furthermore, these observations are in agreement with preliminary gene expression data, revealing modulations in cytoskeletal and apoptosis-related genes after exposure to simulated microgravity. In conclusion, the observed in vitro changes in neuronal network integrity and cell survival induced by space simulated conditions provide us with mechanistic understanding to evaluate health risks and the development of countermeasures to prevent neurological disorders in astronauts over long-term space travels. Acknowledgements: This work is supported partly by the EU-FP7 projects CEREBRAD (n° 295552)

Modifiers and mechanisms of multi-system polyglutamine neurodegenerative disorders: lessons from fly models.

PubMed

Mallik, Moushami; Lakhotia, Subhash C

2010-12-01

Polyglutamine (polyQ) diseases, resulting from a dynamic expansion of glutamine repeats in a polypeptide, are a class of genetically inherited late onset neurodegenerative disorders which, despite expression of the mutated gene widely in brain and other tissues, affect defined subpopulations of neurons in a disease-specific manner. We briefly review the different polyQ-expansion-induced neurodegenerative disorders and the advantages of modelling them in Drosophila. Studies using the fly models have successfully identified a variety of genetic modifiers and have helped in understanding some of the molecular events that follow expression of the abnormal polyQ proteins. Expression of the mutant polyQ proteins causes, as a consequence of intra-cellular and inter-cellular networking, mis-regulation at multiple steps like transcriptional and posttranscriptional regulations, cell signalling, protein quality control systems (protein folding and degradation networks), axonal transport machinery etc., in the sensitive neurons, resulting ultimately in their death. The diversity of genetic modifiers of polyQ toxicity identified through extensive genetic screens in fly and other models clearly reflects a complex network effect of the presence of the mutated protein. Such network effects pose a major challenge for therapeutic applications.

Information Transmission and Anderson Localization in two-dimensional networks of firing-rate neurons

NASA Astrophysics Data System (ADS)

Natale, Joseph; Hentschel, George

Firing-rate networks offer a coarse model of signal propagation in the brain. Here we analyze sparse, 2D planar firing-rate networks with no synapses beyond a certain cutoff distance. Additionally, we impose Dale's Principle to ensure that each neuron makes only or inhibitory outgoing connections. Using spectral methods, we find that the number of neurons participating in excitations of the network becomes insignificant whenever the connectivity cutoff is tuned to a value near or below the average interneuron separation. Further, neural activations exceeding a certain threshold stay confined to a small region of space. This behavior is an instance of Anderson localization, a disorder-induced phase transition by which an information channel is rendered unable to transmit signals. We discuss several potential implications of localization for both local and long-range computation in the brain. This work was supported in part by Grants JSMF/ 220020321 and NSF/IOS/1208126.

The thalamic reticular nucleus in schizophrenia and bipolar disorder: role of parvalbumin-expressing neuron networks and oxidative stress.

PubMed

Steullet, P; Cabungcal, J-H; Bukhari, S A; Ardelt, M I; Pantazopoulos, H; Hamati, F; Salt, T E; Cuenod, M; Do, Kim Q; Berretta, S

2017-11-28

Growing evidence points to a disruption of cortico-thalamo-cortical circuits in schizophrenia (SZ) and bipolar disorder (BD). Clues for a specific involvement of the thalamic reticular nucleus (TRN) come from its unique neuronal characteristics and neural connectivity, allowing it to shape the thalamo-cortical information flow. A direct involvement of the TRN in SZ and BD has not been tested thus far. We used a combination of human postmortem and rodent studies to test the hypothesis that neurons expressing parvalbumin (PV neurons), a main TRN neuronal population, and associated Wisteria floribunda agglutinin-labeled perineuronal nets (WFA/PNNs) are altered in SZ and BD, and that these changes may occur early in the course of the disease as a consequence of oxidative stress. In both disease groups, marked decreases of PV neurons (immunoreactive for PV) and WFA/PNNs were observed in the TRN, with no effects of duration of illness or age at onset. Similarly, in transgenic mice with redox dysregulation, numbers of PV neurons and WFA/PNN+PV neurons were decreased in transgenic compared with wild-type mice; these changes were present at postnatal day (P) 20 for PV neurons and P40 for WFA/PNN+PV neurons, accompanied by alterations of their firing properties. These results show profound abnormalities of PV neurons in the TRN of subjects with SZ and BD, and offer support for the hypothesis that oxidative stress may play a key role in impacting TRN PV neurons at early stages of these disorders. We put forth that these TRN abnormalities may contribute to disruptions of sleep spindles, focused attention and emotion processing in these disorders.Molecular Psychiatry advance online publication, 28 November 2017; doi:10.1038/mp.2017.230.

Regulation of Cerebral Cortical Size and Neuron Number by Fibroblast Growth Factors: Implications for Autism

ERIC Educational Resources Information Center

Vaccarino, Flora M.; Grigorenko, Elena L.; Smith, Karen Muller; Stevens, Hanna E.

2009-01-01

Increased brain size is common in children with autism spectrum disorders. Here we propose that an increased number of cortical excitatory neurons may underlie the increased brain volume, minicolumn pathology and excessive network excitability, leading to sensory hyper-reactivity and seizures, which are often found in autism. We suggest that…

The Intrinsic Electrophysiological Properties of Mammalian Neurons: Insights into Central Nervous System Function

NASA Astrophysics Data System (ADS)

Llinas, Rodolfo R.

1988-12-01

This article reviews the electroresponsive properties of single neurons in the mammalian central nervous system (CNS). In some of these cells the ionic conductances responsible for their excitability also endow them with autorhythmic electrical oscillatory properties. Chemical or electrical synaptic contacts between these neurons often result in network oscillations. In such networks, autorhytmic neurons may act as true oscillators (as pacemakers) or as resonators (responding preferentially to certain firing frequencies). Oscillations and resonance in the CNS are proposed to have diverse functional roles, such as (i) determining global functional states (for example, sleep-wakefulness or attention), (ii) timing in motor coordination, and (iii) specifying connectivity during development. Also, oscillation, especially in the thalamo-cortical circuits, may be related to certain neurological and psychiatric disorders. This review proposes that the autorhythmic electrical properties of central neurons and their connectivity form the basis for an intrinsic functional coordinate system that provides internal context to sensory input.

Exome sequencing links corticospinal motor neuron disease to common neurodegenerative disorders.

PubMed

Novarino, Gaia; Fenstermaker, Ali G; Zaki, Maha S; Hofree, Matan; Silhavy, Jennifer L; Heiberg, Andrew D; Abdellateef, Mostafa; Rosti, Basak; Scott, Eric; Mansour, Lobna; Masri, Amira; Kayserili, Hulya; Al-Aama, Jumana Y; Abdel-Salam, Ghada M H; Karminejad, Ariana; Kara, Majdi; Kara, Bulent; Bozorgmehri, Bita; Ben-Omran, Tawfeg; Mojahedi, Faezeh; El Din Mahmoud, Iman Gamal; Bouslam, Naima; Bouhouche, Ahmed; Benomar, Ali; Hanein, Sylvain; Raymond, Laure; Forlani, Sylvie; Mascaro, Massimo; Selim, Laila; Shehata, Nabil; Al-Allawi, Nasir; Bindu, P S; Azam, Matloob; Gunel, Murat; Caglayan, Ahmet; Bilguvar, Kaya; Tolun, Aslihan; Issa, Mahmoud Y; Schroth, Jana; Spencer, Emily G; Rosti, Rasim O; Akizu, Naiara; Vaux, Keith K; Johansen, Anide; Koh, Alice A; Megahed, Hisham; Durr, Alexandra; Brice, Alexis; Stevanin, Giovanni; Gabriel, Stacy B; Ideker, Trey; Gleeson, Joseph G

2014-01-31

Hereditary spastic paraplegias (HSPs) are neurodegenerative motor neuron diseases characterized by progressive age-dependent loss of corticospinal motor tract function. Although the genetic basis is partly understood, only a fraction of cases can receive a genetic diagnosis, and a global view of HSP is lacking. By using whole-exome sequencing in combination with network analysis, we identified 18 previously unknown putative HSP genes and validated nearly all of these genes functionally or genetically. The pathways highlighted by these mutations link HSP to cellular transport, nucleotide metabolism, and synapse and axon development. Network analysis revealed a host of further candidate genes, of which three were mutated in our cohort. Our analysis links HSP to other neurodegenerative disorders and can facilitate gene discovery and mechanistic understanding of disease.

Cholecystokinin: A multi-functional molecular switch of neuronal circuits

PubMed Central

Lee, Soo Yeun; Soltesz, Ivan

2010-01-01

Cholecystokinin (CCK), a peptide originally discovered in the gastrointestinal tract, is one of the most the abundant and widely distributed neuropeptides in the brain. In spite of its abundance, recent data indicate that that CCK modulates intrinsic neuronal excitability and synaptic transmission in a surprisingly cell-type specific manner, acting as a key molecular switch to regulate the functional output of neuronal circuits. The central importance of CCK in neuronal networks is also reflected in its involvement in a variety of neuropsychiatric and neurological disorders including panic attacks and epilepsy. PMID:21154912

Early Brain Damage and the Development of Motor Behavior in Children: Clues for Therapeutic Intervention?

PubMed Central

Hadders-Algra, Mijna

2001-01-01

The Neuronal Group Selection Theory (NGST) could offer new insights into the mechanisms directing motor disorders, such as cerebral palsy and developmental coordination disorder. According to NGST, normal motor development is characterized by two phases of variability. Variation is not at random but determined by criteria set by genetic information. Development starts with the phase of primary variability,during which variation in motor behavior is not geared to external conditions. At function-specific ages secondary variability starts, during which motor performance can be adapted to specific situations. In both forms, of variability, selection on the basis of afferent information plays a significant role. From the NGST point of view, children with pre- or perinatally acquired brain damage, such as children with cerebral palsy and part of the children with developmental coordination disorder, suffer from stereotyped motor behavior, produced by a limited repertoire or primary (sub)cortical neuronal networks. These children also have roblems in selecting the most efficient neuronal activity, due to deficits in the processing of sensory information. Therefore, NGST suggests that intervention in these children at early age should aim at an enlargement of the primary neuronal networks. With increasing age, the emphasis of intervention could shift to the provision of ample opportunities for active practice, which might form a compensation for the impaired selection. PMID:11530887

[Unconscious sexual desire: fMRI and EEG evidences from self-expansion theory to mirror neurons].

PubMed

Ortigue, Stephanie; Bianchi-Demicheli, Francesco

2010-03-24

Recent advances in cognitive-social neuroscience allow a better understanding of the mechanisms underlying dyadic relationships. From a neuronal viewpoint, desire in dyadic relationships involves a specific fronto-temporo-parietal network and also a subcortical network that mediates conscious and unconscious mechanisms of reward, satisfaction, attention, self representation and self-expansion. The integration of this neuroscientific knowledge on the unconscious neurobiological activation for sexual desire in the human brain will provide physicians with new therapeutical and neuroscientific tools to apprehend sexual disorders in couple.

Synaptic synthesis, dephosphorylation, and degradation: a novel paradigm for an activity-dependent neuronal control of CDKL5.

PubMed

La Montanara, Paolo; Rusconi, Laura; Locarno, Albina; Forti, Lia; Barbiero, Isabella; Tramarin, Marco; Chandola, Chetan; Kilstrup-Nielsen, Charlotte; Landsberger, Nicoletta

2015-02-13

Mutations in the X-linked CDKL5 (cyclin-dependent kinase-like 5) gene have been associated with several forms of neurodevelopmental disorders, including atypical Rett syndrome, autism spectrum disorders, and early infantile epileptic encephalopathy. Accordingly, loss of CDKL5 in mice results in autistic-like features and impaired neuronal communication. Although the biological functions of CDKL5 remain largely unknown, recent pieces of evidence suggest that CDKL5 is involved in neuronal plasticity. Herein, we show that, at all stages of development, neuronal depolarization induces a rapid increase in CDKL5 levels, mostly mediated by extrasomatic synthesis. In young neurons, this induction is prolonged, whereas in more mature neurons, NMDA receptor stimulation induces a protein phosphatase 1-dependent dephosphorylation of CDKL5 that is mandatory for its proteasome-dependent degradation. As a corollary, neuronal activity leads to a prolonged induction of CDKL5 levels in immature neurons but to a short lasting increase of the kinase in mature neurons. Recent results demonstrate that many genes associated with autism spectrum disorders are crucial components of the activity-dependent signaling networks regulating the composition, shape, and strength of the synapse. Thus, we speculate that CDKL5 deficiency disrupts activity-dependent signaling and the consequent synapse development, maturation, and refinement. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

A reevaluation of the vestibulo-ocular reflex: new ideas of its purpose, properties, neural substrate, and disorders

NASA Technical Reports Server (NTRS)

Leigh, R. J.; Brandt, T.

1993-01-01

Conventional views of the vestibulo-ocular reflex (VOR) have emphasized testing with caloric stimuli and by passively rotating patients at low frequencies in a chair. The properties of the VOR tested under these conditions differ from the performance of this reflex during the natural function for which it evolved--locomotion. Only the VOR (and not visually mediated eye movements) can cope with the high-frequency angular and linear perturbations of the head that occur during locomotion; this is achieved by generating eye movements at short latency (< 16 msec). Interpretation of vestibular testing is enhanced by the realization that, although the di- and trisynaptic components of the VOR are essential for this short-latency response, the overall accuracy and plasticity of the VOR depend upon a distributed, parallel network of neurons involving the vestibular nuclei. Neurons in this network variously upon a distributed, parallel network of neurons involving the vestibular nuclei. Neurons in this network variously encode inputs from the labyrinthine semicircular canals and otoliths, as well as from the visual and somatosensory systems. The central vestibular pathways branch to contact vestibular cortex (for perception) and the spinal cord (for control of posture). Thus, the vestibular nuclei basically coordinate the stabilization of gaze and posture, and contribute to the perception of verticality and self-motion. Consequently, brainstem disorders that disrupt the VOR cause not just only nystagmus, but also instability of posture (eg, increased fore-aft sway in patients with downbeat nystagmus) and disturbance of spatial orientation (eg, tilt of the subjective visual vertical in Wallenberg's syndrome).

Integrative transcriptome network analysis of iPSC-derived neurons from schizophrenia and schizoaffective disorder patients with 22q11.2 deletion.

PubMed

Lin, Mingyan; Pedrosa, Erika; Hrabovsky, Anastasia; Chen, Jian; Puliafito, Benjamin R; Gilbert, Stephanie R; Zheng, Deyou; Lachman, Herbert M

2016-11-15

Individuals with 22q11.2 Deletion Syndrome (22q11.2 DS) are a specific high-risk group for developing schizophrenia (SZ), schizoaffective disorder (SAD) and autism spectrum disorders (ASD). Several genes in the deleted region have been implicated in the development of SZ, e.g., PRODH and DGCR8. However, the mechanistic connection between these genes and the neuropsychiatric phenotype remains unclear. To elucidate the molecular consequences of 22q11.2 deletion in early neural development, we carried out RNA-seq analysis to investigate gene expression in early differentiating human neurons derived from induced pluripotent stem cells (iPSCs) of 22q11.2 DS SZ and SAD patients. Eight cases (ten iPSC-neuron samples in total including duplicate clones) and seven controls (nine in total including duplicate clones) were subjected to RNA sequencing. Using a systems level analysis, differentially expressed genes/gene-modules and pathway of interests were identified. Lastly, we related our findings from in vitro neuronal cultures to brain development by mapping differentially expressed genes to BrainSpan transcriptomes. We observed ~2-fold reduction in expression of almost all genes in the 22q11.2 region in SZ (37 genes reached p-value < 0.05, 36 of which reached a false discovery rate < 0.05). Outside of the deleted region, 745 genes showed significant differences in expression between SZ and control neurons (p < 0.05). Function enrichment and network analysis of the differentially expressed genes uncovered converging evidence on abnormal expression in key functional pathways, such as apoptosis, cell cycle and survival, and MAPK signaling in the SZ and SAD samples. By leveraging transcriptome profiles of normal human brain tissues across human development into adulthood, we showed that the differentially expressed genes converge on a sub-network mediated by CDC45 and the cell cycle, which would be disrupted by the 22q11.2 deletion during embryonic brain development, and another sub-network modulated by PRODH, which could contribute to disruption of brain function during adolescence. This study has provided evidence for disruption of potential molecular events in SZ patient with 22q11.2 deletion and related our findings from in vitro neuronal cultures to functional perturbations that can occur during brain development in SZ.

The role of ionotropic glutamate receptors in childhood neurodevelopmental disorders: autism spectrum disorders and fragile x syndrome.

PubMed

Uzunova, Genoveva; Hollander, Eric; Shepherd, Jason

2014-01-01

Autism spectrum disorder (ASD) and Fragile X syndrome (FXS) are relatively common childhood neurodevelopmental disorders with increasing incidence in recent years. They are currently accepted as disorders of the synapse with alterations in different forms of synaptic communication and neuronal network connectivity. The major excitatory neurotransmitter system in brain, the glutamatergic system, is implicated in learning and memory, synaptic plasticity, neuronal development. While much attention is attributed to the role of metabotropic glutamate receptors in ASD and FXS, studies indicate that the ionotropic glutamate receptors (iGluRs) and their regulatory proteins are also altered in several brain regions. Role of iGluRs in the neurobiology of ASD and FXS is supported by a weight of evidence that ranges from human genetics to in vitro cultured neurons. In this review we will discuss clinical, molecular, cellular and functional changes in NMDA, AMPA and kainate receptors and the synaptic proteins that regulate them in the context of ASD and FXS. We will also discuss the significance for the development of translational biomarkers and treatments for the core symptoms of ASD and FXS.

Neuronal network disintegration: common pathways linking neurodegenerative diseases.

PubMed

Ahmed, Rebekah M; Devenney, Emma M; Irish, Muireann; Ittner, Arne; Naismith, Sharon; Ittner, Lars M; Rohrer, Jonathan D; Halliday, Glenda M; Eisen, Andrew; Hodges, John R; Kiernan, Matthew C

2016-11-01

Neurodegeneration refers to a heterogeneous group of brain disorders that progressively evolve. It has been increasingly appreciated that many neurodegenerative conditions overlap at multiple levels and therefore traditional clinicopathological correlation approaches to better classify a disease have met with limited success. Neuronal network disintegration is fundamental to neurodegeneration, and concepts based around such a concept may better explain the overlap between their clinical and pathological phenotypes. In this Review, promoters of overlap in neurodegeneration incorporating behavioural, cognitive, metabolic, motor, and extrapyramidal presentations will be critically appraised. In addition, evidence that may support the existence of large-scale networks that might be contributing to phenotypic differentiation will be considered across a neurodegenerative spectrum. Disintegration of neuronal networks through different pathological processes, such as prion-like spread, may provide a better paradigm of disease and thereby facilitate the identification of novel therapies for neurodegeneration. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Neural networks with excitatory and inhibitory components: Direct and inverse problems by a mean-field approach

NASA Astrophysics Data System (ADS)

di Volo, Matteo; Burioni, Raffaella; Casartelli, Mario; Livi, Roberto; Vezzani, Alessandro

2016-01-01

We study the dynamics of networks with inhibitory and excitatory leak-integrate-and-fire neurons with short-term synaptic plasticity in the presence of depressive and facilitating mechanisms. The dynamics is analyzed by a heterogeneous mean-field approximation, which allows us to keep track of the effects of structural disorder in the network. We describe the complex behavior of different classes of excitatory and inhibitory components, which give rise to a rich dynamical phase diagram as a function of the fraction of inhibitory neurons. Using the same mean-field approach, we study and solve a global inverse problem: reconstructing the degree probability distributions of the inhibitory and excitatory components and the fraction of inhibitory neurons from the knowledge of the average synaptic activity field. This approach unveils new perspectives on the numerical study of neural network dynamics and the possibility of using these models as a test bed for the analysis of experimental data.

A Wnt1-regulated genetic network controls the identity and fate of midbrain-dopaminergic progenitors in vivo.

PubMed

Prakash, Nilima; Brodski, Claude; Naserke, Thorsten; Puelles, Eduardo; Gogoi, Robindra; Hall, Anita; Panhuysen, Markus; Echevarria, Diego; Sussel, Lori; Weisenhorn, Daniela M Vogt; Martinez, Salvador; Arenas, Ernest; Simeone, Antonio; Wurst, Wolfgang

2006-01-01

Midbrain neurons synthesizing the neurotransmitter dopamine play a central role in the modulation of different brain functions and are associated with major neurological and psychiatric disorders. Despite the importance of these cells, the molecular mechanisms controlling their development are still poorly understood. The secreted glycoprotein Wnt1 is expressed in close vicinity to developing midbrain dopaminergic neurons. Here, we show that Wnt1 regulates the genetic network, including Otx2 and Nkx2-2, that is required for the establishment of the midbrain dopaminergic progenitor domain during embryonic development. In addition, Wnt1 is required for the terminal differentiation of midbrain dopaminergic neurons at later stages of embryogenesis. These results identify Wnt1 as a key molecule in the development of midbrain dopaminergic neurons in vivo. They also suggest the Wnt1-controlled signaling pathway as a promising target for new therapeutic strategies in the treatment of Parkinson's disease.

D-serine signalling as a prominent determinant of neuronal-glial dialogue in the healthy and diseased brain.

PubMed

Billard, J-M

2008-10-01

Rather different from their initial image as passive supportive cells of the CNS, the astrocytes are now considered as active partners at synapses, able to release a set of gliotransmitter-like substances to modulate synaptic communication within neuronal networks. Whereas glutamate and ATP were first regarded as main determinants of gliotransmission, growing evidence indicates now that the amino acid D-serine is another important player in the neuronal-glial dialogue. Through the regulation of glutamatergic neurotransmission through both N-methyl-D-aspartate (NMDA-R) and non-NMDA-R, D-serine is helping in modelling the appropriate connections in the developing brain and influencing the functional plasticity within neuronal networks throughout lifespan. The understanding of D-serine signalling, which has increased linearly in the last few years, gives new insights into the critical role of impaired neuronal-glial communication in the diseased brain, and offers new opportunities for developing relevant strategies to treat cognitive deficits associated to brain disorders.

D-serine signalling as a prominent determinant of neuronal-glial dialogue in the healthy and diseased brain

PubMed Central

Billard, J-M

2008-01-01

Rather different from their initial image as passive supportive cells of the CNS, the astrocytes are now considered as active partners at synapses, able to release a set of gliotransmitter-like substances to modulate synaptic communication within neuronal networks. Whereas glutamate and ATP were first regarded as main determinants of gliotransmission, growing evidence indicates now that the amino acid D-serine is another important player in the neuronal-glial dialogue. Through the regulation of glutamatergic neurotransmission through both N-methyl-D-aspartate (NMDA-R) and non-NMDA-R, D-serine is helping in modelling the appropriate connections in the developing brain and influencing the functional plasticity within neuronal networks throughout lifespan. The understanding of D-serine signalling, which has increased linearly in the last few years, gives new insights into the critical role of impaired neuronal-glial communication in the diseased brain, and offers new opportunities for developing relevant strategies to treat cognitive deficits associated to brain disorders. PMID:18363840

New Insights on Temporal Lobe Epilepsy Based on Plasticity-Related Network Changes and High-Order Statistics.

PubMed

Kinjo, Erika Reime; Rodríguez, Pedro Xavier Royero; Dos Santos, Bianca Araújo; Higa, Guilherme Shigueto Vilar; Ferraz, Mariana Sacrini Ayres; Schmeltzer, Christian; Rüdiger, Sten; Kihara, Alexandre Hiroaki

2018-05-01

Epilepsy is a disorder of the brain characterized by the predisposition to generate recurrent unprovoked seizures, which involves reshaping of neuronal circuitries based on intense neuronal activity. In this review, we first detailed the regulation of plasticity-associated genes, such as ARC, GAP-43, PSD-95, synapsin, and synaptophysin. Indeed, reshaping of neuronal connectivity after the primary, acute epileptogenesis event increases the excitability of the temporal lobe. Herein, we also discussed the heterogeneity of neuronal populations regarding the number of synaptic connections, which in the theoretical field is commonly referred as degree. Employing integrate-and-fire neuronal model, we determined that in addition to increased synaptic strength, degree correlations might play essential and unsuspected roles in the control of network activity. Indeed, assortativity, which can be described as a condition where high-degree correlations are observed, increases the excitability of neural networks. In this review, we summarized recent topics in the field, and data were discussed according to newly developed or unusual tools, as provided by mathematical graph analysis and high-order statistics. With this, we were able to present new foundations for the pathological activity observed in temporal lobe epilepsy.

Hyperconnectivity and slow synapses during early development of medial prefrontal cortex in a mouse model for mental retardation and autism.

PubMed

Testa-Silva, Guilherme; Loebel, Alex; Giugliano, Michele; de Kock, Christiaan P J; Mansvelder, Huibert D; Meredith, Rhiannon M

2012-06-01

Neuronal theories of neurodevelopmental disorders (NDDs) of autism and mental retardation propose that abnormal connectivity underlies deficits in attentional processing. We tested this theory by studying unitary synaptic connections between layer 5 pyramidal neurons within medial prefrontal cortex (mPFC) networks in the Fmr1-KO mouse model for mental retardation and autism. In line with predictions from neurocognitive theory, we found that neighboring pyramidal neurons were hyperconnected during a critical period in early mPFC development. Surprisingly, excitatory synaptic connections between Fmr1-KO pyramidal neurons were significantly slower and failed to recover from short-term depression as quickly as wild type (WT) synapses. By 4-5 weeks of mPFC development, connectivity rates were identical for both KO and WT pyramidal neurons and synapse dynamics changed from depressing to facilitating responses with similar properties in both groups. We propose that the early alteration in connectivity and synaptic recovery are tightly linked: using a network model, we show that slower synapses are essential to counterbalance hyperconnectivity in order to maintain a dynamic range of excitatory activity. However, the slow synaptic time constants induce decreased responsiveness to low-frequency stimulation, which may explain deficits in integration and early information processing in attentional neuronal networks in NDDs.

Hyperconnectivity and Slow Synapses during Early Development of Medial Prefrontal Cortex in a Mouse Model for Mental Retardation and Autism

PubMed Central

Testa-Silva, Guilherme; Loebel, Alex; Giugliano, Michele; de Kock, Christiaan P.J.; Mansvelder, Huibert D.; Meredith, Rhiannon M.

2013-01-01

Neuronal theories of neurodevelopmental disorders (NDDs) of autism and mental retardation propose that abnormal connectivity underlies deficits in attentional processing. We tested this theory by studying unitary synaptic connections between layer 5 pyramidal neurons within medial prefrontal cortex (mPFC) networks in the Fmr1-KO mouse model for mental retardation and autism. In line with predictions from neurocognitive theory, we found that neighboring pyramidal neurons were hyperconnected during a critical period in early mPFC development. Surprisingly, excitatory synaptic connections between Fmr1-KO pyramidal neurons were significantly slower and failed to recover from short-term depression as quickly as wild type (WT) synapses. By 4--5 weeks of mPFC development, connectivity rates were identical for both KO and WT pyramidal neurons and synapse dynamics changed from depressing to facilitating responses with similar properties in both groups. We propose that the early alteration in connectivity and synaptic recovery are tightly linked: using a network model, we show that slower synapses are essential to counterbalance hyperconnectivity in order to maintain a dynamic range of excitatory activity. However, the slow synaptic time constants induce decreased responsiveness to low-frequency stimulation, which may explain deficits in integration and early information processing in attentional neuronal networks in NDDs. PMID:21856714

Rescue of deficient amygdala tonic γ-aminobutyric acidergic currents in the Fmr-/y mouse model of fragile X syndrome by a novel γ-aminobutyric acid type A receptor-positive allosteric modulator.

PubMed

Martin, Brandon S; Martinez-Botella, Gabriel; Loya, Carlos M; Salituro, Francesco G; Robichaud, Albert J; Huntsman, Molly M; Ackley, Mike A; Doherty, James J; Corbin, Joshua G

2016-06-01

Alterations in the ratio of excitatory to inhibitory transmission are emerging as a common component of many nervous system disorders, including autism spectrum disorders (ASDs). Tonic γ-aminobutyric acidergic (GABAergic) transmission provided by peri- and extrasynaptic GABA type A (GABAA ) receptors powerfully controls neuronal excitability and plasticity and, therefore, provides a rational therapeutic target for normalizing hyperexcitable networks across a variety of disorders, including ASDs. Our previous studies revealed tonic GABAergic deficits in principal excitatory neurons in the basolateral amygdala (BLA) in the Fmr1(-/y) knockout (KO) mouse model fragile X syndrome. To correct amygdala deficits in tonic GABAergic neurotransmission in Fmr1(-/y) KO mice, we developed a novel positive allosteric modulator of GABAA receptors, SGE-872, based on endogenously active neurosteroids. This study shows that SGE-872 is nearly as potent and twice as efficacious for positively modulating GABAA receptors as its parent molecule, allopregnanolone. Furthermore, at submicromolar concentrations (≤1 μM), SGE-872 is selective for tonic, extrasynaptic α4β3δ-containing GABAA receptors over typical synaptic α1β2γ2 receptors. We further find that SGE-872 strikingly rescues the tonic GABAergic transmission deficit in principal excitatory neurons in the Fmr1(-/y) KO BLA, a structure heavily implicated in the neuropathology of ASDs. Therefore, the potent and selective action of SGE-872 on tonic GABAA receptors containing α4 subunits may represent a novel and highly useful therapeutic avenue for ASDs and related disorders involving hyperexcitability of neuronal networks. © 2015 Wiley Periodicals, Inc.

5-HT in the enteric nervous system: gut function and neuropharmacology.

PubMed

McLean, Peter G; Borman, Richard A; Lee, Kevin

2007-01-01

In recent times, the perception of functional gastrointestinal disorders such as irritable bowel syndrome (IBS) has shifted fundamentally. Such disorders are now thought of as serious diseases characterized by perturbations in the neuronal regulation of gastrointestinal function. The concept of visceral hypersensitivity, the characterization of neuronal networks in the 'brain-gut axis' and the identification of several novel 5-HT-mediated mechanisms have contributed to this shift. Here, we review how some of the more promising of these new mechanisms (e.g. those involving 5-HT transporters and the 5-HT(2B), 5-HT(7) and putative 5-HT(1p) receptors) might lead to a range of second-generation therapies that could revolutionize the treatment of functional gastrointestinal disorders, particularly IBS.

Regulatory Mechanisms Controlling Maturation of Serotonin Neuron Identity and Function

PubMed Central

Spencer, William C.; Deneris, Evan S.

2017-01-01

The brain serotonin (5-hydroxytryptamine; 5-HT) system has been extensively studied for its role in normal physiology and behavior, as well as, neuropsychiatric disorders. The broad influence of 5-HT on brain function, is in part due to the vast connectivity pattern of 5-HT-producing neurons throughout the CNS. 5-HT neurons are born and terminally specified midway through embryogenesis, then enter a protracted period of maturation, where they functionally integrate into CNS circuitry and then are maintained throughout life. The transcriptional regulatory networks controlling progenitor cell generation and terminal specification of 5-HT neurons are relatively well-understood, yet the factors controlling 5-HT neuron maturation are only recently coming to light. In this review, we first provide an update on the regulatory network controlling 5-HT neuron development, then delve deeper into the properties and regulatory strategies governing 5-HT neuron maturation. In particular, we discuss the role of the 5-HT neuron terminal selector transcription factor (TF) Pet-1 as a key regulator of 5-HT neuron maturation. Pet-1 was originally shown to positively regulate genes needed for 5-HT synthesis, reuptake and vesicular transport, hence 5-HT neuron-type transmitter identity. It has now been shown to regulate, both positively and negatively, many other categories of genes in 5-HT neurons including ion channels, GPCRs, transporters, neuropeptides, and other transcription factors. Its function as a terminal selector results in the maturation of 5-HT neuron excitability, firing characteristics, and synaptic modulation by several neurotransmitters. Furthermore, there is a temporal requirement for Pet-1 in the control of postmitotic gene expression trajectories thus indicating a direct role in 5-HT neuron maturation. Proper regulation of the maturation of cellular identity is critical for normal neuronal functioning and perturbations in the gene regulatory networks controlling these processes may result in long-lasting changes in brain function in adulthood. Further study of 5-HT neuron gene regulatory networks is likely to provide additional insight into how neurons acquire their mature identities and how terminal selector-type TFs function in postmitotic vertebrate neurons. PMID:28769770

Regulatory Mechanisms Controlling Maturation of Serotonin Neuron Identity and Function.

PubMed

Spencer, William C; Deneris, Evan S

2017-01-01

The brain serotonin (5-hydroxytryptamine; 5-HT) system has been extensively studied for its role in normal physiology and behavior, as well as, neuropsychiatric disorders. The broad influence of 5-HT on brain function, is in part due to the vast connectivity pattern of 5-HT-producing neurons throughout the CNS. 5-HT neurons are born and terminally specified midway through embryogenesis, then enter a protracted period of maturation, where they functionally integrate into CNS circuitry and then are maintained throughout life. The transcriptional regulatory networks controlling progenitor cell generation and terminal specification of 5-HT neurons are relatively well-understood, yet the factors controlling 5-HT neuron maturation are only recently coming to light. In this review, we first provide an update on the regulatory network controlling 5-HT neuron development, then delve deeper into the properties and regulatory strategies governing 5-HT neuron maturation. In particular, we discuss the role of the 5-HT neuron terminal selector transcription factor (TF) Pet-1 as a key regulator of 5-HT neuron maturation. Pet-1 was originally shown to positively regulate genes needed for 5-HT synthesis, reuptake and vesicular transport, hence 5-HT neuron-type transmitter identity. It has now been shown to regulate, both positively and negatively, many other categories of genes in 5-HT neurons including ion channels, GPCRs, transporters, neuropeptides, and other transcription factors. Its function as a terminal selector results in the maturation of 5-HT neuron excitability, firing characteristics, and synaptic modulation by several neurotransmitters. Furthermore, there is a temporal requirement for Pet-1 in the control of postmitotic gene expression trajectories thus indicating a direct role in 5-HT neuron maturation. Proper regulation of the maturation of cellular identity is critical for normal neuronal functioning and perturbations in the gene regulatory networks controlling these processes may result in long-lasting changes in brain function in adulthood. Further study of 5-HT neuron gene regulatory networks is likely to provide additional insight into how neurons acquire their mature identities and how terminal selector-type TFs function in postmitotic vertebrate neurons.

Development and application of an optogenetic platform for controlling and imaging a large number of individual neurons

NASA Astrophysics Data System (ADS)

Mohammed, Ali Ibrahim Ali

The understanding and treatment of brain disorders as well as the development of intelligent machines is hampered by the lack of knowledge of how the brain fundamentally functions. Over the past century, we have learned much about how individual neurons and neural networks behave, however new tools are critically needed to interrogate how neural networks give rise to complex brain processes and disease conditions. Recent innovations in molecular techniques, such as optogenetics, have enabled neuroscientists unprecedented precision to excite, inhibit and record defined neurons. The impressive sensitivity of currently available optogenetic sensors and actuators has now enabled the possibility of analyzing a large number of individual neurons in the brains of behaving animals. To promote the use of these optogenetic tools, this thesis integrates cutting edge optogenetic molecular sensors which is ultrasensitive for imaging neuronal activity with custom wide field optical microscope to analyze a large number of individual neurons in living brains. Wide-field microscopy provides a large field of view and better spatial resolution approaching the Abbe diffraction limit of fluorescent microscope. To demonstrate the advantages of this optical platform, we imaged a deep brain structure, the Hippocampus, and tracked hundreds of neurons over time while mouse was performing a memory task to investigate how those individual neurons related to behavior. In addition, we tested our optical platform in investigating transient neural network changes upon mechanical perturbation related to blast injuries. In this experiment, all blasted mice show a consistent change in neural network. A small portion of neurons showed a sustained calcium increase for an extended period of time, whereas the majority lost their activities. Finally, using optogenetic silencer to control selective motor cortex neurons, we examined their contributions to the network pathology of basal ganglia related to Parkinson's disease. We found that inhibition of motor cortex does not alter exaggerated beta oscillations in the striatum that are associated with parkinsonianism. Together, these results demonstrate the potential of developing integrated optogenetic system to advance our understanding of the principles underlying neural network computation, which would have broad applications from advancing artificial intelligence to disease diagnosis and treatment.

[Neurobiological foundations underlying normal and disturbed sexuality].

PubMed

Krüger, T H C; Kneer, J

2017-05-01

Sexual functions are regulated by hormonal and neurochemical factors as well as neuronal networks. An understanding of these basic principles is necessary for the diagnostics, counselling and treatment of sexual problems. Description of essential mechanisms of sexual function on a neurochemical and neuronal level. Literature search, selection and discussion of relevant studies. Analogous to the dual control model there are primary inhibitory (e. g. serotonin) and excitatory neurotransmitter systems (e.g. sex steroids and dopamine). Moreover, neuronal structures have been identified that are responsible for processing sexual stimuli. These networks are altered in subjects with sexual disorders or by pharmacological treatment, e. g. antiandrogens and selective serotonin reuptake inhibitors (SSRI) CONCLUSION: Knowledge of the neurobiology of sexuality forms the foundations for the treatment of sexual dysfunctions in psychiatry and other disciplines.

The Network Model of Depression as a Basis for New Therapeutic Strategies for Treating Major Depressive Disorder in Parkinson’s Disease

PubMed Central

D’Ostilio, Kevin; Garraux, Gaëtan

2016-01-01

The high prevalence of major depressive disorder in people with Parkinson’s disease (PD), its negative impact on health-related quality of life and the low response rate to conventional pharmacological therapies call to seek innovative treatments. Here, we review the new approaches for treating major depressive disorder in patients with PD within the framework of the network model of depression. According to this model, major depressive disorder reflects maladaptive neuronal plasticity. Non-invasive brain stimulation (NIBS) using high frequency repetitive transcranial magnetic stimulation (rTMS) over the prefrontal cortex has been proposed as a feasible and effective strategy with minimal risk. The neurobiological basis of its therapeutic effect may involve neuroplastic modifications in limbic and cognitive networks. However, the way this networks reorganize might be strongly influenced by the environment. To address this issue, we propose a combined strategy that includes NIBS together with cognitive and behavioral interventions. PMID:27148016

Dynamic network communication as a unifying neural basis for cognition, development, aging, and disease.

PubMed

Voytek, Bradley; Knight, Robert T

2015-06-15

Perception, cognition, and social interaction depend upon coordinated neural activity. This coordination operates within noisy, overlapping, and distributed neural networks operating at multiple timescales. These networks are built upon a structural scaffolding with intrinsic neuroplasticity that changes with development, aging, disease, and personal experience. In this article, we begin from the perspective that successful interregional communication relies upon the transient synchronization between distinct low-frequency (<80 Hz) oscillations, allowing for brief windows of communication via phase-coordinated local neuronal spiking. From this, we construct a theoretical framework for dynamic network communication, arguing that these networks reflect a balance between oscillatory coupling and local population spiking activity and that these two levels of activity interact. We theorize that when oscillatory coupling is too strong, spike timing within the local neuronal population becomes too synchronous; when oscillatory coupling is too weak, spike timing is too disorganized. Each results in specific disruptions to neural communication. These alterations in communication dynamics may underlie cognitive changes associated with healthy development and aging, in addition to neurological and psychiatric disorders. A number of neurological and psychiatric disorders-including Parkinson's disease, autism, depression, schizophrenia, and anxiety-are associated with abnormalities in oscillatory activity. Although aging, psychiatric and neurological disease, and experience differ in the biological changes to structural gray or white matter, neurotransmission, and gene expression, our framework suggests that any resultant cognitive and behavioral changes in normal or disordered states or their treatment are a product of how these physical processes affect dynamic network communication. Copyright © 2015 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Pathophysiology of ADHD and associated problems—starting points for NF interventions?

PubMed Central

Albrecht, Björn; Uebel-von Sandersleben, Henrik; Gevensleben, Holger; Rothenberger, Aribert

2015-01-01

Attention deficit hyperactivity disorder (ADHD) is characterized by severe and age-inappropriate levels of hyperactivity, impulsivity and inattention. ADHD is a heterogeneous disorder, and the majority of patients show comorbid or associated problems from other psychiatric disorders. Also, ADHD is associated with cognitive and motivational problems as well as resting-state abnormalities, associated with impaired brain activity in distinct neuronal networks. This needs to be considered in a multimodal treatment, of which neurofeedback (NF) may be a promising component. During NF, specific brain activity is fed-back using visual or auditory signals, allowing the participants to gain control over these otherwise unaware neuronal processes. NF may be used to directly improve underlying neuronal deficits, and/or to establish more general self-regulatory skills that may be used to compensate behavioral difficulties. The current manuscript describes pathophysiological characteristics of ADHD, heterogeneity of ADHD subtypes and gender differences, as well as frequently associated behavioral problems such as oppositional defiant/conduct or tic disorder. It is discussed how NF may be helpful as a treatment approach within these contexts. PMID:26157377

Acoustic stimulation can induce a selective neural network response mediated by piezoelectric nanoparticles.

PubMed

Rojas, Camilo; Tedesco, Mariateresa; Massobrio, Paolo; Marino, Attilio; Ciofani, Gianni; Martinoia, Sergio; Raiteri, Roberto

2018-06-01

We aim to develop a novel non-invasive or minimally invasive method for neural stimulation to be applied in the study and treatment of brain (dys)functions and neurological disorders. We investigate the electrophysiological response of in vitro neuronal networks when subjected to low-intensity pulsed acoustic stimulation, mediated by piezoelectric nanoparticles adsorbed on the neuronal membrane. We show that the presence of piezoelectric barium titanate nanoparticles induces, in a reproducible way, an increase in network activity when excited by stationary ultrasound waves in the MHz regime. Such a response can be fully recovered when switching the ultrasound pulse off, depending on the generated pressure field amplitude, whilst it is insensitive to the duration of the ultrasound pulse in the range 0.5 s-1.5 s. We demonstrate that the presence of piezoelectric nanoparticles is necessary, and when applying the same acoustic stimulation to neuronal cultures without nanoparticles or with non-piezoelectric nanoparticles with the same size distribution, no network response is observed. We believe that our results open up an extremely interesting approach when coupled with suitable functionalization strategies of the nanoparticles in order to address specific neurons and/or brain areas and applied in vivo, thus enabling remote, non-invasive, and highly selective modulation of the activity of neuronal subpopulations of the central nervous system of mammalians.

Acoustic stimulation can induce a selective neural network response mediated by piezoelectric nanoparticles

NASA Astrophysics Data System (ADS)

Rojas, Camilo; Tedesco, Mariateresa; Massobrio, Paolo; Marino, Attilio; Ciofani, Gianni; Martinoia, Sergio; Raiteri, Roberto

2018-06-01

Objective. We aim to develop a novel non-invasive or minimally invasive method for neural stimulation to be applied in the study and treatment of brain (dys)functions and neurological disorders. Approach. We investigate the electrophysiological response of in vitro neuronal networks when subjected to low-intensity pulsed acoustic stimulation, mediated by piezoelectric nanoparticles adsorbed on the neuronal membrane. Main results. We show that the presence of piezoelectric barium titanate nanoparticles induces, in a reproducible way, an increase in network activity when excited by stationary ultrasound waves in the MHz regime. Such a response can be fully recovered when switching the ultrasound pulse off, depending on the generated pressure field amplitude, whilst it is insensitive to the duration of the ultrasound pulse in the range 0.5 s–1.5 s. We demonstrate that the presence of piezoelectric nanoparticles is necessary, and when applying the same acoustic stimulation to neuronal cultures without nanoparticles or with non-piezoelectric nanoparticles with the same size distribution, no network response is observed. Significance. We believe that our results open up an extremely interesting approach when coupled with suitable functionalization strategies of the nanoparticles in order to address specific neurons and/or brain areas and applied in vivo, thus enabling remote, non-invasive, and highly selective modulation of the activity of neuronal subpopulations of the central nervous system of mammalians.

The Role of Ionotropic Glutamate Receptors in Childhood Neurodevelopmental Disorders: Autism Spectrum Disorders and Fragile X Syndrome

PubMed Central

Uzunova, Genoveva; Hollander, Eric; Shepherd, Jason

2014-01-01

Autism spectrum disorder (ASD) and Fragile X syndrome (FXS) are relatively common childhood neurodevelopmental disorders with increasing incidence in recent years. They are currently accepted as disorders of the synapse with alterations in different forms of synaptic communication and neuronal network connectivity. The major excitatory neurotransmitter system in brain, the glutamatergic system, is implicated in learning and memory, synaptic plasticity, neuronal development. While much attention is attributed to the role of metabotropic glutamate receptors in ASD and FXS, studies indicate that the ionotropic glutamate receptors (iGluRs) and their regulatory proteins are also altered in several brain regions. Role of iGluRs in the neurobiology of ASD and FXS is supported by a weight of evidence that ranges from human genetics to in vitro cultured neurons. In this review we will discuss clinical, molecular, cellular and functional changes in NMDA, AMPA and kainate receptors and the synaptic proteins that regulate them in the context of ASD and FXS. We will also discuss the significance for the development of translational biomarkers and treatments for the core symptoms of ASD and FXS. PMID:24533017

Modeling the functional genomics of autism using human neurons.

PubMed

Konopka, G; Wexler, E; Rosen, E; Mukamel, Z; Osborn, G E; Chen, L; Lu, D; Gao, F; Gao, K; Lowe, J K; Geschwind, D H

2012-02-01

Human neural progenitors from a variety of sources present new opportunities to model aspects of human neuropsychiatric disease in vitro. Such in vitro models provide the advantages of a human genetic background combined with rapid and easy manipulation, making them highly useful adjuncts to animal models. Here, we examined whether a human neuronal culture system could be utilized to assess the transcriptional program involved in human neural differentiation and to model some of the molecular features of a neurodevelopmental disorder, such as autism. Primary normal human neuronal progenitors (NHNPs) were differentiated into a post-mitotic neuronal state through addition of specific growth factors and whole-genome gene expression was examined throughout a time course of neuronal differentiation. After 4 weeks of differentiation, a significant number of genes associated with autism spectrum disorders (ASDs) are either induced or repressed. This includes the ASD susceptibility gene neurexin 1, which showed a distinct pattern from neurexin 3 in vitro, and which we validated in vivo in fetal human brain. Using weighted gene co-expression network analysis, we visualized the network structure of transcriptional regulation, demonstrating via this unbiased analysis that a significant number of ASD candidate genes are coordinately regulated during the differentiation process. As NHNPs are genetically tractable and manipulable, they can be used to study both the effects of mutations in multiple ASD candidate genes on neuronal differentiation and gene expression in combination with the effects of potential therapeutic molecules. These data also provide a step towards better understanding of the signaling pathways disrupted in ASD.

Inhibition-Based Biomarkers for Autism Spectrum Disorder.

PubMed

Levin, April R; Nelson, Charles A

2015-07-01

Autism spectrum disorder (ASD) is a behaviorally defined and heterogeneous disorder. Biomarkers for ASD offer the opportunity to improve prediction, diagnosis, stratification by severity and subtype, monitoring over time and in response to interventions, and overall understanding of the underlying biology of this disorder. A variety of potential biomarkers, from the level of genes and proteins to network-level interactions, is currently being examined. Many of these biomarkers relate to inhibition, which is of particular interest because in many cases ASD is thought to be a disorder of imbalance between excitation and inhibition. Abnormalities in inhibition at the cellular level lead to emergent properties in networks of neurons. These properties take into account a more complete genetic and cellular background than findings at the level of individual genes or cells, and are able to be measured in live humans, offering additional potential as diagnostic biomarkers and predictors of behaviors. In this review we provide examples of how altered inhibition may inform the search for ASD biomarkers at multiple levels, from genes to cells to networks.

Functional neurological symptom disorder (conversion disorder): A role for microglial-based plasticity mechanisms?

PubMed

Stephenson, Chris P; Baguley, Ian J

2018-02-01

Functional Neurological Symptom Disorder (FND) is a relatively common neurological condition, accounting for approximately 3-6% of neurologist referrals. FND is considered a transient disorder of neuronal function, sometimes linked to physical trauma and psychological stress. Despite this, chronic disability is common, for example, around 40% of adults with motor FND have permanent disability. Building on current theoretical models, this paper proposes that microglial dysfunction could perpetuate functional changes within acute motor FND, thus providing a pathophysiological mechanism underlying the chronic stage of the motor FND phenotypes seen clinically. Core to our argument is microglia's dual role in modulating neuroimmunity and their control of synaptic plasticity, which places them at a pathophysiological nexus wherein coincident physical trauma and psychological stress could cause long-term change in neuronal networks without producing macroscopic structural abnormality. This model proposes a range of hypotheses that are testable with current technologies. Copyright © 2017. Published by Elsevier Ltd.

Brain mechanisms for perceptual and reward-related decision-making.

PubMed

Deco, Gustavo; Rolls, Edmund T; Albantakis, Larissa; Romo, Ranulfo

2013-04-01

Phenomenological models of decision-making, including the drift-diffusion and race models, are compared with mechanistic, biologically plausible models, such as integrate-and-fire attractor neuronal network models. The attractor network models show how decision confidence is an emergent property; and make testable predictions about the neural processes (including neuronal activity and fMRI signals) involved in decision-making which indicate that the medial prefrontal cortex is involved in reward value-based decision-making. Synaptic facilitation in these models can help to account for sequential vibrotactile decision-making, and for how postponed decision-related responses are made. The randomness in the neuronal spiking-related noise that makes the decision-making probabilistic is shown to be increased by the graded firing rate representations found in the brain, to be decreased by the diluted connectivity, and still to be significant in biologically large networks with thousands of synapses onto each neuron. The stability of these systems is shown to be influenced in different ways by glutamatergic and GABAergic efficacy, leading to a new field of dynamical neuropsychiatry with applications to understanding schizophrenia and obsessive-compulsive disorder. The noise in these systems is shown to be advantageous, and to apply to similar attractor networks involved in short-term memory, long-term memory, attention, and associative thought processes. Copyright © 2012 Elsevier Ltd. All rights reserved.

Atypical cross talk between mentalizing and mirror neuron networks in autism spectrum disorder.

PubMed

Fishman, Inna; Keown, Christopher L; Lincoln, Alan J; Pineda, Jaime A; Müller, Ralph-Axel

2014-07-01

Converging evidence indicates that brain abnormalities in autism spectrum disorder (ASD) involve atypical network connectivity, but it is unclear whether altered connectivity is especially prominent in brain networks that participate in social cognition. To investigate whether adolescents with ASD show altered functional connectivity in 2 brain networks putatively impaired in ASD and involved in social processing, theory of mind (ToM) and mirror neuron system (MNS). Cross-sectional study using resting-state functional magnetic resonance imaging involving 25 adolescents with ASD between the ages of 11 and 18 years and 25 typically developing adolescents matched for age, handedness, and nonverbal IQ. Statistical parametric maps testing the degree of whole-brain functional connectivity and social functioning measures. Relative to typically developing controls, participants with ASD showed a mixed pattern of both over- and underconnectivity in the ToM network, which was associated with greater social impairment. Increased connectivity in the ASD group was detected primarily between the regions of the MNS and ToM, and was correlated with sociocommunicative measures, suggesting that excessive ToM-MNS cross talk might be associated with social impairment. In a secondary analysis comparing a subset of the 15 participants with ASD with the most severe symptomology and a tightly matched subset of 15 typically developing controls, participants with ASD showed exclusive overconnectivity effects in both ToM and MNS networks, which were also associated with greater social dysfunction. Adolescents with ASD showed atypically increased functional connectivity involving the mentalizing and mirror neuron systems, largely reflecting greater cross talk between the 2. This finding is consistent with emerging evidence of reduced network segregation in ASD and challenges the prevailing theory of general long-distance underconnectivity in ASD. This excess ToM-MNS connectivity may reflect immature or aberrant developmental processes in 2 brain networks involved in understanding of others, a domain of impairment in ASD. Further, robust links with sociocommunicative symptoms of ASD implicate atypically increased ToM-MNS connectivity in social deficits observed in ASD.

A Brain Centred View of Psychiatric Comorbidity in Tinnitus: From Otology to Hodology

PubMed Central

Minichino, Amedeo; Panico, Roberta; Testugini, Valeria; Altissimi, Giancarlo; Cianfrone, Giancarlo

2014-01-01

Introduction. Comorbid psychiatric disorders are frequent among patients affected by tinnitus. There are mutual clinical influences between tinnitus and psychiatric disorders, as well as neurobiological relations based on partially overlapping hodological and neuroplastic phenomena. The aim of the present paper is to review the evidence of alterations in brain networks underlying tinnitus physiopathology and to discuss them in light of the current knowledge of the neurobiology of psychiatric disorders. Methods. Relevant literature was identified through a search on Medline and PubMed; search terms included tinnitus, brain, plasticity, cortex, network, and pathways. Results. Tinnitus phenomenon results from systemic-neurootological triggers followed by neuronal remapping within several auditory and nonauditory pathways. Plastic reorganization and white matter alterations within limbic system, arcuate fasciculus, insula, salience network, dorsolateral prefrontal cortex, auditory pathways, ffrontocortical, and thalamocortical networks are discussed. Discussion. Several overlapping brain network alterations do exist between tinnitus and psychiatric disorders. Tinnitus, initially related to a clinicoanatomical approach based on a cortical localizationism, could be better explained by an holistic or associationist approach considering psychic functions and tinnitus as emergent properties of partially overlapping large-scale neural networks. PMID:25018882

Noise focusing in neuronal tissues: Symmetry breaking and localization in excitable networks with quenched disorder

NASA Astrophysics Data System (ADS)

Orlandi, Javier G.; Casademunt, Jaume

2017-05-01

We introduce a coarse-grained stochastic model for the spontaneous activity of neuronal cultures to explain the phenomenon of noise focusing, which entails localization of the noise activity in excitable networks with metric correlations. The system is modeled as a continuum excitable medium with a state-dependent spatial coupling that accounts for the dynamics of synaptic connections. The most salient feature is the emergence at the mesoscale of a vector field V (r ) , which acts as an advective carrier of the noise. This entails an explicit symmetry breaking of isotropy and homogeneity that stems from the amplification of the quenched fluctuations of the network by the activity avalanches, concomitant with the excitable dynamics. We discuss the microscopic interpretation of V (r ) and propose an explicit construction of it. The coarse-grained model shows excellent agreement with simulations at the network level. The generic nature of the observed phenomena is discussed.

Biological modelling of a computational spiking neural network with neuronal avalanches.

PubMed

Li, Xiumin; Chen, Qing; Xue, Fangzheng

2017-06-28

In recent years, an increasing number of studies have demonstrated that networks in the brain can self-organize into a critical state where dynamics exhibit a mixture of ordered and disordered patterns. This critical branching phenomenon is termed neuronal avalanches. It has been hypothesized that the homeostatic level balanced between stability and plasticity of this critical state may be the optimal state for performing diverse neural computational tasks. However, the critical region for high performance is narrow and sensitive for spiking neural networks (SNNs). In this paper, we investigated the role of the critical state in neural computations based on liquid-state machines, a biologically plausible computational neural network model for real-time computing. The computational performance of an SNN when operating at the critical state and, in particular, with spike-timing-dependent plasticity for updating synaptic weights is investigated. The network is found to show the best computational performance when it is subjected to critical dynamic states. Moreover, the active-neuron-dominant structure refined from synaptic learning can remarkably enhance the robustness of the critical state and further improve computational accuracy. These results may have important implications in the modelling of spiking neural networks with optimal computational performance.This article is part of the themed issue 'Mathematical methods in medicine: neuroscience, cardiology and pathology'. © 2017 The Author(s).

Biological modelling of a computational spiking neural network with neuronal avalanches

NASA Astrophysics Data System (ADS)

Li, Xiumin; Chen, Qing; Xue, Fangzheng

2017-05-01

In recent years, an increasing number of studies have demonstrated that networks in the brain can self-organize into a critical state where dynamics exhibit a mixture of ordered and disordered patterns. This critical branching phenomenon is termed neuronal avalanches. It has been hypothesized that the homeostatic level balanced between stability and plasticity of this critical state may be the optimal state for performing diverse neural computational tasks. However, the critical region for high performance is narrow and sensitive for spiking neural networks (SNNs). In this paper, we investigated the role of the critical state in neural computations based on liquid-state machines, a biologically plausible computational neural network model for real-time computing. The computational performance of an SNN when operating at the critical state and, in particular, with spike-timing-dependent plasticity for updating synaptic weights is investigated. The network is found to show the best computational performance when it is subjected to critical dynamic states. Moreover, the active-neuron-dominant structure refined from synaptic learning can remarkably enhance the robustness of the critical state and further improve computational accuracy. These results may have important implications in the modelling of spiking neural networks with optimal computational performance. This article is part of the themed issue `Mathematical methods in medicine: neuroscience, cardiology and pathology'.

Common therapeutic mechanisms of pallidal deep brain stimulation for hypo- and hyperkinetic movement disorders

PubMed Central

Iriki, Atsushi; Isoda, Masaki

2015-01-01

Abnormalities in cortico-basal ganglia (CBG) networks can cause a variety of movement disorders ranging from hypokinetic disorders, such as Parkinson's disease (PD), to hyperkinetic conditions, such as Tourette syndrome (TS). Each condition is characterized by distinct patterns of abnormal neural discharge (dysrhythmia) at both the local single-neuron level and the global network level. Despite divergent etiologies, behavioral phenotypes, and neurophysiological profiles, high-frequency deep brain stimulation (HF-DBS) in the basal ganglia has been shown to be effective for both hypo- and hyperkinetic disorders. The aim of this review is to compare and contrast the electrophysiological hallmarks of PD and TS phenotypes in nonhuman primates and discuss why the same treatment (HF-DBS targeted to the globus pallidus internus, GPi-DBS) is capable of ameliorating both symptom profiles. Recent studies have shown that therapeutic GPi-DBS entrains the spiking of neurons located in the vicinity of the stimulating electrode, resulting in strong stimulus-locked modulations in firing probability with minimal changes in the population-scale firing rate. This stimulus effect normalizes/suppresses the pathological firing patterns and dysrhythmia that underlie specific phenotypes in both the PD and TS models. We propose that the elimination of pathological states via stimulus-driven entrainment and suppression, while maintaining thalamocortical network excitability within a normal physiological range, provides a common therapeutic mechanism through which HF-DBS permits information transfer for purposive motor behavior through the CBG while ameliorating conditions with widely different symptom profiles. PMID:26180116

Relationship between neuronal network architecture and naming performance in temporal lobe epilepsy: A connectome based approach using machine learning.

PubMed

Munsell, B C; Wu, G; Fridriksson, J; Thayer, K; Mofrad, N; Desisto, N; Shen, D; Bonilha, L

2017-09-09

Impaired confrontation naming is a common symptom of temporal lobe epilepsy (TLE). The neurobiological mechanisms underlying this impairment are poorly understood but may indicate a structural disorganization of broadly distributed neuronal networks that support naming ability. Importantly, naming is frequently impaired in other neurological disorders and by contrasting the neuronal structures supporting naming in TLE with other diseases, it will become possible to elucidate the common systems supporting naming. We aimed to evaluate the neuronal networks that support naming in TLE by using a machine learning algorithm intended to predict naming performance in subjects with medication refractory TLE using only the structural brain connectome reconstructed from diffusion tensor imaging. A connectome-based prediction framework was developed using network properties from anatomically defined brain regions across the entire brain, which were used in a multi-task machine learning algorithm followed by support vector regression. Nodal eigenvector centrality, a measure of regional network integration, predicted approximately 60% of the variance in naming. The nodes with the highest regression weight were bilaterally distributed among perilimbic sub-networks involving mainly the medial and lateral temporal lobe regions. In the context of emerging evidence regarding the role of large structural networks that support language processing, our results suggest intact naming relies on the integration of sub-networks, as opposed to being dependent on isolated brain areas. In the case of TLE, these sub-networks may be disproportionately indicative naming processes that are dependent semantic integration from memory and lexical retrieval, as opposed to multi-modal perception or motor speech production. Copyright © 2017. Published by Elsevier Inc.

Attentional impulsivity in binge eating disorder modulates response inhibition performance and frontal brain networks.

PubMed

Hege, M A; Stingl, K T; Kullmann, S; Schag, K; Giel, K E; Zipfel, S; Preissl, H

2015-02-01

A subgroup of overweight and obese people is characterized by binge eating disorder (BED). Increased impulsivity has been suggested to cause binge eating and subsequent weight gain. In the current study, neuronal correlates of increased impulsivity in binge eating disorder during behavioral response inhibition were investigated. Magnetic brain activity and behavioral responses of 37 overweight and obese individuals with and without diagnosed BED were recorded while performing a food-related visual go-nogo task. Trait impulsivity was assessed with the Barratt Impulsiveness Scale (BIS-11). Specifically, increased attentional impulsiveness (a subscale of the BIS-11) in BED was related to decreased response inhibition performance and hypoactivity in the prefrontal control network, which was activated when response inhibition was required. Furthermore, participants with BED showed a trend for a food-specific inhibition performance decline. This was possibly related to the absence of a food-specific activity increase in the prefrontal control network in BED, as observed in the control group. In addition, an increase in activity related to the actual button press during prepotent responses and alterations in visual processing were observed. Our results suggest an attentional impulsiveness-related attenuation in response inhibition performance in individuals with BED. This might have been related to increased reward responsiveness and limited resources to activate the prefrontal control network involved in response inhibition. Our results substantiate the importance of neuronal markers for investigating prevention and treatment of obesity, especially in specific subgroups at risk such as BED.

Feedback modulation of neural network synchrony and seizure susceptibility by Mdm2-p53-Nedd4-2 signaling.

PubMed

Jewett, Kathryn A; Christian, Catherine A; Bacos, Jonathan T; Lee, Kwan Young; Zhu, Jiuhe; Tsai, Nien-Pei

2016-03-22

Neural network synchrony is a critical factor in regulating information transmission through the nervous system. Improperly regulated neural network synchrony is implicated in pathophysiological conditions such as epilepsy. Despite the awareness of its importance, the molecular signaling underlying the regulation of neural network synchrony, especially after stimulation, remains largely unknown. In this study, we show that elevation of neuronal activity by the GABA(A) receptor antagonist, Picrotoxin, increases neural network synchrony in primary mouse cortical neuron cultures. The elevation of neuronal activity triggers Mdm2-dependent degradation of the tumor suppressor p53. We show here that blocking the degradation of p53 further enhances Picrotoxin-induced neural network synchrony, while promoting the inhibition of p53 with a p53 inhibitor reduces Picrotoxin-induced neural network synchrony. These data suggest that Mdm2-p53 signaling mediates a feedback mechanism to fine-tune neural network synchrony after activity stimulation. Furthermore, genetically reducing the expression of a direct target gene of p53, Nedd4-2, elevates neural network synchrony basally and occludes the effect of Picrotoxin. Finally, using a kainic acid-induced seizure model in mice, we show that alterations of Mdm2-p53-Nedd4-2 signaling affect seizure susceptibility. Together, our findings elucidate a critical role of Mdm2-p53-Nedd4-2 signaling underlying the regulation of neural network synchrony and seizure susceptibility and reveal potential therapeutic targets for hyperexcitability-associated neurological disorders.

A multiscale method for a robust detection of the default mode network

NASA Astrophysics Data System (ADS)

Baquero, Katherine; Gómez, Francisco; Cifuentes, Christian; Guldenmund, Pieter; Demertzi, Athena; Vanhaudenhuyse, Audrey; Gosseries, Olivia; Tshibanda, Jean-Flory; Noirhomme, Quentin; Laureys, Steven; Soddu, Andrea; Romero, Eduardo

2013-11-01

The Default Mode Network (DMN) is a resting state network widely used for the analysis and diagnosis of mental disorders. It is normally detected in fMRI data, but for its detection in data corrupted by motion artefacts or low neuronal activity, the use of a robust analysis method is mandatory. In fMRI it has been shown that the signal-to-noise ratio (SNR) and the detection sensitivity of neuronal regions is increased with di erent smoothing kernels sizes. Here we propose to use a multiscale decomposition based of a linear scale-space representation for the detection of the DMN. Three main points are proposed in this methodology: rst, the use of fMRI data at di erent smoothing scale-spaces, second, detection of independent neuronal components of the DMN at each scale by using standard preprocessing methods and ICA decomposition at scale-level, and nally, a weighted contribution of each scale by the Goodness of Fit measurement. This method was applied to a group of control subjects and was compared with a standard preprocesing baseline. The detection of the DMN was improved at single subject level and at group level. Based on these results, we suggest to use this methodology to enhance the detection of the DMN in data perturbed with artefacts or applied to subjects with low neuronal activity. Furthermore, the multiscale method could be extended for the detection of other resting state neuronal networks.

A chimeric path to neuronal synchronization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Essaki Arumugam, Easwara Moorthy; Spano, Mark L.

2015-01-15

Synchronization of neuronal activity is associated with neurological disorders such as epilepsy. This process of neuronal synchronization is not fully understood. To further our understanding, we have experimentally studied the progression of this synchronization from normal neuronal firing to full synchronization. We implemented nine FitzHugh-Nagumo neurons (a simplified Hodgkin-Huxley model) via discrete electronics. For different coupling parameters (synaptic strengths), the neurons in the ring were either unsynchronized or completely synchronized when locally coupled in a ring. When a single long-range connection (nonlocal coupling) was introduced, an intermediate state known as a chimera appeared. The results indicate that (1) epilepsy ismore » likely not only a dynamical disease but also a topological disease, strongly tied to the connectivity of the underlying network of neurons, and (2) the synchronization process in epilepsy may not be an “all or none” phenomenon, but can pass through an intermediate stage (chimera)« less

A chimeric path to neuronal synchronization

NASA Astrophysics Data System (ADS)

Essaki Arumugam, Easwara Moorthy; Spano, Mark L.

2015-01-01

Synchronization of neuronal activity is associated with neurological disorders such as epilepsy. This process of neuronal synchronization is not fully understood. To further our understanding, we have experimentally studied the progression of this synchronization from normal neuronal firing to full synchronization. We implemented nine FitzHugh-Nagumo neurons (a simplified Hodgkin-Huxley model) via discrete electronics. For different coupling parameters (synaptic strengths), the neurons in the ring were either unsynchronized or completely synchronized when locally coupled in a ring. When a single long-range connection (nonlocal coupling) was introduced, an intermediate state known as a chimera appeared. The results indicate that (1) epilepsy is likely not only a dynamical disease but also a topological disease, strongly tied to the connectivity of the underlying network of neurons, and (2) the synchronization process in epilepsy may not be an "all or none" phenomenon, but can pass through an intermediate stage (chimera).

Effects of tamoxifen on neuronal morphology, connectivity and biochemistry of hypothalamic ventromedial neurons: Impact on the modulators of sexual behavior.

PubMed

Sá, Susana I; Teixeira, Natércia; Fonseca, Bruno M

2018-01-01

Tamoxifen (TAM) is a selective estrogen receptor modulator, widely used in the treatment and prevention of estrogen-dependent breast cancer. Although with great clinical results, women on TAM therapy still report several side effects, such as sexual dysfunction, which impairs quality of life. The anatomo-functional substrates of the human sexual behavior are still unknown; however, these same substrates are very well characterized in the rodent female sexual behavior, which has advantage of being a very simple reflexive response, dependent on the activation of estrogen receptors (ERs) in the ventrolateral division of the hypothalamic ventromedial nucleus (VMNvl). In fact, in the female rodent, the sexual behavior is triggered by increasing circulation levels of estradiol that changes the nucleus neurochemistry and modulates its intricate neuronal network. Therefore, we considered of notice the examination of the possible neurochemical alterations and the synaptic plasticity impairment in VMNvl neurons of estradiol-primed female rats treated with TAM that may be in the basis of this neurological disorder. Accordingly, we used stereological and biochemical methods to study the action of TAM in axospinous and axodendritic synaptic plasticity and on ER expression. The administration of TAM changed the VMNvl neurochemistry by reducing ERα mRNA and increasing ERβ mRNA expression. Furthermore, present results show that TAM induced neuronal atrophy and reduced synaptic connectivity, favoring electrical inactivity. These data suggest that these cellular and molecular changes may be a possible neuronal mechanism of TAM action in the disruption of the VMNvl network, leading to the development of behavioral disorders. Copyright © 2017 Elsevier Inc. All rights reserved.

Anatomical differences in the mirror neuron system and social cognition network in autism.

PubMed

Hadjikhani, Nouchine; Joseph, Robert M; Snyder, Josh; Tager-Flusberg, Helen

2006-09-01

Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with impaired social and emotional skills, the anatomical substrate of which is still unknown. In this study, we compared a group of 14 high-functioning ASD adults with a group of controls matched for sex, age, intelligence quotient, and handedness. We used an automated technique of analysis that accurately measures the thickness of the cerebral cortex and generates cross-subject statistics in a coordinate system based on cortical anatomy. We found local decreases of gray matter in the ASD group in areas belonging to the mirror neuron system (MNS), argued to be the basis of empathic behavior. Cortical thinning of the MNS was correlated with ASD symptom severity. Cortical thinning was also observed in areas involved in emotion recognition and social cognition. These findings suggest that the social and emotional deficits characteristic of autism may reflect abnormal thinning of the MNS and the broader network of cortical areas subserving social cognition.

Otx genes in neurogenesis of mesencephalic dopaminergic neurons.

PubMed

Simeone, Antonio; Puelles, Eduardo; Omodei, Daniela; Acampora, Dario; Di Giovannantonio, Luca Giovanni; Di Salvio, Michela; Mancuso, Pietro; Tomasetti, Carmine

2011-08-01

Mesencephalic-diencephalic dopaminergic (mdDA) neurons play a relevant role in the control of movement, behavior, and cognition. Indeed loss and/or abnormal functioning of mdDA neurons are responsible for Parkinson's disease as well as for addictive and psychiatric disorders. In the last years a wealth of information has been provided on gene functions controlling identity, fate, and proliferation of mdDA progenitors. This review will focus on the role exerted by Otx genes in early decisions regulating sequential steps required for the neurogenesis of mesencephalic dopaminergic (mesDA) neurons. In this context, the regulatory network involving Otx functional interactions with signaling molecules and transcription factors required to promote or prevent the development of mesDA neurons will be analyzed in detail. Copyright © 2011 Wiley Periodicals, Inc.

Cadherin-8 expression, synaptic localization, and molecular control of neuronal form in prefrontal corticostriatal circuits.

PubMed

Friedman, Lauren G; Riemslagh, Fréderike W; Sullivan, Josefa M; Mesias, Roxana; Williams, Frances M; Huntley, George W; Benson, Deanna L

2015-01-01

Neocortical interactions with the dorsal striatum support many motor and executive functions, and such underlying functional networks are particularly vulnerable to a variety of developmental, neurological, and psychiatric brain disorders, including autism spectrum disorders, Parkinson's disease, and Huntington's disease. Relatively little is known about the development of functional corticostriatal interactions, and in particular, virtually nothing is known of the molecular mechanisms that control generation of prefrontal cortex-striatal circuits. Here, we used regional and cellular in situ hybridization techniques coupled with neuronal tract tracing to show that Cadherin-8 (Cdh8), a homophilic adhesion protein encoded by a gene associated with autism spectrum disorders and learning disability susceptibility, is enriched within striatal projection neurons in the medial prefrontal cortex and in striatal medium spiny neurons forming the direct or indirect pathways. Developmental analysis of quantitative real-time polymerase chain reaction and western blot data show that Cdh8 expression peaks in the prefrontal cortex and striatum at P10, when cortical projections start to form synapses in the striatum. High-resolution immunoelectron microscopy shows that Cdh8 is concentrated at excitatory synapses in the dorsal striatum, and Cdh8 knockdown in cortical neurons impairs dendritic arborization and dendrite self-avoidance. Taken together, our findings indicate that Cdh8 delineates developing corticostriatal circuits where it is a strong candidate for regulating the generation of normal cortical projections, neuronal morphology, and corticostriatal synapses. © 2014 Wiley Periodicals, Inc.

Transplantation of Human Chorion-Derived Cholinergic Progenitor Cells: a Novel Treatment for Neurological Disorders.

PubMed

Mohammadi, Alireza; Maleki-Jamshid, Ali; Sanooghi, Davood; Milan, Peiman Brouki; Rahmani, Arash; Sefat, Farshid; Shahpasand, Koorosh; Soleimani, Mansoureh; Bakhtiari, Mehrdad; Belali, Rafie; Faghihi, Faezeh; Joghataei, Mohammad Taghi; Perry, George; Mozafari, Masoud

2018-03-16

A neurological disorder is any disorder or abnormality in the nervous system. Among different neurological disorders, Alzheimer's disease (AD) is recognized as the sixth leading cause of death globally. Considerable research has been conducted to find pioneer treatments for this devastating disorder among which cell therapy has attracted remarkable attentions over the last decade. Up to now, targeted differentiation into specific desirable cell types has remained a major obstacle to clinical application of cell therapy. Also, potential risks including uncontrolled growth of stem cells could be disastrous. In our novel protocol, we used basal forebrain cholinergic progenitor cells (BFCN) derived from human chorion-derived mesenchymal stem cells (hC-MSCs) which made it possible to obtain high-quality population of cholinergic neurons and in vivo in much shorter time period than previous established methods. Remarkably, the transplanted progenitors fully differentiated to cholinergic neurons which in turn integrated in higher cortical networks of host brains, resulting in significant improvement in cognitive assessments. This method may have profound implications in cell therapies for any other neurodegenerative disorders. Graphical Abstract ᅟ.

Bridging disparate symptoms of schizophrenia: a triple network dysfunction theory

PubMed Central

Nekovarova, Tereza; Fajnerova, Iveta; Horacek, Jiri; Spaniel, Filip

2014-01-01

Schizophrenia is a complex neuropsychiatric disorder with variable symptomatology, traditionally divided into positive and negative symptoms, and cognitive deficits. However, the etiology of this disorder has yet to be fully understood. Recent findings suggest that alteration of the basic sense of self-awareness may be an essential distortion of schizophrenia spectrum disorders. In addition, extensive research of social and mentalizing abilities has stressed the role of distortion of social skills in schizophrenia.This article aims to propose and support a concept of a triple brain network model of the dysfunctional switching between default mode and central executive network (CEN) related to the aberrant activity of the salience network. This model could represent a unitary mechanism of a wide array of symptom domains present in schizophrenia including the deficit of self (self-awareness and self-representation) and theory of mind (ToM) dysfunctions along with the traditional positive, negative and cognitive domains. We review previous studies which document the dysfunctions of self and ToM in schizophrenia together with neuroimaging data that support the triple brain network model as a common neuronal substrate of this dysfunction. PMID:24910597

Antisense reduction of tau in adult mice protects against seizures.

PubMed

DeVos, Sarah L; Goncharoff, Dustin K; Chen, Guo; Kebodeaux, Carey S; Yamada, Kaoru; Stewart, Floy R; Schuler, Dorothy R; Maloney, Susan E; Wozniak, David F; Rigo, Frank; Bennett, C Frank; Cirrito, John R; Holtzman, David M; Miller, Timothy M

2013-07-31

Tau, a microtubule-associated protein, is implicated in the pathogenesis of Alzheimer's Disease (AD) in regard to both neurofibrillary tangle formation and neuronal network hyperexcitability. The genetic ablation of tau substantially reduces hyperexcitability in AD mouse lines, induced seizure models, and genetic in vivo models of epilepsy. These data demonstrate that tau is an important regulator of network excitability. However, developmental compensation in the genetic tau knock-out line may account for the protective effect against seizures. To test the efficacy of a tau reducing therapy for disorders with a detrimental hyperexcitability profile in adult animals, we identified antisense oligonucleotides that selectively decrease endogenous tau expression throughout the entire mouse CNS--brain and spinal cord tissue, interstitial fluid, and CSF--while having no effect on baseline motor or cognitive behavior. In two chemically induced seizure models, mice with reduced tau protein had less severe seizures than control mice. Total tau protein levels and seizure severity were highly correlated, such that those mice with the most severe seizures also had the highest levels of tau. Our results demonstrate that endogenous tau is integral for regulating neuronal hyperexcitability in adult animals and suggest that an antisense oligonucleotide reduction of tau could benefit those with epilepsy and perhaps other disorders associated with tau-mediated neuronal hyperexcitability.

Neurobiologically based interventions for autism spectrum disorders-rationale and new directions.

PubMed

Poustka, Luise; Brandeis, Daniel; Hohmann, Sarah; Holtmann, Martin; Bölte, Sven; Banaschewski, Tobias

2014-01-01

Autism spectrum disorders (ASD) are heterogeneous, neurodevelopmental disorders with early onset, characterized by a triad of impairments in reciprocal interaction and communication as well as repetitive and restricted interests and activities. Though underlying causes still remain largely unknown, there is now evidence for abnormal growth trajectories in the early brain development in ASD during vulnerable periods and subsequent impairment of neuronal organization and differentiation of neuronal networks. A growing number of studies over the last 10 years support the efficacy of behaviorally based interventions in ASD for the improvement of social communication and behavioral functioning. In contrast, research on neurobiologically based therapies for ASD is still at its beginnings. In this article, we will provide a selective overview of novel interventions and trainings based on neurobiological principles. Directions and options for future research on treatment aiming at restoration of normal plasticity in disrupted brain circuits in ASD are discussed.

Neuronal Networks in Children with Continuous Spikes and Waves during Slow Sleep

ERIC Educational Resources Information Center

Siniatchkin, Michael; Groening, Kristina; Moehring, Jan; Moeller, Friederike; Boor, Rainer; Brodbeck, Verena; Michel, Christoph M.; Rodionov, Roman; Lemieux, Louis; Stephani, Ulrich

2010-01-01

Epileptic encephalopathy with continuous spikes and waves during slow sleep is an age-related disorder characterized by the presence of interictal epileptiform discharges during at least greater than 85% of sleep and cognitive deficits associated with this electroencephalography pattern. The pathophysiological mechanisms of continuous spikes and…

Improving and accelerating the differentiation and functional maturation of human stem cell-derived neurons: role of extracellular calcium and GABA.

PubMed

Kemp, Paul J; Rushton, David J; Yarova, Polina L; Schnell, Christian; Geater, Charlene; Hancock, Jane M; Wieland, Annalena; Hughes, Alis; Badder, Luned; Cope, Emma; Riccardi, Daniela; Randall, Andrew D; Brown, Jonathan T; Allen, Nicholas D; Telezhkin, Vsevolod

2016-11-15

Neurons differentiated from pluripotent stem cells using established neural culture conditions often exhibit functional deficits. Recently, we have developed enhanced media which both synchronize the neurogenesis of pluripotent stem cell-derived neural progenitors and accelerate their functional maturation; together these media are termed SynaptoJuice. This pair of media are pro-synaptogenic and generate authentic, mature synaptic networks of connected forebrain neurons from a variety of induced pluripotent and embryonic stem cell lines. Such enhanced rate and extent of synchronized maturation of pluripotent stem cell-derived neural progenitor cells generates neurons which are characterized by a relatively hyperpolarized resting membrane potential, higher spontaneous and induced action potential activity, enhanced synaptic activity, more complete development of a mature inhibitory GABA A receptor phenotype and faster production of electrical network activity when compared to standard differentiation media. This entire process - from pre-patterned neural progenitor to active neuron - takes 3 weeks or less, making it an ideal platform for drug discovery and disease modelling in the fields of human neurodegenerative and neuropsychiatric disorders, such as Huntington's disease, Parkinson's disease, Alzheimer's disease and Schizophrenia. © 2016 The Authors. The Journal of Physiology © 2016 The Physiological Society.

Chaos and Correlated Avalanches in Excitatory Neural Networks with Synaptic Plasticity

NASA Astrophysics Data System (ADS)

Pittorino, Fabrizio; Ibáñez-Berganza, Miguel; di Volo, Matteo; Vezzani, Alessandro; Burioni, Raffaella

2017-03-01

A collective chaotic phase with power law scaling of activity events is observed in a disordered mean field network of purely excitatory leaky integrate-and-fire neurons with short-term synaptic plasticity. The dynamical phase diagram exhibits two transitions from quasisynchronous and asynchronous regimes to the nontrivial, collective, bursty regime with avalanches. In the homogeneous case without disorder, the system synchronizes and the bursty behavior is reflected into a period doubling transition to chaos for a two dimensional discrete map. Numerical simulations show that the bursty chaotic phase with avalanches exhibits a spontaneous emergence of persistent time correlations and enhanced Kolmogorov complexity. Our analysis reveals a mechanism for the generation of irregular avalanches that emerges from the combination of disorder and deterministic underlying chaotic dynamics.

Efficient digital implementation of a conductance-based globus pallidus neuron and the dynamics analysis

NASA Astrophysics Data System (ADS)

Yang, Shuangming; Wei, Xile; Deng, Bin; Liu, Chen; Li, Huiyan; Wang, Jiang

2018-03-01

Balance between biological plausibility of dynamical activities and computational efficiency is one of challenging problems in computational neuroscience and neural system engineering. This paper proposes a set of efficient methods for the hardware realization of the conductance-based neuron model with relevant dynamics, targeting reproducing the biological behaviors with low-cost implementation on digital programmable platform, which can be applied in wide range of conductance-based neuron models. Modified GP neuron models for efficient hardware implementation are presented to reproduce reliable pallidal dynamics, which decode the information of basal ganglia and regulate the movement disorder related voluntary activities. Implementation results on a field-programmable gate array (FPGA) demonstrate that the proposed techniques and models can reduce the resource cost significantly and reproduce the biological dynamics accurately. Besides, the biological behaviors with weak network coupling are explored on the proposed platform, and theoretical analysis is also made for the investigation of biological characteristics of the structured pallidal oscillator and network. The implementation techniques provide an essential step towards the large-scale neural network to explore the dynamical mechanisms in real time. Furthermore, the proposed methodology enables the FPGA-based system a powerful platform for the investigation on neurodegenerative diseases and real-time control of bio-inspired neuro-robotics.

Controlling self-sustained spiking activity by adding or removing one network link

NASA Astrophysics Data System (ADS)

Xu, Kesheng; Huang, Wenwen; Li, Baowen; Dhamala, Mukesh; Liu, Zonghua

2013-06-01

Being able to control the neuronal spiking activity in specific brain regions is central to a treatment scheme in several brain disorders such as epileptic seizures, mental depression, and Parkinson's diseases. Here, we present an approach for controlling self-sustained oscillations by adding or removing one directed network link in coupled neuronal oscillators, in contrast to previous approaches of adding stimuli or noise. We find that such networks can exhibit a variety of activity patterns such as on-off switch, sustained spikes, and short-term spikes. We derive the condition for a specific link to be the controller of the on-off effect. A qualitative analysis is provided to facilitate the understanding of the mechanism for spiking activity by adding one link. Our findings represent the first report on generating spike activity with the addition of only one directed link to a network and provide a deeper understanding of the microscopic roots of self-sustained spiking.

Working memory network alterations in high-functioning adolescents with an autism spectrum disorder.

PubMed

Barendse, Evelien M; Schreuder, Lisanne J; Thoonen, Geert; Hendriks, Marc P H; Kessels, Roy P C; Backes, Walter H; Aldenkamp, Albert P; Jansen, Jacobus F A

2018-02-01

People with autism spectrum disorder (ASD) typically have deficits in the working memory (WM) system. WM is found to be an essential chain in successfully navigating in the social world. We hypothesize that brain networks for WM have an altered network integrity in ASD compared to controls. Thirteen adolescents (one female) with autistic disorder (n = 1), Asperger's disorder (n = 7), or pervasive developmental disorder not otherwise specified (n = 5), and 13 typically developing healthy control adolescents (one female) participated in this study. Functional magnetic resonance imaging (MRI) was performed using an n-back task and in resting state. The analysis of the behavioral data revealed deficits in WM performance in ASD, but only when tested to the limit. Adolescents with ASD showed lower binary global efficiency in the WM network than the healthy control group with n-back and resting-state data. This correlated with diagnostic scores for total problems, reciprocity, and language. Adolescents with higher-functioning autism have difficulty with the WM system, which is typically compensated. Functional MRI markers of brain network organization in ASD are related to characteristics of autism as represented in diagnostic scores. Therefore, functional MRI provides neuronal correlates for memory difficulties in adolescents with ASD. © 2017 The Authors. Psychiatry and Clinical Neurosciences published by John Wiley & Sons Australia, Ltd on behalf of Japanese Society of Psychiatry and Neurology.

Systems Nutrigenomics Reveals Brain Gene Networks Linking Metabolic and Brain Disorders.

PubMed

Meng, Qingying; Ying, Zhe; Noble, Emily; Zhao, Yuqi; Agrawal, Rahul; Mikhail, Andrew; Zhuang, Yumei; Tyagi, Ethika; Zhang, Qing; Lee, Jae-Hyung; Morselli, Marco; Orozco, Luz; Guo, Weilong; Kilts, Tina M; Zhu, Jun; Zhang, Bin; Pellegrini, Matteo; Xiao, Xinshu; Young, Marian F; Gomez-Pinilla, Fernando; Yang, Xia

2016-05-01

Nutrition plays a significant role in the increasing prevalence of metabolic and brain disorders. Here we employ systems nutrigenomics to scrutinize the genomic bases of nutrient-host interaction underlying disease predisposition or therapeutic potential. We conducted transcriptome and epigenome sequencing of hypothalamus (metabolic control) and hippocampus (cognitive processing) from a rodent model of fructose consumption, and identified significant reprogramming of DNA methylation, transcript abundance, alternative splicing, and gene networks governing cell metabolism, cell communication, inflammation, and neuronal signaling. These signals converged with genetic causal risks of metabolic, neurological, and psychiatric disorders revealed in humans. Gene network modeling uncovered the extracellular matrix genes Bgn and Fmod as main orchestrators of the effects of fructose, as validated using two knockout mouse models. We further demonstrate that an omega-3 fatty acid, DHA, reverses the genomic and network perturbations elicited by fructose, providing molecular support for nutritional interventions to counteract diet-induced metabolic and brain disorders. Our integrative approach complementing rodent and human studies supports the applicability of nutrigenomics principles to predict disease susceptibility and to guide personalized medicine. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

From Understanding Cellular Function to Novel Drug Discovery: The Role of Planar Patch-Clamp Array Chip Technology

PubMed Central

Py, Christophe; Martina, Marzia; Diaz-Quijada, Gerardo A.; Luk, Collin C.; Martinez, Dolores; Denhoff, Mike W.; Charrier, Anne; Comas, Tanya; Monette, Robert; Krantis, Anthony; Syed, Naweed I.; Mealing, Geoffrey A. R.

2011-01-01

All excitable cell functions rely upon ion channels that are embedded in their plasma membrane. Perturbations of ion channel structure or function result in pathologies ranging from cardiac dysfunction to neurodegenerative disorders. Consequently, to understand the functions of excitable cells and to remedy their pathophysiology, it is important to understand the ion channel functions under various experimental conditions – including exposure to novel drug targets. Glass pipette patch-clamp is the state of the art technique to monitor the intrinsic and synaptic properties of neurons. However, this technique is labor intensive and has low data throughput. Planar patch-clamp chips, integrated into automated systems, offer high throughputs but are limited to isolated cells from suspensions, thus limiting their use in modeling physiological function. These chips are therefore not most suitable for studies involving neuronal communication. Multielectrode arrays (MEAs), in contrast, have the ability to monitor network activity by measuring local field potentials from multiple extracellular sites, but specific ion channel activity is challenging to extract from these multiplexed signals. Here we describe a novel planar patch-clamp chip technology that enables the simultaneous high-resolution electrophysiological interrogation of individual neurons at multiple sites in synaptically connected neuronal networks, thereby combining the advantages of MEA and patch-clamp techniques. Each neuron can be probed through an aperture that connects to a dedicated subterranean microfluidic channel. Neurons growing in networks are aligned to the apertures by physisorbed or chemisorbed chemical cues. In this review, we describe the design and fabrication process of these chips, approaches to chemical patterning for cell placement, and present physiological data from cultured neuronal cells. PMID:22007170

From understanding cellular function to novel drug discovery: the role of planar patch-clamp array chip technology.

PubMed

Py, Christophe; Martina, Marzia; Diaz-Quijada, Gerardo A; Luk, Collin C; Martinez, Dolores; Denhoff, Mike W; Charrier, Anne; Comas, Tanya; Monette, Robert; Krantis, Anthony; Syed, Naweed I; Mealing, Geoffrey A R

2011-01-01

All excitable cell functions rely upon ion channels that are embedded in their plasma membrane. Perturbations of ion channel structure or function result in pathologies ranging from cardiac dysfunction to neurodegenerative disorders. Consequently, to understand the functions of excitable cells and to remedy their pathophysiology, it is important to understand the ion channel functions under various experimental conditions - including exposure to novel drug targets. Glass pipette patch-clamp is the state of the art technique to monitor the intrinsic and synaptic properties of neurons. However, this technique is labor intensive and has low data throughput. Planar patch-clamp chips, integrated into automated systems, offer high throughputs but are limited to isolated cells from suspensions, thus limiting their use in modeling physiological function. These chips are therefore not most suitable for studies involving neuronal communication. Multielectrode arrays (MEAs), in contrast, have the ability to monitor network activity by measuring local field potentials from multiple extracellular sites, but specific ion channel activity is challenging to extract from these multiplexed signals. Here we describe a novel planar patch-clamp chip technology that enables the simultaneous high-resolution electrophysiological interrogation of individual neurons at multiple sites in synaptically connected neuronal networks, thereby combining the advantages of MEA and patch-clamp techniques. Each neuron can be probed through an aperture that connects to a dedicated subterranean microfluidic channel. Neurons growing in networks are aligned to the apertures by physisorbed or chemisorbed chemical cues. In this review, we describe the design and fabrication process of these chips, approaches to chemical patterning for cell placement, and present physiological data from cultured neuronal cells.

Early Correlated Network Activity in the Hippocampus: Its Putative Role in Shaping Neuronal Circuits.

PubMed

Griguoli, Marilena; Cherubini, Enrico

2017-01-01

Synchronized neuronal activity occurring at different developmental stages in various brain structures represents a hallmark of developmental circuits. This activity, which differs in its specific patterns among animal species may play a crucial role in de novo formation and in shaping neuronal networks. In the rodent hippocampus in vitro , the so-called giant depolarizing potentials (GDPs) constitute a primordial form of neuronal synchrony preceding more organized forms of activity such as oscillations in the theta and gamma frequency range. GDPs are generated at the network level by the interaction of the neurotransmitters glutamate and GABA which, immediately after birth, exert both a depolarizing and excitatory action on their targets. GDPs are triggered by GABAergic interneurons, which in virtue of their extensive axonal branching operate as functional hubs to synchronize large ensembles of cells. Intrinsic bursting activity, driven by a persistent sodium conductance and facilitated by the low expression of Kv7.2 and Kv7.3 channel subunits, responsible for I M , exerts a permissive role in GDP generation. Here, we discuss how GDPs are generated in a probabilistic way when neuronal excitability within a local circuit reaches a certain threshold and how GDP-associated calcium transients act as coincident detectors for enhancing synaptic strength at emerging GABAergic and glutamatergic synapses. We discuss the possible in vivo correlate of this activity. Finally, we debate recent data showing how, in several animal models of neuropsychiatric disorders including autism, a GDPs dysfunction is associated to morphological alterations of neuronal circuits and behavioral deficits reminiscent of those observed in patients.

Transplantation of enteric nervous system stem cells rescues nitric oxide synthase deficient mouse colon

PubMed Central

McCann, Conor J.; Cooper, Julie E.; Natarajan, Dipa; Jevans, Benjamin; Burnett, Laura E.; Burns, Alan J.; Thapar, Nikhil

2017-01-01

Enteric nervous system neuropathy causes a wide range of severe gut motility disorders. Cell replacement of lost neurons using enteric neural stem cells (ENSC) is a possible therapy for these life-limiting disorders. Here we show rescue of gut motility after ENSC transplantation in a mouse model of human enteric neuropathy, the neuronal nitric oxide synthase (nNOS−/−) deficient mouse model, which displays slow transit in the colon. We further show that transplantation of ENSC into the colon rescues impaired colonic motility with formation of extensive networks of transplanted cells, including the development of nNOS+ neurons and subsequent restoration of nitrergic responses. Moreover, post-transplantation non-cell-autonomous mechanisms restore the numbers of interstitial cells of Cajal that are reduced in the nNOS−/− colon. These results provide the first direct evidence that ENSC transplantation can modulate the enteric neuromuscular syncytium to restore function, at the organ level, in a dysmotile gastrointestinal disease model. PMID:28671186

Genetics and Neuroscience in Dyslexia: Perspectives for Education and Remediation

ERIC Educational Resources Information Center

Schulte-Korne, Gerd; Ludwig, Kerstin U.; el Sharkawy, Jennifer; Nothen, Markus M.; Muller-Myhsok, Bertram; Hoffmann, Per

2007-01-01

Our understanding of the causes of a developmental disorder like dyslexia has received recent input from both neuroscience and genetics. The discovery of 4 candidate genes for dyslexia and the identification of neuronal networks engaged when children read and spell are the basis for introducing this knowledge into education. However, the input…

Review of quantitative phase-digital holographic microscopy: promising novel imaging technique to resolve neuronal network activity and identify cellular biomarkers of psychiatric disorders

PubMed Central

Marquet, Pierre; Depeursinge, Christian; Magistretti, Pierre J.

2014-01-01

Abstract. Quantitative phase microscopy (QPM) has recently emerged as a new powerful quantitative imaging technique well suited to noninvasively explore a transparent specimen with a nanometric axial sensitivity. In this review, we expose the recent developments of quantitative phase-digital holographic microscopy (QP-DHM). Quantitative phase-digital holographic microscopy (QP-DHM) represents an important and efficient quantitative phase method to explore cell structure and dynamics. In a second part, the most relevant QPM applications in the field of cell biology are summarized. A particular emphasis is placed on the original biological information, which can be derived from the quantitative phase signal. In a third part, recent applications obtained, with QP-DHM in the field of cellular neuroscience, namely the possibility to optically resolve neuronal network activity and spine dynamics, are presented. Furthermore, potential applications of QPM related to psychiatry through the identification of new and original cell biomarkers that, when combined with a range of other biomarkers, could significantly contribute to the determination of high risk developmental trajectories for psychiatric disorders, are discussed. PMID:26157976

Review of quantitative phase-digital holographic microscopy: promising novel imaging technique to resolve neuronal network activity and identify cellular biomarkers of psychiatric disorders.

PubMed

Marquet, Pierre; Depeursinge, Christian; Magistretti, Pierre J

2014-10-01

Quantitative phase microscopy (QPM) has recently emerged as a new powerful quantitative imaging technique well suited to noninvasively explore a transparent specimen with a nanometric axial sensitivity. In this review, we expose the recent developments of quantitative phase-digital holographic microscopy (QP-DHM). Quantitative phase-digital holographic microscopy (QP-DHM) represents an important and efficient quantitative phase method to explore cell structure and dynamics. In a second part, the most relevant QPM applications in the field of cell biology are summarized. A particular emphasis is placed on the original biological information, which can be derived from the quantitative phase signal. In a third part, recent applications obtained, with QP-DHM in the field of cellular neuroscience, namely the possibility to optically resolve neuronal network activity and spine dynamics, are presented. Furthermore, potential applications of QPM related to psychiatry through the identification of new and original cell biomarkers that, when combined with a range of other biomarkers, could significantly contribute to the determination of high risk developmental trajectories for psychiatric disorders, are discussed.

Planar cell polarity genes control the connectivity of enteric neurons

PubMed Central

Sasselli, Valentina; Boesmans, Werend; Vanden Berghe, Pieter; Tissir, Fadel; Goffinet, André M.; Pachnis, Vassilis

2013-01-01

A highly complex network of intrinsic enteric neurons is required for the digestive and homeostatic functions of the gut. Nevertheless, the genetic and molecular mechanisms that regulate their assembly into functional neuronal circuits are currently unknown. Here we report that the planar cell polarity (PCP) genes Celsr3 and Fzd3 are required during murine embryogenesis to specifically control the guidance and growth of enteric neuronal projections relative to the longitudinal and radial gut axes. Ablation of these genes disrupts the normal organization of nascent neuronal projections, leading to subtle changes of axonal tract configuration in the mature enteric nervous system (ENS), but profound abnormalities in gastrointestinal motility. Our data argue that PCP-dependent modules of connectivity established at early stages of enteric neurogenesis control gastrointestinal function in adult animals and provide the first evidence that developmental deficits in ENS wiring may contribute to the pathogenesis of idiopathic bowel disorders. PMID:23478408

Serotonin neuron development: shaping molecular and structural identities.

PubMed

Deneris, Evan; Gaspar, Patricia

2018-01-01

The continuing fascination with serotonin (5-hydroxytryptamine, 5-HT) as a nervous system chemical messenger began with its discovery in the brains of mammals in 1953. Among the many reasons for this decades-long interest is that the small numbers of neurons that make 5-HT influence the excitability of neural circuits in nearly every region of the brain and spinal cord. A further reason is that 5-HT dysfunction has been linked to a range of psychiatric and neurological disorders many of which have a neurodevelopmental component. This has led to intense interest in understanding 5-HT neuron development with the aim of determining whether early alterations in their generation lead to brain disease susceptibility. Here, we present an overview of the neuroanatomical organization of vertebrate 5-HT neurons, their neurogenesis, and prodigious axonal architectures, which enables the expansive reach of 5-HT neuromodulation in the central nervous system. We review recent findings that have revealed the molecular basis for the tremendous diversity of 5-HT neuron subtypes, the impact of environmental factors on 5-HT neuron development, and how 5-HT axons are topographically organized through disparate signaling pathways. We summarize studies of the gene regulatory networks that control the differentiation, maturation, and maintenance of 5-HT neurons. These studies show that the regulatory factors controlling acquisition of 5-HT-type transmitter identity continue to play critical roles in the functional maturation and the maintenance of 5-HT neurons. New insights are presented into how continuously expressed 5-HT regulatory factors control 5-HT neurons at different stages of life and how the regulatory networks themselves are maintained. WIREs Dev Biol 2018, 7:e301. doi: 10.1002/wdev.301 This article is categorized under: Nervous System Development > Vertebrates: General Principles Gene Expression and Transcriptional Hierarchies > Gene Networks and Genomics Gene Expression and Transcriptional Hierarchies > Cellular Differentiation Nervous System Development > Secondary: Vertebrates: Regional Development. © 2017 Wiley Periodicals, Inc.

Psychedelics as Medicines: An Emerging New Paradigm.

PubMed

Nichols, D E; Johnson, M W; Nichols, C D

2017-02-01

Scientific interest in serotonergic psychedelics (e.g., psilocybin and LSD; 5-HT 2A receptor agonists) has dramatically increased within the last decade. Clinical studies administering psychedelics with psychotherapy have shown preliminary evidence of robust efficacy in treating anxiety and depression, as well as addiction to tobacco and alcohol. Moreover, recent research has suggested that these compounds have potential efficacy against inflammatory diseases through novel mechanisms, with potential advantages over existing antiinflammatory agents. We propose that psychedelics exert therapeutic effects for psychiatric disorders by acutely destabilizing local brain network hubs and global network connectivity via amplification of neuronal avalanches, providing the occasion for brain network "resetting" after the acute effects have resolved. Antiinflammatory effects may hold promise for efficacy in treatment of inflammation-related nonpsychiatric as well as potentially for psychiatric disorders. Serotonergic psychedelics operate through unique mechanisms that show promising effects for a variety of intractable, debilitating, and lethal disorders, and should be rigorously researched. © 2016 American Society for Clinical Pharmacology and Therapeutics.

Emerging themes of ER organization in the development and maintenance of axons

PubMed Central

Renvoisé, Benoît; Blackstone, Craig

2010-01-01

The endoplasmic reticulum (ER) is a continuous membrane system comprising the nuclear envelope, polyribosome-studded peripheral sheets, and a polygonal network of smooth tubules extending throughout the cell. Though protein biosynthesis, transport, and quality control in the ER have been extensively studied, mechanisms underlying the heterogeneous architecture of the ER have been clarified more recently. These insights have increased interest in ER morphology changes associated with the development of neuronal axons and dendrites as well as their integration with pre- and postsynaptic signaling pathways. A number of proteins involved in shaping and distributing the ER network are mutated in neurological disorders, particularly the hereditary spastic paraplegias, emphasizing the importance of proper ER morphology for the establishment and maintenance of highly-polarized neurons. PMID:20678923

Lis1 controls dynamics of neuronal filopodia and spines to impact synaptogenesis and social behaviour

PubMed Central

Sudarov, Anamaria; Gooden, Frank; Tseng, Debbie; Gan, Wen-Biao; Ross, Margaret Elizabeth

2013-01-01

LIS1 (PAFAH1B1) mutation can impair neuronal migration, causing lissencephaly in humans. LIS1 loss is associated with dynein protein motor dysfunction, and disrupts the actin cytoskeleton through disregulated RhoGTPases. Recently, LIS1 was implicated as an important protein-network interaction node with high-risk autism spectrum disorder genes expressed in the synapse. How LIS1 might participate in this disorder has not been investigated. We examined the role of LIS1 in synaptogenesis of post-migrational neurons and social behaviour in mice. Two-photon imaging of actin-rich dendritic filopodia and spines in vivo showed significant reductions in elimination and turnover rates of dendritic protrusions of layer V pyramidal neurons in adolescent Lis1+/− mice. Lis1+/− filopodia on immature hippocampal neurons in vitro exhibited reduced density, length and RhoA dependent impaired dynamics compared to Lis1+/+. Moreover, Lis1+/− adolescent mice exhibited deficits in social interaction. Lis1 inactivation restricted to the postnatal hippocampus resulted in similar deficits in dendritic protrusion density and social interactions. Thus, LIS1 plays prominently in dendritic filopodia dynamics and spine turnover implicating reduced dendritic spine plasticity as contributing to developmental autistic-like behaviour. PMID:23483716

A human neurodevelopmental model for Williams syndrome.

PubMed

Chailangkarn, Thanathom; Trujillo, Cleber A; Freitas, Beatriz C; Hrvoj-Mihic, Branka; Herai, Roberto H; Yu, Diana X; Brown, Timothy T; Marchetto, Maria C; Bardy, Cedric; McHenry, Lauren; Stefanacci, Lisa; Järvinen, Anna; Searcy, Yvonne M; DeWitt, Michelle; Wong, Wenny; Lai, Philip; Ard, M Colin; Hanson, Kari L; Romero, Sarah; Jacobs, Bob; Dale, Anders M; Dai, Li; Korenberg, Julie R; Gage, Fred H; Bellugi, Ursula; Halgren, Eric; Semendeferi, Katerina; Muotri, Alysson R

2016-08-18

Williams syndrome is a genetic neurodevelopmental disorder characterized by an uncommon hypersociability and a mosaic of retained and compromised linguistic and cognitive abilities. Nearly all clinically diagnosed individuals with Williams syndrome lack precisely the same set of genes, with breakpoints in chromosome band 7q11.23 (refs 1-5). The contribution of specific genes to the neuroanatomical and functional alterations, leading to behavioural pathologies in humans, remains largely unexplored. Here we investigate neural progenitor cells and cortical neurons derived from Williams syndrome and typically developing induced pluripotent stem cells. Neural progenitor cells in Williams syndrome have an increased doubling time and apoptosis compared with typically developing neural progenitor cells. Using an individual with atypical Williams syndrome, we narrowed this cellular phenotype to a single gene candidate, frizzled 9 (FZD9). At the neuronal stage, layer V/VI cortical neurons derived from Williams syndrome were characterized by longer total dendrites, increased numbers of spines and synapses, aberrant calcium oscillation and altered network connectivity. Morphometric alterations observed in neurons from Williams syndrome were validated after Golgi staining of post-mortem layer V/VI cortical neurons. This model of human induced pluripotent stem cells fills the current knowledge gap in the cellular biology of Williams syndrome and could lead to further insights into the molecular mechanism underlying the disorder and the human social brain.

A human neurodevelopmental model for Williams syndrome

PubMed Central

Chailangkarn, Thanathom; Trujillo, Cleber A.; Freitas, Beatriz C.; Hrvoj-Mihic, Branka; Herai, Roberto H.; Yu, Diana X.; Brown, Timothy T.; Marchetto, Maria C. N.; Bardy, Cedric; McHenry, Lauren; Stefanacci, Lisa; Järvinen, Anna; Searcy, Yvonne M.; DeWitt, Michelle; Wong, Wenny; Lai, Philip; Ard, M. Colin; Hanson, Kari L.; Romero, Sarah; Jacobs, Bob; Dale, Anders M.; Dai, Li; Korenberg, Julie R.; Gage, Fred H.; Bellugi, Ursula; Halgren, Eric; Semendeferi, Katerina; Muotri, Alysson R.

2016-01-01

Summary Williams syndrome (WS) is a genetic neurodevelopmental disorder characterized by an uncommon hypersociability and a mosaic of retained and compromised linguistic and cognitive abilities. Nearly all clinically diagnosed individuals with WS lack precisely the same set of genes, with breakpoints in chromosome band 7q11.231–5. The contribution of specific genes to the neuroanatomical and functional alterations, leading to behavioral pathologies in humans, remains largely unexplored. Here, we investigate neural progenitor cells (NPCs) and cortical neurons derived from WS and typically developing (TD) induced pluripotent stem cells (iPSCs). WS NPCs have an increased doubling time and apoptosis compared to TD NPCs. Using an atypical WS subject6, 7, we narrowed this cellular phenotype to a single gene candidate, FZD9. At the neuronal stage, WS-derived layers V/VI cortical neurons were characterized by longer total dendrites, increased numbers of spines and synapses, aberrant calcium oscillation and altered network connectivity. Morphometric alterations observed in WS neurons were validated after Golgi staining of postmortem layers V/VI cortical neurons. This human iPSC model8 fills in the current knowledge gap in WS cellular biology and could lead to further insights into the molecular mechanism underlying the disorder and the human social brain. PMID:27509850

Control of Abnormal Synchronization in Neurological Disorders

PubMed Central

Popovych, Oleksandr V.; Tass, Peter A.

2014-01-01

In the nervous system, synchronization processes play an important role, e.g., in the context of information processing and motor control. However, pathological, excessive synchronization may strongly impair brain function and is a hallmark of several neurological disorders. This focused review addresses the question of how an abnormal neuronal synchronization can specifically be counteracted by invasive and non-invasive brain stimulation as, for instance, by deep brain stimulation for the treatment of Parkinson’s disease, or by acoustic stimulation for the treatment of tinnitus. On the example of coordinated reset (CR) neuromodulation, we illustrate how insights into the dynamics of complex systems contribute to successful model-based approaches, which use methods from synergetics, non-linear dynamics, and statistical physics, for the development of novel therapies for normalization of brain function and synaptic connectivity. Based on the intrinsic multistability of the neuronal populations induced by spike timing-dependent plasticity (STDP), CR neuromodulation utilizes the mutual interdependence between synaptic connectivity and dynamics of the neuronal networks in order to restore more physiological patterns of connectivity via desynchronization of neuronal activity. The very goal is to shift the neuronal population by stimulation from an abnormally coupled and synchronized state to a desynchronized regime with normalized synaptic connectivity, which significantly outlasts the stimulation cessation, so that long-lasting therapeutic effects can be achieved. PMID:25566174

Molecular Regulation of DNA Damage-Induced Apoptosis in Neurons of Cerebral Cortex

PubMed Central

Liu, Zhiping; Pipino, Jacqueline; Chestnut, Barry; Landek, Melissa A.

2009-01-01

Cerebral cortical neuron degeneration occurs in brain disorders manifesting throughout life, but the mechanisms are understood poorly. We used cultured embryonic mouse cortical neurons and an in vivo mouse model to study mechanisms of DNA damaged-induced apoptosis in immature and differentiated neurons. p53 drives apoptosis of immature and differentiated cortical neurons through its rapid and prominent activation stimulated by DNA strand breaks induced by topoisomerase-I and -II inhibition. Blocking p53-DNA transactivation with α-pifithrin protects immature neurons; blocking p53-mitochondrial functions with μ-pifithrin protects differentiated neurons. Mitochondrial death proteins are upregulated in apoptotic immature and differentiated neurons and have nonredundant proapoptotic functions; Bak is more dominant than Bax in differentiated neurons. p53 phosphorylation is mediated by ataxia telangiectasia mutated (ATM) kinase. ATM inactivation is antiapoptotic, particularly in differentiated neurons, whereas inhibition of c-Abl protects immature neurons but not differentiated neurons. Cell death protein expression patterns in mouse forebrain are mostly similar to cultured neurons. DNA damage induces prominent p53 activation and apoptosis in cerebral cortex in vivo. Thus, DNA strand breaks in cortical neurons induce rapid p53-mediated apoptosis through actions of upstream ATM and c-Abl kinases and downstream mitochondrial death proteins. This molecular network operates through variations depending on neuron maturity. PMID:18820287

Network feedback regulates motor output across a range of modulatory neuron activity

PubMed Central

Spencer, Robert M.

2016-01-01

Modulatory projection neurons alter network neuron synaptic and intrinsic properties to elicit multiple different outputs. Sensory and other inputs elicit a range of modulatory neuron activity that is further shaped by network feedback, yet little is known regarding how the impact of network feedback on modulatory neurons regulates network output across a physiological range of modulatory neuron activity. Identified network neurons, a fully described connectome, and a well-characterized, identified modulatory projection neuron enabled us to address this issue in the crab (Cancer borealis) stomatogastric nervous system. The modulatory neuron modulatory commissural neuron 1 (MCN1) activates and modulates two networks that generate rhythms via different cellular mechanisms and at distinct frequencies. MCN1 is activated at rates of 5–35 Hz in vivo and in vitro. Additionally, network feedback elicits MCN1 activity time-locked to motor activity. We asked how network activation, rhythm speed, and neuron activity levels are regulated by the presence or absence of network feedback across a physiological range of MCN1 activity rates. There were both similarities and differences in responses of the two networks to MCN1 activity. Many parameters in both networks were sensitive to network feedback effects on MCN1 activity. However, for most parameters, MCN1 activity rate did not determine the extent to which network output was altered by the addition of network feedback. These data demonstrate that the influence of network feedback on modulatory neuron activity is an important determinant of network output and feedback can be effective in shaping network output regardless of the extent of network modulation. PMID:27030739

"The developmental and functional logic of neuronal circuits": commentary on the Kavli Prize in Neuroscience.

PubMed

Glover, J C

2009-11-10

The first Kavli Prize in Neuroscience recognizes a confluence of career achievements that together provide a fundamental understanding of how brain and spinal cord circuits are assembled during development and function in the adult. The members of the Kavli Neuroscience Prize Committee have decided to reward three scientists (Sten Grillner, Thomas Jessell, and Pasko Rakic) jointly "for discoveries on the developmental and functional logic of neuronal circuits". Pasko Rakic performed groundbreaking studies of the developing cerebral cortex, including the discovery of how radial glia guide the neuronal migration that establishes cortical layers and for the radial unit hypothesis and its implications for cortical connectivity and evolution. Thomas Jessell discovered molecular principles governing the specification and patterning of different neuron types and the development of their synaptic interconnection into sensorimotor circuits. Sten Grillner elucidated principles of network organization in the vertebrate locomotor central pattern generator, along with its command systems and sensory and higher order control. The discoveries of Rakic, Jessell and Grillner provide a framework for how neurons obtain their identities and ultimate locations, establish appropriate connections with each other, and how the resultant neuronal networks operate. Their work has significantly advanced our understanding of brain development and function and created new opportunities for the treatment of neurological disorders. Each has pioneered an important area of neuroscience research and left a legacy of exceptional scientific achievement, insight, communication, mentoring and leadership.

Basal ganglia, movement disorders and deep brain stimulation: advances made through non-human primate research.

PubMed

Wichmann, Thomas; Bergman, Hagai; DeLong, Mahlon R

2018-03-01

Studies in non-human primates (NHPs) have led to major advances in our understanding of the function of the basal ganglia and of the pathophysiologic mechanisms of hypokinetic movement disorders such as Parkinson's disease and hyperkinetic disorders such as chorea and dystonia. Since the brains of NHPs are anatomically very close to those of humans, disease states and the effects of medical and surgical approaches, such as deep brain stimulation (DBS), can be more faithfully modeled in NHPs than in other species. According to the current model of the basal ganglia circuitry, which was strongly influenced by studies in NHPs, the basal ganglia are viewed as components of segregated networks that emanate from specific cortical areas, traverse the basal ganglia, and ventral thalamus, and return to the frontal cortex. Based on the presumed functional domains of the different cortical areas involved, these networks are designated as 'motor', 'oculomotor', 'associative' and 'limbic' circuits. The functions of these networks are strongly modulated by the release of dopamine in the striatum. Striatal dopamine release alters the activity of striatal projection neurons which, in turn, influences the (inhibitory) basal ganglia output. In parkinsonism, the loss of striatal dopamine results in the emergence of oscillatory burst patterns of firing of basal ganglia output neurons, increased synchrony of the discharge of neighboring basal ganglia neurons, and an overall increase in basal ganglia output. The relevance of these findings is supported by the demonstration, in NHP models of parkinsonism, of the antiparkinsonian effects of inactivation of the motor circuit at the level of the subthalamic nucleus, one of the major components of the basal ganglia. This finding also contributed strongly to the revival of the use of surgical interventions to treat patients with Parkinson's disease. While ablative procedures were first used for this purpose, they have now been largely replaced by DBS of the subthalamic nucleus or internal pallidal segment. These procedures are not only effective in the treatment of parkinsonism, but also in the treatment of hyperkinetic conditions (such as chorea or dystonia) which result from pathophysiologic changes different from those underlying Parkinson's disease. Thus, these interventions probably do not counteract specific aspects of the pathophysiology of movement disorders, but non-specifically remove the influence of the different types of disruptive basal ganglia output from the relatively intact portions of the motor circuitry downstream from the basal ganglia. Knowledge gained from studies in NHPs remains critical for our understanding of the pathophysiology of movement disorders, of the effects of DBS on brain network activity, and the development of better treatments for patients with movement disorders and other neurologic or psychiatric conditions.

Asynchronous Cholinergic Drive Correlates with Excitation-Inhibition Imbalance via a Neuronal Ca2+ Sensor Protein.

PubMed

Zhou, Keming; Cherra, Salvatore J; Goncharov, Alexandr; Jin, Yishi

2017-05-09

Excitation-inhibition imbalance in neural networks is widely linked to neurological and neuropsychiatric disorders. However, how genetic factors alter neuronal activity, leading to excitation-inhibition imbalance, remains unclear. Here, using the C. elegans locomotor circuit, we examine how altering neuronal activity for varying time periods affects synaptic release pattern and animal behavior. We show that while short-duration activation of excitatory cholinergic neurons elicits a reversible enhancement of presynaptic strength, persistent activation results to asynchronous and reduced cholinergic drive, inducing imbalance between endogenous excitation and inhibition. We find that the neuronal calcium sensor protein NCS-2 is required for asynchronous cholinergic release in an activity-dependent manner and dampens excitability of inhibitory neurons non-cell autonomously. The function of NCS-2 requires its Ca 2+ binding and membrane association domains. These results reveal a synaptic mechanism implicating asynchronous release in regulation of excitation-inhibition balance. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

Functional Neuroimaging of Spike-Wave Seizures

PubMed Central

Motelow, Joshua E.; Blumenfeld, Hal

2013-01-01

Generalized spike-wave seizures are typically brief events associated with dynamic changes in brain physiology, metabolism, and behavior. Functional magnetic resonance imaging (fMRI) provides a relatively high spatio-temporal resolution method for imaging cortical-subcortical network activity during spike-wave seizures. Patients with spike-wave seizures often have episodes of staring and unresponsiveness which interfere with normal behavior. Results from human fMRI studies suggest that spike-wave seizures disrupt specific networks in the thalamus and fronto-parietal association cortex which are critical for normal attentive consciousness. However, the neuronal activity underlying imaging changes seen during fMRI is not well understood, particularly in abnormal conditions such as seizures. Animal models have begun to provide important fundamental insights into the neuronal basis for fMRI changes during spike-wave activity. Work from these models including both fMRI and direct neuronal recordings suggest that, like in humans, specific cortical-subcortical networks are involved in spike-wave, while other regions are spared. Regions showing fMRI increases demonstrate correlated increases in neuronal activity in animal models. The mechanisms of fMRI decreases in spike-wave will require further investigation. A better understanding of the specific brain regions involved in generating spike-wave seizures may help guide efforts to develop targeted therapies aimed at preventing or reversing abnormal excitability in these brain regions, ultimately leading to a cure for this disorder. PMID:18839093

Assessment of the Effects of Endocrine Disrupting Compounds on the Development of Vertebrate Neural Network Function Using Multi-electrode Arrays.

PubMed

Sanchez, Karla R; Mersha, Mahlet D; Dhillon, Harbinder S; Temburni, Murali K

2018-04-26

Bis-phenols, such as bis-phenol A (BPA) and bis-phenol-S (BPS), are polymerizing agents widely used in the production of plastics and numerous everyday products. They are classified as endocrine disrupting compounds (EDC) with estradiol-like properties. Long-term exposure to EDCs, even at low doses, has been linked with various health defects including cancer, behavioral disorders, and infertility, with greater vulnerability during early developmental periods. To study the effects of BPA on the development of neuronal function, we used an in vitro neuronal network derived from the early chick embryonic brain as a model. We found that exposure to BPA affected the development of network activity, specifically spiking activity and synchronization. A change in network activity is the crucial link between the molecular target of a drug or compound and its effect on behavioral outcome. Multi-electrode arrays are increasingly becoming useful tools to study the effects of drugs on network activity in vitro. There are several systems available in the market and, although there are variations in the number of electrodes, the type and quality of the electrode array and the analysis software, the basic underlying principles, and the data obtained is the same across the different systems. Although currently limited to analysis of two-dimensional in vitro cultures, these MEA systems are being improved to enable in vivo network activity in brain slices. Here, we provide a detailed protocol for embryonic exposure and recording neuronal network activity and synchrony, along with representative results.

Ketamine: differential neurophysiological dynamics in functional networks in the rat brain

PubMed Central

Ahnaou, A; Huysmans, H; Biermans, R; Manyakov, N V; Drinkenburg, W H I M

2017-01-01

Recently, the N-methyl-d-aspartate-receptor (NMDAR) antagonist ketamine has emerged as a fast-onset mechanism to achieve antidepressant activity, whereas its psychomimetic, dissociative and amnestic effects have been well documented to pharmacologically model schizophrenia features in rodents. Sleep–wake architecture, neuronal oscillations and network connectivity are key mechanisms supporting brain plasticity and cognition, which are disrupted in mood disorders such as depression and schizophrenia. In rats, we investigated the dynamic effects of acute and chronic subcutaneous administration of ketamine (2.5, 5 and 10 mg kg−1) on sleep–wake cycle, multichannels network interactions assessed by coherence and phase–amplitude cross-frequency coupling, locomotor activity (LMA), cognitive information processing as reflected by the mismatch negativity-like (MMN) component of event-related brain potentials (ERPs). Acute ketamine elicited a short, lasting inhibition of rapid eye movement (REM) sleep, increased coherence in higher gamma frequency oscillations independent of LMA, altered theta-gamma phase–amplitude coupling, increased MMN peak-amplitude response and evoked higher gamma oscillations. In contrast, chronic ketamine reduced large-scale communication among cortical regions by decreasing oscillations and coherent activity in the gamma frequency range, shifted networks activity towards slow alpha rhythm, decreased MMN peak response and enhanced aberrant higher gamma neuronal network oscillations. Altogether, our data show that acute and chronic ketamine elicited differential changes in network connectivity, ERPs and event-related oscillations (EROs), supporting possible underlying alterations in NMDAR–GABAergic signaling. The findings underscore the relevance of intermittent dosing of ketamine to accurately maintain the functional integrity of neuronal networks for long-term plastic changes and therapeutic effect. PMID:28926001

Synaptic inhibition and γ-aminobutyric acid in the mammalian central nervous system

PubMed Central

OBATA, Kunihiko

2013-01-01

Signal transmission through synapses connecting two neurons is mediated by release of neurotransmitter from the presynaptic axon terminals and activation of its receptor at the postsynaptic neurons. γ-Aminobutyric acid (GABA), non-protein amino acid formed by decarboxylation of glutamic acid, is a principal neurotransmitter at inhibitory synapses of vertebrate and invertebrate nervous system. On one hand glutamic acid serves as a principal excitatory neurotransmitter. This article reviews GABA researches on; (1) synaptic inhibition by membrane hyperpolarization, (2) exclusive localization in inhibitory neurons, (3) release from inhibitory neurons, (4) excitatory action at developmental stage, (5) phenotype of GABA-deficient mouse produced by gene-targeting, (6) developmental adjustment of neural network and (7) neurological/psychiatric disorder. In the end, GABA functions in simple nervous system and plants, and non-amino acid neurotransmitters were supplemented. PMID:23574805

Network feedback regulates motor output across a range of modulatory neuron activity.

PubMed

Spencer, Robert M; Blitz, Dawn M

2016-06-01

Modulatory projection neurons alter network neuron synaptic and intrinsic properties to elicit multiple different outputs. Sensory and other inputs elicit a range of modulatory neuron activity that is further shaped by network feedback, yet little is known regarding how the impact of network feedback on modulatory neurons regulates network output across a physiological range of modulatory neuron activity. Identified network neurons, a fully described connectome, and a well-characterized, identified modulatory projection neuron enabled us to address this issue in the crab (Cancer borealis) stomatogastric nervous system. The modulatory neuron modulatory commissural neuron 1 (MCN1) activates and modulates two networks that generate rhythms via different cellular mechanisms and at distinct frequencies. MCN1 is activated at rates of 5-35 Hz in vivo and in vitro. Additionally, network feedback elicits MCN1 activity time-locked to motor activity. We asked how network activation, rhythm speed, and neuron activity levels are regulated by the presence or absence of network feedback across a physiological range of MCN1 activity rates. There were both similarities and differences in responses of the two networks to MCN1 activity. Many parameters in both networks were sensitive to network feedback effects on MCN1 activity. However, for most parameters, MCN1 activity rate did not determine the extent to which network output was altered by the addition of network feedback. These data demonstrate that the influence of network feedback on modulatory neuron activity is an important determinant of network output and feedback can be effective in shaping network output regardless of the extent of network modulation. Copyright © 2016 the American Physiological Society.

Stress induced neural reorganization: A conceptual framework linking depression and Alzheimer's disease.

PubMed

Ross, Jennifer A; Gliebus, Gediminas; Van Bockstaele, Elisabeth J

2018-07-13

Chronic stress is a risk factor for a number of physiological disorders including cardiovascular disease, obesity and gastrointestinal disorders, as well as psychiatric and neurodegenerative disorders. There are a number of underlying molecular and cellular mechanisms altered in the course of chronic stress, which may increase the vulnerability of individuals to develop psychiatric disorders such as depression, and neurodegenerative disorders such as Alzheimer's Disease (AD). This is evident in the influence of stress on large-scale brain networks, including the resting state Default Mode Network (DMN), the effects of stress on neuronal circuitry and architecture, and the cellular and molecular adaptations to stress, which may render individuals with stress related psychiatric disorders more vulnerable to neurodegenerative disease later in life. These alterations include decreased negative feedback inhibition of the hypothalamic pituitary axis (HPA) axis, decreased dendritic arborization and spine density in the prefrontal cortex (PFC) and hippocampus, and the release of proinflammatory cytokines, which may suppress neurogenesis and promote neuronal cell death. Each of these factors are thought to play a role in stress-related psychiatric disease as well as AD, and have been observed in clinical and post-mortem studies of individuals with depression and AD. The goal of the current review is to summarize clinical and preclinical evidence supporting a role for chronic stress as a putative link between neuropsychiatric and neurodegenerative disease. Moreover, we provide a rationale for the importance of taking a medical history of stress-related psychiatric diseases into consideration during clinical trial design, as they may play an important role in the etiology of AD in stratified patient populations. Copyright © 2017 Elsevier Inc. All rights reserved.

Towards systems neuroscience of ADHD: A meta-analysis of 55 fMRI studies

PubMed Central

Cortese, Samuele; Kelly, Clare; Chabernaud, Camille; Proal, Erika; Di Martino, Adriana; Milham, Michael P.; Castellanos, F. Xavier

2013-01-01

Objective To perform a comprehensive meta-analysis of task-based functional MRI studies of Attention-Deficit/Hyperactivity Disorder (ADHD). Method PubMed, Ovid, EMBASE, Web of Science, ERIC, CINHAL, and NeuroSynth were searched for studies published through 06/30/2011. Significant differences in activation of brain regions between individuals with ADHD and comparisons were detected using activation likelihood estimation meta-analysis (p<0.05, corrected). Dysfunctional regions in ADHD were related to seven reference neuronal systems. We performed a set of meta-analyses focused on age groups (children; adults), clinical characteristics (history of stimulant treatment; presence of psychiatric comorbidities), and specific neuropsychological tasks (inhibition; working memory; vigilance/attention). Results Fifty-five studies were included (39 in children, 16 in adults). In children, hypoactivation in ADHD vs. comparisons was found mostly in systems involved in executive functions (frontoparietal network) and attention (ventral attentional network). Significant hyperactivation in ADHD vs. comparisons was observed predominantly within the default, ventral attention, and somatomotor networks. In adults, ADHD-related hypoactivation was predominant in the frontoparietal system, while ADHD-related hyperactivation was present in the visual, dorsal attention, and default networks. Significant ADHD-related dysfunction largely reflected task features and was detected even in the absence of comorbid mental disorders or history of stimulant treatment. Conclusions A growing literature provides evidence of ADHD-related dysfunction within multiple neuronal systems involved in higher-level cognitive functions but also in sensorimotor processes, including the visual system, and in the default network. This meta-analytic evidence extends early models of ADHD pathophysiology focused on prefrontal-striatal circuits. PMID:22983386

Network dysfunction of emotional and cognitive processes in those at genetic risk of bipolar disorder.

PubMed

Breakspear, Michael; Roberts, Gloria; Green, Melissa J; Nguyen, Vinh T; Frankland, Andrew; Levy, Florence; Lenroot, Rhoshel; Mitchell, Philip B

2015-11-01

The emotional and cognitive vulnerabilities that precede the development of bipolar disorder are poorly understood. The inferior frontal gyrus-a key cortical hub for the integration of cognitive and emotional processes-exhibits both structural and functional changes in bipolar disorder, and is also functionally impaired in unaffected first-degree relatives, showing diminished engagement during inhibition of threat-related emotional stimuli. We hypothesized that this functional impairment of the inferior frontal gyrus in those at genetic risk of bipolar disorder reflects the dysfunction of broader network dynamics underlying the coordination of emotion perception and cognitive control. To test this, we studied effective connectivity in functional magnetic resonance imaging data acquired from 41 first-degree relatives of patients with bipolar disorder, 45 matched healthy controls and 55 participants with established bipolar disorder. Dynamic causal modelling was used to model the neuronal interaction between key regions associated with fear perception (the anterior cingulate), inhibition (the left dorsolateral prefrontal cortex) and the region upon which these influences converge, namely the inferior frontal gyrus. Network models that embodied non-linear, hierarchical relationships were the most strongly supported by data from our healthy control and bipolar participants. We observed a marked difference in the hierarchical influence of the anterior cingulate on the effective connectivity from the dorsolateral prefrontal cortex to the inferior frontal gyrus that is unique to the at-risk cohort. Non-specific, non-hierarchical mechanisms appear to compensate for this network disturbance. We thus establish a specific network disturbance suggesting dysfunction in the processes that support hierarchical relationships between emotion and cognitive control in those at high genetic risk for bipolar disorder. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Network Modeling of Adult Neurogenesis: Shifting Rates of Neuronal Turnover Optimally Gears Network Learning according to Novelty Gradient

PubMed Central

Chambers, R. Andrew; Conroy, Susan K.

2010-01-01

Apoptotic and neurogenic events in the adult hippocampus are hypothesized to play a role in cognitive responses to new contexts. Corticosteroid-mediated stress responses and other neural processes invoked by substantially novel contextual changes may regulate these processes. Using elementary three-layer neural networks that learn by incremental synaptic plasticity, we explored whether the cognitive effects of differential regimens of neuronal turnover depend on the environmental context in terms of the degree of novelty in the new information to be learned. In “adult” networks that had achieved mature synaptic connectivity upon prior learning of the Roman alphabet, imposition of apoptosis/neurogenesis before learning increasingly novel information (alternate Roman < Russian < Hebrew) reveals optimality of informatic cost benefits when rates of turnover are geared in proportion to the degree of novelty. These findings predict that flexible control of rates of apoptosis–neurogenesis within plastic, mature neural systems optimizes learning attributes under varying degrees of contextual change, and that failures in this regulation may define a role for adult hippocampal neurogenesis in novelty- and stress-responsive psychiatric disorders. PMID:17214558

Network modeling of adult neurogenesis: shifting rates of neuronal turnover optimally gears network learning according to novelty gradient.

PubMed

Chambers, R Andrew; Conroy, Susan K

2007-01-01

Apoptotic and neurogenic events in the adult hippocampus are hypothesized to play a role in cognitive responses to new contexts. Corticosteroid-mediated stress responses and other neural processes invoked by substantially novel contextual changes may regulate these processes. Using elementary three-layer neural networks that learn by incremental synaptic plasticity, we explored whether the cognitive effects of differential regimens of neuronal turnover depend on the environmental context in terms of the degree of novelty in the new information to be learned. In "adult" networks that had achieved mature synaptic connectivity upon prior learning of the Roman alphabet, imposition of apoptosis/neurogenesis before learning increasingly novel information (alternate Roman < Russian < Hebrew) reveals optimality of informatic cost benefits when rates of turnover are geared in proportion to the degree of novelty. These findings predict that flexible control of rates of apoptosis-neurogenesis within plastic, mature neural systems optimizes learning attributes under varying degrees of contextual change, and that failures in this regulation may define a role for adult hippocampal neurogenesis in novelty- and stress-responsive psychiatric disorders.

Mean-field equations for neuronal networks with arbitrary degree distributions.

PubMed

Nykamp, Duane Q; Friedman, Daniel; Shaker, Sammy; Shinn, Maxwell; Vella, Michael; Compte, Albert; Roxin, Alex

2017-04-01

The emergent dynamics in networks of recurrently coupled spiking neurons depends on the interplay between single-cell dynamics and network topology. Most theoretical studies on network dynamics have assumed simple topologies, such as connections that are made randomly and independently with a fixed probability (Erdös-Rényi network) (ER) or all-to-all connected networks. However, recent findings from slice experiments suggest that the actual patterns of connectivity between cortical neurons are more structured than in the ER random network. Here we explore how introducing additional higher-order statistical structure into the connectivity can affect the dynamics in neuronal networks. Specifically, we consider networks in which the number of presynaptic and postsynaptic contacts for each neuron, the degrees, are drawn from a joint degree distribution. We derive mean-field equations for a single population of homogeneous neurons and for a network of excitatory and inhibitory neurons, where the neurons can have arbitrary degree distributions. Through analysis of the mean-field equations and simulation of networks of integrate-and-fire neurons, we show that such networks have potentially much richer dynamics than an equivalent ER network. Finally, we relate the degree distributions to so-called cortical motifs.

Mean-field equations for neuronal networks with arbitrary degree distributions

NASA Astrophysics Data System (ADS)

Nykamp, Duane Q.; Friedman, Daniel; Shaker, Sammy; Shinn, Maxwell; Vella, Michael; Compte, Albert; Roxin, Alex

2017-04-01

The emergent dynamics in networks of recurrently coupled spiking neurons depends on the interplay between single-cell dynamics and network topology. Most theoretical studies on network dynamics have assumed simple topologies, such as connections that are made randomly and independently with a fixed probability (Erdös-Rényi network) (ER) or all-to-all connected networks. However, recent findings from slice experiments suggest that the actual patterns of connectivity between cortical neurons are more structured than in the ER random network. Here we explore how introducing additional higher-order statistical structure into the connectivity can affect the dynamics in neuronal networks. Specifically, we consider networks in which the number of presynaptic and postsynaptic contacts for each neuron, the degrees, are drawn from a joint degree distribution. We derive mean-field equations for a single population of homogeneous neurons and for a network of excitatory and inhibitory neurons, where the neurons can have arbitrary degree distributions. Through analysis of the mean-field equations and simulation of networks of integrate-and-fire neurons, we show that such networks have potentially much richer dynamics than an equivalent ER network. Finally, we relate the degree distributions to so-called cortical motifs.

iPlasticity: induced juvenile-like plasticity in the adult brain as a mechanism of antidepressants.

PubMed

Umemori, Juzoh; Winkel, Frederike; Didio, Giuliano; Llach Pou, Maria; Castrén, Eero

2018-05-26

The network hypothesis of depression proposes that mood disorders reflect problems in information processing within particular neural networks. Antidepressants, including selective serotonin reuptake inhibitors (SSRIs), function by gradually improving information processing within these networks. Antidepressants have been shown to induce a state of juvenile-like plasticity comparable to that observed during developmental critical periods: such critical-period-like plasticity allows brain networks to better adapt to extrinsic and intrinsic signals. We have coined this drug-induced state of juvenile-like plasticity iPlasticity. A combination of iPlasticity induced by chronic SSRI treatment together with training, rehabilitation, or psychotherapy improves symptoms of neuropsychiatric disorders and issues underlying the developmentally- or genetically-malfunctioning networks. We have proposed that iPlasticity might be a critical component of antidepressant action. We have demonstrated that iPlasticity occurs in the visual cortex, fear erasure network, extinction of aggression caused by social isolation, and spatial reversal memory in rodent models. Chronic SSRI treatment is known to promote neurogenesis and to cause dematuration of granule cells in the dentate gyrus and of interneurons, especially parvalbumin interneurons enwrapped by perineuronal nets in the prefrontal cortex, visual cortex, and amygdala. Brain-derived neurotrophic factor (BDNF), via its receptor Tropomyosin kinase receptor B (TrkB), is involved in processes of the synaptic plasticity, including neurogenesis, neuronal differentiation, weight of synapses, and gene regulation of synaptic formation. BDNF can be activated by both chronic SSRI treatment and neuronal activity. Accordingly, the BDNF/TrkB pathway is critical for iPlasticity, but further analyses will be needed to provide mechanical insight into the processes of iPlasticity. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Global dysrhythmia of cerebro-basal ganglia-cerebellar networks underlies motor tics following striatal disinhibition.

PubMed

McCairn, Kevin W; Iriki, Atsushi; Isoda, Masaki

2013-01-09

Motor tics, a cardinal symptom of Tourette syndrome (TS), are hypothesized to arise from abnormalities within cerebro-basal ganglia circuits. Yet noninvasive neuroimaging of TS has previously identified robust activation in the cerebellum. To date, electrophysiological properties of cerebellar activation and its role in basal ganglia-mediated tic expression remain unknown. We performed multisite, multielectrode recordings of single-unit activity and local field potentials from the cerebellum, basal ganglia, and primary motor cortex using a pharmacologic monkey model of motor tics/TS. Following microinjections of bicuculline into the sensorimotor putamen, periodic tics occurred predominantly in the orofacial region, and a sizable number of cerebellar neurons showed phasic changes in activity associated with tic episodes. Specifically, 64% of the recorded cerebellar cortex neurons exhibited increases in activity, and 85% of the dentate nucleus neurons displayed excitatory, inhibitory, or multiphasic responses. Critically, abnormal discharges of cerebellar cortex neurons and excitatory-type dentate neurons mostly preceded behavioral tic onset, indicating their central origins. Latencies of pathological activity in the cerebellum and primary motor cortex substantially overlapped, suggesting that aberrant signals may be traveling along divergent pathways to these structures from the basal ganglia. Furthermore, the occurrence of tic movement was most closely associated with local field potential spikes in the cerebellum and primary motor cortex, implying that these structures may function as a gate to release overt tic movements. These findings indicate that tic-generating networks in basal ganglia mediated tic disorders extend beyond classical cerebro-basal ganglia circuits, leading to global network dysrhythmia including cerebellar circuits.

Rhythmogenic neuronal networks, emergent leaders, and k-cores.

PubMed

Schwab, David J; Bruinsma, Robijn F; Feldman, Jack L; Levine, Alex J

2010-11-01

Neuronal network behavior results from a combination of the dynamics of individual neurons and the connectivity of the network that links them together. We study a simplified model, based on the proposal of Feldman and Del Negro (FDN) [Nat. Rev. Neurosci. 7, 232 (2006)], of the preBötzinger Complex, a small neuronal network that participates in the control of the mammalian breathing rhythm through periodic firing bursts. The dynamics of this randomly connected network of identical excitatory neurons differ from those of a uniformly connected one. Specifically, network connectivity determines the identity of emergent leader neurons that trigger the firing bursts. When neuronal desensitization is controlled by the number of input signals to the neurons (as proposed by FDN), the network's collective desensitization--required for successful burst termination--is mediated by k-core clusters of neurons.

Protein Homeostasis in Amyotrophic Lateral Sclerosis: Therapeutic Opportunities?

PubMed Central

Webster, Christopher P.; Smith, Emma F.; Shaw, Pamela J.; De Vos, Kurt J.

2017-01-01

Protein homeostasis (proteostasis), the correct balance between production and degradation of proteins, is essential for the health and survival of cells. Proteostasis requires an intricate network of protein quality control pathways (the proteostasis network) that work to prevent protein aggregation and maintain proteome health throughout the lifespan of the cell. Collapse of proteostasis has been implicated in the etiology of a number of neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), the most common adult onset motor neuron disorder. Here, we review the evidence linking dysfunctional proteostasis to the etiology of ALS and discuss how ALS-associated insults affect the proteostasis network. Finally, we discuss the potential therapeutic benefit of proteostasis network modulation in ALS. PMID:28512398

The Schizophrenia Risk Gene MIR137 Acts as a Hippocampal Gene Network Node Orchestrating the Expression of Genes Relevant to Nervous System Development and Function

PubMed Central

Loohuis, Nikkie FM Olde; Kasri, Nael Nadif; Glennon, Jeffrey C; van Bokhoven, Hans; Hébert, Sébastien S; Kaplan, Barry B.; Martens, Gerard JM; Aschrafi, Armaz

2016-01-01

MicroRNAs (miRs) are small regulatory molecules, which orchestrate neuronal development and plasticity through modulation of complex gene networks. microRNA-137 (miR-137) is a brain-enriched RNA with a critical role in regulating brain development and in mediating synaptic plasticity. Importantly, mutations in this miR are associated with the pathoetiology of schizophrenia (SZ), and there is a widespread assumption that disruptions in miR-137 expression lead to aberrant expression of gene regulatory networks associated with SZ. To systematically identify the mRNA targets for this miR, we performed miR-137 gain- and loss-of-function experiments in primary rat hippocampal neurons and profiled differentially expressed mRNAs through next-generation sequencing. We identified 500 genes that were bidirectionally activated or repressed in their expression by the modulation of miR-137 levels. Gene ontology analysis using two independent software resources suggested functions for these miR-137-regulated genes in neurodevelopmental processes, neuronal maturation processes and cell maintenance, all of which known to be critical for proper brain circuitry formation. Since many of the putative miR-137 targets identified here also have been previously shown to be associated with SZ, we propose that this miR acts as a critical gene network hub contributing to the pathophysiology of this neurodevelopmental disorder. PMID:26925706

Disorder-induced localization of excitability in an array of coupled lasers

NASA Astrophysics Data System (ADS)

Lamperti, M.; Perego, A. M.

2017-10-01

We report on the localization of excitability induced by disorder in an array of coupled semiconductor lasers with a saturable absorber. Through numerical simulations we show that the exponential localization of excitable waves occurs if a certain critical amount of randomness is present in the coupling coefficients among the lasers. The results presented in this Rapid Communication demonstrate that disorder can induce localization in lattices of excitable nonlinear oscillators, and can be of interest in the study of photonics-based random networks, neuromorphic systems, and, by analogy, in biology, in particular, in the investigation of the collective dynamics of neuronal cell populations.

Intermediate-filaments: from disordered building blocks to well-ordered cells

NASA Astrophysics Data System (ADS)

Kornreich, Micha; Malka-Gibor, Eti; Laser-Azogui, Adi; Doron, Ofer; Avinery, Ram; Herrmann, Harald; Beck, Roy

In the past decade it was found that ~50% of human proteins contain long disordered regions, which play significant functional roles. As these regions lack a defined 3D folded structure, their ensemble conformations can be studied using polymer physics statistical-mechanics arguments. We measure the structure and mechanical response of hydrogels composed of neuronal intermediate filaments proteins. In the nervous system, these proteins provide cells with their mechanical support and shape, via interactions of their long, highly charged and disordered protein chains. We employ synchrotron small-angle X-ray scattering and various microscopy techniques to investigate such hydrogels from the nano- to the macro-scale. In contrast to previous polymer physics theories and experiments, we find that shorter and less charged chains can promote network expansion. The results are explained by intricate interactions between specific domains on the interacting chains, and also suggest a novel structural justification for the changing protein compositions observed during neuronal development. We address the following questions: Can protein disorder have an important role in cellular architecture? Can structural disorder in the micro-scale induce orientational and translational order on the macro-scale? How do the physical properties of disordered protein regions, such as charge, length, and hydrophobicity, modulate the cellular super-structure?

[Predictive factors of anxiety disorders].

PubMed

Domschke, K

2014-10-01

Anxiety disorders are among the most frequent mental disorders in Europe (12-month prevalence 14%) and impose a high socioeconomic burden. The pathogenesis of anxiety disorders is complex with an interaction of biological, environmental and psychosocial factors contributing to the overall disease risk (diathesis-stress model). In this article, risk factors for anxiety disorders will be presented on several levels, e.g. genetic factors, environmental factors, gene-environment interactions, epigenetic mechanisms, neuronal networks ("brain fear circuit"), psychophysiological factors (e.g. startle response and CO2 sensitivity) and dimensional/subclinical phenotypes of anxiety (e.g. anxiety sensitivity and behavioral inhibition), and critically discussed regarding their potential predictive value. The identification of factors predictive of anxiety disorders will possibly allow for effective preventive measures or early treatment interventions, respectively, and reduce the individual patient's suffering as well as the overall socioeconomic burden of anxiety disorders.

Dynamical estimation of neuron and network properties III: network analysis using neuron spike times.

PubMed

Knowlton, Chris; Meliza, C Daniel; Margoliash, Daniel; Abarbanel, Henry D I

2014-06-01

Estimating the behavior of a network of neurons requires accurate models of the individual neurons along with accurate characterizations of the connections among them. Whereas for a single cell, measurements of the intracellular voltage are technically feasible and sufficient to characterize a useful model of its behavior, making sufficient numbers of simultaneous intracellular measurements to characterize even small networks is infeasible. This paper builds on prior work on single neurons to explore whether knowledge of the time of spiking of neurons in a network, once the nodes (neurons) have been characterized biophysically, can provide enough information to usefully constrain the functional architecture of the network: the existence of synaptic links among neurons and their strength. Using standardized voltage and synaptic gating variable waveforms associated with a spike, we demonstrate that the functional architecture of a small network of model neurons can be established.

Chronic disorders of consciousness: role of neuroimaging

NASA Astrophysics Data System (ADS)

Kremneva, E.; Sergeev, D.; Zmeykina, E.; Legostaeva, L.; Piradov, M.

2017-08-01

Chronic disorders of consciousness are clinically challenging conditions, and advanced methods of imaging for better understanding of diagnosis and prognosis are needed. Recent functional neuroradiological studies utilizing PET and fMRI demonstrated that besides widespread neuronal loss disruption of interconnection between certain cortical networks after the injury may also play the leading role in the development of behaviourally assessed unresponsiveness. Functional and structural connectivity, evaluated by neuroimaging approaches, may correlate with clinical status and may also play prognostic role. Integration of data from various diagnostic modalities is needed for further progress in this area.

Synchronization properties of heterogeneous neuronal networks with mixed excitability type

NASA Astrophysics Data System (ADS)

Leone, Michael J.; Schurter, Brandon N.; Letson, Benjamin; Booth, Victoria; Zochowski, Michal; Fink, Christian G.

2015-03-01

We study the synchronization of neuronal networks with dynamical heterogeneity, showing that network structures with the same propensity for synchronization (as quantified by master stability function analysis) may develop dramatically different synchronization properties when heterogeneity is introduced with respect to neuronal excitability type. Specifically, we investigate networks composed of neurons with different types of phase response curves (PRCs), which characterize how oscillating neurons respond to excitatory perturbations. Neurons exhibiting type 1 PRC respond exclusively with phase advances, while neurons exhibiting type 2 PRC respond with either phase delays or phase advances, depending on when the perturbation occurs. We find that Watts-Strogatz small world networks transition to synchronization gradually as the proportion of type 2 neurons increases, whereas scale-free networks may transition gradually or rapidly, depending upon local correlations between node degree and excitability type. Random placement of type 2 neurons results in gradual transition to synchronization, whereas placement of type 2 neurons as hubs leads to a much more rapid transition, showing that type 2 hub cells easily "hijack" neuronal networks to synchronization. These results underscore the fact that the degree of synchronization observed in neuronal networks is determined by a complex interplay between network structure and the dynamical properties of individual neurons, indicating that efforts to recover structural connectivity from dynamical correlations must in general take both factors into account.

The cells of cajal-retzius: still a mystery one century after.

PubMed

Soriano, Eduardo; Del Río, José Antonio

2005-05-05

Cajal-Retzius (CR) cells are an enigmatic class of neurons located at the surface of the cerebral cortex, playing a major role in cortical development. In this review, we discuss several distinct features of these neurons and the mechanisms by which they regulate cortical development. Many CR cells likely have extracortical origin and undergo cell death during development. Recent genetic studies report unique patterns of gene expression in CR cells, which may help to explain the developmental processes in which they participate. Moreover, a number of studies indicate that CR cells, and their secreted gene product, reelin, are involved in neuronal migration by acting on two key partners, migrating neurons and radial glial cells. Emerging data show that these neurons are a critical part of an early and complex network of neural activity in layer I, supporting the notion that CR cells modulate cortical maturation. Given these key and complex developmental properties, it is therefore conceivable for CR cells to be implicated in the pathogenesis of a variety of neurological disorders.

ASIC-dependent LTP at multiple glutamatergic synapses in amygdala network is required for fear memory

PubMed Central

Chiang, Po-Han; Chien, Ta-Chun; Chen, Chih-Cheng; Yanagawa, Yuchio; Lien, Cheng-Chang

2015-01-01

Genetic variants in the human ortholog of acid-sensing ion channel-1a subunit (ASIC1a) gene are associated with panic disorder and amygdala dysfunction. Both fear learning and activity-induced long-term potentiation (LTP) of cortico-basolateral amygdala (BLA) synapses are impaired in ASIC1a-null mice, suggesting a critical role of ASICs in fear memory formation. In this study, we found that ASICs were differentially expressed within the amygdala neuronal population, and the extent of LTP at various glutamatergic synapses correlated with the level of ASIC expression in postsynaptic neurons. Importantly, selective deletion of ASIC1a in GABAergic cells, including amygdala output neurons, eliminated LTP in these cells and reduced fear learning to the same extent as that found when ASIC1a was selectively abolished in BLA glutamatergic neurons. Thus, fear learning requires ASIC-dependent LTP at multiple amygdala synapses, including both cortico-BLA input synapses and intra-amygdala synapses on output neurons. PMID:25988357

Pathological effect of homeostatic synaptic scaling on network dynamics in diseases of the cortex.

PubMed

Fröhlich, Flavio; Bazhenov, Maxim; Sejnowski, Terrence J

2008-02-13

Slow periodic EEG discharges are common in CNS disorders. The pathophysiology of this aberrant rhythmic activity is poorly understood. We used a computational model of a neocortical network with a dynamic homeostatic scaling rule to show that loss of input (partial deafferentation) can trigger network reorganization that results in pathological periodic discharges. The decrease in average firing rate in the network by deafferentation was compensated by homeostatic synaptic scaling of recurrent excitation among pyramidal cells. Synaptic scaling succeeded in recovering the network target firing rate for all degrees of deafferentation (fraction of deafferented cells), but there was a critical degree of deafferentation for pathological network reorganization. For deafferentation degrees below this value, homeostatic upregulation of recurrent excitation had minimal effect on the macroscopic network dynamics. For deafferentation above this threshold, however, a slow periodic oscillation appeared, patterns of activity were less sparse, and bursting occurred in individual neurons. Also, comparison of spike-triggered afferent and recurrent excitatory conductances revealed that information transmission was strongly impaired. These results suggest that homeostatic plasticity can lead to secondary functional impairment in case of cortical disorders associated with cell loss.

Simulation of Code Spectrum and Code Flow of Cultured Neuronal Networks.

PubMed

Tamura, Shinichi; Nishitani, Yoshi; Hosokawa, Chie; Miyoshi, Tomomitsu; Sawai, Hajime

2016-01-01

It has been shown that, in cultured neuronal networks on a multielectrode, pseudorandom-like sequences (codes) are detected, and they flow with some spatial decay constant. Each cultured neuronal network is characterized by a specific spectrum curve. That is, we may consider the spectrum curve as a "signature" of its associated neuronal network that is dependent on the characteristics of neurons and network configuration, including the weight distribution. In the present study, we used an integrate-and-fire model of neurons with intrinsic and instantaneous fluctuations of characteristics for performing a simulation of a code spectrum from multielectrodes on a 2D mesh neural network. We showed that it is possible to estimate the characteristics of neurons such as the distribution of number of neurons around each electrode and their refractory periods. Although this process is a reverse problem and theoretically the solutions are not sufficiently guaranteed, the parameters seem to be consistent with those of neurons. That is, the proposed neural network model may adequately reflect the behavior of a cultured neuronal network. Furthermore, such prospect is discussed that code analysis will provide a base of communication within a neural network that will also create a base of natural intelligence.

Dynamics of human subthalamic neuron phase-locking to motor and sensory cortical oscillations during movement.

PubMed

Lipski, Witold J; Wozny, Thomas A; Alhourani, Ahmad; Kondylis, Efstathios D; Turner, Robert S; Crammond, Donald J; Richardson, Robert Mark

2017-09-01

Coupled oscillatory activity recorded between sensorimotor regions of the basal ganglia-thalamocortical loop is thought to reflect information transfer relevant to movement. A neuronal firing-rate model of basal ganglia-thalamocortical circuitry, however, has dominated thinking about basal ganglia function for the past three decades, without knowledge of the relationship between basal ganglia single neuron firing and cortical population activity during movement itself. We recorded activity from 34 subthalamic nucleus (STN) neurons, simultaneously with cortical local field potentials and motor output, in 11 subjects with Parkinson's disease (PD) undergoing awake deep brain stimulator lead placement. STN firing demonstrated phase synchronization to both low- and high-beta-frequency cortical oscillations, and to the amplitude envelope of gamma oscillations, in motor cortex. We found that during movement, the magnitude of this synchronization was dynamically modulated in a phase-frequency-specific manner. Importantly, we found that phase synchronization was not correlated with changes in neuronal firing rate. Furthermore, we found that these relationships were not exclusive to motor cortex, because STN firing also demonstrated phase synchronization to both premotor and sensory cortex. The data indicate that models of basal ganglia function ultimately will need to account for the activity of populations of STN neurons that are bound in distinct functional networks with both motor and sensory cortices and code for movement parameters independent of changes in firing rate. NEW & NOTEWORTHY Current models of basal ganglia-thalamocortical networks do not adequately explain simple motor functions, let alone dysfunction in movement disorders. Our findings provide data that inform models of human basal ganglia function by demonstrating how movement is encoded by networks of subthalamic nucleus (STN) neurons via dynamic phase synchronization with cortex. The data also demonstrate, for the first time in humans, a mechanism through which the premotor and sensory cortices are functionally connected to the STN. Copyright © 2017 the American Physiological Society.

Endoplasmic reticulum proteostasis impairment in aging.

PubMed

Martínez, Gabriela; Duran-Aniotz, Claudia; Cabral-Miranda, Felipe; Vivar, Juan P; Hetz, Claudio

2017-08-01

Perturbed neuronal proteostasis is a salient feature shared by both aging and protein misfolding disorders. The proteostasis network controls the health of the proteome by integrating pathways involved in protein synthesis, folding, trafficking, secretion, and their degradation. A reduction in the buffering capacity of the proteostasis network during aging may increase the risk to undergo neurodegeneration by enhancing the accumulation of misfolded proteins. As almost one-third of the proteome is synthetized at the endoplasmic reticulum (ER), maintenance of its proper function is fundamental to sustain neuronal function. In fact, ER stress is a common feature of most neurodegenerative diseases. The unfolded protein response (UPR) operates as central player to maintain ER homeostasis or the induction of cell death of chronically damaged cells. Here, we discuss recent evidence placing ER stress as a driver of brain aging, and the emerging impact of neuronal UPR in controlling global proteostasis at the whole organismal level. Finally, we discuss possible therapeutic interventions to improve proteostasis and prevent pathological brain aging. © 2017 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.

APC/CCdh1-Rock2 pathway controls dendritic integrity and memory

PubMed Central

Bobo-Jiménez, Verónica; Delgado-Esteban, María; Angibaud, Julie; Sánchez-Morán, Irene; de la Fuente, Antonio; Yajeya, Javier; Nägerl, U. Valentin; Castillo, José; Bolaños, Juan P.

2017-01-01

Disruption of neuronal morphology contributes to the pathology of neurodegenerative disorders such as Alzheimer’s disease (AD). However, the underlying molecular mechanisms are unknown. Here, we show that postnatal deletion of Cdh1, a cofactor of the anaphase-promoting complex/cyclosome (APC/C) ubiquitin ligase in neurons [Cdh1 conditional knockout (cKO)], disrupts dendrite arborization and causes dendritic spine and synapse loss in the cortex and hippocampus, concomitant with memory impairment and neurodegeneration, in adult mice. We found that the dendrite destabilizer Rho protein kinase 2 (Rock2), which accumulates in the brain of AD patients, is an APC/CCdh1 substrate in vivo and that Rock2 protein and activity increased in the cortex and hippocampus of Cdh1 cKO mice. In these animals, inhibition of Rock activity, using the clinically approved drug fasudil, prevented dendritic network disorganization, memory loss, and neurodegeneration. Thus, APC/CCdh1-mediated degradation of Rock2 maintains the dendritic network, memory formation, and neuronal survival, suggesting that pharmacological inhibition of aberrantly accumulated Rock2 may be a suitable therapeutic strategy against neurodegeneration. PMID:28396402

Subthalamic, not striatal, activity correlates with basal ganglia downstream activity in normal and parkinsonian monkeys

PubMed Central

Deffains, Marc; Iskhakova, Liliya; Katabi, Shiran; Haber, Suzanne N; Israel, Zvi; Bergman, Hagai

2016-01-01

The striatum and the subthalamic nucleus (STN) constitute the input stage of the basal ganglia (BG) network and together innervate BG downstream structures using GABA and glutamate, respectively. Comparison of the neuronal activity in BG input and downstream structures reveals that subthalamic, not striatal, activity fluctuations correlate with modulations in the increase/decrease discharge balance of BG downstream neurons during temporal discounting classical condition task. After induction of parkinsonism with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), abnormal low beta (8-15 Hz) spiking and local field potential (LFP) oscillations resonate across the BG network. Nevertheless, LFP beta oscillations entrain spiking activity of STN, striatal cholinergic interneurons and BG downstream structures, but do not entrain spiking activity of striatal projection neurons. Our results highlight the pivotal role of STN divergent projections in BG physiology and pathophysiology and may explain why STN is such an effective site for invasive treatment of advanced Parkinson's disease and other BG-related disorders. DOI: http://dx.doi.org/10.7554/eLife.16443.001 PMID:27552049

Identification of neuronal network properties from the spectral analysis of calcium imaging signals in neuronal cultures.

PubMed

Tibau, Elisenda; Valencia, Miguel; Soriano, Jordi

2013-01-01

Neuronal networks in vitro are prominent systems to study the development of connections in living neuronal networks and the interplay between connectivity, activity and function. These cultured networks show a rich spontaneous activity that evolves concurrently with the connectivity of the underlying network. In this work we monitor the development of neuronal cultures, and record their activity using calcium fluorescence imaging. We use spectral analysis to characterize global dynamical and structural traits of the neuronal cultures. We first observe that the power spectrum can be used as a signature of the state of the network, for instance when inhibition is active or silent, as well as a measure of the network's connectivity strength. Second, the power spectrum identifies prominent developmental changes in the network such as GABAA switch. And third, the analysis of the spatial distribution of the spectral density, in experiments with a controlled disintegration of the network through CNQX, an AMPA-glutamate receptor antagonist in excitatory neurons, reveals the existence of communities of strongly connected, highly active neurons that display synchronous oscillations. Our work illustrates the interest of spectral analysis for the study of in vitro networks, and its potential use as a network-state indicator, for instance to compare healthy and diseased neuronal networks.

Gene network and pathway analysis of mice with conditional ablation of Dicer in post-mitotic neurons.

PubMed

Dorval, Véronique; Smith, Pascal Y; Delay, Charlotte; Calvo, Ezequiel; Planel, Emmanuel; Zommer, Nadège; Buée, Luc; Hébert, Sébastien S

2012-01-01

The small non-protein-coding microRNAs (miRNAs) have emerged as critical regulators of neuronal differentiation, identity and survival. To date, however, little is known about the genes and molecular networks regulated by neuronal miRNAs in vivo, particularly in the adult mammalian brain. We analyzed whole genome microarrays from mice lacking Dicer, the enzyme responsible for miRNA production, specifically in postnatal forebrain neurons. A total of 755 mRNA transcripts were significantly (P<0.05, FDR<0.25) misregulated in the conditional Dicer knockout mice. Ten genes, including Tnrc6c, Dnmt3a, and Limk1, were validated by real time quantitative RT-PCR. Upregulated transcripts were enriched in nonneuronal genes, which is consistent with previous studies in vitro. Microarray data mining showed that upregulated genes were enriched in biological processes related to gene expression regulation, while downregulated genes were associated with neuronal functions. Molecular pathways associated with neurological disorders, cellular organization and cellular maintenance were altered in the Dicer mutant mice. Numerous miRNA target sites were enriched in the 3'untranslated region (3'UTR) of upregulated genes, the most significant corresponding to the miR-124 seed sequence. Interestingly, our results suggest that, in addition to miR-124, a large fraction of the neuronal miRNome participates, by order of abundance, in coordinated gene expression regulation and neuronal maintenance. Taken together, these results provide new clues into the role of specific miRNA pathways in the regulation of brain identity and maintenance in adult mice.

Nerve growth factor, interoception, and sympathetic neuron: lesson from congenital insensitivity to pain with anhidrosis.

PubMed

Indo, Yasuhiro

2009-05-11

Nerve growth factor (NGF) is a well-known neurotrophic factor essential for the survival and maintenance of sensory and sympathetic neurons. Congenital insensitivity to pain with anhidrosis (CIPA) is a genetic disorder due to loss-of-function mutations in the NTRK1 (also known as TRKA) gene encoding TrkA, a receptor tyrosine kinase for NGF. Patients with CIPA provide us a rare opportunity to explore the developmental and physiological function of the NGF-dependent neurons in behavior, cognitive, and mental activities that are not available in animal studies. Here, I discuss the significance of findings that patients with CIPA lack NGF-dependent neurons, including interoceptive polymodal receptors, sympathetic postganglionic neurons, and probably several types of neurons in the brain. They also exhibit characteristic emotional behavior or problems. Together, the NGF-TrkA system is essential for the establishment of a neural network for interoception and homeostasis that may underlie 'gut feelings'. Thus, NGF-dependent neurons play a crucial role in emotional experiences and decision-making processes. Prospective studies focused on these neurons might provide further insights into the neural basis of human emotion and feeling.

Computational properties of networks of synchronous groups of spiking neurons.

PubMed

Dayhoff, Judith E

2007-09-01

We demonstrate a model in which synchronously firing ensembles of neurons are networked to produce computational results. Each ensemble is a group of biological integrate-and-fire spiking neurons, with probabilistic interconnections between groups. An analogy is drawn in which each individual processing unit of an artificial neural network corresponds to a neuronal group in a biological model. The activation value of a unit in the artificial neural network corresponds to the fraction of active neurons, synchronously firing, in a biological neuronal group. Weights of the artificial neural network correspond to the product of the interconnection density between groups, the group size of the presynaptic group, and the postsynaptic potential heights in the synchronous group model. All three of these parameters can modulate connection strengths between neuronal groups in the synchronous group models. We give an example of nonlinear classification (XOR) and a function approximation example in which the capability of the artificial neural network can be captured by a neural network model with biological integrate-and-fire neurons configured as a network of synchronously firing ensembles of such neurons. We point out that the general function approximation capability proven for feedforward artificial neural networks appears to be approximated by networks of neuronal groups that fire in synchrony, where the groups comprise integrate-and-fire neurons. We discuss the advantages of this type of model for biological systems, its possible learning mechanisms, and the associated timing relationships.

Plastic Synaptic Networks of the Amygdala for the Acquisition, Expression, and Extinction of Conditioned Fear

PubMed Central

Pape, Hans-Christian; Pare, Denis

2009-01-01

The last ten years have witnessed a surge of interest for the mechanisms underlying the acquisition and extinction of classically conditioned fear responses. In part, this results from the realization that abnormalities in fear learning mechanisms likely participate to the development and/or maintenance of human anxiety disorders. The simplicity and robustness of this learning paradigm, coupled to the fact that the underlying circuitry is evolutionarily well conserved makes it an ideal model to study the basic biology of memory and identify genetic factors and neuronal systems that regulate the normal and pathological expressions of learned fear. Critical advances have been made in determining how modified neuronal functions upon fear acquisition become stabilized during fear memory consolidation and how these processes are controlled in the course of fear memory extinction. With these advances, came the realization that activity in remote neuronal networks must be coordinated for these events to take place. In this paper, we review these mechanisms of coordinated network activity and the molecular cascades leading to enduring fear memory, and allowing for their extinction. We will focus on Pavlovian fear conditioning as a model and the amygdala as a key component for the acquisition and extinction of fear responses. PMID:20393190

Foxp2 Regulates Gene Networks Implicated in Neurite Outgrowth in the Developing Brain

PubMed Central

Vernes, Sonja C.; Oliver, Peter L.; Spiteri, Elizabeth; Lockstone, Helen E.; Puliyadi, Rathi; Taylor, Jennifer M.; Ho, Joses; Mombereau, Cedric; Brewer, Ariel; Lowy, Ernesto; Nicod, Jérôme; Groszer, Matthias; Baban, Dilair; Sahgal, Natasha; Cazier, Jean-Baptiste; Ragoussis, Jiannis; Davies, Kay E.; Geschwind, Daniel H.; Fisher, Simon E.

2011-01-01

Forkhead-box protein P2 is a transcription factor that has been associated with intriguing aspects of cognitive function in humans, non-human mammals, and song-learning birds. Heterozygous mutations of the human FOXP2 gene cause a monogenic speech and language disorder. Reduced functional dosage of the mouse version (Foxp2) causes deficient cortico-striatal synaptic plasticity and impairs motor-skill learning. Moreover, the songbird orthologue appears critically important for vocal learning. Across diverse vertebrate species, this well-conserved transcription factor is highly expressed in the developing and adult central nervous system. Very little is known about the mechanisms regulated by Foxp2 during brain development. We used an integrated functional genomics strategy to robustly define Foxp2-dependent pathways, both direct and indirect targets, in the embryonic brain. Specifically, we performed genome-wide in vivo ChIP–chip screens for Foxp2-binding and thereby identified a set of 264 high-confidence neural targets under strict, empirically derived significance thresholds. The findings, coupled to expression profiling and in situ hybridization of brain tissue from wild-type and mutant mouse embryos, strongly highlighted gene networks linked to neurite development. We followed up our genomics data with functional experiments, showing that Foxp2 impacts on neurite outgrowth in primary neurons and in neuronal cell models. Our data indicate that Foxp2 modulates neuronal network formation, by directly and indirectly regulating mRNAs involved in the development and plasticity of neuronal connections. PMID:21765815

Foxp2 regulates gene networks implicated in neurite outgrowth in the developing brain.

PubMed

Vernes, Sonja C; Oliver, Peter L; Spiteri, Elizabeth; Lockstone, Helen E; Puliyadi, Rathi; Taylor, Jennifer M; Ho, Joses; Mombereau, Cedric; Brewer, Ariel; Lowy, Ernesto; Nicod, Jérôme; Groszer, Matthias; Baban, Dilair; Sahgal, Natasha; Cazier, Jean-Baptiste; Ragoussis, Jiannis; Davies, Kay E; Geschwind, Daniel H; Fisher, Simon E

2011-07-01

Forkhead-box protein P2 is a transcription factor that has been associated with intriguing aspects of cognitive function in humans, non-human mammals, and song-learning birds. Heterozygous mutations of the human FOXP2 gene cause a monogenic speech and language disorder. Reduced functional dosage of the mouse version (Foxp2) causes deficient cortico-striatal synaptic plasticity and impairs motor-skill learning. Moreover, the songbird orthologue appears critically important for vocal learning. Across diverse vertebrate species, this well-conserved transcription factor is highly expressed in the developing and adult central nervous system. Very little is known about the mechanisms regulated by Foxp2 during brain development. We used an integrated functional genomics strategy to robustly define Foxp2-dependent pathways, both direct and indirect targets, in the embryonic brain. Specifically, we performed genome-wide in vivo ChIP-chip screens for Foxp2-binding and thereby identified a set of 264 high-confidence neural targets under strict, empirically derived significance thresholds. The findings, coupled to expression profiling and in situ hybridization of brain tissue from wild-type and mutant mouse embryos, strongly highlighted gene networks linked to neurite development. We followed up our genomics data with functional experiments, showing that Foxp2 impacts on neurite outgrowth in primary neurons and in neuronal cell models. Our data indicate that Foxp2 modulates neuronal network formation, by directly and indirectly regulating mRNAs involved in the development and plasticity of neuronal connections.

A human brain network derived from coma-causing brainstem lesions.

PubMed

Fischer, David B; Boes, Aaron D; Demertzi, Athena; Evrard, Henry C; Laureys, Steven; Edlow, Brian L; Liu, Hesheng; Saper, Clifford B; Pascual-Leone, Alvaro; Fox, Michael D; Geerling, Joel C

2016-12-06

To characterize a brainstem location specific to coma-causing lesions, and its functional connectivity network. We compared 12 coma-causing brainstem lesions to 24 control brainstem lesions using voxel-based lesion-symptom mapping in a case-control design to identify a site significantly associated with coma. We next used resting-state functional connectivity from a healthy cohort to identify a network of regions functionally connected to this brainstem site. We further investigated the cortical regions of this network by comparing their spatial topography to that of known networks and by evaluating their functional connectivity in patients with disorders of consciousness. A small region in the rostral dorsolateral pontine tegmentum was significantly associated with coma-causing lesions. In healthy adults, this brainstem site was functionally connected to the ventral anterior insula (AI) and pregenual anterior cingulate cortex (pACC). These cortical areas aligned poorly with previously defined resting-state networks, better matching the distribution of von Economo neurons. Finally, connectivity between the AI and pACC was disrupted in patients with disorders of consciousness, and to a greater degree than other brain networks. Injury to a small region in the pontine tegmentum is significantly associated with coma. This brainstem site is functionally connected to 2 cortical regions, the AI and pACC, which become disconnected in disorders of consciousness. This network of brain regions may have a role in the maintenance of human consciousness. © 2016 American Academy of Neurology.

A human brain network derived from coma-causing brainstem lesions

PubMed Central

Boes, Aaron D.; Demertzi, Athena; Evrard, Henry C.; Laureys, Steven; Edlow, Brian L.; Liu, Hesheng; Saper, Clifford B.; Pascual-Leone, Alvaro; Geerling, Joel C.

2016-01-01

Objective: To characterize a brainstem location specific to coma-causing lesions, and its functional connectivity network. Methods: We compared 12 coma-causing brainstem lesions to 24 control brainstem lesions using voxel-based lesion-symptom mapping in a case-control design to identify a site significantly associated with coma. We next used resting-state functional connectivity from a healthy cohort to identify a network of regions functionally connected to this brainstem site. We further investigated the cortical regions of this network by comparing their spatial topography to that of known networks and by evaluating their functional connectivity in patients with disorders of consciousness. Results: A small region in the rostral dorsolateral pontine tegmentum was significantly associated with coma-causing lesions. In healthy adults, this brainstem site was functionally connected to the ventral anterior insula (AI) and pregenual anterior cingulate cortex (pACC). These cortical areas aligned poorly with previously defined resting-state networks, better matching the distribution of von Economo neurons. Finally, connectivity between the AI and pACC was disrupted in patients with disorders of consciousness, and to a greater degree than other brain networks. Conclusions: Injury to a small region in the pontine tegmentum is significantly associated with coma. This brainstem site is functionally connected to 2 cortical regions, the AI and pACC, which become disconnected in disorders of consciousness. This network of brain regions may have a role in the maintenance of human consciousness. PMID:27815400

Network reconfiguration and neuronal plasticity in rhythm-generating networks.

PubMed

Koch, Henner; Garcia, Alfredo J; Ramirez, Jan-Marino

2011-12-01

Neuronal networks are highly plastic and reconfigure in a state-dependent manner. The plasticity at the network level emerges through multiple intrinsic and synaptic membrane properties that imbue neurons and their interactions with numerous nonlinear properties. These properties are continuously regulated by neuromodulators and homeostatic mechanisms that are critical to maintain not only network stability and also adapt networks in a short- and long-term manner to changes in behavioral, developmental, metabolic, and environmental conditions. This review provides concrete examples from neuronal networks in invertebrates and vertebrates, and illustrates that the concepts and rules that govern neuronal networks and behaviors are universal.

BDNF in schizophrenia, depression and corresponding animal models.

PubMed

Angelucci, F; Brenè, S; Mathé, A A

2005-04-01

Understanding the etiology and pathogenesis schizophrenia and depression is a major challenge facing psychiatry. One hypothesis is that these disorders are secondary to a malfunction of neurotrophic factors. Inappropriate neurotrophic support during brain development could lead to structural disorganisation in which neuronal networks are established in a nonoptimal manner. Inadequate neurotrophic support in adult individuals could ultimately be an underlying mechanism leading to decreased capacity of brain to adaptive changes and increased vulnerability to neurotoxic damage. Brain-derived neurotrophic factor (BDNF) is a mediator involved in neuronal survival and plasticity of dopaminergic, cholinergic, and serotonergic neurons in the central nervous system (CNS). In this review, we summarize findings regarding altered BDNF in schizophrenia and depression and animal models, as well as the effects of antipsychotic and antidepressive treatments on the expression of BDNF.

Intrinsic Cellular Properties and Connectivity Density Determine Variable Clustering Patterns in Randomly Connected Inhibitory Neural Networks

PubMed Central

Rich, Scott; Booth, Victoria; Zochowski, Michal

2016-01-01

The plethora of inhibitory interneurons in the hippocampus and cortex play a pivotal role in generating rhythmic activity by clustering and synchronizing cell firing. Results of our simulations demonstrate that both the intrinsic cellular properties of neurons and the degree of network connectivity affect the characteristics of clustered dynamics exhibited in randomly connected, heterogeneous inhibitory networks. We quantify intrinsic cellular properties by the neuron's current-frequency relation (IF curve) and Phase Response Curve (PRC), a measure of how perturbations given at various phases of a neurons firing cycle affect subsequent spike timing. We analyze network bursting properties of networks of neurons with Type I or Type II properties in both excitability and PRC profile; Type I PRCs strictly show phase advances and IF curves that exhibit frequencies arbitrarily close to zero at firing threshold while Type II PRCs display both phase advances and delays and IF curves that have a non-zero frequency at threshold. Type II neurons whose properties arise with or without an M-type adaptation current are considered. We analyze network dynamics under different levels of cellular heterogeneity and as intrinsic cellular firing frequency and the time scale of decay of synaptic inhibition are varied. Many of the dynamics exhibited by these networks diverge from the predictions of the interneuron network gamma (ING) mechanism, as well as from results in all-to-all connected networks. Our results show that randomly connected networks of Type I neurons synchronize into a single cluster of active neurons while networks of Type II neurons organize into two mutually exclusive clusters segregated by the cells' intrinsic firing frequencies. Networks of Type II neurons containing the adaptation current behave similarly to networks of either Type I or Type II neurons depending on network parameters; however, the adaptation current creates differences in the cluster dynamics compared to those in networks of Type I or Type II neurons. To understand these results, we compute neuronal PRCs calculated with a perturbation matching the profile of the synaptic current in our networks. Differences in profiles of these PRCs across the different neuron types reveal mechanisms underlying the divergent network dynamics. PMID:27812323

BDNF in fragile X syndrome.

PubMed

Castrén, Maija L; Castrén, Eero

2014-01-01

Fragile X syndrome (FXS) is a monogenic disorder that is caused by the absence of FMR1 protein (FMRP). FXS serves as an excellent model disorder for studies investigating disturbed molecular mechanisms and synapse function underlying cognitive impairment, autism, and behavioral disturbance. Abnormalities in dendritic spines and synaptic transmission in the brain of FXS individuals and mouse models for FXS indicate perturbations in the development, maintenance, and plasticity of neuronal network connectivity. However, numerous alterations are found during the early development in FXS, including abnormal differentiation of neural progenitors and impaired migration of newly born neurons. Several aspects of FMRP function are modulated by brain-derived neurotrophic factor (BDNF) signaling. Here, we review the evidence of the role for BDNF in the developing and adult FXS brain. This article is part of the Special Issue entitled 'BDNF Regulation of Synaptic Structure, Function, and Plasticity'. Copyright © 2013 Elsevier Ltd. All rights reserved.

Aberrant within- and between-network connectivity of the mirror neuron system network and the mentalizing network in first episode psychosis.

PubMed

Choe, Eugenie; Lee, Tae Young; Kim, Minah; Hur, Ji-Won; Yoon, Youngwoo Bryan; Cho, Kang-Ik K; Kwon, Jun Soo

2018-03-26

It has been suggested that the mentalizing network and the mirror neuron system network support important social cognitive processes that are impaired in schizophrenia. However, the integrity and interaction of these two networks have not been sufficiently studied, and their effects on social cognition in schizophrenia remain unclear. Our study included 26 first-episode psychosis (FEP) patients and 26 healthy controls. We utilized resting-state functional connectivity to examine the a priori-defined mirror neuron system network and the mentalizing network and to assess the within- and between-network connectivities of the networks in FEP patients. We also assessed the correlation between resting-state functional connectivity measures and theory of mind performance. FEP patients showed altered within-network connectivity of the mirror neuron system network, and aberrant between-network connectivity between the mirror neuron system network and the mentalizing network. The within-network connectivity of the mirror neuron system network was noticeably correlated with theory of mind task performance in FEP patients. The integrity and interaction of the mirror neuron system network and the mentalizing network may be altered during the early stages of psychosis. Additionally, this study suggests that alterations in the integrity of the mirror neuron system network are highly related to deficient theory of mind in schizophrenia, and this problem would be present from the early stage of psychosis. Copyright © 2018 Elsevier B.V. All rights reserved.

NRSF-dependent epigenetic mechanisms contribute to programming of stress-sensitive neurons by neonatal experience, promoting resilience.

PubMed

Singh-Taylor, A; Molet, J; Jiang, S; Korosi, A; Bolton, J L; Noam, Y; Simeone, K; Cope, J; Chen, Y; Mortazavi, A; Baram, T Z

2018-03-01

Resilience to stress-related emotional disorders is governed in part by early-life experiences. Here we demonstrate experience-dependent re-programming of stress-sensitive hypothalamic neurons, which takes place through modification of neuronal gene expression via epigenetic mechanisms. Specifically, we found that augmented maternal care reduced glutamatergic synapses onto stress-sensitive hypothalamic neurons and repressed expression of the stress-responsive gene, Crh. In hypothalamus in vitro, reduced glutamatergic neurotransmission recapitulated the repressive effects of augmented maternal care on Crh, and this required recruitment of the transcriptional repressor repressor element-1 silencing transcription factor/neuron restrictive silencing factor (NRSF). Increased NRSF binding to chromatin was accompanied by sequential repressive epigenetic changes which outlasted NRSF binding. chromatin immunoprecipitation-seq analyses of NRSF targets identified gene networks that, in addition to Crh, likely contributed to the augmented care-induced phenotype, including diminished depression-like and anxiety-like behaviors. Together, we believe these findings provide the first causal link between enriched neonatal experience, synaptic refinement and induction of epigenetic processes within specific neurons. They uncover a novel mechanistic pathway from neonatal environment to emotional resilience.

Computational exploration of neuron and neural network models in neurobiology.

PubMed

Prinz, Astrid A

2007-01-01

The electrical activity of individual neurons and neuronal networks is shaped by the complex interplay of a large number of non-linear processes, including the voltage-dependent gating of ion channels and the activation of synaptic receptors. These complex dynamics make it difficult to understand how individual neuron or network parameters-such as the number of ion channels of a given type in a neuron's membrane or the strength of a particular synapse-influence neural system function. Systematic exploration of cellular or network model parameter spaces by computational brute force can overcome this difficulty and generate comprehensive data sets that contain information about neuron or network behavior for many different combinations of parameters. Searching such data sets for parameter combinations that produce functional neuron or network output provides insights into how narrowly different neural system parameters have to be tuned to produce a desired behavior. This chapter describes the construction and analysis of databases of neuron or neuronal network models and describes some of the advantages and downsides of such exploration methods.

Mirror neurons as a model for the science and treatment of stuttering.

PubMed

Snyder, Gregory J; Waddell, Dwight E; Blanchet, Paul

2016-01-06

Persistent developmental stuttering is generally considered a speech disorder and affects ∼1% of the global population. While mainstream treatments continue to rely on unreliable behavioral speech motor targets, an emerging research perspective utilizes the mirror neuron system hypothesis as a neural substrate in the science and treatment of stuttering. The purpose of this exploratory study is to test the viability of the mirror neuron system hypothesis in the fluency enhancement of those who stutter. Participants were asked to speak while they were producing self-generated manual gestures, producing and visually perceiving self-generated manual gestures, and visually perceiving manual gestures, relative to a nonmanual gesture control speaking condition. Data reveal that all experimental speaking conditions enhanced fluent speech in all research participants, and the simultaneous perception and production of manual gesturing trended toward greater efficacious fluency enhancement. Coupled with existing research, we interpret these data as suggestive of fluency enhancement through subcortical involvement within multiple levels of an action understanding mirror neuron network. In addition, incidental findings report that stuttering moments were observed to simultaneously occur both orally and manually. Consequently, these data suggest that stuttering behaviors are compensatory, distal manifestations over multiple expressive modalities to an underlying centralized genetic neural substrate of the disorder.

Cannabinoid Receptors Mediate Methamphetamine Induction of High Frequency Gamma Oscillations in the Nucleus Accumbens

PubMed Central

Morra, Joshua T.; Glick, Stanley D.; Cheer, Joseph F.

2012-01-01

Patients suffering from amphetamine---induced psychosis display repetitive behaviors, partially alleviated by antipsychotics, which are reminiscent of rodent stereotypies. Due to recent evidence implicating endocannabinoid involvement in brain disorders, including psychosis, we studied the effects of endocannabinoid signaling on neuronal oscillations of rats exhibiting methamphetamine stereotypy. Neuronal network oscillations were recorded with multiple single electrode arrays aimed at the nucleus accumbens of freely moving rats. During the experiments, animals were dosed intravenously with the CB1 receptor antagonist rimonabant (0.3 mg/kg) or vehicle followed by an ascending dose regimen of methamphetamine (0.01, 0.1, 1, and 3 mg/kg; cumulative dosing). The effects of drug administration on stereotypy and local gamma oscillations were evaluated. Methamphetamine treatment significantly increased high frequency gamma oscillations (~ 80 Hz). Entrainment of a subpopulation of nucleus accumbens neurons to high frequency gamma was associated with stereotypy encoding in putative fast-spiking interneurons, but not in putative medium spiny neurons. The observed ability of methamphetamine to induce both stereotypy and high frequency gamma power was potently disrupted following CB1 receptor blockade. The present data suggest that CB1 receptor-dependent mechanisms are recruited by methamphetamine to modify striatal interneuron oscillations that accompany changes in psychomotor state, further supporting the link between endocannabinoids and schizophrenia spectrum disorders. PMID:22609048

Uniting functional network topology and oscillations in the fronto-parietal single unit network of behaving primates.

PubMed

Dann, Benjamin; Michaels, Jonathan A; Schaffelhofer, Stefan; Scherberger, Hansjörg

2016-08-15

The functional communication of neurons in cortical networks underlies higher cognitive processes. Yet, little is known about the organization of the single neuron network or its relationship to the synchronization processes that are essential for its formation. Here, we show that the functional single neuron network of three fronto-parietal areas during active behavior of macaque monkeys is highly complex. The network was closely connected (small-world) and consisted of functional modules spanning these areas. Surprisingly, the importance of different neurons to the network was highly heterogeneous with a small number of neurons contributing strongly to the network function (hubs), which were in turn strongly inter-connected (rich-club). Examination of the network synchronization revealed that the identified rich-club consisted of neurons that were synchronized in the beta or low frequency range, whereas other neurons were mostly non-oscillatory synchronized. Therefore, oscillatory synchrony may be a central communication mechanism for highly organized functional spiking networks.

Neuronal avalanches of a self-organized neural network with active-neuron-dominant structure.

PubMed

Li, Xiumin; Small, Michael

2012-06-01

Neuronal avalanche is a spontaneous neuronal activity which obeys a power-law distribution of population event sizes with an exponent of -3/2. It has been observed in the superficial layers of cortex both in vivo and in vitro. In this paper, we analyze the information transmission of a novel self-organized neural network with active-neuron-dominant structure. Neuronal avalanches can be observed in this network with appropriate input intensity. We find that the process of network learning via spike-timing dependent plasticity dramatically increases the complexity of network structure, which is finally self-organized to be active-neuron-dominant connectivity. Both the entropy of activity patterns and the complexity of their resulting post-synaptic inputs are maximized when the network dynamics are propagated as neuronal avalanches. This emergent topology is beneficial for information transmission with high efficiency and also could be responsible for the large information capacity of this network compared with alternative archetypal networks with different neural connectivity.

The Drosophila Clock Neuron Network Features Diverse Coupling Modes and Requires Network-wide Coherence for Robust Circadian Rhythms.

PubMed

Yao, Zepeng; Bennett, Amelia J; Clem, Jenna L; Shafer, Orie T

2016-12-13

In animals, networks of clock neurons containing molecular clocks orchestrate daily rhythms in physiology and behavior. However, how various types of clock neurons communicate and coordinate with one another to produce coherent circadian rhythms is not well understood. Here, we investigate clock neuron coupling in the brain of Drosophila and demonstrate that the fly's various groups of clock neurons display unique and complex coupling relationships to core pacemaker neurons. Furthermore, we find that coordinated free-running rhythms require molecular clock synchrony not only within the well-characterized lateral clock neuron classes but also between lateral clock neurons and dorsal clock neurons. These results uncover unexpected patterns of coupling in the clock neuron network and reveal that robust free-running behavioral rhythms require a coherence of molecular oscillations across most of the fly's clock neuron network. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

GABA neuron alterations, cortical circuit dysfunction and cognitive deficits in schizophrenia.

PubMed

Gonzalez-Burgos, Guillermo; Fish, Kenneth N; Lewis, David A

2011-01-01

Schizophrenia is a brain disorder associated with cognitive deficits that severely affect the patients' capacity for daily functioning. Whereas our understanding of its pathophysiology is limited, postmortem studies suggest that schizophrenia is associated with deficits of GABA-mediated synaptic transmission. A major role of GABA-mediated transmission may be producing synchronized network oscillations which are currently hypothesized to be essential for normal cognitive function. Therefore, cognitive deficits in schizophrenia may result from a GABA synapse dysfunction that disturbs neural synchrony. Here, we highlight recent studies further suggesting alterations of GABA transmission and network oscillations in schizophrenia. We also review current models for the mechanisms of GABA-mediated synchronization of neural activity, focusing on parvalbumin-positive GABA neurons, which are altered in schizophrenia and whose function has been strongly linked to the production of neural synchrony. Alterations of GABA signaling that impair gamma oscillations and, as a result, cognitive function suggest paths for novel therapeutic interventions.

Circuit to Construct Mapping: A Mathematical Tool for Assisting the Diagnosis and Treatment in Major Depressive Disorder

PubMed Central

Bielczyk, Natalia Z.; Buitelaar, Jan K.; Glennon, Jeffrey C.; Tiesinga, Paul H. E.

2015-01-01

Major depressive disorder (MDD) is a serious condition with a lifetime prevalence exceeding 16% worldwide. MDD is a heterogeneous disorder that involves multiple behavioral symptoms on the one hand and multiple neuronal circuits on the other hand. In this review, we integrate the literature on cognitive and physiological biomarkers of MDD with the insights derived from mathematical models of brain networks, especially models that can be used for fMRI datasets. We refer to the recent NIH research domain criteria initiative, in which a concept of “constructs” as functional units of mental disorders is introduced. Constructs are biomarkers present at multiple levels of brain functioning – cognition, genetics, brain anatomy, and neurophysiology. In this review, we propose a new approach which we called circuit to construct mapping (CCM), which aims to characterize causal relations between the underlying network dynamics (as the cause) and the constructs referring to the clinical symptoms of MDD (as the effect). CCM involves extracting diagnostic categories from behavioral data, linking circuits that are causal to these categories with use of clinical neuroimaging data, and modeling the dynamics of the emerging circuits with attractor dynamics in order to provide new, neuroimaging-related biomarkers for MDD. The CCM approach optimizes the clinical diagnosis and patient stratification. It also addresses the recent demand for linking circuits to behavior, and provides a new insight into clinical treatment by investigating the dynamics of neuronal circuits underneath cognitive dimensions of MDD. CCM can serve as a new regime toward personalized medicine, assisting the diagnosis and treatment of MDD. PMID:25767450

Relationship between inter-stimulus-intervals and intervals of autonomous activities in a neuronal network.

PubMed

Ito, Hidekatsu; Minoshima, Wataru; Kudoh, Suguru N

2015-08-01

To investigate relationships between neuronal network activity and electrical stimulus, we analyzed autonomous activity before and after electrical stimulus. Recordings of autonomous activity were performed using dissociated culture of rat hippocampal neurons on a multi-electrodes array (MEA) dish. Single stimulus and pared stimuli were applied to a cultured neuronal network. Single stimulus was applied every 1 min, and paired stimuli was performed by two sequential stimuli every 1 min. As a result, the patterns of synchronized activities of a neuronal network were changed after stimulus. Especially, long range synchronous activities were induced by paired stimuli. When 1 s inter-stimulus-intervals (ISI) and 1.5 s ISI paired stimuli are applied to a neuronal network, relatively long range synchronous activities expressed in case of 1.5 s ISI. Temporal synchronous activity of neuronal network is changed according to inter-stimulus-intervals (ISI) of electrical stimulus. In other words, dissociated neuronal network can maintain given information in temporal pattern and a certain type of an information maintenance mechanism was considered to be implemented in a semi-artificial dissociated neuronal network. The result is useful toward manipulation technology of neuronal activity in a brain system.

Protecting Neural Structures and Cognitive Function During Prolonged Space Flight by Targeting the Brain Derived Neurotrophic Factor Molecular Network

NASA Technical Reports Server (NTRS)

Schmidt, M. A.; Goodwin, T. J.

2014-01-01

Brain derived neurotrophic factor (BDNF) is the main activity-dependent neurotrophin in the human nervous system. BDNF is implicated in production of new neurons from dentate gyrus stem cells (hippocampal neurogenesis), synapse formation, sprouting of new axons, growth of new axons, sprouting of new dendrites, and neuron survival. Alterations in the amount or activity of BDNF can produce significant detrimental changes to cortical function and synaptic transmission in the human brain. This can result in glial and neuronal dysfunction, which may contribute to a range of clinical conditions, spanning a number of learning, behavioral, and neurological disorders. There is an extensive body of work surrounding the BDNF molecular network, including BDNF gene polymorphisms, methylated BDNF gene promoters, multiple gene transcripts, varied BDNF functional proteins, and different BDNF receptors (whose activation differentially drive the neuron to neurogenesis or apoptosis). BDNF is also closely linked to mitochondrial biogenesis through PGC-1alpha, which can influence brain and muscle metabolic efficiency. BDNF AS A HUMAN SPACE FLIGHT COUNTERMEASURE TARGET Earth-based studies reveal that BDNF is negatively impacted by many of the conditions encountered in the space environment, including oxidative stress, radiation, psychological stressors, sleep deprivation, and many others. A growing body of work suggests that the BDNF network is responsive to a range of diet, nutrition, exercise, drug, and other types of influences. This section explores the BDNF network in the context of 1) protecting the brain and nervous system in the space environment, 2) optimizing neurobehavioral performance in space, and 3) reducing the residual effects of space flight on the nervous system on return to Earth

Human embryonic stem cell-derived neurons adopt and regulate the activity of an established neural network

PubMed Central

Weick, Jason P.; Liu, Yan; Zhang, Su-Chun

2011-01-01

Whether hESC-derived neurons can fully integrate with and functionally regulate an existing neural network remains unknown. Here, we demonstrate that hESC-derived neurons receive unitary postsynaptic currents both in vitro and in vivo and adopt the rhythmic firing behavior of mouse cortical networks via synaptic integration. Optical stimulation of hESC-derived neurons expressing Channelrhodopsin-2 elicited both inhibitory and excitatory postsynaptic currents and triggered network bursting in mouse neurons. Furthermore, light stimulation of hESC-derived neurons transplanted to the hippocampus of adult mice triggered postsynaptic currents in host pyramidal neurons in acute slice preparations. Thus, hESC-derived neurons can participate in and modulate neural network activity through functional synaptic integration, suggesting they are capable of contributing to neural network information processing both in vitro and in vivo. PMID:22106298

Loss of Balance between Striatal Feedforward Inhibition and Corticostriatal Excitation Leads to Tremor.

PubMed

Oran, Yael; Bar-Gad, Izhar

2018-02-14

Fast-spiking interneurons (FSIs) exert powerful inhibitory control over the striatum and are hypothesized to balance the massive excitatory cortical and thalamic input to this structure. We recorded neuronal activity in the dorsolateral striatum and globus pallidus (GP) concurrently with the detailed movement kinematics of freely behaving female rats before and after selective inhibition of FSI activity using IEM-1460 microinjections. The inhibition led to the appearance of episodic rest tremor in the body part that depended on the somatotopic location of the injection within the striatum. The tremor was accompanied by coherent oscillations in the local field potential (LFP). Individual neuron activity patterns became oscillatory and coherent in the tremor frequency. Striatal neurons, but not GP neurons, displayed additional temporal, nonoscillatory correlations. The subsequent reduction in the corticostriatal input following muscimol injection to the corresponding somatotopic location in the primary motor cortex led to disruption of the tremor and a reduction of the LFP oscillations and individual neuron's phase-locked activity. The breakdown of the normal balance of excitation and inhibition in the striatum has been shown previously to be related to different motor abnormalities. Our results further indicate that the balance between excitatory corticostriatal input and feedforward FSI inhibition is sufficient to break down the striatal decorrelation process and generate oscillations resulting in rest tremor typical of multiple basal ganglia disorders. SIGNIFICANCE STATEMENT Fast-spiking interneurons (FSIs) play a key role in normal striatal processing by exerting powerful inhibitory control over the network. FSI malfunctions have been associated with abnormal processing of information within the striatum that leads to multiple movement disorders. Here, we study the changes in neuronal activity and movement kinematics following selective inhibition of these neurons. The injections led to the appearance of episodic rest tremor, accompanied by coherent oscillations in neuronal activity, which was reversed following corticostriatal inhibition. These results suggest that the balance between corticostriatal excitation and feedforward FSI inhibition is crucial for maintaining the striatal decorrelation process, and that its breakdown leads to the formation of oscillations resulting in rest tremor typical of multiple basal ganglia disorders. Copyright © 2018 the authors 0270-6474/18/381699-12$15.00/0.

Cultured Neuronal Networks Express Complex Patterns of Activity and Morphological Memory

NASA Astrophysics Data System (ADS)

Raichman, Nadav; Rubinsky, Liel; Shein, Mark; Baruchi, Itay; Volman, Vladislav; Ben-Jacob, Eshel

The following sections are included: * Cultured Neuronal Networks * Recording the Network Activity * Network Engineering * The Formation of Synchronized Bursting Events * The Characterization of the SBEs * Highly-Active Neurons * Function-Form Relations in Cultured Networks * Analyzing the SBEs Motifs * Network Repertoire * Network under Hypothermia * Summary * Acknowledgments * References

The carbohydrate deposits detected by histochemical methods in the molecular layer of the dentate gyrus in the hippocampal formation of patients with schizophrenia, Down's syndrome and dementia, and aged person.

PubMed

Nishimura, A; Ikemoto, K; Satoh, K; Yamamoto, Y; Rand, S; Brinkmann, B; Nishi, K

2000-11-01

Post-mortem brain tissue was obtained from 28 patients with brain disorders, of which 15 had clinically diagnosed schizophrenia, 6 Alzheimer type dementia, 5 dementia with tangles and 2 cases of Down's syndrome. The controls were 22 cases from autopsies without brain disorders or with no known episodes of brain disorder. The tissues were stained for the detection of carbohydrate deposits in the hippocampal formation, using lectin, immunohistochemical and conventional staining methods. The staining revealed the existence of spherical deposits in the inner and middle molecular layers of the dentate gyrus in the hippocampal formation which contained fucose, galactose, N-acetyl galactosamine, N-acetyl glucosamine, sialic acid, mannose and chondroitin sulfate. The number of the deposits was higher in patients with brain disorder such as schizophrenia, Alzheimer type dementia, dementia with tangles or Down's syndrome, and in some aged individuals, in comparison to those in younger individuals. No deposits were detected in a few younger or aged individuals. Spherical deposits 3-10 microm in diameter may be an immature form of the corpora amylacea, since they were similar in the histochemical characteristics with lectin, immunohistochemical and conventional staining methods. However, differing staining ability by hematoxylin, periodic acid Schiff's reagent and antibodies against the intracellular degraded proteins such as ubiquitin and tau-protein was observed. The antibodies against ubiquitin and tau-protein showed clear reactivity with the corpora amylacea and no reactivity with spherical deposits, indicating that the corpora amylacea has an intracellular origin and spherical deposits an extracellular matrix origin. The results obtained in this study indicate that not only neuronal degeneration but also unusual glycometabolism in neurons may disturb the neuronal function and cause brain disorders, and that spherical deposits may cause dysfunction of the neuronal network in the dentate gyrus of the hippocampus which is closely linked with recognition and memory functions.

Three-dimensional neural cultures produce networks that mimic native brain activity.

PubMed

Bourke, Justin L; Quigley, Anita F; Duchi, Serena; O'Connell, Cathal D; Crook, Jeremy M; Wallace, Gordon G; Cook, Mark J; Kapsa, Robert M I

2018-02-01

Development of brain function is critically dependent on neuronal networks organized through three dimensions. Culture of central nervous system neurons has traditionally been limited to two dimensions, restricting growth patterns and network formation to a single plane. Here, with the use of multichannel extracellular microelectrode arrays, we demonstrate that neurons cultured in a true three-dimensional environment recapitulate native neuronal network formation and produce functional outcomes more akin to in vivo neuronal network activity. Copyright © 2017 John Wiley & Sons, Ltd.

Numerical simulation of coherent resonance in a model network of Rulkov neurons

NASA Astrophysics Data System (ADS)

Andreev, Andrey V.; Runnova, Anastasia E.; Pisarchik, Alexander N.

2018-04-01

In this paper we study the spiking behaviour of a neuronal network consisting of Rulkov elements. We find that the regularity of this behaviour maximizes at a certain level of environment noise. This effect referred to as coherence resonance is demonstrated in a random complex network of Rulkov neurons. An external stimulus added to some of neurons excites them, and then activates other neurons in the network. The network coherence is also maximized at the certain stimulus amplitude.

Propagating waves can explain irregular neural dynamics.

PubMed

Keane, Adam; Gong, Pulin

2015-01-28

Cortical neurons in vivo fire quite irregularly. Previous studies about the origin of such irregular neural dynamics have given rise to two major models: a balanced excitation and inhibition model, and a model of highly synchronized synaptic inputs. To elucidate the network mechanisms underlying synchronized synaptic inputs and account for irregular neural dynamics, we investigate a spatially extended, conductance-based spiking neural network model. We show that propagating wave patterns with complex dynamics emerge from the network model. These waves sweep past neurons, to which they provide highly synchronized synaptic inputs. On the other hand, these patterns only emerge from the network with balanced excitation and inhibition; our model therefore reconciles the two major models of irregular neural dynamics. We further demonstrate that the collective dynamics of propagating wave patterns provides a mechanistic explanation for a range of irregular neural dynamics, including the variability of spike timing, slow firing rate fluctuations, and correlated membrane potential fluctuations. In addition, in our model, the distributions of synaptic conductance and membrane potential are non-Gaussian, consistent with recent experimental data obtained using whole-cell recordings. Our work therefore relates the propagating waves that have been widely observed in the brain to irregular neural dynamics. These results demonstrate that neural firing activity, although appearing highly disordered at the single-neuron level, can form dynamical coherent structures, such as propagating waves at the population level. Copyright © 2015 the authors 0270-6474/15/351591-15$15.00/0.

Differential Patterns of Dysconnectivity in Mirror Neuron and Mentalizing Networks in Schizophrenia

PubMed Central

Schilbach, Leonhard; Derntl, Birgit; Aleman, Andre; Caspers, Svenja; Clos, Mareike; Diederen, Kelly M. J.; Gruber, Oliver; Kogler, Lydia; Liemburg, Edith J.; Sommer, Iris E.; Müller, Veronika I.; Cieslik, Edna C.; Eickhoff, Simon B.

2016-01-01

Impairments of social cognition are well documented in patients with schizophrenia (SCZ), but the neural basis remains poorly understood. In light of evidence that suggests that the “mirror neuron system” (MNS) and the “mentalizing network” (MENT) are key substrates of intersubjectivity and joint action, it has been suggested that dysfunction of these neural networks may underlie social difficulties in SCZ patients. Additionally, MNS and MENT might be associated differently with positive vs negative symptoms, given prior social cognitive and symptom associations. We assessed resting state functional connectivity (RSFC) in meta-analytically defined MNS and MENT networks in this patient group. Magnetic resonance imaging (MRI) scans were obtained from 116 patients and 133 age-, gender- and movement-matched healthy controls (HC) at 5 different MRI sites. Network connectivity was analyzed for group differences and correlations with clinical symptoms. Results demonstrated decreased connectivity within the MNS and also the MENT in patients compared to controls. Notably, dysconnectivity of the MNS was related to symptom severity, while no such relationship was observed for the MENT. In sum, these findings demonstrate that differential patterns of dysconnectivity exist in SCZ patients, which may contribute differently to the interpersonal difficulties commonly observed in the disorder. PMID:26940699

Uniting functional network topology and oscillations in the fronto-parietal single unit network of behaving primates

PubMed Central

Dann, Benjamin; Michaels, Jonathan A; Schaffelhofer, Stefan; Scherberger, Hansjörg

2016-01-01

The functional communication of neurons in cortical networks underlies higher cognitive processes. Yet, little is known about the organization of the single neuron network or its relationship to the synchronization processes that are essential for its formation. Here, we show that the functional single neuron network of three fronto-parietal areas during active behavior of macaque monkeys is highly complex. The network was closely connected (small-world) and consisted of functional modules spanning these areas. Surprisingly, the importance of different neurons to the network was highly heterogeneous with a small number of neurons contributing strongly to the network function (hubs), which were in turn strongly inter-connected (rich-club). Examination of the network synchronization revealed that the identified rich-club consisted of neurons that were synchronized in the beta or low frequency range, whereas other neurons were mostly non-oscillatory synchronized. Therefore, oscillatory synchrony may be a central communication mechanism for highly organized functional spiking networks. DOI: http://dx.doi.org/10.7554/eLife.15719.001 PMID:27525488

Solving Constraint Satisfaction Problems with Networks of Spiking Neurons

PubMed Central

Jonke, Zeno; Habenschuss, Stefan; Maass, Wolfgang

2016-01-01

Network of neurons in the brain apply—unlike processors in our current generation of computer hardware—an event-based processing strategy, where short pulses (spikes) are emitted sparsely by neurons to signal the occurrence of an event at a particular point in time. Such spike-based computations promise to be substantially more power-efficient than traditional clocked processing schemes. However, it turns out to be surprisingly difficult to design networks of spiking neurons that can solve difficult computational problems on the level of single spikes, rather than rates of spikes. We present here a new method for designing networks of spiking neurons via an energy function. Furthermore, we show how the energy function of a network of stochastically firing neurons can be shaped in a transparent manner by composing the networks of simple stereotypical network motifs. We show that this design approach enables networks of spiking neurons to produce approximate solutions to difficult (NP-hard) constraint satisfaction problems from the domains of planning/optimization and verification/logical inference. The resulting networks employ noise as a computational resource. Nevertheless, the timing of spikes plays an essential role in their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines) and Gibbs sampling. PMID:27065785

Altered Astrocyte-Neuron Interactions and Epileptogenesis in Tuberous Sclerosis Complex Disorder

DTIC Science & Technology

2015-06-01

Tsc1-deficient astrocytes on neuronal morphology and neuronal activity associated with seizures . 2. KEY WORDS epilepsy , seizure , tuberous sclerosis...AWARD NUMBER: W81XWH-12-1-0196 TITLE: Altered Astrocyte-Neuron Interactions and Epileptogenesis in Tuberous Sclerosis Complex Disorder PRINCIPAL...TITLE AND SUBTITLE Altered Astrocyte-Neuron Interactions and Epileptogenesis in Tuberous Sclerosis Complex Disorder 5a. CONTRACT NUMBER 5b. GRANT

Sleep Impairment and Reduced Interneuron Excitability in a Mouse Model of Dravet Syndrome

PubMed Central

Kalume, Franck; Oakley, John C.; Westenbroek, Ruth E.; Gile, Jennifer; de la Iglesia, Horacio O.; Scheuer, Todd; Catterall, William A.

2015-01-01

Dravet Syndrome (DS) is caused by heterozygous loss-of-function mutations in voltage-gated sodium channel NaV1.1. Our genetic mouse model of DS recapitulates its severe seizures and premature death. Sleep disturbance is common in DS, but its mechanism is unknown. Electroencephalographic studies revealed abnormal sleep in DS mice, including reduced delta wave power, reduced sleep spindles, increased brief wakes, and numerous interictal spikes in Non-Rapid-Eye-Movement sleep. Theta power was reduced in Rapid-Eye-Movement sleep. Mice with NaV1.1 deleted specifically in forebrain interneurons exhibited similar sleep pathology to DS mice, but without changes in circadian rhythm. Sleep architecture depends on oscillatory activity in the thalamocortical network generated by excitatory neurons in the ventrobasal nucleus (VBN) of the thalamus and inhibitory GABAergic neurons in the reticular nucleus of the thalamus (RNT). Whole-cell NaV current was reduced in GABAergic RNT neurons but not in VBN neurons. Rebound firing of action potentials following hyperpolarization, the signature firing pattern of RNT neurons during sleep, was also reduced. These results demonstrate imbalance of excitatory vs. inhibitory neurons in this circuit. As predicted from this functional impairment, we found substantial deficit in homeostatic rebound of slow wave activity following sleep deprivation. Although sleep disorders in epilepsies have been attributed to anti-epileptic drugs, our results show that sleep disorder in DS mice arises from loss of NaV1.1 channels in forebrain GABAergic interneurons without drug treatment. Impairment of NaV currents and excitability of GABAergic RNT neurons are correlated with impaired sleep quality and homeostasis in these mice. PMID:25766678

Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks.

PubMed

Hagen, Espen; Dahmen, David; Stavrinou, Maria L; Lindén, Henrik; Tetzlaff, Tom; van Albada, Sacha J; Grün, Sonja; Diesmann, Markus; Einevoll, Gaute T

2016-12-01

With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network model for a ∼1 mm 2 patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its public implementation in hybridLFPy form the basis for LFP predictions from other and larger point-neuron network models, as well as extensions of the current application with additional biological detail. © The Author 2016. Published by Oxford University Press.

Computational model of electrically coupled, intrinsically distinct pacemaker neurons.

PubMed

Soto-Treviño, Cristina; Rabbah, Pascale; Marder, Eve; Nadim, Farzan

2005-07-01

Electrical coupling between neurons with similar properties is often studied. Nonetheless, the role of electrical coupling between neurons with widely different intrinsic properties also occurs, but is less well understood. Inspired by the pacemaker group of the crustacean pyloric network, we developed a multicompartment, conductance-based model of a small network of intrinsically distinct, electrically coupled neurons. In the pyloric network, a small intrinsically bursting neuron, through gap junctions, drives 2 larger, tonically spiking neurons to reliably burst in-phase with it. Each model neuron has 2 compartments, one responsible for spike generation and the other for producing a slow, large-amplitude oscillation. We illustrate how these compartments interact and determine the dynamics of the model neurons. Our model captures the dynamic oscillation range measured from the isolated and coupled biological neurons. At the network level, we explore the range of coupling strengths for which synchronous bursting oscillations are possible. The spatial segregation of ionic currents significantly enhances the ability of the 2 neurons to burst synchronously, and the oscillation range of the model pacemaker network depends not only on the strength of the electrical synapse but also on the identity of the neuron receiving inputs. We also compare the activity of the electrically coupled, distinct neurons with that of a network of coupled identical bursting neurons. For small to moderate coupling strengths, the network of identical elements, when receiving asymmetrical inputs, can have a smaller dynamic range of oscillation than that of its constituent neurons in isolation.

Shaping Neuronal Network Activity by Presynaptic Mechanisms

PubMed Central

Ashery, Uri

2015-01-01

Neuronal microcircuits generate oscillatory activity, which has been linked to basic functions such as sleep, learning and sensorimotor gating. Although synaptic release processes are well known for their ability to shape the interaction between neurons in microcircuits, most computational models do not simulate the synaptic transmission process directly and hence cannot explain how changes in synaptic parameters alter neuronal network activity. In this paper, we present a novel neuronal network model that incorporates presynaptic release mechanisms, such as vesicle pool dynamics and calcium-dependent release probability, to model the spontaneous activity of neuronal networks. The model, which is based on modified leaky integrate-and-fire neurons, generates spontaneous network activity patterns, which are similar to experimental data and robust under changes in the model's primary gain parameters such as excitatory postsynaptic potential and connectivity ratio. Furthermore, it reliably recreates experimental findings and provides mechanistic explanations for data obtained from microelectrode array recordings, such as network burst termination and the effects of pharmacological and genetic manipulations. The model demonstrates how elevated asynchronous release, but not spontaneous release, synchronizes neuronal network activity and reveals that asynchronous release enhances utilization of the recycling vesicle pool to induce the network effect. The model further predicts a positive correlation between vesicle priming at the single-neuron level and burst frequency at the network level; this prediction is supported by experimental findings. Thus, the model is utilized to reveal how synaptic release processes at the neuronal level govern activity patterns and synchronization at the network level. PMID:26372048

Can simple rules control development of a pioneer vertebrate neuronal network generating behavior?

PubMed

Roberts, Alan; Conte, Deborah; Hull, Mike; Merrison-Hort, Robert; al Azad, Abul Kalam; Buhl, Edgar; Borisyuk, Roman; Soffe, Stephen R

2014-01-08

How do the pioneer networks in the axial core of the vertebrate nervous system first develop? Fundamental to understanding any full-scale neuronal network is knowledge of the constituent neurons, their properties, synaptic interconnections, and normal activity. Our novel strategy uses basic developmental rules to generate model networks that retain individual neuron and synapse resolution and are capable of reproducing correct, whole animal responses. We apply our developmental strategy to young Xenopus tadpoles, whose brainstem and spinal cord share a core vertebrate plan, but at a tractable complexity. Following detailed anatomical and physiological measurements to complete a descriptive library of each type of spinal neuron, we build models of their axon growth controlled by simple chemical gradients and physical barriers. By adding dendrites and allowing probabilistic formation of synaptic connections, we reconstruct network connectivity among up to 2000 neurons. When the resulting "network" is populated by model neurons and synapses, with properties based on physiology, it can respond to sensory stimulation by mimicking tadpole swimming behavior. This functioning model represents the most complete reconstruction of a vertebrate neuronal network that can reproduce the complex, rhythmic behavior of a whole animal. The findings validate our novel developmental strategy for generating realistic networks with individual neuron- and synapse-level resolution. We use it to demonstrate how early functional neuronal connectivity and behavior may in life result from simple developmental "rules," which lay out a scaffold for the vertebrate CNS without specific neuron-to-neuron recognition.

Unveiling causal activity of complex networks

NASA Astrophysics Data System (ADS)

Williams-García, Rashid V.; Beggs, John M.; Ortiz, Gerardo

2017-07-01

We introduce a novel tool for analyzing complex network dynamics, allowing for cascades of causally-related events, which we call causal webs (c-webs), to be separated from other non-causally-related events. This tool shows that traditionally-conceived avalanches may contain mixtures of spatially-distinct but temporally-overlapping cascades of events, and dynamical disorder or noise. In contrast, c-webs separate these components, unveiling previously hidden features of the network and dynamics. We apply our method to mouse cortical data with resulting statistics which demonstrate for the first time that neuronal avalanches are not merely composed of causally-related events. The original version of this article was uploaded to the arXiv on March 17th, 2016 [1].

Constrained synaptic connectivity in functional mammalian neuronal networks grown on patterned surfaces.

PubMed

Wyart, Claire; Ybert, Christophe; Bourdieu, Laurent; Herr, Catherine; Prinz, Christelle; Chatenay, Didier

2002-06-30

The use of ordered neuronal networks in vitro is a promising approach to study the development and the activity of small neuronal assemblies. However, in previous attempts, sufficient growth control and physiological maturation of neurons could not be achieved. Here we describe an original protocol in which polylysine patterns confine the adhesion of cellular bodies to prescribed spots and the neuritic growth to thin lines. Hippocampal neurons in these networks are maintained healthy in serum free medium up to 5 weeks in vitro. Electrophysiology and immunochemistry show that neurons exhibit mature excitatory and inhibitory synapses and calcium imaging reveals spontaneous activity of neurons in isolated networks. We demonstrate that neurons in these geometrical networks form functional synapses preferentially to their first neighbors. We have, therefore, established a simple and robust protocol to constrain both the location of neuronal cell bodies and their pattern of connectivity. Moreover, the long term maintenance of the geometry and the physiology of the networks raises the possibility of new applications for systematic screening of pharmacological agents and for electronic to neuron devices.

Redistribution of neural phase coherence reflects establishment of feedforward map in speech motor adaptation

PubMed Central

Sengupta, Ranit

2015-01-01

Despite recent progress in our understanding of sensorimotor integration in speech learning, a comprehensive framework to investigate its neural basis is lacking at behaviorally relevant timescales. Structural and functional imaging studies in humans have helped us identify brain networks that support speech but fail to capture the precise spatiotemporal coordination within the networks that takes place during speech learning. Here we use neuronal oscillations to investigate interactions within speech motor networks in a paradigm of speech motor adaptation under altered feedback with continuous recording of EEG in which subjects adapted to the real-time auditory perturbation of a target vowel sound. As subjects adapted to the task, concurrent changes were observed in the theta-gamma phase coherence during speech planning at several distinct scalp regions that is consistent with the establishment of a feedforward map. In particular, there was an increase in coherence over the central region and a decrease over the fronto-temporal regions, revealing a redistribution of coherence over an interacting network of brain regions that could be a general feature of error-based motor learning in general. Our findings have implications for understanding the neural basis of speech motor learning and could elucidate how transient breakdown of neuronal communication within speech networks relates to speech disorders. PMID:25632078

Uncovering Neuronal Networks Defined by Consistent Between-Neuron Spike Timing from Neuronal Spike Recordings

PubMed Central

2018-01-01

Abstract It is widely assumed that distributed neuronal networks are fundamental to the functioning of the brain. Consistent spike timing between neurons is thought to be one of the key principles for the formation of these networks. This can involve synchronous spiking or spiking with time delays, forming spike sequences when the order of spiking is consistent. Finding networks defined by their sequence of time-shifted spikes, denoted here as spike timing networks, is a tremendous challenge. As neurons can participate in multiple spike sequences at multiple between-spike time delays, the possible complexity of networks is prohibitively large. We present a novel approach that is capable of (1) extracting spike timing networks regardless of their sequence complexity, and (2) that describes their spiking sequences with high temporal precision. We achieve this by decomposing frequency-transformed neuronal spiking into separate networks, characterizing each network’s spike sequence by a time delay per neuron, forming a spike sequence timeline. These networks provide a detailed template for an investigation of the experimental relevance of their spike sequences. Using simulated spike timing networks, we show network extraction is robust to spiking noise, spike timing jitter, and partial occurrences of the involved spike sequences. Using rat multineuron recordings, we demonstrate the approach is capable of revealing real spike timing networks with sub-millisecond temporal precision. By uncovering spike timing networks, the prevalence, structure, and function of complex spike sequences can be investigated in greater detail, allowing us to gain a better understanding of their role in neuronal functioning. PMID:29789811

A neural mass model of basal ganglia nuclei simulates pathological beta rhythm in Parkinson's disease

NASA Astrophysics Data System (ADS)

Liu, Fei; Wang, Jiang; Liu, Chen; Li, Huiyan; Deng, Bin; Fietkiewicz, Chris; Loparo, Kenneth A.

2016-12-01

An increase in beta oscillations within the basal ganglia nuclei has been shown to be associated with movement disorder, such as Parkinson's disease. The motor cortex and an excitatory-inhibitory neuronal network composed of the subthalamic nucleus (STN) and the external globus pallidus (GPe) are thought to play an important role in the generation of these oscillations. In this paper, we propose a neuron mass model of the basal ganglia on the population level that reproduces the Parkinsonian oscillations in a reciprocal excitatory-inhibitory network. Moreover, it is shown that the generation and frequency of these pathological beta oscillations are varied by the coupling strength and the intrinsic characteristics of the basal ganglia. Simulation results reveal that increase of the coupling strength induces the generation of the beta oscillation, as well as enhances the oscillation frequency. However, for the intrinsic properties of each nucleus in the excitatory-inhibitory network, the STN primarily influences the generation of the beta oscillation while the GPe mainly determines its frequency. Interestingly, describing function analysis applied on this model theoretically explains the mechanism of pathological beta oscillations.

Transgenic expression of neuronal dystonin isoform 2 partially rescues the disease phenotype of the dystonia musculorum mouse model of hereditary sensory autonomic neuropathy VI

PubMed Central

Ferrier, Andrew; Sato, Tadasu; De Repentigny, Yves; Gibeault, Sabrina; Bhanot, Kunal; O'Meara, Ryan W.; Lynch-Godrei, Anisha; Kornfeld, Samantha F.; Young, Kevin G.; Kothary, Rashmi

2014-01-01

A newly identified lethal form of hereditary sensory and autonomic neuropathy (HSAN), designated HSAN-VI, is caused by a homozygous mutation in the bullous pemphigoid antigen 1 (BPAG1)/dystonin gene (DST). The HSAN-VI mutation impacts all major neuronal BPAG1/dystonin protein isoforms: dystonin-a1, -a2 and -a3. Homozygous mutations in the murine Dst gene cause a severe sensory neuropathy termed dystonia musculorum (dt). Phenotypically, dt mice are similar to HSAN-VI patients, manifesting progressive limb contractures, dystonia, dysautonomia and early postnatal death. To obtain a better molecular understanding of disease pathogenesis in HSAN-VI patients and the dt disorder, we generated transgenic mice expressing a myc-tagged dystonin-a2 protein under the regulation of the neuronal prion protein promoter on the dtTg4/Tg4 background, which is devoid of endogenous dystonin-a1 and -a2, but does express dystonin-a3. Restoring dystonin-a2 expression in the nervous system, particularly within sensory neurons, prevented the disorganization of organelle membranes and microtubule networks, attenuated the degeneration of sensory neuron subtypes and ameliorated the phenotype and increased life span in these mice. Despite these improvements, complete rescue was not observed likely because of inadequate expression of the transgene. Taken together, this study provides needed insight into the molecular basis of the dt disorder and other peripheral neuropathies including HSAN-VI. PMID:24381311

The nature of catecholamine-containing neurons in the enteric nervous system in relationship with organogenesis, normal human anatomy and neurodegeneration.

PubMed

Natale, G; Ryskalin, L; Busceti, C L; Biagioni, F; Fornai, F

2017-09-01

The gastrointestinal tract is provided with extrinsic and intrinsic innervation. The extrinsic innervation includes the classic vagal parasympathetic and sympathetic components, with afferent sensitive and efferent secretomotor fibers. The intrinsic innervations is represented by the enteric nervous system (ENS), which is recognized as a complex neural network controlling a variety of cell populations, including smooth muscle cells, mucosal secretory cells, endocrine cells, microvasculature, immune and inflammatory cells. This is finalized to regulate gastrointestinal secretion, absorption and motility. In particular, this network is organized in several plexuses each one providing quite autonomous control of gastrointestinal functions (hence the definition of "second brain"). The similarity between ENS and CNS is further substantiated by the presence of local sensitive pseudo- unipolar ganglionic neurons with both peripheral and central branching which terminate in the enteric wall. A large variety of neurons and neurotransmitters takes part in the ENS. However, the nature of these neurons and their role in the regulation of gastrointestinal functions is debatable. In particular, the available literature reporting the specific nature of catecholamine- containing neurons provides conflicting evidence. This is critical both for understanding the specific role of each catecholamine in the gut and, mostly, to characterize specifically the enteric neuropathology occurring in a variety of diseases. An emphasis is posed on neurodegenerative disorders, such as Parkinson's disease, which is associated with the loss of catecholamine neurons. In this respect, the recognition of the nature of such neurons within the ENS would contribute to elucidate the pathological mechanisms which produce both CNS and ENS degeneration and to achieve more effective therapeutic approaches. Despite a great emphasis is posed on the role of noradrenaline to regulate enteric activities only a few reports are available on the anatomy and physiology of enteric dopamine neurons. Remarkably, this review limits the presence of enteric noradrenaline (and adrenaline) only within extrinsic sympathetic nerve terminals. This is based on careful morphological studies showing that the only catecholamine-containing neurons within ENS would be dopaminergic. This means that enteric pathology of catecholamine neurons should be conceived as axon pathology for noradrenaline neurons and whole cell pathology for dopamine neurons which would be the sole catecholamine cell within intrinsic circuitries affecting gut motility and secretions.The gastrointestinal tract is provided with extrinsic and intrinsic innervation. The extrinsic innervation includes the classic vagal parasympathetic and sympathetic components, with afferent sensitive and efferent secretomotor fibers. The intrinsic innervations is represented by the enteric nervous system (ENS), which is recognized as a complex neural network  controlling a variety of cell populations, including smooth muscle cells, mucosal secretory cells, endocrine cells, microvasculature, immune and inflammatory cells. This is finalized to regulate gastrointestinal secretion, absorption and motility. In particular, this network is organized in several plexuses each one providing quite autonomous control of gastrointestinal functions (hence the definition of "second brain"). The similarity between ENS and CNS is further substantiated by the presence of local sensitive pseudounipolar ganglionic neurons with both peripheral and central branching which terminate in the enteric wall. A large variety of neurons and neurotransmitters takes part in the ENS. However, the nature of these neurons and their role in the regulation of gastrointestinal functions is debatable. In particular, the available literature reporting the specific nature of catecholamine-containing neurons provides conflicting evidence. This is critical both for understanding the specific role of each catecholamine in the gut and, mostly, to characterize specifically the enteric neuropathology occurring in a variety of diseases. An emphasis is posed on neurodegenerative disorders, such as including Parkinson's disease, which is associated with the loss of catecholamine neurons. In this respect, the recognition of the nature of such neurons within the ENS would contribute to elucidate the pathological mechanisms which produce both CNS and ENS degeneration and to achieve more effective therapeutic approaches. Despite a great emphasis is posed on the role of noradrenaline to regulate enteric activities only a few reports are available on the anatomy and physiology of enteric dopamine neurons. Remarkably, this review limits the presence of enteric noradrenaline (and adrenaline) only within extrinsic sympathetic nerve terminals. This is based on careful morphological studies showing that the only catecholamine-containing neurons within ENS would be dopaminergic. This means that enteric pathology of catecholamine neurons should be conceived as axon pathology for noradrenaline neurons and whole cell pathology for dopamine neurons which would be the sole catecholamine cell within intrinsic circuitries affecting gut motility and secretions.

Simplicity and efficiency of integrate-and-fire neuron models.

PubMed

Plesser, Hans E; Diesmann, Markus

2009-02-01

Lovelace and Cios (2008) recently proposed a very simple spiking neuron (VSSN) model for simulations of large neuronal networks as an efficient replacement for the integrate-and-fire neuron model. We argue that the VSSN model falls behind key advances in neuronal network modeling over the past 20 years, in particular, techniques that permit simulators to compute the state of the neuron without repeated summation over the history of input spikes and to integrate the subthreshold dynamics exactly. State-of-the-art solvers for networks of integrate-and-fire model neurons are substantially more efficient than the VSSN simulator and allow routine simulations of networks of some 10(5) neurons and 10(9) connections on moderate computer clusters.

Transcranial Direct Current Stimulation Modulates Neuronal Networks in Attention Deficit Hyperactivity Disorder.

PubMed

Sotnikova, Anna; Soff, Cornelia; Tagliazucchi, Enzo; Becker, Katja; Siniatchkin, Michael

2017-09-01

Anodal transcranial direct current stimulation (tDCS) of the prefrontal cortex has been repeatedly shown to improve working memory (WM). Since patients with attention deficit hyperactivity disorder (ADHD) are characterized by both underactivation of the prefrontal cortex and deficits in WM, the modulation of prefrontal activity with tDCS in ADHD patients may increase their WM performance as well as improve the activation and connectivity of the WM network. In the present study, this hypothesis was tested using a double-blind sham-controlled experimental design. After randomization, sixteen adolescents with ADHD underwent either anodal tDCS over the left dorsolateral prefrontal cortex (DLPFC, 1 mA, 20 min) or sham stimulation with simultaneous fMRI during n-back WM task. Both in one-back and two-back conditions, tDCS led to a greater activation (compared with sham stimulation) of the left DLPFC (under the electrode), left premotor cortex, left supplementary motor cortex, and precuneus. The effects of tDCS were long-lasting and influenced resting state functional connectivity even 20 min after the stimulation, with patterns of strengthened DLPFC connectivity after tDCS outlining the WM network. In summary, anodal tDCS caused increased neuronal activation and connectivity, not only in the brain area under the stimulating electrode (i.e. left DLPFC) but also in other, more remote brain regions. Because of moderate behavioral effects of tDCS, the significance of this technique for ADHD treatment has to be investigated in further studies.

Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.

PubMed

Xu, Tao; Xiao, Na; Zhai, Xiaolong; Kwan Chan, Pak; Tin, Chung

2018-02-01

Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

The role of central noradrenergic dysregulation in anxiety disorders: evidence from clinical studies.

PubMed

Kalk, N J; Nutt, D J; Lingford-Hughes, A R

2011-01-01

The nature of the noradrenergic dysregulation in clinical anxiety disorders remains unclear. In panic disorder, the predominant view has been that central noradrenergic neuronal networks and/or the sympathetic nervous system was normal in patients at rest, but hyper-reactive to specific stimuli, for example carbon dioxide. These ideas have been extended to other anxiety disorders, which share with panic disorder characteristic subjective anxiety and physiological symptoms of excess sympathetic activity. For example, Generalized Anxiety Disorder is characterized by chronic free-floating anxiety, muscle tension, palpitation and insomnia. It has been proposed that there is chronic central hypersecretion of noradrenaline in Generalized Anxiety Disorder, with consequent hyporesponsiveness of central post-synaptic receptors. With regards to other disorders, it has been suggested that there is noradrenergic involvement or derangement, but a more specific hypothesis has not been enunciated. This paper reviews the evidence for noradrenergic dysfunction in anxiety disorders, derived from indirect measures of noradrenergic function in clinical populations.

Quantification of changes in language-related brain areas in autism spectrum disorders using large-scale network analysis.

PubMed

Goch, Caspar J; Stieltjes, Bram; Henze, Romy; Hering, Jan; Poustka, Luise; Meinzer, Hans-Peter; Maier-Hein, Klaus H

2014-05-01

Diagnosis of autism spectrum disorders (ASD) is difficult, as symptoms vary greatly and are difficult to quantify objectively. Recent work has focused on the assessment of non-invasive diffusion tensor imaging-based biomarkers that reflect the microstructural characteristics of neuronal pathways in the brain. While tractography-based approaches typically analyze specific structures of interest, a graph-based large-scale network analysis of the connectome can yield comprehensive measures of larger-scale architectural patterns in the brain. Commonly applied global network indices, however, do not provide any specificity with respect to functional areas or anatomical structures. Aim of this work was to assess the concept of network centrality as a tool to perform locally specific analysis without disregarding the global network architecture and compare it to other popular network indices. We create connectome networks from fiber tractographies and parcellations of the human brain and compute global network indices as well as local indices for Wernicke's Area, Broca's Area and the Motor Cortex. Our approach was evaluated on 18 children suffering from ASD and 18 typically developed controls using magnetic resonance imaging-based cortical parcellations in combination with diffusion tensor imaging tractography. We show that the network centrality of Wernicke's area is significantly (p<0.001) reduced in ASD, while the motor cortex, which was used as a control region, did not show significant alterations. This could reflect the reduced capacity for comprehension of language in ASD. The betweenness centrality could potentially be an important metric in the development of future diagnostic tools in the clinical context of ASD diagnosis. Our results further demonstrate the applicability of large-scale network analysis tools in the domain of region-specific analysis with a potential application in many different psychological disorders.

FOXP2 drives neuronal differentiation by interacting with retinoic acid signaling pathways.

PubMed

Devanna, Paolo; Middelbeek, Jeroen; Vernes, Sonja C

2014-01-01

FOXP2 was the first gene shown to cause a Mendelian form of speech and language disorder. Although developmentally expressed in many organs, loss of a single copy of FOXP2 leads to a phenotype that is largely restricted to orofacial impairment during articulation and linguistic processing deficits. Why perturbed FOXP2 function affects specific aspects of the developing brain remains elusive. We investigated the role of FOXP2 in neuronal differentiation and found that FOXP2 drives molecular changes consistent with neuronal differentiation in a human model system. We identified a network of FOXP2 regulated genes related to retinoic acid signaling and neuronal differentiation. FOXP2 also produced phenotypic changes associated with neuronal differentiation including increased neurite outgrowth and reduced migration. Crucially, cells expressing FOXP2 displayed increased sensitivity to retinoic acid exposure. This suggests a mechanism by which FOXP2 may be able to increase the cellular differentiation response to environmental retinoic acid cues for specific subsets of neurons in the brain. These data demonstrate that FOXP2 promotes neuronal differentiation by interacting with the retinoic acid signaling pathway and regulates key processes required for normal circuit formation such as neuronal migration and neurite outgrowth. In this way, FOXP2, which is found only in specific subpopulations of neurons in the brain, may drive precise neuronal differentiation patterns and/or control localization and connectivity of these FOXP2 positive cells.

FOXP2 drives neuronal differentiation by interacting with retinoic acid signaling pathways

PubMed Central

Devanna, Paolo; Middelbeek, Jeroen; Vernes, Sonja C.

2014-01-01

FOXP2 was the first gene shown to cause a Mendelian form of speech and language disorder. Although developmentally expressed in many organs, loss of a single copy of FOXP2 leads to a phenotype that is largely restricted to orofacial impairment during articulation and linguistic processing deficits. Why perturbed FOXP2 function affects specific aspects of the developing brain remains elusive. We investigated the role of FOXP2 in neuronal differentiation and found that FOXP2 drives molecular changes consistent with neuronal differentiation in a human model system. We identified a network of FOXP2 regulated genes related to retinoic acid signaling and neuronal differentiation. FOXP2 also produced phenotypic changes associated with neuronal differentiation including increased neurite outgrowth and reduced migration. Crucially, cells expressing FOXP2 displayed increased sensitivity to retinoic acid exposure. This suggests a mechanism by which FOXP2 may be able to increase the cellular differentiation response to environmental retinoic acid cues for specific subsets of neurons in the brain. These data demonstrate that FOXP2 promotes neuronal differentiation by interacting with the retinoic acid signaling pathway and regulates key processes required for normal circuit formation such as neuronal migration and neurite outgrowth. In this way, FOXP2, which is found only in specific subpopulations of neurons in the brain, may drive precise neuronal differentiation patterns and/or control localization and connectivity of these FOXP2 positive cells. PMID:25309332

Human pluripotent stem cell models of autism spectrum disorder: emerging frontiers, opportunities, and challenges towards neuronal networks in a dish.

PubMed

Aigner, Stefan; Heckel, Tobias; Zhang, Jitao D; Andreae, Laura C; Jagasia, Ravi

2014-03-01

Autism spectrum disorder (ASD) is characterized by deficits in language development and social cognition and the manifestation of repetitive and restrictive behaviors. Despite recent major advances, our understanding of the pathophysiological mechanisms leading to ASD is limited. Although most ASD cases have unknown genetic underpinnings, animal and human cellular models of several rare, genetically defined syndromic forms of ASD have provided evidence for shared pathophysiological mechanisms that may extend to idiopathic cases. Here, we review our current knowledge of the genetic basis and molecular etiology of ASD and highlight how human pluripotent stem cell-based disease models have the potential to advance our understanding of molecular dysfunction. We summarize landmark studies in which neuronal cell populations generated from human embryonic stem cells and patient-derived induced pluripotent stem cells have served to model disease mechanisms, and we discuss recent technological advances that may ultimately allow in vitro modeling of specific human neuronal circuitry dysfunction in ASD. We propose that these advances now offer an unprecedented opportunity to help better understand ASD pathophysiology. This should ultimately enable the development of cellular models for ASD, allowing drug screening and the identification of molecular biomarkers for patient stratification.

The protein interactome of collapsin response mediator protein-2 (CRMP2/DPYSL2) reveals novel partner proteins in brain tissue.

PubMed

Martins-de-Souza, Daniel; Cassoli, Juliana S; Nascimento, Juliana M; Hensley, Kenneth; Guest, Paul C; Pinzon-Velasco, Andres M; Turck, Christoph W

2015-10-01

Collapsin response mediator protein-2 (CRMP2) is a CNS protein involved in neuronal development, axonal and neuronal growth, cell migration, and protein trafficking. Recent studies have linked perturbations in CRMP2 function to neurodegenerative disorders such as Alzheimer's disease, neuropathic pain, and Batten disease, and to psychiatric disorders such as schizophrenia. Like most proteins, CRMP2 functions though interactions with a molecular network of proteins and other molecules. Here, we have attempted to identify additional proteins of the CRMP2 interactome to provide further leads about its roles in neurological functions. We used a combined co-immunoprecipitation and shotgun proteomic approach in order to identify CRMP2 protein partners. We identified 78 CRMP2 protein partners not previously reported in public protein interaction databases. These were involved in seven biological processes, which included cell signaling, growth, metabolism, trafficking, and immune function, according to Gene Ontology classifications. Furthermore, 32 different molecular functions were found to be associated with these proteins, such as RNA binding, ribosomal functions, transporter activity, receptor activity, serine/threonine phosphatase activity, cell adhesion, cytoskeletal protein binding and catalytic activity. In silico pathway interactome construction revealed a highly connected network with the most overrepresented functions corresponding to semaphorin interactions, along with axon guidance and WNT5A signaling. Taken together, these findings suggest that the CRMP2 pathway is critical for regulating neuronal and synaptic architecture. Further studies along these lines might uncover novel biomarkers and drug targets for use in drug discovery. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Network and neuronal membrane properties in hybrid networks reciprocally regulate selectivity to rapid thalamocortical inputs.

PubMed

Pesavento, Michael J; Pinto, David J

2012-11-01

Rapidly changing environments require rapid processing from sensory inputs. Varying deflection velocities of a rodent's primary facial vibrissa cause varying temporal neuronal activity profiles within the ventral posteromedial thalamic nucleus. Local neuron populations in a single somatosensory layer 4 barrel transform sparsely coded input into a spike count based on the input's temporal profile. We investigate this transformation by creating a barrel-like hybrid network with whole cell recordings of in vitro neurons from a cortical slice preparation, embedding the biological neuron in the simulated network by presenting virtual synaptic conductances via a conductance clamp. Utilizing the hybrid network, we examine the reciprocal network properties (local excitatory and inhibitory synaptic convergence) and neuronal membrane properties (input resistance) by altering the barrel population response to diverse thalamic input. In the presence of local network input, neurons are more selective to thalamic input timing; this arises from strong feedforward inhibition. Strongly inhibitory (damping) network regimes are more selective to timing and less selective to the magnitude of input but require stronger initial input. Input selectivity relies heavily on the different membrane properties of excitatory and inhibitory neurons. When inhibitory and excitatory neurons had identical membrane properties, the sensitivity of in vitro neurons to temporal vs. magnitude features of input was substantially reduced. Increasing the mean leak conductance of the inhibitory cells decreased the network's temporal sensitivity, whereas increasing excitatory leak conductance enhanced magnitude sensitivity. Local network synapses are essential in shaping thalamic input, and differing membrane properties of functional classes reciprocally modulate this effect.

[Neurological and technical aspects of deep brain stimulation].

PubMed

Voges, J; Krauss, J K

2010-06-01

Deep brain stimulation (DBS) is an important component of the therapy of movement disorders and has almost completely replaced high-frequency coagulation of brain tissue in stereotactic neurosurgery. Despite the functional efficacy of DBS, which in parts is documented on the highest evidence level, the underlying mechanisms are still not completely understood. According to the current state of knowledge electrophysiological and functional data give evidence that high-frequency DBS has an inhibitory effect around the stimulation electrode whilst at the same time axons entering or leaving the stimulated brain area are excited leading to modulation of neuronal networks. The latter effect modifies pathological discharges of neurons in key structures of the basal ganglia network (e.g. irregular bursting activity, oscillations or synchronization) which are found in particular movement disorders such as Parkinson' s disease or dystonia. The introduction of technical standards, such as the integration of high resolution MRI into computer-assisted treatment planning, in combination with special treatment planning software have contributed significantly to the reduction of severe surgical complications (frequency of intracranial hemorrhaging 1-3%) in recent years. Future developments will address the modification of hardware components of the stimulation system, the evaluation of new brain target areas, the simultaneous stimulation of different brain areas and the assessment of different stimulation paradigms (high-frequency vs low-frequency DBS).

Population coding in sparsely connected networks of noisy neurons.

PubMed

Tripp, Bryan P; Orchard, Jeff

2012-01-01

This study examines the relationship between population coding and spatial connection statistics in networks of noisy neurons. Encoding of sensory information in the neocortex is thought to require coordinated neural populations, because individual cortical neurons respond to a wide range of stimuli, and exhibit highly variable spiking in response to repeated stimuli. Population coding is rooted in network structure, because cortical neurons receive information only from other neurons, and because the information they encode must be decoded by other neurons, if it is to affect behavior. However, population coding theory has often ignored network structure, or assumed discrete, fully connected populations (in contrast with the sparsely connected, continuous sheet of the cortex). In this study, we modeled a sheet of cortical neurons with sparse, primarily local connections, and found that a network with this structure could encode multiple internal state variables with high signal-to-noise ratio. However, we were unable to create high-fidelity networks by instantiating connections at random according to spatial connection probabilities. In our models, high-fidelity networks required additional structure, with higher cluster factors and correlations between the inputs to nearby neurons.

Constructing Precisely Computing Networks with Biophysical Spiking Neurons.

PubMed

Schwemmer, Michael A; Fairhall, Adrienne L; Denéve, Sophie; Shea-Brown, Eric T

2015-07-15

While spike timing has been shown to carry detailed stimulus information at the sensory periphery, its possible role in network computation is less clear. Most models of computation by neural networks are based on population firing rates. In equivalent spiking implementations, firing is assumed to be random such that averaging across populations of neurons recovers the rate-based approach. Recently, however, Denéve and colleagues have suggested that the spiking behavior of neurons may be fundamental to how neuronal networks compute, with precise spike timing determined by each neuron's contribution to producing the desired output (Boerlin and Denéve, 2011; Boerlin et al., 2013). By postulating that each neuron fires to reduce the error in the network's output, it was demonstrated that linear computations can be performed by networks of integrate-and-fire neurons that communicate through instantaneous synapses. This left open, however, the possibility that realistic networks, with conductance-based neurons with subthreshold nonlinearity and the slower timescales of biophysical synapses, may not fit into this framework. Here, we show how the spike-based approach can be extended to biophysically plausible networks. We then show that our network reproduces a number of key features of cortical networks including irregular and Poisson-like spike times and a tight balance between excitation and inhibition. Lastly, we discuss how the behavior of our model scales with network size or with the number of neurons "recorded" from a larger computing network. These results significantly increase the biological plausibility of the spike-based approach to network computation. We derive a network of neurons with standard spike-generating currents and synapses with realistic timescales that computes based upon the principle that the precise timing of each spike is important for the computation. We then show that our network reproduces a number of key features of cortical networks including irregular, Poisson-like spike times, and a tight balance between excitation and inhibition. These results significantly increase the biological plausibility of the spike-based approach to network computation, and uncover how several components of biological networks may work together to efficiently carry out computation. Copyright © 2015 the authors 0270-6474/15/3510112-23$15.00/0.

Neural networks with local receptive fields and superlinear VC dimension.

PubMed

Schmitt, Michael

2002-04-01

Local receptive field neurons comprise such well-known and widely used unit types as radial basis function (RBF) neurons and neurons with center-surround receptive field. We study the Vapnik-Chervonenkis (VC) dimension of feedforward neural networks with one hidden layer of these units. For several variants of local receptive field neurons, we show that the VC dimension of these networks is superlinear. In particular, we establish the bound Omega(W log k) for any reasonably sized network with W parameters and k hidden nodes. This bound is shown to hold for discrete center-surround receptive field neurons, which are physiologically relevant models of cells in the mammalian visual system, for neurons computing a difference of gaussians, which are popular in computational vision, and for standard RBF neurons, a major alternative to sigmoidal neurons in artificial neural networks. The result for RBF neural networks is of particular interest since it answers a question that has been open for several years. The results also give rise to lower bounds for networks with fixed input dimension. Regarding constants, all bounds are larger than those known thus far for similar architectures with sigmoidal neurons. The superlinear lower bounds contrast with linear upper bounds for single local receptive field neurons also derived here.

Results on a binding neuron model and their implications for modified hourglass model for neuronal network.

PubMed

Arunachalam, Viswanathan; Akhavan-Tabatabaei, Raha; Lopez, Cristina

2013-01-01

The classical models of single neuron like Hodgkin-Huxley point neuron or leaky integrate and fire neuron assume the influence of postsynaptic potentials to last till the neuron fires. Vidybida (2008) in a refreshing departure has proposed models for binding neurons in which the trace of an input is remembered only for a finite fixed period of time after which it is forgotten. The binding neurons conform to the behaviour of real neurons and are applicable in constructing fast recurrent networks for computer modeling. This paper develops explicitly several useful results for a binding neuron like the firing time distribution and other statistical characteristics. We also discuss the applicability of the developed results in constructing a modified hourglass network model in which there are interconnected neurons with excitatory as well as inhibitory inputs. Limited simulation results of the hourglass network are presented.

Role of physical and mental training in brain network configuration.

PubMed

Foster, Philip P

2015-01-01

It is hypothesized that the topology of brain networks is constructed by connecting nodes which may be continuously remodeled by appropriate training. Efficiency of physical and/or mental training on the brain relies on the flexibility of networks' architecture molded by local remodeling of proteins and synapses of excitatory neurons producing transformations in network topology. Continuous remodeling of proteins of excitatory neurons is fine-tuning the scaling and strength of excitatory synapses up or down via regulation of intra-cellular metabolic and regulatory networks of the genome-transcriptome-proteome interface. Alzheimer's disease is a model of "energy cost-driven small-world network disorder" with dysfunction of high-energy cost wiring as the network global efficiency is impaired by the deposition of an informed agent, the amyloid-β, selectively targeting high-degree nodes. In schizophrenia, the interconnectivity and density of rich-club networks are significantly reduced. Training-induced homeostatic synaptogenesis-enhancement, presumably via reconfiguration of brain networks into greater small-worldness, appears essential in learning, memory, and executive functions. A macroscopic cartography of creation-removal of synaptic connections in a macro-network, and at the intra-cellular scale, micro-networks regulate the physiological mechanisms for the preferential attachment of synapses. The strongest molecular relationship of exercise and functional connectivity was identified for brain-derived neurotrophic factor (BDNF). The allele variant, rs7294919, also shows a powerful relationship with the hippocampal volume. How the brain achieves this unique quest of reconfiguration remains a puzzle. What are the underlying mechanisms of synaptogenesis promoting communications brain ↔ muscle and brain ↔ brain in such trainings? What is the respective role of independent mental, physical, or combined-mental-physical trainings? Physical practice seems to be playing an instrumental role in the cognitive enhancement (brain ↔ muscle com.). However, mental training, meditation or virtual reality (films, games) require only minimal motor activity and cardio-respiratory stimulation. Therefore, other potential paths (brain ↔ brain com.) molding brain networks are nonetheless essential. Patients with motor neuron disease/injury (e.g., amyotrophic lateral sclerosis, traumatism) also achieve successful cognitive enhancement albeit they may only elicit mental practice.

The transcription factor NRSF contributes to epileptogenesis by selective repression of a subset of target genes

PubMed Central

McClelland, Shawn; Brennan, Gary P; Dubé, Celine; Rajpara, Seeta; Iyer, Shruti; Richichi, Cristina; Bernard, Christophe; Baram, Tallie Z

2014-01-01

The mechanisms generating epileptic neuronal networks following insults such as severe seizures are unknown. We have previously shown that interfering with the function of the neuron-restrictive silencer factor (NRSF/REST), an important transcription factor that influences neuronal phenotype, attenuated development of this disorder. In this study, we found that epilepsy-provoking seizures increased the low NRSF levels in mature hippocampus several fold yet surprisingly, provoked repression of only a subset (∼10%) of potential NRSF target genes. Accordingly, the repressed gene-set was rescued when NRSF binding to chromatin was blocked. Unexpectedly, genes selectively repressed by NRSF had mid-range binding frequencies to the repressor, a property that rendered them sensitive to moderate fluctuations of NRSF levels. Genes selectively regulated by NRSF during epileptogenesis coded for ion channels, receptors, and other crucial contributors to neuronal function. Thus, dynamic, selective regulation of NRSF target genes may play a role in influencing neuronal properties in pathological and physiological contexts. DOI: http://dx.doi.org/10.7554/eLife.01267.001 PMID:25117540

Microglia in CNS development: Shaping the brain for the future.

PubMed

Mosser, Coralie-Anne; Baptista, Sofia; Arnoux, Isabelle; Audinat, Etienne

Microglial cells are the resident macrophages of the central nervous system (CNS) and are mainly known for their roles in neuropathologies. However, major recent developments have revealed that these immune cells actively interact with neurons in physiological conditions and can modulate the fate and functions of synapses. Originating from myeloid precursors born in the yolk sac, microglial cells invade the CNS during early embryonic development. As a consequence they can potentially influence neuronal proliferation, migration and differentiation as well as the formation and maturation of neuronal networks, thereby contributing to the entire shaping of the CNS. We review here recent evidence indicating that microglial cells are indeed involved in crucial steps of the CNS development, including neuronal survival and apoptosis, axonal growth, migration of neurons, pruning of supernumerary synapses and functional maturation of developing synapses. We also discuss current hypotheses proposing that diverting microglial cells of their physiological functions, by promoting the expression of an immune phenotype during development, may be central to neurodevelopmental disorders such as autism, schizophrenia and epilepsy. Copyright © 2017 Elsevier Ltd. All rights reserved.

Continuing the search for the engram: examining the mechanism of fear memories.

PubMed

Josselyn, Sheena A

2010-07-01

The goal of my research is to gain insight using rodent models into the fundamental molecular, cellular and systems that make up the base of memory formation. My work focuses on fear memories. Aberrant fear and/or anxiety may be at the heart of many psychiatric disorders. In this article, I review the results of my research group; these results show that particular neurons in the lateral amygdala, a brain region important for fear, are specifically involved in particular fear memories. We started by showing that the transcription factor CREB (cAMP/Ca(2+) response element binding protein) plays a key role in the formation of fear memories. Next, we used viral vectors to overexpress CREB in a subset of lateral amygdala neurons. This not only facilitated fear memory formation but also "drove" the memory into the neurons with relatively increased CREB function. Finally, we showed that selective ablation of the neurons overexpressing CREB in the lateral amygdala selectively erased the fear memory. These findings are the first to show disruption of a specific memory by disrupting select neurons within a distributed network.

Dynamical state of the network determines the efficacy of single neuron properties in shaping the network activity

PubMed Central

Sahasranamam, Ajith; Vlachos, Ioannis; Aertsen, Ad; Kumar, Arvind

2016-01-01

Spike patterns are among the most common electrophysiological descriptors of neuron types. Surprisingly, it is not clear how the diversity in firing patterns of the neurons in a network affects its activity dynamics. Here, we introduce the state-dependent stochastic bursting neuron model allowing for a change in its firing patterns independent of changes in its input-output firing rate relationship. Using this model, we show that the effect of single neuron spiking on the network dynamics is contingent on the network activity state. While spike bursting can both generate and disrupt oscillations, these patterns are ineffective in large regions of the network state space in changing the network activity qualitatively. Finally, we show that when single-neuron properties are made dependent on the population activity, a hysteresis like dynamics emerges. This novel phenomenon has important implications for determining the network response to time-varying inputs and for the network sensitivity at different operating points. PMID:27212008

Dynamical state of the network determines the efficacy of single neuron properties in shaping the network activity.

PubMed

Sahasranamam, Ajith; Vlachos, Ioannis; Aertsen, Ad; Kumar, Arvind

2016-05-23

Spike patterns are among the most common electrophysiological descriptors of neuron types. Surprisingly, it is not clear how the diversity in firing patterns of the neurons in a network affects its activity dynamics. Here, we introduce the state-dependent stochastic bursting neuron model allowing for a change in its firing patterns independent of changes in its input-output firing rate relationship. Using this model, we show that the effect of single neuron spiking on the network dynamics is contingent on the network activity state. While spike bursting can both generate and disrupt oscillations, these patterns are ineffective in large regions of the network state space in changing the network activity qualitatively. Finally, we show that when single-neuron properties are made dependent on the population activity, a hysteresis like dynamics emerges. This novel phenomenon has important implications for determining the network response to time-varying inputs and for the network sensitivity at different operating points.

BDNF and the maturation of posttranscriptional regulatory networks in human SH-SY5Y neuroblast differentiation.

PubMed

Goldie, Belinda J; Barnett, Michelle M; Cairns, Murray J

2014-01-01

The SH-SY5Y culture system is a convenient neuronal model with the potential to elaborate human/primate-specific transcription networks and pathways related to human cognitive disorders. While this system allows for the exploration of specialized features in the human genome, there is still significant debate about how this model should be implemented, and its appropriateness for answering complex functional questions related to human neural architecture. In view of these questions we sought to characterize the posttranscriptional regulatory structure of the two-stage ATRA differentiation, BDNF maturation protocol proposed by Encinas et al. (2000) using integrative whole-genome gene and microRNA (miRNA) expression analysis. We report that ATRA-BDNF induced significant increases in expression of key synaptic genes, brain-specific miRNA and miRNA biogenesis machinery, and in AChE activity, compared with ATRA alone. Functional annotation clustering associated BDNF more significantly with neuronal terms, and with synaptic terms not found in ATRA-only clusters. While our results support use of SH-SY5Y as a neuronal model, we advocate considered selection of the differentiation agent/s relative to the system being modeled.

Augmented brain function by coordinated reset stimulation with slowly varying sequences.

PubMed

Zeitler, Magteld; Tass, Peter A

2015-01-01

Several brain disorders are characterized by abnormally strong neuronal synchrony. Coordinated Reset (CR) stimulation was developed to selectively counteract abnormal neuronal synchrony by desynchronization. For this, phase resetting stimuli are delivered to different subpopulations in a timely coordinated way. In neural networks with spike timing-dependent plasticity CR stimulation may eventually lead to an anti-kindling, i.e., an unlearning of abnormal synaptic connectivity and abnormal synchrony. The spatiotemporal sequence by which all stimulation sites are stimulated exactly once is called the stimulation site sequence, or briefly sequence. So far, in simulations, pre-clinical and clinical applications CR was applied either with fixed sequences or rapidly varying sequences (RVS). In this computational study we show that appropriate repetition of the sequence with occasional random switching to the next sequence may significantly improve the anti-kindling effect of CR. To this end, a sequence is applied many times before randomly switching to the next sequence. This new method is called SVS CR stimulation, i.e., CR with slowly varying sequences. In a neuronal network with strong short-range excitatory and weak long-range inhibitory dynamic couplings SVS CR stimulation turns out to be superior to CR stimulation with fixed sequences or RVS.

Augmented brain function by coordinated reset stimulation with slowly varying sequences

PubMed Central

Zeitler, Magteld; Tass, Peter A.

2015-01-01

Several brain disorders are characterized by abnormally strong neuronal synchrony. Coordinated Reset (CR) stimulation was developed to selectively counteract abnormal neuronal synchrony by desynchronization. For this, phase resetting stimuli are delivered to different subpopulations in a timely coordinated way. In neural networks with spike timing-dependent plasticity CR stimulation may eventually lead to an anti-kindling, i.e., an unlearning of abnormal synaptic connectivity and abnormal synchrony. The spatiotemporal sequence by which all stimulation sites are stimulated exactly once is called the stimulation site sequence, or briefly sequence. So far, in simulations, pre-clinical and clinical applications CR was applied either with fixed sequences or rapidly varying sequences (RVS). In this computational study we show that appropriate repetition of the sequence with occasional random switching to the next sequence may significantly improve the anti-kindling effect of CR. To this end, a sequence is applied many times before randomly switching to the next sequence. This new method is called SVS CR stimulation, i.e., CR with slowly varying sequences. In a neuronal network with strong short-range excitatory and weak long-range inhibitory dynamic couplings SVS CR stimulation turns out to be superior to CR stimulation with fixed sequences or RVS. PMID:25873867

Proteostasis and Diseases of the Motor Unit.

PubMed

Rinaldi, Carlo; Mäger, Imre; Wood, Matthew J

2016-01-01

The accumulation in neurons of aberrant protein species, the pathological hallmark of many neurodegenerative diseases, results from a global impairment of key cellular processes governing protein synthesis/degradation and repair mechanisms, also known as the proteostasis network (PN). The growing number of connections between dysfunction of this intricate network of pathways and diseases of the motor unit, where both motor neurons and muscle are primarily affected, has provided momentum to investigate the muscle- and motor neuron-specific response to physiological and pathological stressors and to explore the therapeutic opportunities that manipulation of this process may offer. Furthermore, these diseases offer an unparalleled opportunity to deepen our understanding of the molecular mechanisms behind the intertissue communication and transfer of signals of proteostasis. The most compelling aspect of these investigations is their immediate potential for therapeutic impact: targeting muscle to stem degeneration of the motor unit would represent a dramatic paradigm therapeutic shift for treating these devastating diseases. Here we will review the current state of the art of the research on the alterations of the PN in diseases of the motor unit and its potential to result in effective treatments for these devastating neuromuscular disorders.

Recurrent Network models of sequence generation and memory

PubMed Central

Rajan, Kanaka; Harvey, Christopher D; Tank, David W

2016-01-01

SUMMARY Sequential activation of neurons is a common feature of network activity during a variety of behaviors, including working memory and decision making. Previous network models for sequences and memory emphasized specialized architectures in which a principled mechanism is pre-wired into their connectivity. Here, we demonstrate that starting from random connectivity and modifying a small fraction of connections, a largely disordered recurrent network can produce sequences and implement working memory efficiently. We use this process, called Partial In-Network training (PINning), to model and match cellular-resolution imaging data from the posterior parietal cortex during a virtual memory-guided two-alternative forced choice task [Harvey, Coen and Tank, 2012]. Analysis of the connectivity reveals that sequences propagate by the cooperation between recurrent synaptic interactions and external inputs, rather than through feedforward or asymmetric connections. Together our results suggest that neural sequences may emerge through learning from largely unstructured network architectures. PMID:26971945

Synchronization in a chaotic neural network with time delay depending on the spatial distance between neurons

NASA Astrophysics Data System (ADS)

Tang, Guoning; Xu, Kesheng; Jiang, Luoluo

2011-10-01

The synchronization is investigated in a two-dimensional Hindmarsh-Rose neuronal network by introducing a global coupling scheme with time delay, where the length of time delay is proportional to the spatial distance between neurons. We find that the time delay always disturbs synchronization of the neuronal network. When both the coupling strength and length of time delay per unit distance (i.e., enlargement factor) are large enough, the time delay induces the abnormal membrane potential oscillations in neurons. Specifically, the abnormal membrane potential oscillations for the symmetrically placed neurons form an antiphase, so that the large coupling strength and enlargement factor lead to the desynchronization of the neuronal network. The complete and intermittently complete synchronization of the neuronal network are observed for the right choice of parameters. The physical mechanism underlying these phenomena is analyzed.

Transition to subthreshold activity with the use of phase shifting in a model thalamic network

NASA Astrophysics Data System (ADS)

Thomas, Elizabeth; Grisar, Thierry

1997-05-01

Absence epilepsy involves a state of low frequency synchronous oscillations by the involved neuronal networks. These oscillations may be either above or subthreshold. In this investigation, we studied the methods which could be utilized to transform the threshold activity of neurons in the network to a subthreshold state. A model thalamic network was constructed using the Hodgkin Huxley framework. Subthreshold activity was achieved by the application of stimuli to the network which caused phase shifts in the oscillatory activity of selected neurons in the network. In some instances the stimulus was a periodic pulse train of low frequency to the reticular thalamic neurons of the network while in others, it was a constant hyperpolarizing current applied to the thalamocortical neurons.

Brain profiling and clinical-neuroscience.

PubMed

Peled, Avi

2006-01-01

The current psychiatric diagnostic system, the diagnostic statistic manual, has recently come under increasing criticism. The major reason for the shortcomings of the current psychiatric diagnosis is the lack of a scientific brain-related etiological knowledge about mental disorders. The advancement toward such knowledge is further hampered by the lack of a theoretical framework or "language" that translates clinical findings of mental disorders to brain disturbances and insufficiencies. Here such a theoretical construct is proposed based on insights from neuroscience and neural-computation models. Correlates between clinical manifestations and presumed neuronal network disturbances are proposed in the form of a practical diagnostic system titled "Brain Profiling". Three dimensions make-up brain profiling, "neural complexity disorders", "neuronal resilience insufficiency", and "context-sensitive processing decline". The first dimension relates to disturbances occurring to fast neuronal activations in the millisecond range, it incorporates connectivity and hierarchical imbalances appertaining typically to psychotic and schizophrenic clinical manifestations. The second dimension relates to disturbances that alter slower changes namely long-term synaptic modulations, and incorporates disturbances to optimization and constraint satisfactions within relevant neuronal circuitry. Finally, the level of internal representations related to personality disorders is presented by a "context-sensitive process decline" as the third dimension. For practical use of brain profiling diagnosis a consensual list of psychiatric clinical manifestations provides a "diagnostic input vector", clinical findings are coded 1 for "detection" and 0 for "non-detection", 0.5 is coded for "questionable". The entries are clustered according to their presumed neuronal dynamic relationships and coefficients determine their relevance to the specific related brain disturbance. Relevant equations calculate and normalize the different values attributed to relevant brain disturbances culminating in a three-digit estimation representing the three diagnostic dimensions. brain profiling has the promise for a future brain-related diagnosis. It offers testable predictions about the etiology of mental disorders because being brain-related it lends readily to brain imaging investigations. Being presented also as a one-point representation in a three-dimensional space, multiple follow-up diagnoses trace a trajectory representing an easy-to-see clinical history of the patient. Additional, more immediate, advantages involve reduced stigma because it relaters the disorder to the brain not the person, in addition the three-digit diagnostic code is clinically informative unlike the DSM codes that have no clinical relevance. To conclude, brain profiling diagnosis of mental disorders could be a bold new step toward a "clinical-neuroscience" substituting "psychiatry".

Cannabinoid receptors mediate methamphetamine induction of high frequency gamma oscillations in the nucleus accumbens.

PubMed

Morra, Joshua T; Glick, Stanley D; Cheer, Joseph F

2012-09-01

Patients suffering from amphetamine-induced psychosis display repetitive behaviors, partially alleviated by antipsychotics, which are reminiscent of rodent stereotypies. Due to recent evidence implicating endocannabinoid involvement in brain disorders, including psychosis, we studied the effects of endocannabinoid signaling on neuronal oscillations of rats exhibiting methamphetamine stereotypy. Neuronal network oscillations were recorded with multiple single electrode arrays aimed at the nucleus accumbens of freely-moving rats. During the experiments, animals were dosed intravenously with the CB1 receptor antagonist rimonabant (0.3 mg/kg) or vehicle followed by an ascending dose regimen of methamphetamine (0.01, 0.1, 1, and 3 mg/kg; cumulative dosing). The effects of drug administration on stereotypy and local gamma oscillations were evaluated. Methamphetamine treatment significantly increased high frequency gamma oscillations (∼80 Hz). Entrainment of a subpopulation of nucleus accumbens neurons to high frequency gamma was associated with stereotypy encoding in putative fast-spiking interneurons, but not in putative medium spiny neurons. The observed ability of methamphetamine to induce both stereotypy and high frequency gamma power was potently disrupted following CB1 receptor blockade. The present data suggest that CB1 receptor-dependent mechanisms are recruited by methamphetamine to modify striatal interneuron oscillations that accompany changes in psychomotor state, further supporting the link between endocannabinoids and schizophrenia spectrum disorders. Copyright © 2012 Elsevier Ltd. All rights reserved.

Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks.

PubMed

Pena, Rodrigo F O; Vellmer, Sebastian; Bernardi, Davide; Roque, Antonio C; Lindner, Benjamin

2018-01-01

Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power spectrum. Depending on cellular and network parameters, correlations display diverse patterns (ranging from simple refractory-period effects and stochastic oscillations to slow fluctuations) and it is generally not well-understood how these dependencies come about. Previous work has explored how the single-cell correlations in a homogeneous network (excitatory and inhibitory integrate-and-fire neurons with nearly balanced mean recurrent input) can be determined numerically from an iterative single-neuron simulation. Such a scheme is based on the fact that every neuron is driven by the network noise (i.e., the input currents from all its presynaptic partners) but also contributes to the network noise, leading to a self-consistency condition for the input and output spectra. Here we first extend this scheme to homogeneous networks with strong recurrent inhibition and a synaptic filter, in which instabilities of the previous scheme are avoided by an averaging procedure. We then extend the scheme to heterogeneous networks in which (i) different neural subpopulations (e.g., excitatory and inhibitory neurons) have different cellular or connectivity parameters; (ii) the number and strength of the input connections are random (Erdős-Rényi topology) and thus different among neurons. In all heterogeneous cases, neurons are lumped in different classes each of which is represented by a single neuron in the iterative scheme; in addition, we make a Gaussian approximation of the input current to the neuron. These approximations seem to be justified over a broad range of parameters as indicated by comparison with simulation results of large recurrent networks. Our method can help to elucidate how network heterogeneity shapes the asynchronous state in recurrent neural networks.

A Multiple-Plasticity Spiking Neural Network Embedded in a Closed-Loop Control System to Model Cerebellar Pathologies.

PubMed

Geminiani, Alice; Casellato, Claudia; Antonietti, Alberto; D'Angelo, Egidio; Pedrocchi, Alessandra

2018-06-01

The cerebellum plays a crucial role in sensorimotor control and cerebellar disorders compromise adaptation and learning of motor responses. However, the link between alterations at network level and cerebellar dysfunction is still unclear. In principle, this understanding would benefit of the development of an artificial system embedding the salient neuronal and plastic properties of the cerebellum and operating in closed-loop. To this aim, we have exploited a realistic spiking computational model of the cerebellum to analyze the network correlates of cerebellar impairment. The model was modified to reproduce three different damages of the cerebellar cortex: (i) a loss of the main output neurons (Purkinje Cells), (ii) a lesion to the main cerebellar afferents (Mossy Fibers), and (iii) a damage to a major mechanism of synaptic plasticity (Long Term Depression). The modified network models were challenged with an Eye-Blink Classical Conditioning test, a standard learning paradigm used to evaluate cerebellar impairment, in which the outcome was compared to reference results obtained in human or animal experiments. In all cases, the model reproduced the partial and delayed conditioning typical of the pathologies, indicating that an intact cerebellar cortex functionality is required to accelerate learning by transferring acquired information to the cerebellar nuclei. Interestingly, depending on the type of lesion, the redistribution of synaptic plasticity and response timing varied greatly generating specific adaptation patterns. Thus, not only the present work extends the generalization capabilities of the cerebellar spiking model to pathological cases, but also predicts how changes at the neuronal level are distributed across the network, making it usable to infer cerebellar circuit alterations occurring in cerebellar pathologies.

Multiplex Networks of Cortical and Hippocampal Neurons Revealed at Different Timescales

PubMed Central

Timme, Nicholas; Ito, Shinya; Myroshnychenko, Maxym; Yeh, Fang-Chin; Hiolski, Emma; Hottowy, Pawel; Beggs, John M.

2014-01-01

Recent studies have emphasized the importance of multiplex networks – interdependent networks with shared nodes and different types of connections – in systems primarily outside of neuroscience. Though the multiplex properties of networks are frequently not considered, most networks are actually multiplex networks and the multiplex specific features of networks can greatly affect network behavior (e.g. fault tolerance). Thus, the study of networks of neurons could potentially be greatly enhanced using a multiplex perspective. Given the wide range of temporally dependent rhythms and phenomena present in neural systems, we chose to examine multiplex networks of individual neurons with time scale dependent connections. To study these networks, we used transfer entropy – an information theoretic quantity that can be used to measure linear and nonlinear interactions – to systematically measure the connectivity between individual neurons at different time scales in cortical and hippocampal slice cultures. We recorded the spiking activity of almost 12,000 neurons across 60 tissue samples using a 512-electrode array with 60 micrometer inter-electrode spacing and 50 microsecond temporal resolution. To the best of our knowledge, this preparation and recording method represents a superior combination of number of recorded neurons and temporal and spatial recording resolutions to any currently available in vivo system. We found that highly connected neurons (“hubs”) were localized to certain time scales, which, we hypothesize, increases the fault tolerance of the network. Conversely, a large proportion of non-hub neurons were not localized to certain time scales. In addition, we found that long and short time scale connectivity was uncorrelated. Finally, we found that long time scale networks were significantly less modular and more disassortative than short time scale networks in both tissue types. As far as we are aware, this analysis represents the first systematic study of temporally dependent multiplex networks among individual neurons. PMID:25536059

Serotonin receptor 1A–modulated phosphorylation of glycine receptor α3 controls breathing in mice

PubMed Central

Manzke, Till; Niebert, Marcus; Koch, Uwe R.; Caley, Alex; Vogelgesang, Steffen; Hülsmann, Swen; Ponimaskin, Evgeni; Müller, Ulrike; Smart, Trevor G.; Harvey, Robert J.; Richter, Diethelm W.

2010-01-01

Rhythmic breathing movements originate from a dispersed neuronal network in the medulla and pons. Here, we demonstrate that rhythmic activity of this respiratory network is affected by the phosphorylation status of the inhibitory glycine receptor α3 subtype (GlyRα3), which controls glutamatergic and glycinergic neuronal discharges, subject to serotonergic modulation. Serotonin receptor type 1A–specific (5-HTR1A–specific) modulation directly induced dephosphorylation of GlyRα3 receptors, which augmented inhibitory glycine-activated chloride currents in HEK293 cells coexpressing 5-HTR1A and GlyRα3. The 5-HTR1A–GlyRα3 signaling pathway was distinct from opioid receptor signaling and efficiently counteracted opioid-induced depression of breathing and consequential apnea in mice. Paradoxically, this rescue of breathing originated from enhanced glycinergic synaptic inhibition of glutamatergic and glycinergic neurons and caused disinhibition of their target neurons. Together, these effects changed respiratory phase alternations and ensured rhythmic breathing in vivo. GlyRα3-deficient mice had an irregular respiratory rhythm under baseline conditions, and systemic 5-HTR1A activation failed to remedy opioid-induced respiratory depression in these mice. Delineation of this 5-HTR1A–GlyRα3 signaling pathway offers a mechanistic basis for pharmacological treatment of opioid-induced apnea and other breathing disturbances caused by disorders of inhibitory synaptic transmission, such as hyperekplexia, hypoxia/ischemia, and brainstem infarction. PMID:20978350

Computational Models of Neuron-Astrocyte Interactions Lead to Improved Efficacy in the Performance of Neural Networks

PubMed Central

Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B.

2012-01-01

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem. PMID:22649480

Computational models of neuron-astrocyte interactions lead to improved efficacy in the performance of neural networks.

PubMed

Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B

2012-01-01

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem.

A modeling comparison of projection neuron- and neuromodulator-elicited oscillations in a central pattern generating network.

PubMed

Kintos, Nickolas; Nusbaum, Michael P; Nadim, Farzan

2008-06-01

Many central pattern generating networks are influenced by synaptic input from modulatory projection neurons. The network response to a projection neuron is sometimes mimicked by bath applying the neuronally-released modulator, despite the absence of network interactions with the projection neuron. One interesting example occurs in the crab stomatogastric ganglion (STG), where bath applying the neuropeptide pyrokinin (PK) elicits a gastric mill rhythm which is similar to that elicited by the projection neuron modulatory commissural neuron 1 (MCN1), despite the absence of PK in MCN1 and the fact that MCN1 is not active during the PK-elicited rhythm. MCN1 terminals have fast and slow synaptic actions on the gastric mill network and are presynaptically inhibited by this network in the STG. These local connections are inactive in the PK-elicited rhythm, and the mechanism underlying this rhythm is unknown. We use mathematical and biophysically-realistic modeling to propose potential mechanisms by which PK can elicit a gastric mill rhythm that is similar to the MCN1-elicited rhythm. We analyze slow-wave network oscillations using simplified mathematical models and, in parallel, develop biophysically-realistic models that account for fast, action potential-driven oscillations and some spatial structure of the network neurons. Our results illustrate how the actions of bath-applied neuromodulators can mimic those of descending projection neurons through mathematically similar but physiologically distinct mechanisms.

Simulating synchronization in neuronal networks

NASA Astrophysics Data System (ADS)

Fink, Christian G.

2016-06-01

We discuss several techniques used in simulating neuronal networks by exploring how a network's connectivity structure affects its propensity for synchronous spiking. Network connectivity is generated using the Watts-Strogatz small-world algorithm, and two key measures of network structure are described. These measures quantify structural characteristics that influence collective neuronal spiking, which is simulated using the leaky integrate-and-fire model. Simulations show that adding a small number of random connections to an otherwise lattice-like connectivity structure leads to a dramatic increase in neuronal synchronization.

Impact of Partial Time Delay on Temporal Dynamics of Watts-Strogatz Small-World Neuronal Networks

NASA Astrophysics Data System (ADS)

Yan, Hao; Sun, Xiaojuan

2017-06-01

In this paper, we mainly discuss effects of partial time delay on temporal dynamics of Watts-Strogatz (WS) small-world neuronal networks by controlling two parameters. One is the time delay τ and the other is the probability of partial time delay pdelay. Temporal dynamics of WS small-world neuronal networks are discussed with the aid of temporal coherence and mean firing rate. With the obtained simulation results, it is revealed that for small time delay τ, the probability pdelay could weaken temporal coherence and increase mean firing rate of neuronal networks, which indicates that it could improve neuronal firings of the neuronal networks while destroying firing regularity. For large time delay τ, temporal coherence and mean firing rate do not have great changes with respect to pdelay. Time delay τ always has great influence on both temporal coherence and mean firing rate no matter what is the value of pdelay. Moreover, with the analysis of spike trains and histograms of interspike intervals of neurons inside neuronal networks, it is found that the effects of partial time delays on temporal coherence and mean firing rate could be the result of locking between the period of neuronal firing activities and the value of time delay τ. In brief, partial time delay could have great influence on temporal dynamics of the neuronal networks.

Emergent Oscillations in Networks of Stochastic Spiking Neurons

PubMed Central

van Drongelen, Wim; Cowan, Jack D.

2011-01-01

Networks of neurons produce diverse patterns of oscillations, arising from the network's global properties, the propensity of individual neurons to oscillate, or a mixture of the two. Here we describe noisy limit cycles and quasi-cycles, two related mechanisms underlying emergent oscillations in neuronal networks whose individual components, stochastic spiking neurons, do not themselves oscillate. Both mechanisms are shown to produce gamma band oscillations at the population level while individual neurons fire at a rate much lower than the population frequency. Spike trains in a network undergoing noisy limit cycles display a preferred period which is not found in the case of quasi-cycles, due to the even faster decay of phase information in quasi-cycles. These oscillations persist in sparsely connected networks, and variation of the network's connectivity results in variation of the oscillation frequency. A network of such neurons behaves as a stochastic perturbation of the deterministic Wilson-Cowan equations, and the network undergoes noisy limit cycles or quasi-cycles depending on whether these have limit cycles or a weakly stable focus. These mechanisms provide a new perspective on the emergence of rhythmic firing in neural networks, showing the coexistence of population-level oscillations with very irregular individual spike trains in a simple and general framework. PMID:21573105

Identified Serotonergic Modulatory Neurons Have Heterogeneous Synaptic Connectivity within the Olfactory System of Drosophila.

PubMed

Coates, Kaylynn E; Majot, Adam T; Zhang, Xiaonan; Michael, Cole T; Spitzer, Stacy L; Gaudry, Quentin; Dacks, Andrew M

2017-08-02

Modulatory neurons project widely throughout the brain, dynamically altering network processing based on an animal's physiological state. The connectivity of individual modulatory neurons can be complex, as they often receive input from a variety of sources and are diverse in their physiology, structure, and gene expression profiles. To establish basic principles about the connectivity of individual modulatory neurons, we examined a pair of identified neurons, the "contralaterally projecting, serotonin-immunoreactive deutocerebral neurons" (CSDns), within the olfactory system of Drosophila Specifically, we determined the neuronal classes providing synaptic input to the CSDns within the antennal lobe (AL), an olfactory network targeted by the CSDns, and the degree to which CSDn active zones are uniformly distributed across the AL. Using anatomical techniques, we found that the CSDns received glomerulus-specific input from olfactory receptor neurons (ORNs) and projection neurons (PNs), and networkwide input from local interneurons (LNs). Furthermore, we quantified the number of CSDn active zones in each glomerulus and found that CSDn output is not uniform, but rather heterogeneous, across glomeruli and stereotyped from animal to animal. Finally, we demonstrate that the CSDns synapse broadly onto LNs and PNs throughout the AL but do not synapse upon ORNs. Our results demonstrate that modulatory neurons do not necessarily provide purely top-down input but rather receive neuron class-specific input from the networks that they target, and that even a two cell modulatory network has highly heterogeneous, yet stereotyped, pattern of connectivity. SIGNIFICANCE STATEMENT Modulatory neurons often project broadly throughout the brain to alter processing based on physiological state. However, the connectivity of individual modulatory neurons to their target networks is not well understood, as modulatory neuron populations are heterogeneous in their physiology, morphology, and gene expression. In this study, we use a pair of identified serotonergic neurons within the Drosophila olfactory system as a model to establish a framework for modulatory neuron connectivity. We demonstrate that individual modulatory neurons can integrate neuron class-specific input from their target network, which is often nonreciprocal. Additionally, modulatory neuron output can be stereotyped, yet nonuniform, across network regions. Our results provide new insight into the synaptic relationships that underlie network function of modulatory neurons. Copyright © 2017 the authors 0270-6474/17/377318-14$15.00/0.

Neural correlates of somatoform disorders from a meta-analytic perspective on neuroimaging studies.

PubMed

Boeckle, Markus; Schrimpf, Marlene; Liegl, Gregor; Pieh, Christoph

2016-01-01

Somatoform disorders (SD) are common medical disorders with prevalence rates between 3.5% and 18.4%, depending on country and medical setting. SD as outlined in the ICD-10 exhibits various biological, social, and psychological pathogenic factors. Little is known about the neural correlates of SD. The aims of this meta-analysis are to identify neuronal areas that are involved in SD and consistently differ between patients and healthy controls. We conducted a systematic literature research on neuroimaging studies of SD. Ten out of 686 studies fulfilled the inclusion criteria and were analyzed using activation likelihood estimation. Five neuronal areas differ between patients with SD and healthy controls namely the premotor and supplementary motor cortexes, the middle frontal gyrus, the anterior cingulate cortex, the insula, and the posterior cingulate cortex. These areas seem to have a particular importance for the occurrence of SD. Out of the ten studies two did not contribute to any of the clusters. Our results seem to largely overlap with the circuit network model of somatosensory amplification for SD. It is conceivable that functional disorders, independent of the clinical impression, show similar neurobiological processes. While overlaps do occur it is necessary to understand single functional somatic syndromes and their aetiology for future research, terminology, and treatment guidelines.

Is insulin growth factor-1 the future for treating autism spectrum disorder and/or schizophrenia?

PubMed

Bou Khalil, Rami

2017-02-01

To date, no curative psychopharmacologic treatment exists for the core symptoms of autism spectrum disorder (ASD) as well as for schizophrenia. Bumatenide is a specific antagonist of the first isoform of the Na-K-Cl cotransporter (NKCC1). It is usually used as a diuretic but may also promote a decrease in intraneuronal chloride ion concentration leading to hyperpolarization in neuronal membrane and subsequent decrease in neuronal hyperexcitability. This physiologic effect has been considered to be behind the relative efficacy of bumetanide in improving symptoms of ASD and, to a lesser extent, schizophrenia. However, insulin growth factor-1 (IGF-1) shows the same physiologic effect. In addition, it may improve brain network dysconnectivity which is known to be an important neurobiological feature in ASD and schizophrenia. IGF-1 has started to prove its efficacy in improving symptoms of children with Rett syndrome, a genetic disorder that shares several clinical similarities with ASD. IGF-1 may also improve oxytocin secretion through the enhancement of the transient potential receptor V2 channel function. Accordingly, IGF-1 should be studied as a potential treatment of ASD and other mental disorders characterized with brain dysconnectivity such as schizophrenia. Copyright © 2016 Elsevier Ltd. All rights reserved.

A real-time hybrid neuron network for highly parallel cognitive systems.

PubMed

Christiaanse, Gerrit Jan; Zjajo, Amir; Galuzzi, Carlo; van Leuken, Rene

2016-08-01

For comprehensive understanding of how neurons communicate with each other, new tools need to be developed that can accurately mimic the behaviour of such neurons and neuron networks under `real-time' constraints. In this paper, we propose an easily customisable, highly pipelined, neuron network design, which executes optimally scheduled floating-point operations for maximal amount of biophysically plausible neurons per FPGA family type. To reduce the required amount of resources without adverse effect on the calculation latency, a single exponent instance is used for multiple neuron calculation operations. Experimental results indicate that the proposed network design allows the simulation of up to 1188 neurons on Virtex7 (XC7VX550T) device in brain real-time yielding a speed-up of x12.4 compared to the state-of-the art.

An FPGA-Based Silicon Neuronal Network with Selectable Excitability Silicon Neurons

PubMed Central

Li, Jing; Katori, Yuichi; Kohno, Takashi

2012-01-01

This paper presents a digital silicon neuronal network which simulates the nerve system in creatures and has the ability to execute intelligent tasks, such as associative memory. Two essential elements, the mathematical-structure-based digital spiking silicon neuron (DSSN) and the transmitter release based silicon synapse, allow us to tune the excitability of silicon neurons and are computationally efficient for hardware implementation. We adopt mixed pipeline and parallel structure and shift operations to design a sufficient large and complex network without excessive hardware resource cost. The network with 256 full-connected neurons is built on a Digilent Atlys board equipped with a Xilinx Spartan-6 LX45 FPGA. Besides, a memory control block and USB control block are designed to accomplish the task of data communication between the network and the host PC. This paper also describes the mechanism of associative memory performed in the silicon neuronal network. The network is capable of retrieving stored patterns if the inputs contain enough information of them. The retrieving probability increases with the similarity between the input and the stored pattern increasing. Synchronization of neurons is observed when the successful stored pattern retrieval occurs. PMID:23269911

[Application of a mathematical algorithm for the detection of electroneuromyographic results in the pathogenesis study of facial dyskinesia].

PubMed

Gribova, N P; Iudel'son, Ia B; Golubev, V L; Abramenkova, I V

2003-01-01

To carry out a differential diagnosis of two facial dyskinesia (FD) models--facial hemispasm (FH) and facial paraspasm (FP), a combined program of electroneuromyographic (ENMG) examination has been created, using statistical analyses, including that for objects identification based on hybrid neural network with the application of adaptive fuzzy logic method and standard statistics programs (Wilcoxon, Student statistics). In FH, a lesion of peripheral facial neuromotor apparatus with augmentation of functions of inter-neurons in segmental and upper segmental stem levels predominated. In FP, primary afferent strengthening in mimic muscles was accompanied by increased motor neurons activity and reciprocal augmentation of inter-neurons, inhibiting motor portion of V pair. Mathematical algorithm for ENMG results recognition worked out in the study provides a precise differentiation of two FD models and opens possibilities for differential diagnosis of other facial motor disorders.

Neuronal migration, apoptosis and bipolar disorder.

PubMed

Uribe, Ezequiel; Wix, Richard

2012-01-01

Bipolar disorder, like the majority of psychiatric disorders, is considered a neurodevelopment disease of neurodevelopment. There is an increased rate of neuronal birth and death during this development period. In the particular case of the processes that determine neuronal death, it is known that those neurons that establish connections have to be removed from the central nervous system. There is a deficit of GABAergic interneurons in the cerebral cortex in bipolar disorder, accompanied by overexpression of proapoptic genes. There is also an alteration in the expression of molecules that mediate in the migration of these neurons and their inclusion in functional synapsis during the foetal stage. The role of these molecules in the neuronal death pathways by apoptosis will be reviewed here in an attempt to establish biological hypotheses of the genesis of bipolar disorder. Copyright © 2011 SEP y SEPB. Published by Elsevier Espana. All rights reserved.

Intrinsically active and pacemaker neurons in pluripotent stem cell-derived neuronal populations.

PubMed

Illes, Sebastian; Jakab, Martin; Beyer, Felix; Gelfert, Renate; Couillard-Despres, Sébastien; Schnitzler, Alfons; Ritter, Markus; Aigner, Ludwig

2014-03-11

Neurons generated from pluripotent stem cells (PSCs) self-organize into functional neuronal assemblies in vitro, generating synchronous network activities. Intriguingly, PSC-derived neuronal assemblies develop spontaneous activities that are independent of external stimulation, suggesting the presence of thus far undetected intrinsically active neurons (IANs). Here, by using mouse embryonic stem cells, we provide evidence for the existence of IANs in PSC-neuronal networks based on extracellular multielectrode array and intracellular patch-clamp recordings. IANs remain active after pharmacological inhibition of fast synaptic communication and possess intrinsic mechanisms required for autonomous neuronal activity. PSC-derived IANs are functionally integrated in PSC-neuronal populations, contribute to synchronous network bursting, and exhibit pacemaker properties. The intrinsic activity and pacemaker properties of the neuronal subpopulation identified herein may be particularly relevant for interventions involving transplantation of neural tissues. IANs may be a key element in the regulation of the functional activity of grafted as well as preexisting host neuronal networks.

Intrinsically Active and Pacemaker Neurons in Pluripotent Stem Cell-Derived Neuronal Populations

PubMed Central

Illes, Sebastian; Jakab, Martin; Beyer, Felix; Gelfert, Renate; Couillard-Despres, Sébastien; Schnitzler, Alfons; Ritter, Markus; Aigner, Ludwig

2014-01-01

Summary Neurons generated from pluripotent stem cells (PSCs) self-organize into functional neuronal assemblies in vitro, generating synchronous network activities. Intriguingly, PSC-derived neuronal assemblies develop spontaneous activities that are independent of external stimulation, suggesting the presence of thus far undetected intrinsically active neurons (IANs). Here, by using mouse embryonic stem cells, we provide evidence for the existence of IANs in PSC-neuronal networks based on extracellular multielectrode array and intracellular patch-clamp recordings. IANs remain active after pharmacological inhibition of fast synaptic communication and possess intrinsic mechanisms required for autonomous neuronal activity. PSC-derived IANs are functionally integrated in PSC-neuronal populations, contribute to synchronous network bursting, and exhibit pacemaker properties. The intrinsic activity and pacemaker properties of the neuronal subpopulation identified herein may be particularly relevant for interventions involving transplantation of neural tissues. IANs may be a key element in the regulation of the functional activity of grafted as well as preexisting host neuronal networks. PMID:24672755

Basal ganglia dysfunction in idiopathic REM sleep behaviour disorder parallels that in early Parkinson's disease.

PubMed

Rolinski, Michal; Griffanti, Ludovica; Piccini, Paola; Roussakis, Andreas A; Szewczyk-Krolikowski, Konrad; Menke, Ricarda A; Quinnell, Timothy; Zaiwalla, Zenobia; Klein, Johannes C; Mackay, Clare E; Hu, Michele T M

2016-08-01

SEE POSTUMA DOI101093/AWW131 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE: Resting state functional magnetic resonance imaging dysfunction within the basal ganglia network is a feature of early Parkinson's disease and may be a diagnostic biomarker of basal ganglia dysfunction. Currently, it is unclear whether these changes are present in so-called idiopathic rapid eye movement sleep behaviour disorder, a condition associated with a high rate of future conversion to Parkinson's disease. In this study, we explore the utility of resting state functional magnetic resonance imaging to detect basal ganglia network dysfunction in rapid eye movement sleep behaviour disorder. We compare these data to a set of healthy control subjects, and to a set of patients with established early Parkinson's disease. Furthermore, we explore the relationship between resting state functional magnetic resonance imaging basal ganglia network dysfunction and loss of dopaminergic neurons assessed with dopamine transporter single photon emission computerized tomography, and perform morphometric analyses to assess grey matter loss. Twenty-six patients with polysomnographically-established rapid eye movement sleep behaviour disorder, 48 patients with Parkinson's disease and 23 healthy control subjects were included in this study. Resting state networks were isolated from task-free functional magnetic resonance imaging data using dual regression with a template derived from a separate cohort of 80 elderly healthy control participants. Resting state functional magnetic resonance imaging parameter estimates were extracted from the study subjects in the basal ganglia network. In addition, eight patients with rapid eye movement sleep behaviour disorder, 10 with Parkinson's disease and 10 control subjects received (123)I-ioflupane single photon emission computerized tomography. We tested for reduction of basal ganglia network connectivity, and for loss of tracer uptake in rapid eye movement sleep behaviour disorder and Parkinson's disease relative to each other and to controls. Connectivity measures of basal ganglia network dysfunction differentiated both rapid eye movement sleep behaviour disorder and Parkinson's disease from controls with high sensitivity (96%) and specificity (74% for rapid eye movement sleep behaviour disorder, 78% for Parkinson's disease), indicating its potential as an indicator of early basal ganglia dysfunction. Rapid eye movement sleep behaviour disorder was indistinguishable from Parkinson's disease on resting state functional magnetic resonance imaging despite obvious differences on dopamine transported single photon emission computerized tomography. Basal ganglia connectivity is a promising biomarker for the detection of early basal ganglia network dysfunction, and may help to identify patients at risk of developing Parkinson's disease in the future. Future risk stratification using a polymodal approach could combine basal ganglia network connectivity with clinical and other imaging measures, with important implications for future neuroprotective trials in rapid eye movement sleep behaviour disorder. © The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain.

Basal ganglia dysfunction in idiopathic REM sleep behaviour disorder parallels that in early Parkinson’s disease

PubMed Central

Rolinski, Michal; Griffanti, Ludovica; Piccini, Paola; Roussakis, Andreas A.; Szewczyk-Krolikowski, Konrad; Menke, Ricarda A.; Quinnell, Timothy; Zaiwalla, Zenobia; Klein, Johannes C.; Mackay, Clare E.

2016-01-01

Abstract See Postuma (doi:10.1093/aww131) for a scientific commentary on this article. Resting state functional magnetic resonance imaging dysfunction within the basal ganglia network is a feature of early Parkinson’s disease and may be a diagnostic biomarker of basal ganglia dysfunction. Currently, it is unclear whether these changes are present in so-called idiopathic rapid eye movement sleep behaviour disorder, a condition associated with a high rate of future conversion to Parkinson’s disease. In this study, we explore the utility of resting state functional magnetic resonance imaging to detect basal ganglia network dysfunction in rapid eye movement sleep behaviour disorder. We compare these data to a set of healthy control subjects, and to a set of patients with established early Parkinson’s disease. Furthermore, we explore the relationship between resting state functional magnetic resonance imaging basal ganglia network dysfunction and loss of dopaminergic neurons assessed with dopamine transporter single photon emission computerized tomography, and perform morphometric analyses to assess grey matter loss. Twenty-six patients with polysomnographically-established rapid eye movement sleep behaviour disorder, 48 patients with Parkinson’s disease and 23 healthy control subjects were included in this study. Resting state networks were isolated from task-free functional magnetic resonance imaging data using dual regression with a template derived from a separate cohort of 80 elderly healthy control participants. Resting state functional magnetic resonance imaging parameter estimates were extracted from the study subjects in the basal ganglia network. In addition, eight patients with rapid eye movement sleep behaviour disorder, 10 with Parkinson’s disease and 10 control subjects received 123I-ioflupane single photon emission computerized tomography. We tested for reduction of basal ganglia network connectivity, and for loss of tracer uptake in rapid eye movement sleep behaviour disorder and Parkinson’s disease relative to each other and to controls. Connectivity measures of basal ganglia network dysfunction differentiated both rapid eye movement sleep behaviour disorder and Parkinson’s disease from controls with high sensitivity (96%) and specificity (74% for rapid eye movement sleep behaviour disorder, 78% for Parkinson’s disease), indicating its potential as an indicator of early basal ganglia dysfunction. Rapid eye movement sleep behaviour disorder was indistinguishable from Parkinson’s disease on resting state functional magnetic resonance imaging despite obvious differences on dopamine transported single photon emission computerized tomography. Basal ganglia connectivity is a promising biomarker for the detection of early basal ganglia network dysfunction, and may help to identify patients at risk of developing Parkinson’s disease in the future. Future risk stratification using a polymodal approach could combine basal ganglia network connectivity with clinical and other imaging measures, with important implications for future neuroprotective trials in rapid eye movement sleep behaviour disorder. PMID:27297241

Neurometaplasticity: Glucoallostasis control of plasticity of the neural networks of error commission, detection, and correction modulates neuroplasticity to influence task precision

NASA Astrophysics Data System (ADS)

Welcome, Menizibeya O.; Dane, Şenol; Mastorakis, Nikos E.; Pereverzev, Vladimir A.

2017-12-01

The term "metaplasticity" is a recent one, which means plasticity of synaptic plasticity. Correspondingly, neurometaplasticity simply means plasticity of neuroplasticity, indicating that a previous plastic event determines the current plasticity of neurons. Emerging studies suggest that neurometaplasticity underlie many neural activities and neurobehavioral disorders. In our previous work, we indicated that glucoallostasis is essential for the control of plasticity of the neural network that control error commission, detection and correction. Here we review recent works, which suggest that task precision depends on the modulatory effects of neuroplasticity on the neural networks of error commission, detection, and correction. Furthermore, we discuss neurometaplasticity and its role in error commission, detection, and correction.

A spatially resolved network spike in model neuronal cultures reveals nucleation centers, circular traveling waves and drifting spiral waves.

PubMed

Paraskevov, A V; Zendrikov, D K

2017-03-23

We show that in model neuronal cultures, where the probability of interneuronal connection formation decreases exponentially with increasing distance between the neurons, there exists a small number of spatial nucleation centers of a network spike, from where the synchronous spiking activity starts propagating in the network typically in the form of circular traveling waves. The number of nucleation centers and their spatial locations are unique and unchanged for a given realization of neuronal network but are different for different networks. In contrast, if the probability of interneuronal connection formation is independent of the distance between neurons, then the nucleation centers do not arise and the synchronization of spiking activity during a network spike occurs spatially uniform throughout the network. Therefore one can conclude that spatial proximity of connections between neurons is important for the formation of nucleation centers. It is also shown that fluctuations of the spatial density of neurons at their random homogeneous distribution typical for the experiments in vitro do not determine the locations of the nucleation centers. The simulation results are qualitatively consistent with the experimental observations.

A spatially resolved network spike in model neuronal cultures reveals nucleation centers, circular traveling waves and drifting spiral waves

NASA Astrophysics Data System (ADS)

Paraskevov, A. V.; Zendrikov, D. K.

2017-04-01

We show that in model neuronal cultures, where the probability of interneuronal connection formation decreases exponentially with increasing distance between the neurons, there exists a small number of spatial nucleation centers of a network spike, from where the synchronous spiking activity starts propagating in the network typically in the form of circular traveling waves. The number of nucleation centers and their spatial locations are unique and unchanged for a given realization of neuronal network but are different for different networks. In contrast, if the probability of interneuronal connection formation is independent of the distance between neurons, then the nucleation centers do not arise and the synchronization of spiking activity during a network spike occurs spatially uniform throughout the network. Therefore one can conclude that spatial proximity of connections between neurons is important for the formation of nucleation centers. It is also shown that fluctuations of the spatial density of neurons at their random homogeneous distribution typical for the experiments in vitro do not determine the locations of the nucleation centers. The simulation results are qualitatively consistent with the experimental observations.

A new cross-correlation algorithm for the analysis of "in vitro" neuronal network activity aimed at pharmacological studies.

PubMed

Biffi, E; Menegon, A; Regalia, G; Maida, S; Ferrigno, G; Pedrocchi, A

2011-08-15

Modern drug discovery for Central Nervous System pathologies has recently focused its attention to in vitro neuronal networks as models for the study of neuronal activities. Micro Electrode Arrays (MEAs), a widely recognized tool for pharmacological investigations, enable the simultaneous study of the spiking activity of discrete regions of a neuronal culture, providing an insight into the dynamics of networks. Taking advantage of MEAs features and making the most of the cross-correlation analysis to assess internal parameters of a neuronal system, we provide an efficient method for the evaluation of comprehensive neuronal network activity. We developed an intra network burst correlation algorithm, we evaluated its sensitivity and we explored its potential use in pharmacological studies. Our results demonstrate the high sensitivity of this algorithm and the efficacy of this methodology in pharmacological dose-response studies, with the advantage of analyzing the effect of drugs on the comprehensive correlative properties of integrated neuronal networks. Copyright © 2011 Elsevier B.V. All rights reserved.

Neural networks with multiple general neuron models: a hybrid computational intelligence approach using Genetic Programming.

PubMed

Barton, Alan J; Valdés, Julio J; Orchard, Robert

2009-01-01

Classical neural networks are composed of neurons whose nature is determined by a certain function (the neuron model), usually pre-specified. In this paper, a type of neural network (NN-GP) is presented in which: (i) each neuron may have its own neuron model in the form of a general function, (ii) any layout (i.e network interconnection) is possible, and (iii) no bias nodes or weights are associated to the connections, neurons or layers. The general functions associated to a neuron are learned by searching a function space. They are not provided a priori, but are rather built as part of an Evolutionary Computation process based on Genetic Programming. The resulting network solutions are evaluated based on a fitness measure, which may, for example, be based on classification or regression errors. Two real-world examples are presented to illustrate the promising behaviour on classification problems via construction of a low-dimensional representation of a high-dimensional parameter space associated to the set of all network solutions.

Altered olfactory processing of stress-related body odors and artificial odors in patients with panic disorder.

PubMed

Wintermann, Gloria-Beatrice; Donix, Markus; Joraschky, Peter; Gerber, Johannes; Petrowski, Katja

2013-01-01

Patients with Panic Disorder (PD) direct their attention towards potential threat, followed by panic attacks, and increased sweat production. Onés own anxiety sweat odor influences the attentional focus, and discrimination of threat or non-threat. Since olfactory projection areas overlap with neuronal areas of a panic-specific fear network, the present study investigated the neuronal processing of odors in general and of stress-related sweat odors in particular in patients with PD. A sample of 13 patients with PD with/ without agoraphobia and 13 age- and gender-matched healthy controls underwent an fMRI investigation during olfactory stimulation with their stress-related sweat odors (TSST, ergometry) as well as artificial odors (peach, artificial sweat) as non-fearful non-body odors. The two groups did not differ with respect to their olfactory identification ability. Independent of the kind of odor, the patients with PD showed activations in fronto-cortical areas in contrast to the healthy controls who showed activations in olfaction-related areas such as the amygdalae and the hippocampus. For artificial odors, the patients with PD showed a decreased neuronal activation of the thalamus, the posterior cingulate cortex and the anterior cingulate cortex. Under the presentation of sweat odor caused by ergometric exercise, the patients with PD showed an increased activation in the superior temporal gyrus, the supramarginal gyrus, and the cingulate cortex which was positively correlated with the severity of the psychopathology. For the sweat odor from the anxiety condition, the patients with PD showed an increased activation in the gyrus frontalis inferior, which was positively correlated with the severity of the psychopathology. The results suggest altered neuronal processing of olfactory stimuli in PD. Both artificial odors and stress-related body odors activate specific parts of a fear-network which is associated with an increased severity of the psychopathology.

Self-organization of synchronous activity propagation in neuronal networks driven by local excitation

PubMed Central

Bayati, Mehdi; Valizadeh, Alireza; Abbassian, Abdolhossein; Cheng, Sen

2015-01-01

Many experimental and theoretical studies have suggested that the reliable propagation of synchronous neural activity is crucial for neural information processing. The propagation of synchronous firing activity in so-called synfire chains has been studied extensively in feed-forward networks of spiking neurons. However, it remains unclear how such neural activity could emerge in recurrent neuronal networks through synaptic plasticity. In this study, we investigate whether local excitation, i.e., neurons that fire at a higher frequency than the other, spontaneously active neurons in the network, can shape a network to allow for synchronous activity propagation. We use two-dimensional, locally connected and heterogeneous neuronal networks with spike-timing dependent plasticity (STDP). We find that, in our model, local excitation drives profound network changes within seconds. In the emergent network, neural activity propagates synchronously through the network. This activity originates from the site of the local excitation and propagates through the network. The synchronous activity propagation persists, even when the local excitation is removed, since it derives from the synaptic weight matrix. Importantly, once this connectivity is established it remains stable even in the presence of spontaneous activity. Our results suggest that synfire-chain-like activity can emerge in a relatively simple way in realistic neural networks by locally exciting the desired origin of the neuronal sequence. PMID:26089794

Synaptic Impairment and Robustness of Excitatory Neuronal Networks with Different Topologies

PubMed Central

Mirzakhalili, Ehsan; Gourgou, Eleni; Booth, Victoria; Epureanu, Bogdan

2017-01-01

Synaptic deficiencies are a known hallmark of neurodegenerative diseases, but the diagnosis of impaired synapses on the cellular level is not an easy task. Nonetheless, changes in the system-level dynamics of neuronal networks with damaged synapses can be detected using techniques that do not require high spatial resolution. This paper investigates how the structure/topology of neuronal networks influences their dynamics when they suffer from synaptic loss. We study different neuronal network structures/topologies by specifying their degree distributions. The modes of the degree distribution can be used to construct networks that consist of rich clubs and resemble small world networks, as well. We define two dynamical metrics to compare the activity of networks with different structures: persistent activity (namely, the self-sustained activity of the network upon removal of the initial stimulus) and quality of activity (namely, percentage of neurons that participate in the persistent activity of the network). Our results show that synaptic loss affects the persistent activity of networks with bimodal degree distributions less than it affects random networks. The robustness of neuronal networks enhances when the distance between the modes of the degree distribution increases, suggesting that the rich clubs of networks with distinct modes keep the whole network active. In addition, a tradeoff is observed between the quality of activity and the persistent activity. For a range of distributions, both of these dynamical metrics are considerably high for networks with bimodal degree distribution compared to random networks. We also propose three different scenarios of synaptic impairment, which may correspond to different pathological or biological conditions. Regardless of the network structure/topology, results demonstrate that synaptic loss has more severe effects on the activity of the network when impairments are correlated with the activity of the neurons. PMID:28659765

PhotoMEA: an opto-electronic biosensor for monitoring in vitro neuronal network activity.

PubMed

Ghezzi, Diego; Pedrocchi, Alessandra; Menegon, Andrea; Mantero, Sara; Valtorta, Flavia; Ferrigno, Giancarlo

2007-02-01

PhotoMEA is a biosensor useful for the analysis of an in vitro neuronal network, fully based on optical methods. Its function is based on the stimulation of neurons with caged glutamate and the recording of neuronal activity by Voltage-Sensitive fluorescent Dyes (VSD). The main advantage is that it will be possible to stimulate even at sub-single neuron level and to record with high resolution the activity of the entire network in the culture. A large-scale view of neuronal intercommunications offers a unique opportunity for testing the ability of drugs to affect neuronal properties as well as alterations in the behaviour of the entire network. The concept and a prototype for validation is described here in detail.

CDYL Deficiency Disrupts Neuronal Migration and Increases Susceptibility to Epilepsy.

PubMed

Qin, Rui; Cao, Shuai; Lyu, Tianjie; Qi, Cai; Zhang, Weiguang; Wang, Yun

2017-01-10

During brain development, the correct migration of newborn neurons is one of the determinants of circuit formation, and neuronal migration defects may lead to neurological and psychiatric disorders. The molecular mechanisms underlying neuronal migration and related disorders are poorly understood. Here, we report that Chromodomain Y-like (CDYL) is critical for neuronal migration in mice. Knocking down CDYL caused neuronal migration defects and disrupted both mobility and multipolar-to-bipolar transition of migrating neurons. We find that CDYL regulates neuronal migration by transcriptionally repressing RhoA. In addition, CDYL deficiency increased the excitability of cortical pyramidal neurons and the susceptibility of mice to convulsant-induced seizures. These results demonstrate that CDYL is a regulator of neuronal migration and shed light on the pathogenesis of seizure-related neurodevelopmental disorders. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

Synaptic dynamics regulation in response to high frequency stimulation in neuronal networks

NASA Astrophysics Data System (ADS)

Su, Fei; Wang, Jiang; Li, Huiyan; Wei, Xile; Yu, Haitao; Deng, Bin

2018-02-01

High frequency stimulation (HFS) has confirmed its ability in modulating the pathological neural activities. However its detailed mechanism is unclear. This study aims to explore the effects of HFS on neuronal networks dynamics. First, the two-neuron FitzHugh-Nagumo (FHN) networks with static coupling strength and the small-world FHN networks with spike-time-dependent plasticity (STDP) modulated synaptic coupling strength are constructed. Then, the multi-scale method is used to transform the network models into equivalent averaged models, where the HFS intensity is modeled as the ratio between stimulation amplitude and frequency. Results show that in static two-neuron networks, there is still synaptic current projected to the postsynaptic neuron even if the presynaptic neuron is blocked by the HFS. In the small-world networks, the effects of the STDP adjusting rate parameter on the inactivation ratio and synchrony degree increase with the increase of HFS intensity. However, only when the HFS intensity becomes very large can the STDP time window parameter affect the inactivation ratio and synchrony index. Both simulation and numerical analysis demonstrate that the effects of HFS on neuronal network dynamics are realized through the adjustment of synaptic variable and conductance.

Training a Network of Electronic Neurons for Control of a Mobile Robot

NASA Astrophysics Data System (ADS)

Vromen, T. G. M.; Steur, E.; Nijmeijer, H.

An adaptive training procedure is developed for a network of electronic neurons, which controls a mobile robot driving around in an unknown environment while avoiding obstacles. The neuronal network controls the angular velocity of the wheels of the robot based on the sensor readings. The nodes in the neuronal network controller are clusters of neurons rather than single neurons. The adaptive training procedure ensures that the input-output behavior of the clusters is identical, even though the constituting neurons are nonidentical and have, in isolation, nonidentical responses to the same input. In particular, we let the neurons interact via a diffusive coupling, and the proposed training procedure modifies the diffusion interaction weights such that the neurons behave synchronously with a predefined response. The working principle of the training procedure is experimentally validated and results of an experiment with a mobile robot that is completely autonomously driving in an unknown environment with obstacles are presented.

How environmental and genetic factors combine to cause autism: A redox/methylation hypothesis.

PubMed

Deth, Richard; Muratore, Christina; Benzecry, Jorge; Power-Charnitsky, Verna-Ann; Waly, Mostafa

2008-01-01

Recently higher rates of autism diagnosis suggest involvement of environmental factors in causing this developmental disorder, in concert with genetic risk factors. Autistic children exhibit evidence of oxidative stress and impaired methylation, which may reflect effects of toxic exposure on sulfur metabolism. We review the metabolic relationship between oxidative stress and methylation, with particular emphasis on adaptive responses that limit activity of cobalamin and folate-dependent methionine synthase. Methionine synthase activity is required for dopamine-stimulated phospholipid methylation, a unique membrane-delimited signaling process mediated by the D4 dopamine receptor that promotes neuronal synchronization and attention, and synchrony is impaired in autism. Genetic polymorphisms adversely affecting sulfur metabolism, methylation, detoxification, dopamine signaling and the formation of neuronal networks occur more frequently in autistic subjects. On the basis of these observations, a "redox/methylation hypothesis of autism" is described, in which oxidative stress, initiated by environment factors in genetically vulnerable individuals, leads to impaired methylation and neurological deficits secondary to reductions in the capacity for synchronizing neural networks.

Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism

PubMed Central

Willsey, A. Jeremy; Sanders, Stephan J.; Li, Mingfeng; Dong, Shan; Tebbenkamp, Andrew T.; Muhle, Rebecca A.; Reilly, Steven K.; Lin, Leon; Fertuzinhos, Sofia; Miller, Jeremy A.; Murtha, Michael T.; Bichsel, Candace; Niu, Wei; Cotney, Justin; Ercan-Sencicek, A. Gulhan; Gockley, Jake; Gupta, Abha; Han, Wenqi; He, Xin; Hoffman, Ellen; Klei, Lambertus; Lei, Jing; Liu, Wenzhong; Liu, Li; Lu, Cong; Xu, Xuming; Zhu, Ying; Mane, Shrikant M.; Lein, Edward S.; Wei, Liping; Noonan, James P.; Roeder, Kathryn; Devlin, Bernie; Šestan, Nenad; State, Matthew W.

2013-01-01

SUMMARY Autism spectrum disorder (ASD) is a complex developmental syndrome of unknown etiology. Recent studies employing exome- and genome-wide sequencing have identified nine high-confidence ASD (hcASD) genes. Working from the hypothesis that ASD-associated mutations in these biologically pleiotropic genes will disrupt intersecting developmental processes to contribute to a common phenotype, we have attempted to identify time periods, brain regions, and cell types in which these genes converge. We have constructed coexpression networks based on the hcASD “seed” genes, leveraging a rich expression data set encompassing multiple human brain regions across human development and into adulthood. By assessing enrichment of an independent set of probable ASD (pASD) genes, derived from the same sequencing studies, we demonstrate a key point of convergence in midfetal layer 5/6 cortical projection neurons. This approach informs when, where, and in what cell types mutations in these specific genes may be productively studied to clarify ASD pathophysiology. PMID:24267886

GABA Neuron Alterations, Cortical Circuit Dysfunction and Cognitive Deficits in Schizophrenia

PubMed Central

Gonzalez-Burgos, Guillermo; Fish, Kenneth N.; Lewis, David A.

2011-01-01

Schizophrenia is a brain disorder associated with cognitive deficits that severely affect the patients' capacity for daily functioning. Whereas our understanding of its pathophysiology is limited, postmortem studies suggest that schizophrenia is associated with deficits of GABA-mediated synaptic transmission. A major role of GABA-mediated transmission may be producing synchronized network oscillations which are currently hypothesized to be essential for normal cognitive function. Therefore, cognitive deficits in schizophrenia may result from a GABA synapse dysfunction that disturbs neural synchrony. Here, we highlight recent studies further suggesting alterations of GABA transmission and network oscillations in schizophrenia. We also review current models for the mechanisms of GABA-mediated synchronization of neural activity, focusing on parvalbumin-positive GABA neurons, which are altered in schizophrenia and whose function has been strongly linked to the production of neural synchrony. Alterations of GABA signaling that impair gamma oscillations and, as a result, cognitive function suggest paths for novel therapeutic interventions. PMID:21904685

Dysfunction of sensory oscillations in Autism Spectrum Disorder

PubMed Central

Simon, David M.; Wallace, Mark T.

2016-01-01

Autism Spectrum Disorder (ASD) is a highly prevalent developmental disability characterized by deficits in social communication and interaction, restricted interests, and repetitive behaviors. Recently, anomalous sensory and perceptual function has gained an increased level of recognition as an important feature of ASD. A specific impairment in the ability to integrate information across brain networks has been proposed to contribute to these disruptions. A crucial mechanism for these integrative processes is the rhythmic synchronization of neuronal excitability across neural populations; collectively known as oscillations. In ASD there is believed to be a deficit in the ability to efficiently couple functional neural networks using these oscillations. This review discusses evidence for disruptions in oscillatory synchronization in ASD, and how disturbance of this neural mechanism contributes to alterations in sensory and perceptual function. The review also frames oscillatory data from the perspective of prevailing neurobiologically-inspired theories of ASD. PMID:27451342

Establishment of a Human Neuronal Network Assessment System by Using a Human Neuron/Astrocyte Co-Culture Derived from Fetal Neural Stem/Progenitor Cells.

PubMed

Fukushima, Kazuyuki; Miura, Yuji; Sawada, Kohei; Yamazaki, Kazuto; Ito, Masashi

2016-01-01

Using human cell models mimicking the central nervous system (CNS) provides a better understanding of the human CNS, and it is a key strategy to improve success rates in CNS drug development. In the CNS, neurons function as networks in which astrocytes play important roles. Thus, an assessment system of neuronal network functions in a co-culture of human neurons and astrocytes has potential to accelerate CNS drug development. We previously demonstrated that human hippocampus-derived neural stem/progenitor cells (HIP-009 cells) were a novel tool to obtain human neurons and astrocytes in the same culture. In this study, we applied HIP-009 cells to a multielectrode array (MEA) system to detect neuronal signals as neuronal network functions. We observed spontaneous firings of HIP-009 neurons, and validated functional formation of neuronal networks pharmacologically. By using this assay system, we investigated effects of several reference compounds, including agonists and antagonists of glutamate and γ-aminobutyric acid receptors, and sodium, potassium, and calcium channels, on neuronal network functions using firing and burst numbers, and synchrony as readouts. These results indicate that the HIP-009/MEA assay system is applicable to the pharmacological assessment of drug candidates affecting synaptic functions for CNS drug development. © 2015 Society for Laboratory Automation and Screening.

Synaptic plasticity and neuronal refractory time cause scaling behaviour of neuronal avalanches

NASA Astrophysics Data System (ADS)

Michiels van Kessenich, L.; de Arcangelis, L.; Herrmann, H. J.

2016-08-01

Neuronal avalanches measured in vitro and in vivo in different cortical networks consistently exhibit power law behaviour for the size and duration distributions with exponents typical for a mean field self-organized branching process. These exponents are also recovered in neuronal network simulations implementing various neuronal dynamics on different network topologies. They can therefore be considered a very robust feature of spontaneous neuronal activity. Interestingly, this scaling behaviour is also observed on regular lattices in finite dimensions, which raises the question about the origin of the mean field behavior observed experimentally. In this study we provide an answer to this open question by investigating the effect of activity dependent plasticity in combination with the neuronal refractory time in a neuronal network. Results show that the refractory time hinders backward avalanches forcing a directed propagation. Hebbian plastic adaptation plays the role of sculpting these directed avalanche patterns into the topology of the network slowly changing it into a branched structure where loops are marginal.

Synaptic plasticity and neuronal refractory time cause scaling behaviour of neuronal avalanches.

PubMed

Michiels van Kessenich, L; de Arcangelis, L; Herrmann, H J

2016-08-18

Neuronal avalanches measured in vitro and in vivo in different cortical networks consistently exhibit power law behaviour for the size and duration distributions with exponents typical for a mean field self-organized branching process. These exponents are also recovered in neuronal network simulations implementing various neuronal dynamics on different network topologies. They can therefore be considered a very robust feature of spontaneous neuronal activity. Interestingly, this scaling behaviour is also observed on regular lattices in finite dimensions, which raises the question about the origin of the mean field behavior observed experimentally. In this study we provide an answer to this open question by investigating the effect of activity dependent plasticity in combination with the neuronal refractory time in a neuronal network. Results show that the refractory time hinders backward avalanches forcing a directed propagation. Hebbian plastic adaptation plays the role of sculpting these directed avalanche patterns into the topology of the network slowly changing it into a branched structure where loops are marginal.

Dynamic range in small-world networks of Hodgkin-Huxley neurons with chemical synapses

NASA Astrophysics Data System (ADS)

Batista, C. A. S.; Viana, R. L.; Lopes, S. R.; Batista, A. M.

2014-09-01

According to Stevens' law the relationship between stimulus and response is a power-law within an interval called the dynamic range. The dynamic range of sensory organs is found to be larger than that of a single neuron, suggesting that the network structure plays a key role in the behavior of both the scaling exponent and the dynamic range of neuron assemblies. In order to verify computationally the relationships between stimulus and response for spiking neurons, we investigate small-world networks of neurons described by the Hodgkin-Huxley equations connected by chemical synapses. We found that the dynamic range increases with the network size, suggesting that the enhancement of the dynamic range observed in sensory organs, with respect to single neurons, is an emergent property of complex network dynamics.

The relevance of network micro-structure for neural dynamics.

PubMed

Pernice, Volker; Deger, Moritz; Cardanobile, Stefano; Rotter, Stefan

2013-01-01

The activity of cortical neurons is determined by the input they receive from presynaptic neurons. Many previous studies have investigated how specific aspects of the statistics of the input affect the spike trains of single neurons and neurons in recurrent networks. However, typically very simple random network models are considered in such studies. Here we use a recently developed algorithm to construct networks based on a quasi-fractal probability measure which are much more variable than commonly used network models, and which therefore promise to sample the space of recurrent networks in a more exhaustive fashion than previously possible. We use the generated graphs as the underlying network topology in simulations of networks of integrate-and-fire neurons in an asynchronous and irregular state. Based on an extensive dataset of networks and neuronal simulations we assess statistical relations between features of the network structure and the spiking activity. Our results highlight the strong influence that some details of the network structure have on the activity dynamics of both single neurons and populations, even if some global network parameters are kept fixed. We observe specific and consistent relations between activity characteristics like spike-train irregularity or correlations and network properties, for example the distributions of the numbers of in- and outgoing connections or clustering. Exploiting these relations, we demonstrate that it is possible to estimate structural characteristics of the network from activity data. We also assess higher order correlations of spiking activity in the various networks considered here, and find that their occurrence strongly depends on the network structure. These results provide directions for further theoretical studies on recurrent networks, as well as new ways to interpret spike train recordings from neural circuits.

Hysteresis, neural avalanches, and critical behavior near a first-order transition of a spiking neural network

NASA Astrophysics Data System (ADS)

Scarpetta, Silvia; Apicella, Ilenia; Minati, Ludovico; de Candia, Antonio

2018-06-01

Many experimental results, both in vivo and in vitro, support the idea that the brain cortex operates near a critical point and at the same time works as a reservoir of precise spatiotemporal patterns. However, the mechanism at the basis of these observations is still not clear. In this paper we introduce a model which combines both these features, showing that scale-free avalanches are the signature of a system posed near the spinodal line of a first-order transition, with many spatiotemporal patterns stored as dynamical metastable attractors. Specifically, we studied a network of leaky integrate-and-fire neurons whose connections are the result of the learning of multiple spatiotemporal dynamical patterns, each with a randomly chosen ordering of the neurons. We found that the network shows a first-order transition between a low-spiking-rate disordered state (down), and a high-rate state characterized by the emergence of collective activity and the replay of one of the stored patterns (up). The transition is characterized by hysteresis, or alternation of up and down states, depending on the lifetime of the metastable states. In both cases, critical features and neural avalanches are observed. Notably, critical phenomena occur at the edge of a discontinuous phase transition, as recently observed in a network of glow lamps.

Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons

PubMed Central

Pecevski, Dejan; Buesing, Lars; Maass, Wolfgang

2011-01-01

An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows (“explaining away”) and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons. PMID:22219717

Synaptic Plasticity and Spike Synchronisation in Neuronal Networks

NASA Astrophysics Data System (ADS)

Borges, Rafael R.; Borges, Fernando S.; Lameu, Ewandson L.; Protachevicz, Paulo R.; Iarosz, Kelly C.; Caldas, Iberê L.; Viana, Ricardo L.; Macau, Elbert E. N.; Baptista, Murilo S.; Grebogi, Celso; Batista, Antonio M.

2017-12-01

Brain plasticity, also known as neuroplasticity, is a fundamental mechanism of neuronal adaptation in response to changes in the environment or due to brain injury. In this review, we show our results about the effects of synaptic plasticity on neuronal networks composed by Hodgkin-Huxley neurons. We show that the final topology of the evolved network depends crucially on the ratio between the strengths of the inhibitory and excitatory synapses. Excitation of the same order of inhibition revels an evolved network that presents the rich-club phenomenon, well known to exist in the brain. For initial networks with considerably larger inhibitory strengths, we observe the emergence of a complex evolved topology, where neurons sparsely connected to other neurons, also a typical topology of the brain. The presence of noise enhances the strength of both types of synapses, but if the initial network has synapses of both natures with similar strengths. Finally, we show how the synchronous behaviour of the evolved network will reflect its evolved topology.

Diverse types of genetic variation converge on functional gene networks involved in schizophrenia.

PubMed

Gilman, Sarah R; Chang, Jonathan; Xu, Bin; Bawa, Tejdeep S; Gogos, Joseph A; Karayiorgou, Maria; Vitkup, Dennis

2012-12-01

Despite the successful identification of several relevant genomic loci, the underlying molecular mechanisms of schizophrenia remain largely unclear. We developed a computational approach (NETBAG+) that allows an integrated analysis of diverse disease-related genetic data using a unified statistical framework. The application of this approach to schizophrenia-associated genetic variations, obtained using unbiased whole-genome methods, allowed us to identify several cohesive gene networks related to axon guidance, neuronal cell mobility, synaptic function and chromosomal remodeling. The genes forming the networks are highly expressed in the brain, with higher brain expression during prenatal development. The identified networks are functionally related to genes previously implicated in schizophrenia, autism and intellectual disability. A comparative analysis of copy number variants associated with autism and schizophrenia suggests that although the molecular networks implicated in these distinct disorders may be related, the mutations associated with each disease are likely to lead, at least on average, to different functional consequences.

Homeostatic Scaling of Excitability in Recurrent Neural Networks

PubMed Central

Remme, Michiel W. H.; Wadman, Wytse J.

2012-01-01

Neurons adjust their intrinsic excitability when experiencing a persistent change in synaptic drive. This process can prevent neural activity from moving into either a quiescent state or a saturated state in the face of ongoing plasticity, and is thought to promote stability of the network in which neurons reside. However, most neurons are embedded in recurrent networks, which require a delicate balance between excitation and inhibition to maintain network stability. This balance could be disrupted when neurons independently adjust their intrinsic excitability. Here, we study the functioning of activity-dependent homeostatic scaling of intrinsic excitability (HSE) in a recurrent neural network. Using both simulations of a recurrent network consisting of excitatory and inhibitory neurons that implement HSE, and a mean-field description of adapting excitatory and inhibitory populations, we show that the stability of such adapting networks critically depends on the relationship between the adaptation time scales of both neuron populations. In a stable adapting network, HSE can keep all neurons functioning within their dynamic range, while the network is undergoing several (patho)physiologically relevant types of plasticity, such as persistent changes in external drive, changes in connection strengths, or the loss of inhibitory cells from the network. However, HSE cannot prevent the unstable network dynamics that result when, due to such plasticity, recurrent excitation in the network becomes too strong compared to feedback inhibition. This suggests that keeping a neural network in a stable and functional state requires the coordination of distinct homeostatic mechanisms that operate not only by adjusting neural excitability, but also by controlling network connectivity. PMID:22570604

Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning

NASA Astrophysics Data System (ADS)

Xu, Tao; Xiao, Na; Zhai, Xiaolong; Chan, Pak Kwan; Tin, Chung

2018-02-01

Objective. Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). Approach. The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. Main results. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. Significance. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

A VLSI recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory.

PubMed

Chicca, E; Badoni, D; Dante, V; D'Andreagiovanni, M; Salina, G; Carota, L; Fusi, S; Del Giudice, P

2003-01-01

Electronic neuromorphic devices with on-chip, on-line learning should be able to modify quickly the synaptic couplings to acquire information about new patterns to be stored (synaptic plasticity) and, at the same time, preserve this information on very long time scales (synaptic stability). Here, we illustrate the electronic implementation of a simple solution to this stability-plasticity problem, recently proposed and studied in various contexts. It is based on the observation that reducing the analog depth of the synapses to the extreme (bistable synapses) does not necessarily disrupt the performance of the device as an associative memory, provided that 1) the number of neurons is large enough; 2) the transitions between stable synaptic states are stochastic; and 3) learning is slow. The drastic reduction of the analog depth of the synaptic variable also makes this solution appealing from the point of view of electronic implementation and offers a simple methodological alternative to the technological solution based on floating gates. We describe the full custom analog very large-scale integration (VLSI) realization of a small network of integrate-and-fire neurons connected by bistable deterministic plastic synapses which can implement the idea of stochastic learning. In the absence of stimuli, the memory is preserved indefinitely. During the stimulation the synapse undergoes quick temporary changes through the activities of the pre- and postsynaptic neurons; those changes stochastically result in a long-term modification of the synaptic efficacy. The intentionally disordered pattern of connectivity allows the system to generate a randomness suited to drive the stochastic selection mechanism. We check by a suitable stimulation protocol that the stochastic synaptic plasticity produces the expected pattern of potentiation and depression in the electronic network.

Detection of 5-hydroxytryptamine (5-HT) in vitro using a hippocampal neuronal network-based biosensor with extracellular potential analysis of neurons.

PubMed

Hu, Liang; Wang, Qin; Qin, Zhen; Su, Kaiqi; Huang, Liquan; Hu, Ning; Wang, Ping

2015-04-15

5-hydroxytryptamine (5-HT) is an important neurotransmitter in regulating emotions and related behaviors in mammals. To detect and monitor the 5-HT, effective and convenient methods are demanded in investigation of neuronal network. In this study, hippocampal neuronal networks (HNNs) endogenously expressing 5-HT receptors were employed as sensing elements to build an in vitro neuronal network-based biosensor. The electrophysiological characteristics were analyzed in both neuron and network levels. The firing rates and amplitudes were derived from signal to determine the biosensor response characteristics. The experimental results demonstrate a dose-dependent inhibitory effect of 5-HT on hippocampal neuron activities, indicating the effectiveness of this hybrid biosensor in detecting 5-HT with a response range from 0.01μmol/L to 10μmol/L. In addition, the cross-correlation analysis of HNNs activities suggests 5-HT could weaken HNN connectivity reversibly, providing more specificity of this biosensor in detecting 5-HT. Moreover, 5-HT induced spatiotemporal firing pattern alterations could be monitored in neuron and network levels simultaneously by this hybrid biosensor in a convenient and direct way. With those merits, this neuronal network-based biosensor will be promising to be a valuable and utility platform for the study of neurotransmitter in vitro. Copyright © 2014 Elsevier B.V. All rights reserved.

[The hypothalamic-pituitary-adrenal axis and depressive disorder: recent progress].

PubMed

Kunugi, Hiroshi; Hori, Hiroaki; Numakawa, Tadahiro; Ota, Miho

2012-08-01

Depression is a stress-induced disorder and there is compelling evidence for the involvement of hypothalamic-pituitary-adrenal (HPA) axis abnormalities in the disease. Chronic hyperactivity of the HPA axis and resultant excessive glucocorticoid (hypercortisolism) may be causal to depression. We demonstrated that the dexamethasone (DEX)/CRH test is a sensitive state-dependent marker to monitor HPA axis abnormalities. Restoration from HPA axis abnormalities occurs with clinical responses to treatment. Brain-derived neurotrophic factor (BDNF) has also been implicated in depression. We found that glucocorticoid (DEX) suppresses BDNF-induced dendrite outgrowth and synaptic formation via blocking the MAPK pathway in early-developing cultured hippocampal neurons. Furthermore, we demonstrated that glucocorticoid receptor (GR) and TrkB (a specific receptor of BDNF) interact and that DEX acutely suppresses BDNF-induced glutamate release by affecting the PLC-gamma pathway in cultured cortical neurons, indicating a mechanism underlying the effect of excessive glucocorticoid on BDNF function and resultant damage in cortical neurons. In a macroscopic view using magnetic resonance imaging (MRI), we found that individuals with hypercortisolism detected by the DEX/CRH test demonstrated volume loss in gray matter and reduced neural network assessed with diffusion tensor imaging in several brain regions. Finally, we observed that individuals with hypocortisolism detected by the DEX/CRH test tend to present more distress symptoms, maladaptive coping styles, and schizotypal personality traits than their counterparts, which points to the important role of hypocortisolism as well as hypercortisolism in depression spectrum disorders.

WONOEP appraisal: new genetic approaches to study epilepsy

PubMed Central

Rossignol, Elsa; Kobow, Katja; Simonato, Michele; Loeb, Jeffrey A.; Grisar, Thierry; Gilby, Krista L.; Vinet, Jonathan; Kadam, Shilpa D.; Becker, Albert J.

2014-01-01

Objective New genetic investigation techniques, including next-generation sequencing, epigenetic profiling, cell lineage mapping, targeted genetic manipulation of specific neuronal cell types, stem cell reprogramming and optogenetic manipulations within epileptic networks are progressively unravelling the mysteries of epileptogenesis and ictogenesis. These techniques have opened new avenues to discover the molecular basis of epileptogenesis and to study the physiological impacts of mutations in epilepsy-associated genes on a multilayer level, from cells to circuits. Methods This manuscript reviews recently published applications of these new genetic technologies in the study of epilepsy, as well as work presented by the authors at the genetic session of the XII Workshop on the Neurobiology of Epilepsy in Quebec, Canada. Results Next-generation sequencing is providing investigators with an unbiased means to assess the molecular causes of sporadic forms of epilepsy and have revealed the complexity and genetic heterogeneity of sporadic epilepsy disorders. To assess the functional impact of mutations in these newly identified genes on specific neuronal cell-types during brain development, new modeling strategies in animals, including conditional genetics in mice and in utero knockdown approaches, are enabling functional validation with exquisite cell-type and temporal specificity. In addition, optogenetics, using cell-type specific Cre recombinase driver lines, is enabling investigators to dissect networks involved in epilepsy. Genetically-encoded cell-type labeling is also providing new means to assess the role of the non-neuronal components of epileptic networks such as glial cells. Furthermore, beyond its role in revealing coding variants involved in epileptogenesis, next-generation sequencing can be used to assess the epigenetic modifications that lead to sustained network hyperexcitability in epilepsy, including methylation changes in gene promoters and non-coding RNAs involved in modifying gene expression following seizures. In addition, genetically-based bioluminescent reporters are providing new opportunities to assess neuronal activity and neurotransmitter levels both in vitro and in vivo in the context of epilepsy. Finally, genetically rederived neurons generated from patient iPS cells and genetically-modified zebrafish have become high-throughput means to investigate disease mechanisms and potential new therapies. Significance Genetics has considerably changed the field of epilepsy research and is paving the way for better diagnosis and therapies for patients with epilepsy. PMID:24965021

Extensive excitatory network interactions shape temporal processing of communication signals in a model sensory system.

PubMed

Ma, Xiaofeng; Kohashi, Tsunehiko; Carlson, Bruce A

2013-07-01

Many sensory brain regions are characterized by extensive local network interactions. However, we know relatively little about the contribution of this microcircuitry to sensory coding. Detailed analyses of neuronal microcircuitry are usually performed in vitro, whereas sensory processing is typically studied by recording from individual neurons in vivo. The electrosensory pathway of mormyrid fish provides a unique opportunity to link in vitro studies of synaptic physiology with in vivo studies of sensory processing. These fish communicate by actively varying the intervals between pulses of electricity. Within the midbrain posterior exterolateral nucleus (ELp), the temporal filtering of afferent spike trains establishes interval tuning by single neurons. We characterized pairwise neuronal connectivity among ELp neurons with dual whole cell recording in an in vitro whole brain preparation. We found a densely connected network in which single neurons influenced the responses of other neurons throughout the network. Similarly tuned neurons were more likely to share an excitatory synaptic connection than differently tuned neurons, and synaptic connections between similarly tuned neurons were stronger than connections between differently tuned neurons. We propose a general model for excitatory network interactions in which strong excitatory connections both reinforce and adjust tuning and weak excitatory connections make smaller modifications to tuning. The diversity of interval tuning observed among this population of neurons can be explained, in part, by each individual neuron receiving a different complement of local excitatory inputs.

Activity of cardiorespiratory networks revealed by transsynaptic virus expressing GFP.

PubMed

Irnaten, M; Neff, R A; Wang, J; Loewy, A D; Mettenleiter, T C; Mendelowitz, D

2001-01-01

A fluorescent transneuronal marker capable of labeling individual neurons in a central network while maintaining their normal physiology would permit functional studies of neurons within entire networks responsible for complex behaviors such as cardiorespiratory reflexes. The Bartha strain of pseudorabies virus (PRV), an attenuated swine alpha herpesvirus, can be used as a transsynaptic marker of neural circuits. Bartha PRV invades neuronal networks in the CNS through peripherally projecting axons, replicates in these parent neurons, and then travels transsynaptically to continue labeling the second- and higher-order neurons in a time-dependent manner. A Bartha PRV mutant that expresses green fluorescent protein (GFP) was used to visualize and record from neurons that determine the vagal motor outflow to the heart. Here we show that Bartha PRV-GFP-labeled neurons retain their normal electrophysiological properties and that the labeled baroreflex pathways that control heart rate are unaltered by the virus. This novel transynaptic virus permits in vitro studies of identified neurons within functionally defined neuronal systems including networks that mediate cardiovascular and respiratory function and interactions. We also demonstrate superior laryngeal motorneurons fire spontaneously and synapse on cardiac vagal neurons in the nucleus ambiguus. This cardiorespiratory pathway provides a neural basis of respiratory sinus arrhythmias.

Changes in basal ganglia processing of cortical input following magnetic stimulation in Parkinsonism.

PubMed

Tischler, Hadass; Moran, Anan; Belelovsky, Katya; Bronfeld, Maya; Korngreen, Alon; Bar-Gad, Izhar

2012-12-01

Parkinsonism is associated with major changes in neuronal activity throughout the cortico-basal ganglia loop. Current measures quantify changes in baseline neuronal and network activity but do not capture alterations in information propagation throughout the system. Here, we applied a novel non-invasive magnetic stimulation approach using a custom-made mini-coil that enabled us to study transmission of neuronal activity throughout the cortico-basal ganglia loop in both normal and parkinsonian primates. By magnetically perturbing cortical activity while simultaneously recording neuronal responses along the cortico-basal ganglia loop, we were able to directly investigate modifications in descending cortical activity transmission. We found that in both the normal and parkinsonian states, cortical neurons displayed similar multi-phase firing rate modulations in response to magnetic stimulation. However, in the basal ganglia, large synaptically driven stereotypic neuronal modulation was present in the parkinsonian state that was mostly absent in the normal state. The stimulation-induced neuronal activity pattern highlights the change in information propagation along the cortico-basal ganglia loop. Our findings thus point to the role of abnormal dynamic activity transmission rather than changes in baseline activity as a major component in parkinsonian pathophysiology. Moreover, our results hint that the application of transcranial magnetic stimulation (TMS) in human patients of different disorders may result in different neuronal effects than the one induced in normal subjects. Copyright © 2012 Elsevier Inc. All rights reserved.

Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks

PubMed Central

Pena, Rodrigo F. O.; Vellmer, Sebastian; Bernardi, Davide; Roque, Antonio C.; Lindner, Benjamin

2018-01-01

Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power spectrum. Depending on cellular and network parameters, correlations display diverse patterns (ranging from simple refractory-period effects and stochastic oscillations to slow fluctuations) and it is generally not well-understood how these dependencies come about. Previous work has explored how the single-cell correlations in a homogeneous network (excitatory and inhibitory integrate-and-fire neurons with nearly balanced mean recurrent input) can be determined numerically from an iterative single-neuron simulation. Such a scheme is based on the fact that every neuron is driven by the network noise (i.e., the input currents from all its presynaptic partners) but also contributes to the network noise, leading to a self-consistency condition for the input and output spectra. Here we first extend this scheme to homogeneous networks with strong recurrent inhibition and a synaptic filter, in which instabilities of the previous scheme are avoided by an averaging procedure. We then extend the scheme to heterogeneous networks in which (i) different neural subpopulations (e.g., excitatory and inhibitory neurons) have different cellular or connectivity parameters; (ii) the number and strength of the input connections are random (Erdős-Rényi topology) and thus different among neurons. In all heterogeneous cases, neurons are lumped in different classes each of which is represented by a single neuron in the iterative scheme; in addition, we make a Gaussian approximation of the input current to the neuron. These approximations seem to be justified over a broad range of parameters as indicated by comparison with simulation results of large recurrent networks. Our method can help to elucidate how network heterogeneity shapes the asynchronous state in recurrent neural networks. PMID:29551968

Nanostructured superhydrophobic substrates trigger the development of 3D neuronal networks.

PubMed

Limongi, Tania; Cesca, Fabrizia; Gentile, Francesco; Marotta, Roberto; Ruffilli, Roberta; Barberis, Andrea; Dal Maschio, Marco; Petrini, Enrica Maria; Santoriello, Stefania; Benfenati, Fabio; Di Fabrizio, Enzo

2013-02-11

The generation of 3D networks of primary neurons is a big challenge in neuroscience. Here, a novel method is presented for a 3D neuronal culture on superhydrophobic (SH) substrates. How nano-patterned SH devices stimulate neurons to build 3D networks is investigated. Scanning electron microscopy and confocal imaging show that soon after plating neurites adhere to the nanopatterned pillar sidewalls and they are subsequently pulled between pillars in a suspended position. These neurons display an enhanced survival rate compared to standard cultures and develop mature networks with physiological excitability. These findings underline the importance of using nanostructured SH surfaces for directing 3D neuronal growth, as well as for the design of biomaterials for neuronal regeneration. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Self-Organized Supercriticality and Oscillations in Networks of Stochastic Spiking Neurons

NASA Astrophysics Data System (ADS)

Costa, Ariadne; Brochini, Ludmila; Kinouchi, Osame

2017-08-01

Networks of stochastic spiking neurons are interesting models in the area of Theoretical Neuroscience, presenting both continuous and discontinuous phase transitions. Here we study fully connected networks analytically, numerically and by computational simulations. The neurons have dynamic gains that enable the network to converge to a stationary slightly supercritical state (self-organized supercriticality or SOSC) in the presence of the continuous transition. We show that SOSC, which presents power laws for neuronal avalanches plus some large events, is robust as a function of the main parameter of the neuronal gain dynamics. We discuss the possible applications of the idea of SOSC to biological phenomena like epilepsy and dragon king avalanches. We also find that neuronal gains can produce collective oscillations that coexists with neuronal avalanches, with frequencies compatible with characteristic brain rhythms.

Toward Understanding How Early-Life Stress Reprograms Cognitive and Emotional Brain Networks.

PubMed

Chen, Yuncai; Baram, Tallie Z

2016-01-01

Vulnerability to emotional disorders including depression derives from interactions between genes and environment, especially during sensitive developmental periods. Adverse early-life experiences provoke the release and modify the expression of several stress mediators and neurotransmitters within specific brain regions. The interaction of these mediators with developing neurons and neuronal networks may lead to long-lasting structural and functional alterations associated with cognitive and emotional consequences. Although a vast body of work has linked quantitative and qualitative aspects of stress to adolescent and adult outcomes, a number of questions are unclear. What distinguishes 'normal' from pathologic or toxic stress? How are the effects of stress transformed into structural and functional changes in individual neurons and neuronal networks? Which ones are affected? We review these questions in the context of established and emerging studies. We introduce a novel concept regarding the origin of toxic early-life stress, stating that it may derive from specific patterns of environmental signals, especially those derived from the mother or caretaker. Fragmented and unpredictable patterns of maternal care behaviors induce a profound chronic stress. The aberrant patterns and rhythms of early-life sensory input might also directly and adversely influence the maturation of cognitive and emotional brain circuits, in analogy to visual and auditory brain systems. Thus, unpredictable, stress-provoking early-life experiences may influence adolescent cognitive and emotional outcomes by disrupting the maturation of the underlying brain networks. Comprehensive approaches and multiple levels of analysis are required to probe the protean consequences of early-life adversity on the developing brain. These involve integrated human and animal-model studies, and approaches ranging from in vivo imaging to novel neuroanatomical, molecular, epigenomic, and computational methodologies. Because early-life adversity is a powerful determinant of subsequent vulnerabilities to emotional and cognitive pathologies, understanding the underlying processes will have profound implications for the world's current and future children.

Estimating network parameters from combined dynamics of firing rate and irregularity of single neurons.

PubMed

Hamaguchi, Kosuke; Riehle, Alexa; Brunel, Nicolas

2011-01-01

High firing irregularity is a hallmark of cortical neurons in vivo, and modeling studies suggest a balance of excitation and inhibition is necessary to explain this high irregularity. Such a balance must be generated, at least partly, from local interconnected networks of excitatory and inhibitory neurons, but the details of the local network structure are largely unknown. The dynamics of the neural activity depends on the local network structure; this in turn suggests the possibility of estimating network structure from the dynamics of the firing statistics. Here we report a new method to estimate properties of the local cortical network from the instantaneous firing rate and irregularity (CV(2)) under the assumption that recorded neurons are a part of a randomly connected sparse network. The firing irregularity, measured in monkey motor cortex, exhibits two features; many neurons show relatively stable firing irregularity in time and across different task conditions; the time-averaged CV(2) is widely distributed from quasi-regular to irregular (CV(2) = 0.3-1.0). For each recorded neuron, we estimate the three parameters of a local network [balance of local excitation-inhibition, number of recurrent connections per neuron, and excitatory postsynaptic potential (EPSP) size] that best describe the dynamics of the measured firing rates and irregularities. Our analysis shows that optimal parameter sets form a two-dimensional manifold in the three-dimensional parameter space that is confined for most of the neurons to the inhibition-dominated region. High irregularity neurons tend to be more strongly connected to the local network, either in terms of larger EPSP and inhibitory PSP size or larger number of recurrent connections, compared with the low irregularity neurons, for a given excitatory/inhibitory balance. Incorporating either synaptic short-term depression or conductance-based synapses leads many low CV(2) neurons to move to the excitation-dominated region as well as to an increase of EPSP size.

High-resolution CMOS MEA platform to study neurons at subcellular, cellular, and network levels†

PubMed Central

Müller, Jan; Ballini, Marco; Livi, Paolo; Chen, Yihui; Radivojevic, Milos; Shadmani, Amir; Viswam, Vijay; Jones, Ian L.; Fiscella, Michele; Diggelmann, Roland; Stettler, Alexander; Frey, Urs; Bakkum, Douglas J.; Hierlemann, Andreas

2017-01-01

Studies on information processing and learning properties of neuronal networks would benefit from simultaneous and parallel access to the activity of a large fraction of all neurons in such networks. Here, we present a CMOS-based device, capable of simultaneously recording the electrical activity of over a thousand cells in in vitro neuronal networks. The device provides sufficiently high spatiotemporal resolution to enable, at the same time, access to neuronal preparations on subcellular, cellular, and network level. The key feature is a rapidly reconfigurable array of 26 400 microelectrodes arranged at low pitch (17.5 μm) within a large overall sensing area (3.85 × 2.10 mm2). An arbitrary subset of the electrodes can be simultaneously connected to 1024 low-noise readout channels as well as 32 stimulation units. Each electrode or electrode subset can be used to electrically stimulate or record the signals of virtually any neuron on the array. We demonstrate the applicability and potential of this device for various different experimental paradigms: large-scale recordings from whole networks of neurons as well as investigations of axonal properties of individual neurons. PMID:25973786

High-resolution CMOS MEA platform to study neurons at subcellular, cellular, and network levels.

PubMed

Müller, Jan; Ballini, Marco; Livi, Paolo; Chen, Yihui; Radivojevic, Milos; Shadmani, Amir; Viswam, Vijay; Jones, Ian L; Fiscella, Michele; Diggelmann, Roland; Stettler, Alexander; Frey, Urs; Bakkum, Douglas J; Hierlemann, Andreas

2015-07-07

Studies on information processing and learning properties of neuronal networks would benefit from simultaneous and parallel access to the activity of a large fraction of all neurons in such networks. Here, we present a CMOS-based device, capable of simultaneously recording the electrical activity of over a thousand cells in in vitro neuronal networks. The device provides sufficiently high spatiotemporal resolution to enable, at the same time, access to neuronal preparations on subcellular, cellular, and network level. The key feature is a rapidly reconfigurable array of 26 400 microelectrodes arranged at low pitch (17.5 μm) within a large overall sensing area (3.85 × 2.10 mm(2)). An arbitrary subset of the electrodes can be simultaneously connected to 1024 low-noise readout channels as well as 32 stimulation units. Each electrode or electrode subset can be used to electrically stimulate or record the signals of virtually any neuron on the array. We demonstrate the applicability and potential of this device for various different experimental paradigms: large-scale recordings from whole networks of neurons as well as investigations of axonal properties of individual neurons.

An Overview of the Protective Effects of Chitosan and Acetylated Chitosan Oligosaccharides against Neuronal Disorders.

PubMed

Hao, Cui; Wang, Wei; Wang, Shuyao; Zhang, Lijuan; Guo, Yunliang

2017-03-23

Chitin is the second most abundant biopolymer on Earth and is mainly comprised of a marine invertebrate, consisting of repeating β-1,4 linked N-acetylated glucosamine units, whereas its N-deacetylated product, chitosan, has broad medical applications. Interestingly, chitosan oligosaccharides have therapeutic effects on different types of neuronal disorders, including, but not limited to, Alzheimer's disease, Parkinson's disease, and nerve crush injury. A common link among neuronal disorders is observed at a sub-cellular level, such as atypical protein assemblies and induced neuronal death. Chronic activation of innate immune responses that lead to neuronal injury is also common in these diseases. Thus, the common mechanisms of neuronal disorders might explain the general therapeutic effects of chitosan oligosaccharides and their derivatives in these diseases. This review provides an update on the pathogenesis and therapy for neuronal disorders and will be mainly focused on the recent progress made towards the neuroprotective properties of chitosan and acetylated chitosan oligosaccharides. Their structural features and the underlying molecular mechanisms will also be discussed.

Nanoparticles: A Challenging Vehicle for Neural Stimulation

PubMed Central

Colombo, Elisabetta; Feyen, Paul; Antognazza, Maria Rosa; Lanzani, Guglielmo; Benfenati, Fabio

2016-01-01

Neurostimulation represents a powerful and well-established tool for the treatment of several diseases affecting the central nervous system. Although, effective in reducing the symptoms or the progression of brain disorders, the poor accessibility of the deepest areas of the brain currently hampers the possibility of a more specific and controlled therapeutic stimulation, depending on invasive surgical approaches and long-term stability, and biocompatibility issues. The massive research of the last decades on nanomaterials and nanoscale devices favored the development of new tools to address the limitations of the available neurostimulation approaches. This mini-review focuses on the employment of nanoparticles for the modulation of the electrophysiological activity of neuronal networks and the related transduction mechanisms underlying the nanostructure-neuron interfaces. PMID:27047327

Phase synchronization of bursting neurons in clustered small-world networks

NASA Astrophysics Data System (ADS)

Batista, C. A. S.; Lameu, E. L.; Batista, A. M.; Lopes, S. R.; Pereira, T.; Zamora-López, G.; Kurths, J.; Viana, R. L.

2012-07-01

We investigate the collective dynamics of bursting neurons on clustered networks. The clustered network model is composed of subnetworks, each of them presenting the so-called small-world property. This model can also be regarded as a network of networks. In each subnetwork a neuron is connected to other ones with regular as well as random connections, the latter with a given intracluster probability. Moreover, in a given subnetwork each neuron has an intercluster probability to be connected to the other subnetworks. The local neuron dynamics has two time scales (fast and slow) and is modeled by a two-dimensional map. In such small-world network the neuron parameters are chosen to be slightly different such that, if the coupling strength is large enough, there may be synchronization of the bursting (slow) activity. We give bounds for the critical coupling strength to obtain global burst synchronization in terms of the network structure, that is, the probabilities of intracluster and intercluster connections. We find that, as the heterogeneity in the network is reduced, the network global synchronizability is improved. We show that the transitions to global synchrony may be abrupt or smooth depending on the intercluster probability.

Effects of partial time delays on phase synchronization in Watts-Strogatz small-world neuronal networks

NASA Astrophysics Data System (ADS)

Sun, Xiaojuan; Perc, Matjaž; Kurths, Jürgen

2017-05-01

In this paper, we study effects of partial time delays on phase synchronization in Watts-Strogatz small-world neuronal networks. Our focus is on the impact of two parameters, namely the time delay τ and the probability of partial time delay pdelay, whereby the latter determines the probability with which a connection between two neurons is delayed. Our research reveals that partial time delays significantly affect phase synchronization in this system. In particular, partial time delays can either enhance or decrease phase synchronization and induce synchronization transitions with changes in the mean firing rate of neurons, as well as induce switching between synchronized neurons with period-1 firing to synchronized neurons with period-2 firing. Moreover, in comparison to a neuronal network where all connections are delayed, we show that small partial time delay probabilities have especially different influences on phase synchronization of neuronal networks.

Effects of partial time delays on phase synchronization in Watts-Strogatz small-world neuronal networks.

PubMed

Sun, Xiaojuan; Perc, Matjaž; Kurths, Jürgen

2017-05-01

In this paper, we study effects of partial time delays on phase synchronization in Watts-Strogatz small-world neuronal networks. Our focus is on the impact of two parameters, namely the time delay τ and the probability of partial time delay p delay , whereby the latter determines the probability with which a connection between two neurons is delayed. Our research reveals that partial time delays significantly affect phase synchronization in this system. In particular, partial time delays can either enhance or decrease phase synchronization and induce synchronization transitions with changes in the mean firing rate of neurons, as well as induce switching between synchronized neurons with period-1 firing to synchronized neurons with period-2 firing. Moreover, in comparison to a neuronal network where all connections are delayed, we show that small partial time delay probabilities have especially different influences on phase synchronization of neuronal networks.

Long-term optical stimulation of channelrhodopsin-expressing neurons to study network plasticity

PubMed Central

Lignani, Gabriele; Ferrea, Enrico; Difato, Francesco; Amarù, Jessica; Ferroni, Eleonora; Lugarà, Eleonora; Espinoza, Stefano; Gainetdinov, Raul R.; Baldelli, Pietro; Benfenati, Fabio

2013-01-01

Neuronal plasticity produces changes in excitability, synaptic transmission, and network architecture in response to external stimuli. Network adaptation to environmental conditions takes place in time scales ranging from few seconds to days, and modulates the entire network dynamics. To study the network response to defined long-term experimental protocols, we setup a system that combines optical and electrophysiological tools embedded in a cell incubator. Primary hippocampal neurons transduced with lentiviruses expressing channelrhodopsin-2/H134R were subjected to various photostimulation protocols in a time window in the order of days. To monitor the effects of light-induced gating of network activity, stimulated transduced neurons were simultaneously recorded using multi-electrode arrays (MEAs). The developed experimental model allows discerning short-term, long-lasting, and adaptive plasticity responses of the same neuronal network to distinct stimulation frequencies applied over different temporal windows. PMID:23970852

Long-term optical stimulation of channelrhodopsin-expressing neurons to study network plasticity.

PubMed

Lignani, Gabriele; Ferrea, Enrico; Difato, Francesco; Amarù, Jessica; Ferroni, Eleonora; Lugarà, Eleonora; Espinoza, Stefano; Gainetdinov, Raul R; Baldelli, Pietro; Benfenati, Fabio

2013-01-01

Neuronal plasticity produces changes in excitability, synaptic transmission, and network architecture in response to external stimuli. Network adaptation to environmental conditions takes place in time scales ranging from few seconds to days, and modulates the entire network dynamics. To study the network response to defined long-term experimental protocols, we setup a system that combines optical and electrophysiological tools embedded in a cell incubator. Primary hippocampal neurons transduced with lentiviruses expressing channelrhodopsin-2/H134R were subjected to various photostimulation protocols in a time window in the order of days. To monitor the effects of light-induced gating of network activity, stimulated transduced neurons were simultaneously recorded using multi-electrode arrays (MEAs). The developed experimental model allows discerning short-term, long-lasting, and adaptive plasticity responses of the same neuronal network to distinct stimulation frequencies applied over different temporal windows.

Sodium Pumps Mediate Activity-Dependent Changes in Mammalian Motor Networks

PubMed Central

Picton, Laurence D.; Nascimento, Filipe; Broadhead, Matthew J.; Sillar, Keith T.

2017-01-01

Ubiquitously expressed sodium pumps are best known for maintaining the ionic gradients and resting membrane potential required for generating action potentials. However, activity- and state-dependent changes in pump activity can also influence neuronal firing and regulate rhythmic network output. Here we demonstrate that changes in sodium pump activity regulate locomotor networks in the spinal cord of neonatal mice. The sodium pump inhibitor, ouabain, increased the frequency and decreased the amplitude of drug-induced locomotor bursting, effects that were dependent on the presence of the neuromodulator dopamine. Conversely, activating the pump with the sodium ionophore monensin decreased burst frequency. When more “natural” locomotor output was evoked using dorsal-root stimulation, ouabain increased burst frequency and extended locomotor episode duration, whereas monensin slowed and shortened episodes. Decreasing the time between dorsal-root stimulation, and therefore interepisode interval, also shortened and slowed activity, suggesting that pump activity encodes information about past network output and contributes to feedforward control of subsequent locomotor bouts. Using whole-cell patch-clamp recordings from spinal motoneurons and interneurons, we describe a long-duration (∼60 s), activity-dependent, TTX- and ouabain-sensitive, hyperpolarization (∼5 mV), which is mediated by spike-dependent increases in pump activity. The duration of this dynamic pump potential is enhanced by dopamine. Our results therefore reveal sodium pumps as dynamic regulators of mammalian spinal motor networks that can also be affected by neuromodulatory systems. Given the involvement of sodium pumps in movement disorders, such as amyotrophic lateral sclerosis and rapid-onset dystonia parkinsonism, knowledge of their contribution to motor network regulation also has considerable clinical importance. SIGNIFICANCE STATEMENT The sodium pump is ubiquitously expressed and responsible for at least half of total brain energy consumption. The pumps maintain ionic gradients and the resting membrane potential of neurons, but increasing evidence suggests that activity- and state-dependent changes in pump activity also influence neuronal firing. Here we demonstrate that changes in sodium pump activity regulate locomotor output in the spinal cord of neonatal mice. We describe a sodium pump-mediated afterhyperpolarization in spinal neurons, mediated by spike-dependent increases in pump activity, which is affected by dopamine. Understanding how sodium pumps contribute to network regulation and are targeted by neuromodulators, including dopamine, has clinical relevance due to the role of the sodium pump in diseases, including amyotrophic lateral sclerosis, parkinsonism, epilepsy, and hemiplegic migraine. PMID:28123025

Improved Autoassociative Neural Networks

NASA Technical Reports Server (NTRS)

Hand, Charles

2003-01-01

Improved autoassociative neural networks, denoted nexi, have been proposed for use in controlling autonomous robots, including mobile exploratory robots of the biomorphic type. In comparison with conventional autoassociative neural networks, nexi would be more complex but more capable in that they could be trained to do more complex tasks. A nexus would use bit weights and simple arithmetic in a manner that would enable training and operation without a central processing unit, programs, weight registers, or large amounts of memory. Only a relatively small amount of memory (to hold the bit weights) and a simple logic application- specific integrated circuit would be needed. A description of autoassociative neural networks is prerequisite to a meaningful description of a nexus. An autoassociative network is a set of neurons that are completely connected in the sense that each neuron receives input from, and sends output to, all the other neurons. (In some instantiations, a neuron could also send output back to its own input terminal.) The state of a neuron is completely determined by the inner product of its inputs with weights associated with its input channel. Setting the weights sets the behavior of the network. The neurons of an autoassociative network are usually regarded as comprising a row or vector. Time is a quantized phenomenon for most autoassociative networks in the sense that time proceeds in discrete steps. At each time step, the row of neurons forms a pattern: some neurons are firing, some are not. Hence, the current state of an autoassociative network can be described with a single binary vector. As time goes by, the network changes the vector. Autoassociative networks move vectors over hyperspace landscapes of possibilities.

Energetic Constraints Produce Self-sustained Oscillatory Dynamics in Neuronal Networks

PubMed Central

Burroni, Javier; Taylor, P.; Corey, Cassian; Vachnadze, Tengiz; Siegelmann, Hava T.

2017-01-01

Overview: We model energy constraints in a network of spiking neurons, while exploring general questions of resource limitation on network function abstractly. Background: Metabolic states like dietary ketosis or hypoglycemia have a large impact on brain function and disease outcomes. Glia provide metabolic support for neurons, among other functions. Yet, in computational models of glia-neuron cooperation, there have been no previous attempts to explore the effects of direct realistic energy costs on network activity in spiking neurons. Currently, biologically realistic spiking neural networks assume that membrane potential is the main driving factor for neural spiking, and do not take into consideration energetic costs. Methods: We define local energy pools to constrain a neuron model, termed Spiking Neuron Energy Pool (SNEP), which explicitly incorporates energy limitations. Each neuron requires energy to spike, and resources in the pool regenerate over time. Our simulation displays an easy-to-use GUI, which can be run locally in a web browser, and is freely available. Results: Energy dependence drastically changes behavior of these neural networks, causing emergent oscillations similar to those in networks of biological neurons. We analyze the system via Lotka-Volterra equations, producing several observations: (1) energy can drive self-sustained oscillations, (2) the energetic cost of spiking modulates the degree and type of oscillations, (3) harmonics emerge with frequencies determined by energy parameters, and (4) varying energetic costs have non-linear effects on energy consumption and firing rates. Conclusions: Models of neuron function which attempt biological realism may benefit from including energy constraints. Further, we assert that observed oscillatory effects of energy limitations exist in networks of many kinds, and that these findings generalize to abstract graphs and technological applications. PMID:28289370

Developing a tissue-engineered neural-electrical relay using encapsulated neuronal constructs on conducting polymer fibers.

PubMed

Cullen, D Kacy; R Patel, Ankur; Doorish, John F; Smith, Douglas H; Pfister, Bryan J

2008-12-01

Neural-electrical interface platforms are being developed to extracellularly monitor neuronal population activity. Polyaniline-based electrically conducting polymer fibers are attractive substrates for sustained functional interfaces with neurons due to their flexibility, tailored geometry and controlled electro-conductive properties. In this study, we addressed the neurobiological considerations of utilizing small diameter (<400 microm) fibers consisting of a blend of electrically conductive polyaniline and polypropylene (PA-PP) as the backbone of encapsulated tissue-engineered neural-electrical relays. We devised new approaches to promote survival, adhesion and neurite outgrowth of primary dorsal root ganglion neurons on PA-PP fibers. We attained a greater than ten-fold increase in the density of viable neurons on fiber surfaces to approximately 700 neurons mm(-2) by manipulating surrounding surface charges to bias settling neuronal suspensions toward fibers coated with cell-adhesive ligands. This stark increase in neuronal density resulted in robust neuritic extension and network formation directly along the fibers. Additionally, we encapsulated these neuronal networks on PA-PP fibers using agarose to form a protective barrier while potentially facilitating network stability. Following encapsulation, the neuronal networks maintained integrity, high viability (>85%) and intimate adhesion to PA-PP fibers. These efforts accomplished key prerequisites for the establishment of functional electrical interfaces with neuronal populations using small diameter PA-PP fibers-specifically, improved neurocompatibility, high-density neuronal adhesion and neuritic network development directly on fiber surfaces.

A reanalysis of "Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons".

PubMed

Engelken, Rainer; Farkhooi, Farzad; Hansel, David; van Vreeswijk, Carl; Wolf, Fred

2016-01-01

Neuronal activity in the central nervous system varies strongly in time and across neuronal populations. It is a longstanding proposal that such fluctuations generically arise from chaotic network dynamics. Various theoretical studies predict that the rich dynamics of rate models operating in the chaotic regime can subserve circuit computation and learning. Neurons in the brain, however, communicate via spikes and it is a theoretical challenge to obtain similar rate fluctuations in networks of spiking neuron models. A recent study investigated spiking balanced networks of leaky integrate and fire (LIF) neurons and compared their dynamics to a matched rate network with identical topology, where single unit input-output functions were chosen from isolated LIF neurons receiving Gaussian white noise input. A mathematical analogy between the chaotic instability in networks of rate units and the spiking network dynamics was proposed. Here we revisit the behavior of the spiking LIF networks and these matched rate networks. We find expected hallmarks of a chaotic instability in the rate network: For supercritical coupling strength near the transition point, the autocorrelation time diverges. For subcritical coupling strengths, we observe critical slowing down in response to small external perturbations. In the spiking network, we found in contrast that the timescale of the autocorrelations is insensitive to the coupling strength and that rate deviations resulting from small input perturbations rapidly decay. The decay speed even accelerates for increasing coupling strength. In conclusion, our reanalysis demonstrates fundamental differences between the behavior of pulse-coupled spiking LIF networks and rate networks with matched topology and input-output function. In particular there is no indication of a corresponding chaotic instability in the spiking network.

Cultured neuronal networks as environmental biosensors.

PubMed

O'Shaughnessy, Thomas J; Gray, Samuel A; Pancrazio, Joseph J

2004-01-01

Contamination of water by toxins, either intentionally or unintentionally, is a growing concern for both military and civilian agencies and thus there is a need for systems capable of monitoring a wide range of natural and industrial toxicants. The EILATox-Oregon Workshop held in September 2002 provided an opportunity to test the capabilities of a prototype neuronal network-based biosensor with unknown contaminants in water samples. The biosensor is a portable device capable of recording the action potential activity from a network of mammalian neurons grown on glass microelectrode arrays. Changes in the action potential fi ring rate across the network are monitored to determine exposure to toxicants. A series of three neuronal networks derived from mice was used to test seven unknown samples. Two of these unknowns later were revealed to be blanks, to which the neuronal networks did not respond. Of the five remaining unknowns, a significant change in network activity was detected for four of the compounds at concentrations below a lethal level for humans: mercuric chloride, sodium arsenite, phosdrin and chlordimeform. These compounds--two heavy metals, an organophosphate and an insecticide--demonstrate the breadth of detection possible with neuronal networks. The results generated at the workshop show the promise of the neuronal network biosensor as an environmental detector but there is still considerable effort needed to produce a device suitable for routine environmental threat monitoring.

General Anesthetics to Treat Major Depressive Disorder: Clinical Relevance and Underlying Mechanisms.

PubMed

Vutskits, Laszlo

2018-01-01

Major depressive disorder is a frequent and devastating psychological condition with tremendous public health impact. The underlying pathophysiological mechanisms involve abnormal neurotransmission and a relatedly impaired synaptic plasticity. Since general anesthetics are potent modulators of neuronal activity and, thereby, can exert long-term context-dependent impact on neural networks, an intriguing hypothesis is that these drugs could enhance impaired neural plasticity associated with certain psychiatric diseases. Clinical observations over the past few decades appear to confirm this possibility. Indeed, equipotency of general anesthesia alone in comparison with electroconvulsive therapy under general anesthesia has been demonstrated in several clinical trials. Importantly, in the past 15 years, intravenous administration of subanesthetic doses of ketamine have also been demonstrated to have rapid antidepressant effects. The molecular, cellular, and network mechanisms underlying these therapeutic effects have been partially identified. Although several important questions remain to be addressed, the ensemble of these experimental and clinical observations opens new therapeutic possibilities in the treatment of depressive disorders. Importantly, they also suggest a new therapeutic role for anesthetics that goes beyond their principal use in the perioperative period to facilitate surgery.

Neurons from the adult human dentate nucleus: neural networks in the neuron classification.

PubMed

Grbatinić, Ivan; Marić, Dušica L; Milošević, Nebojša T

2015-04-07

Topological (central vs. border neuron type) and morphological classification of adult human dentate nucleus neurons according to their quantified histomorphological properties using neural networks on real and virtual neuron samples. In the real sample 53.1% and 14.1% of central and border neurons, respectively, are classified correctly with total of 32.8% of misclassified neurons. The most important result present 62.2% of misclassified neurons in border neurons group which is even greater than number of correctly classified neurons (37.8%) in that group, showing obvious failure of network to classify neurons correctly based on computational parameters used in our study. On the virtual sample 97.3% of misclassified neurons in border neurons group which is much greater than number of correctly classified neurons (2.7%) in that group, again confirms obvious failure of network to classify neurons correctly. Statistical analysis shows that there is no statistically significant difference in between central and border neurons for each measured parameter (p>0.05). Total of 96.74% neurons are morphologically classified correctly by neural networks and each one belongs to one of the four histomorphological types: (a) neurons with small soma and short dendrites, (b) neurons with small soma and long dendrites, (c) neuron with large soma and short dendrites, (d) neurons with large soma and long dendrites. Statistical analysis supports these results (p<0.05). Human dentate nucleus neurons can be classified in four neuron types according to their quantitative histomorphological properties. These neuron types consist of two neuron sets, small and large ones with respect to their perykarions with subtypes differing in dendrite length i.e. neurons with short vs. long dendrites. Besides confirmation of neuron classification on small and large ones, already shown in literature, we found two new subtypes i.e. neurons with small soma and long dendrites and with large soma and short dendrites. These neurons are most probably equally distributed throughout the dentate nucleus as no significant difference in their topological distribution is observed. Copyright © 2015 Elsevier Ltd. All rights reserved.

Connectomic constraints on computation in feedforward networks of spiking neurons.

PubMed

Ramaswamy, Venkatakrishnan; Banerjee, Arunava

2014-10-01

Several efforts are currently underway to decipher the connectome or parts thereof in a variety of organisms. Ascertaining the detailed physiological properties of all the neurons in these connectomes, however, is out of the scope of such projects. It is therefore unclear to what extent knowledge of the connectome alone will advance a mechanistic understanding of computation occurring in these neural circuits, especially when the high-level function of the said circuit is unknown. We consider, here, the question of how the wiring diagram of neurons imposes constraints on what neural circuits can compute, when we cannot assume detailed information on the physiological response properties of the neurons. We call such constraints-that arise by virtue of the connectome-connectomic constraints on computation. For feedforward networks equipped with neurons that obey a deterministic spiking neuron model which satisfies a small number of properties, we ask if just by knowing the architecture of a network, we can rule out computations that it could be doing, no matter what response properties each of its neurons may have. We show results of this form, for certain classes of network architectures. On the other hand, we also prove that with the limited set of properties assumed for our model neurons, there are fundamental limits to the constraints imposed by network structure. Thus, our theory suggests that while connectomic constraints might restrict the computational ability of certain classes of network architectures, we may require more elaborate information on the properties of neurons in the network, before we can discern such results for other classes of networks.

Clique of Functional Hubs Orchestrates Population Bursts in Developmentally Regulated Neural Networks

PubMed Central

Luccioli, Stefano; Ben-Jacob, Eshel; Barzilai, Ari; Bonifazi, Paolo; Torcini, Alessandro

2014-01-01

It has recently been discovered that single neuron stimulation can impact network dynamics in immature and adult neuronal circuits. Here we report a novel mechanism which can explain in neuronal circuits, at an early stage of development, the peculiar role played by a few specific neurons in promoting/arresting the population activity. For this purpose, we consider a standard neuronal network model, with short-term synaptic plasticity, whose population activity is characterized by bursting behavior. The addition of developmentally inspired constraints and correlations in the distribution of the neuronal connectivities and excitabilities leads to the emergence of functional hub neurons, whose stimulation/deletion is critical for the network activity. Functional hubs form a clique, where a precise sequential activation of the neurons is essential to ignite collective events without any need for a specific topological architecture. Unsupervised time-lagged firings of supra-threshold cells, in connection with coordinated entrainments of near-threshold neurons, are the key ingredients to orchestrate population activity. PMID:25255443

Revealing degree distribution of bursting neuron networks.

PubMed

Shen, Yu; Hou, Zhonghuai; Xin, Houwen

2010-03-01

We present a method to infer the degree distribution of a bursting neuron network from its dynamics. Burst synchronization (BS) of coupled Morris-Lecar neurons has been studied under the weak coupling condition. In the BS state, all the neurons start and end bursting almost simultaneously, while the spikes inside the burst are incoherent among the neurons. Interestingly, we find that the spike amplitude of a given neuron shows an excellent linear relationship with its degree, which makes it possible to estimate the degree distribution of the network by simple statistics of the spike amplitudes. We demonstrate the validity of this scheme on scale-free as well as small-world networks. The underlying mechanism of such a method is also briefly discussed.

Speed and segmentation control mechanisms characterized in rhythmically-active circuits created from spinal neurons produced from genetically-tagged embryonic stem cells

PubMed Central

Sternfeld, Matthew J; Hinckley, Christopher A; Moore, Niall J; Pankratz, Matthew T; Hilde, Kathryn L; Driscoll, Shawn P; Hayashi, Marito; Amin, Neal D; Bonanomi, Dario; Gifford, Wesley D; Sharma, Kamal; Goulding, Martyn; Pfaff, Samuel L

2017-01-01

Flexible neural networks, such as the interconnected spinal neurons that control distinct motor actions, can switch their activity to produce different behaviors. Both excitatory (E) and inhibitory (I) spinal neurons are necessary for motor behavior, but the influence of recruiting different ratios of E-to-I cells remains unclear. We constructed synthetic microphysical neural networks, called circuitoids, using precise combinations of spinal neuron subtypes derived from mouse stem cells. Circuitoids of purified excitatory interneurons were sufficient to generate oscillatory bursts with properties similar to in vivo central pattern generators. Inhibitory V1 neurons provided dual layers of regulation within excitatory rhythmogenic networks - they increased the rhythmic burst frequency of excitatory V3 neurons, and segmented excitatory motor neuron activity into sub-networks. Accordingly, the speed and pattern of spinal circuits that underlie complex motor behaviors may be regulated by quantitatively gating the intra-network cellular activity ratio of E-to-I neurons. DOI: http://dx.doi.org/10.7554/eLife.21540.001 PMID:28195039

Orientation selectivity in inhibition-dominated networks of spiking neurons: effect of single neuron properties and network dynamics.

PubMed

Sadeh, Sadra; Rotter, Stefan

2015-01-01

The neuronal mechanisms underlying the emergence of orientation selectivity in the primary visual cortex of mammals are still elusive. In rodents, visual neurons show highly selective responses to oriented stimuli, but neighboring neurons do not necessarily have similar preferences. Instead of a smooth map, one observes a salt-and-pepper organization of orientation selectivity. Modeling studies have recently confirmed that balanced random networks are indeed capable of amplifying weakly tuned inputs and generating highly selective output responses, even in absence of feature-selective recurrent connectivity. Here we seek to elucidate the neuronal mechanisms underlying this phenomenon by resorting to networks of integrate-and-fire neurons, which are amenable to analytic treatment. Specifically, in networks of perfect integrate-and-fire neurons, we observe that highly selective and contrast invariant output responses emerge, very similar to networks of leaky integrate-and-fire neurons. We then demonstrate that a theory based on mean firing rates and the detailed network topology predicts the output responses, and explains the mechanisms underlying the suppression of the common-mode, amplification of modulation, and contrast invariance. Increasing inhibition dominance in our networks makes the rectifying nonlinearity more prominent, which in turn adds some distortions to the otherwise essentially linear prediction. An extension of the linear theory can account for all the distortions, enabling us to compute the exact shape of every individual tuning curve in our networks. We show that this simple form of nonlinearity adds two important properties to orientation selectivity in the network, namely sharpening of tuning curves and extra suppression of the modulation. The theory can be further extended to account for the nonlinearity of the leaky model by replacing the rectifier by the appropriate smooth input-output transfer function. These results are robust and do not depend on the state of network dynamics, and hold equally well for mean-driven and fluctuation-driven regimes of activity.

Orientation Selectivity in Inhibition-Dominated Networks of Spiking Neurons: Effect of Single Neuron Properties and Network Dynamics

PubMed Central

Sadeh, Sadra; Rotter, Stefan

2015-01-01

The neuronal mechanisms underlying the emergence of orientation selectivity in the primary visual cortex of mammals are still elusive. In rodents, visual neurons show highly selective responses to oriented stimuli, but neighboring neurons do not necessarily have similar preferences. Instead of a smooth map, one observes a salt-and-pepper organization of orientation selectivity. Modeling studies have recently confirmed that balanced random networks are indeed capable of amplifying weakly tuned inputs and generating highly selective output responses, even in absence of feature-selective recurrent connectivity. Here we seek to elucidate the neuronal mechanisms underlying this phenomenon by resorting to networks of integrate-and-fire neurons, which are amenable to analytic treatment. Specifically, in networks of perfect integrate-and-fire neurons, we observe that highly selective and contrast invariant output responses emerge, very similar to networks of leaky integrate-and-fire neurons. We then demonstrate that a theory based on mean firing rates and the detailed network topology predicts the output responses, and explains the mechanisms underlying the suppression of the common-mode, amplification of modulation, and contrast invariance. Increasing inhibition dominance in our networks makes the rectifying nonlinearity more prominent, which in turn adds some distortions to the otherwise essentially linear prediction. An extension of the linear theory can account for all the distortions, enabling us to compute the exact shape of every individual tuning curve in our networks. We show that this simple form of nonlinearity adds two important properties to orientation selectivity in the network, namely sharpening of tuning curves and extra suppression of the modulation. The theory can be further extended to account for the nonlinearity of the leaky model by replacing the rectifier by the appropriate smooth input-output transfer function. These results are robust and do not depend on the state of network dynamics, and hold equally well for mean-driven and fluctuation-driven regimes of activity. PMID:25569445

Widespread receptivity to neuropeptide PDF throughout the neuronal circadian clock network of Drosophila revealed by real-time cyclic AMP imaging.

PubMed

Shafer, Orie T; Kim, Dong Jo; Dunbar-Yaffe, Richard; Nikolaev, Viacheslav O; Lohse, Martin J; Taghert, Paul H

2008-04-24

The neuropeptide PDF is released by sixteen clock neurons in Drosophila and helps maintain circadian activity rhythms by coordinating a network of approximately 150 neuronal clocks. Whether PDF acts directly on elements of this neural network remains unknown. We address this question by adapting Epac1-camps, a genetically encoded cAMP FRET sensor, for use in the living brain. We find that a subset of the PDF-expressing neurons respond to PDF with long-lasting cAMP increases and confirm that such responses require the PDF receptor. In contrast, an unrelated Drosophila neuropeptide, DH31, stimulates large cAMP increases in all PDF-expressing clock neurons. Thus, the network of approximately 150 clock neurons displays widespread, though not uniform, PDF receptivity. This work introduces a sensitive means of measuring cAMP changes in a living brain with subcellular resolution. Specifically, it experimentally confirms the longstanding hypothesis that PDF is a direct modulator of most neurons in the Drosophila clock network.

Qualitative validation of the reduction from two reciprocally coupled neurons to one self-coupled neuron in a respiratory network model.

PubMed

Dunmyre, Justin R

2011-06-01

The pre-Bötzinger complex of the mammalian brainstem is a heterogeneous neuronal network, and individual neurons within the network have varying strengths of the persistent sodium and calcium-activated nonspecific cationic currents. Individually, these currents have been the focus of modeling efforts. Previously, Dunmyre et al. (J Comput Neurosci 1-24, 2011) proposed a model and studied the interactions of these currents within one self-coupled neuron. In this work, I consider two identical, reciprocally coupled model neurons and validate the reduction to the self-coupled case. I find that all of the dynamics of the two model neuron network and the regions of parameter space where these distinct dynamics are found are qualitatively preserved in the reduction to the self-coupled case.

Structure-function analysis of genetically defined neuronal populations.

PubMed

Groh, Alexander; Krieger, Patrik

2013-10-01

Morphological and functional classification of individual neurons is a crucial aspect of the characterization of neuronal networks. Systematic structural and functional analysis of individual neurons is now possible using transgenic mice with genetically defined neurons that can be visualized in vivo or in brain slice preparations. Genetically defined neurons are useful for studying a particular class of neurons and also for more comprehensive studies of the neuronal content of a network. Specific subsets of neurons can be identified by fluorescence imaging of enhanced green fluorescent protein (eGFP) or another fluorophore expressed under the control of a cell-type-specific promoter. The advantages of such genetically defined neurons are not only their homogeneity and suitability for systematic descriptions of networks, but also their tremendous potential for cell-type-specific manipulation of neuronal networks in vivo. This article describes a selection of procedures for visualizing and studying the anatomy and physiology of genetically defined neurons in transgenic mice. We provide information about basic equipment, reagents, procedures, and analytical approaches for obtaining three-dimensional (3D) cell morphologies and determining the axonal input and output of genetically defined neurons. We exemplify with genetically labeled cortical neurons, but the procedures are applicable to other brain regions with little or no alterations.

Developmental time windows for axon growth influence neuronal network topology.

PubMed

Lim, Sol; Kaiser, Marcus

2015-04-01

Early brain connectivity development consists of multiple stages: birth of neurons, their migration and the subsequent growth of axons and dendrites. Each stage occurs within a certain period of time depending on types of neurons and cortical layers. Forming synapses between neurons either by growing axons starting at similar times for all neurons (much-overlapped time windows) or at different time points (less-overlapped) may affect the topological and spatial properties of neuronal networks. Here, we explore the extreme cases of axon formation during early development, either starting at the same time for all neurons (parallel, i.e., maximally overlapped time windows) or occurring for each neuron separately one neuron after another (serial, i.e., no overlaps in time windows). For both cases, the number of potential and established synapses remained comparable. Topological and spatial properties, however, differed: Neurons that started axon growth early on in serial growth achieved higher out-degrees, higher local efficiency and longer axon lengths while neurons demonstrated more homogeneous connectivity patterns for parallel growth. Second, connection probability decreased more rapidly with distance between neurons for parallel growth than for serial growth. Third, bidirectional connections were more numerous for parallel growth. Finally, we tested our predictions with C. elegans data. Together, this indicates that time windows for axon growth influence the topological and spatial properties of neuronal networks opening up the possibility to a posteriori estimate developmental mechanisms based on network properties of a developed network.

Spiking Neurons for Analysis of Patterns

NASA Technical Reports Server (NTRS)

Huntsberger, Terrance

2008-01-01

Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern-analysis and pattern-recognition computational systems. These neurons are represented by a mathematical model denoted the state-variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets. Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a tree-like interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers. The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological neurons). These features enable the neurons to adapt their responses to high-rate inputs from sensors, and to adapt their firing thresholds to mitigate noise or effects of potential sensor failure. The mathematical derivation of the SVM starts from a prior model, known in the art as the point soma model, which captures all of the salient properties of neuronal response while keeping the computational cost low. The point-soma latency time is modified to be an exponentially decaying function of the strength of the applied potential. Choosing computational efficiency over biological fidelity, the dendrites surrounding a neuron are represented by simplified compartmental submodels and there are no dendritic spines. Updates to the dendritic potential, calcium-ion concentrations and conductances, and potassium-ion conductances are done by use of equations similar to those of the point soma. Diffusion processes in dendrites are modeled by averaging among nearest-neighbor compartments. Inputs to each of the dendritic compartments come from sensors. Alternatively or in addition, when an affected neuron is part of a pool, inputs can come from other spiking neurons. At present, SVM neural networks are implemented by computational simulation, using algorithms that encode the SVM and its submodels. However, it should be possible to implement these neural networks in hardware: The differential equations for the dendritic and cellular processes in the SVM model of spiking neurons map to equivalent circuits that can be implemented directly in analog very-large-scale integrated (VLSI) circuits.

Reliability and synchronization in a delay-coupled neuronal network with synaptic plasticity

NASA Astrophysics Data System (ADS)

Pérez, Toni; Uchida, Atsushi

2011-06-01

We investigate the characteristics of reliability and synchronization of a neuronal network of delay-coupled integrate and fire neurons. Reliability and synchronization appear in separated regions of the phase space of the parameters considered. The effect of including synaptic plasticity and different delay values between the connections are also considered. We found that plasticity strongly changes the characteristics of reliability and synchronization in the parameter space of the coupling strength and the drive amplitude for the neuronal network. We also found that delay does not affect the reliability of the network but has a determinant influence on the synchronization of the neurons.

Reducing Neuronal Networks to Discrete Dynamics

PubMed Central

Terman, David; Ahn, Sungwoo; Wang, Xueying; Just, Winfried

2008-01-01

We consider a general class of purely inhibitory and excitatory-inhibitory neuronal networks, with a general class of network architectures, and characterize the complex firing patterns that emerge. Our strategy for studying these networks is to first reduce them to a discrete model. In the discrete model, each neuron is represented as a finite number of states and there are rules for how a neuron transitions from one state to another. In this paper, we rigorously demonstrate that the continuous neuronal model can be reduced to the discrete model if the intrinsic and synaptic properties of the cells are chosen appropriately. In a companion paper [1], we analyze the discrete model. PMID:18443649

Prenatal stress and inhibitory neuron systems: implications for neuropsychiatric disorders

PubMed Central

Fine, Rebecca; Zhang, Jie; Stevens, Hanna E.

2014-01-01

Prenatal stress is a risk factor for several psychiatric disorders in which inhibitory neuron pathology is implicated. A growing body of research demonstrates that inhibitory circuitry in the brain is directly and persistently affected by prenatal stress. This review synthesizes research that elucidates how this early, developmental risk factor impacts inhibitory neurons and how these findings intersect with research on risk factors and inhibitory neuron pathophysiology in schizophrenia, anxiety, autism and Tourette syndrome. The specific impact of prenatal stress on inhibitory neurons, particularly developmental mechanisms, may elucidate further the pathophysiology of these disorders. PMID:24751963

Distinct transcriptomes define rostral and caudal serotonin neurons

PubMed Central

Wylie, Christi J.; Hendricks, Timothy J.; Zhang, Bing; Wang, Lily; Lu, Pengcheng; Leahy, Patrick; Fox, Stephanie; Maeno, Hiroshi; Deneris, Evan S.

2012-01-01

The molecular architecture of developing serotonin (5HT) neurons is poorly understood yet its determination is likely to be essential for elucidating functional heterogeneity of these cells and the contribution of serotonergic dysfunction to disease pathogenesis. Here, we describe the purification of postmitotic embryonic 5HT neurons by flow cytometry for whole genome microarray expression profiling of this unitary monoaminergic neuron type. Our studies identified significantly enriched expression of hundreds of unique genes in 5HT neurons thus providing an abundance of new serotonergic markers. Furthermore, we identified several hundred transcripts encoding homeodomain, axon guidance, cell adhesion, intracellular signaling, ion transport, and imprinted genes associated with various neurodevelopmental disorders that were differentially enriched in developing rostral and caudal 5HT neurons. These findings suggested a homeodomain code that distinguishes rostral and caudal 5HT neurons. Indeed, verification studies demonstrated that Hmx homeodomain and Hox gene expression defined an Hmx+ rostral subtype and Hox+ caudal subtype. Expression of engrailed genes in a subset of 5HT neurons in the rostral domain further distinguished two subtypes defined as Hmx+En+ and Hmx+En-. The differential enrichment of gene sets for different canonical pathways and gene ontology categories provided additional evidence for heterogeneity between rostral and caudal 5HT neurons. These findings demonstrate a deep transcriptome and biological pathway duality for neurons that give rise to the ascending and descending serotonergic subsystems. Our databases provide a rich, clinically relevant, resource for definition of 5HT neuron subtypes and elucidation of the genetic networks required for serotonergic function. PMID:20071532

Effects of channel noise on firing coherence of small-world Hodgkin-Huxley neuronal networks

NASA Astrophysics Data System (ADS)

Sun, X. J.; Lei, J. Z.; Perc, M.; Lu, Q. S.; Lv, S. J.

2011-01-01

We investigate the effects of channel noise on firing coherence of Watts-Strogatz small-world networks consisting of biophysically realistic HH neurons having a fraction of blocked voltage-gated sodium and potassium ion channels embedded in their neuronal membranes. The intensity of channel noise is determined by the number of non-blocked ion channels, which depends on the fraction of working ion channels and the membrane patch size with the assumption of homogeneous ion channel density. We find that firing coherence of the neuronal network can be either enhanced or reduced depending on the source of channel noise. As shown in this paper, sodium channel noise reduces firing coherence of neuronal networks; in contrast, potassium channel noise enhances it. Furthermore, compared with potassium channel noise, sodium channel noise plays a dominant role in affecting firing coherence of the neuronal network. Moreover, we declare that the observed phenomena are independent of the rewiring probability.

Study on algorithm of process neural network for soft sensing in sewage disposal system

NASA Astrophysics Data System (ADS)

Liu, Zaiwen; Xue, Hong; Wang, Xiaoyi; Yang, Bin; Lu, Siying

2006-11-01

A new method of soft sensing based on process neural network (PNN) for sewage disposal system is represented in the paper. PNN is an extension of traditional neural network, in which the inputs and outputs are time-variation. An aggregation operator is introduced to process neuron, and it makes the neuron network has the ability to deal with the information of space-time two dimensions at the same time, so the data processing enginery of biological neuron is imitated better than traditional neuron. Process neural network with the structure of three layers in which hidden layer is process neuron and input and output are common neurons for soft sensing is discussed. The intelligent soft sensing based on PNN may be used to fulfill measurement of the effluent BOD (Biochemical Oxygen Demand) from sewage disposal system, and a good training result of soft sensing was obtained by the method.

A combined Bodian-Nissl stain for improved network analysis in neuronal cell culture.

PubMed

Hightower, M; Gross, G W

1985-11-01

Bodian and Nissl procedures were combined to stain dissociated mouse spinal cord cells cultured on coverslips. The Bodian technique stains fine neuronal processes in great detail as well as an intracellular fibrillar network concentrated around the nucleus and in proximal neurites. The Nissl stain clearly delimits neuronal cytoplasm in somata and in large dendrites. A combination of these techniques allows the simultaneous depiction of neuronal perikarya and all afferent and efferent processes. Costaining with little background staining by either procedure suggests high specificity for neurons. This procedure could be exploited for routine network analysis of cultured neurons.

Cortical Dynamics in Presence of Assemblies of Densely Connected Weight-Hub Neurons

PubMed Central

Setareh, Hesam; Deger, Moritz; Petersen, Carl C. H.; Gerstner, Wulfram

2017-01-01

Experimental measurements of pairwise connection probability of pyramidal neurons together with the distribution of synaptic weights have been used to construct randomly connected model networks. However, several experimental studies suggest that both wiring and synaptic weight structure between neurons show statistics that differ from random networks. Here we study a network containing a subset of neurons which we call weight-hub neurons, that are characterized by strong inward synapses. We propose a connectivity structure for excitatory neurons that contain assemblies of densely connected weight-hub neurons, while the pairwise connection probability and synaptic weight distribution remain consistent with experimental data. Simulations of such a network with generalized integrate-and-fire neurons display regular and irregular slow oscillations akin to experimentally observed up/down state transitions in the activity of cortical neurons with a broad distribution of pairwise spike correlations. Moreover, stimulation of a model network in the presence or absence of assembly structure exhibits responses similar to light-evoked responses of cortical layers in optogenetically modified animals. We conclude that a high connection probability into and within assemblies of excitatory weight-hub neurons, as it likely is present in some but not all cortical layers, changes the dynamics of a layer of cortical microcircuitry significantly. PMID:28690508

The Role of Adult-Born Neurons in the Constantly Changing Olfactory Bulb Network

PubMed Central

Malvaut, Sarah; Saghatelyan, Armen

2016-01-01

The adult mammalian brain is remarkably plastic and constantly undergoes structurofunctional modifications in response to environmental stimuli. In many regions plasticity is manifested by modifications in the efficacy of existing synaptic connections or synapse formation and elimination. In a few regions, however, plasticity is brought by the addition of new neurons that integrate into established neuronal networks. This type of neuronal plasticity is particularly prominent in the olfactory bulb (OB) where thousands of neuronal progenitors are produced on a daily basis in the subventricular zone (SVZ) and migrate along the rostral migratory stream (RMS) towards the OB. In the OB, these neuronal precursors differentiate into local interneurons, mature, and functionally integrate into the bulbar network by establishing output synapses with principal neurons. Despite continuous progress, it is still not well understood how normal functioning of the OB is preserved in the constantly remodelling bulbar network and what role adult-born neurons play in odor behaviour. In this review we will discuss different levels of morphofunctional plasticity effected by adult-born neurons and their functional role in the adult OB and also highlight the possibility that different subpopulations of adult-born cells may fulfill distinct functions in the OB neuronal network and odor behaviour. PMID:26839709

The frequency preference of neurons and synapses in a recurrent oscillatory network.

PubMed

Tseng, Hua-an; Martinez, Diana; Nadim, Farzan

2014-09-17

A variety of neurons and synapses shows a maximal response at a preferred frequency, generally considered to be important in shaping network activity. We are interested in whether all neurons and synapses in a recurrent oscillatory network can have preferred frequencies and, if so, whether these frequencies are the same or correlated, and whether they influence the network activity. We address this question using identified neurons in the pyloric network of the crab Cancer borealis. Previous work has shown that the pyloric pacemaker neurons exhibit membrane potential resonance whose resonance frequency is correlated with the network frequency. The follower lateral pyloric (LP) neuron makes reciprocally inhibitory synapses with the pacemakers. We find that LP shows resonance at a higher frequency than the pacemakers and the network frequency falls between the two. We also find that the reciprocal synapses between the pacemakers and LP have preferred frequencies but at significantly lower values. The preferred frequency of the LP to pacemaker synapse is correlated with the presynaptic preferred frequency, which is most pronounced when the peak voltage of the LP waveform is within the dynamic range of the synaptic activation curve and a shift in the activation curve by the modulatory neuropeptide proctolin shifts the frequency preference. Proctolin also changes the power of the LP neuron resonance without significantly changing the resonance frequency. These results indicate that different neuron types and synapses in a network may have distinct preferred frequencies, which are subject to neuromodulation and may interact to shape network oscillations. Copyright © 2014 the authors 0270-6474/14/3412933-13$15.00/0.

Neural computational modeling reveals a major role of corticospinal gating of central oscillations in the generation of essential tremor.

PubMed

Qu, Hong-En; Niu, Chuanxin M; Li, Si; Hao, Man-Zhao; Hu, Zi-Xiang; Xie, Qing; Lan, Ning

2017-12-01

Essential tremor, also referred to as familial tremor, is an autosomal dominant genetic disease and the most common movement disorder. It typically involves a postural and motor tremor of the hands, head or other part of the body. Essential tremor is driven by a central oscillation signal in the brain. However, the corticospinal mechanisms involved in the generation of essential tremor are unclear. Therefore, in this study, we used a neural computational model that includes both monosynaptic and multisynaptic corticospinal pathways interacting with a propriospinal neuronal network. A virtual arm model is driven by the central oscillation signal to simulate tremor activity behavior. Cortical descending commands are classified as alpha or gamma through monosynaptic or multisynaptic corticospinal pathways, which converge respectively on alpha or gamma motoneurons in the spinal cord. Several scenarios are evaluated based on the central oscillation signal passing down to the spinal motoneurons via each descending pathway. The simulated behaviors are compared with clinical essential tremor characteristics to identify the corticospinal pathways responsible for transmitting the central oscillation signal. A propriospinal neuron with strong cortical inhibition performs a gating function in the generation of essential tremor. Our results indicate that the propriospinal neuronal network is essential for relaying the central oscillation signal and the production of essential tremor.

On the Dynamics of the Spontaneous Activity in Neuronal Networks

PubMed Central

Bonifazi, Paolo; Ruaro, Maria Elisabetta; Torre, Vincent

2007-01-01

Most neuronal networks, even in the absence of external stimuli, produce spontaneous bursts of spikes separated by periods of reduced activity. The origin and functional role of these neuronal events are still unclear. The present work shows that the spontaneous activity of two very different networks, intact leech ganglia and dissociated cultures of rat hippocampal neurons, share several features. Indeed, in both networks: i) the inter-spike intervals distribution of the spontaneous firing of single neurons is either regular or periodic or bursting, with the fraction of bursting neurons depending on the network activity; ii) bursts of spontaneous spikes have the same broad distributions of size and duration; iii) the degree of correlated activity increases with the bin width, and the power spectrum of the network firing rate has a 1/f behavior at low frequencies, indicating the existence of long-range temporal correlations; iv) the activity of excitatory synaptic pathways mediated by NMDA receptors is necessary for the onset of the long-range correlations and for the presence of large bursts; v) blockage of inhibitory synaptic pathways mediated by GABAA receptors causes instead an increase in the correlation among neurons and leads to a burst distribution composed only of very small and very large bursts. These results suggest that the spontaneous electrical activity in neuronal networks with different architectures and functions can have very similar properties and common dynamics. PMID:17502919

Tau-Dependent Kv4.2 Depletion and Dendritic Hyperexcitability in a Mouse Model of Alzheimer's Disease

PubMed Central

Hall, Alicia M.; Throesch, Benjamin T.; Buckingham, Susan C.; Markwardt, Sean J.; Peng, Yin; Wang, Qin

2015-01-01

Neuronal hyperexcitability occurs early in the pathogenesis of Alzheimer's disease (AD) and contributes to network dysfunction in AD patients. In other disorders with neuronal hyperexcitability, dysfunction in the dendrites often contributes, but dendritic excitability has not been directly examined in AD models. We used dendritic patch-clamp recordings to measure dendritic excitability in the CA1 region of the hippocampus. We found that dendrites, more so than somata, of hippocampal neurons were hyperexcitable in mice overexpressing Aβ. This dendritic hyperexcitability was associated with depletion of Kv4.2, a dendritic potassium channel important for regulating dendritic excitability and synaptic plasticity. The antiepileptic drug, levetiracetam, blocked Kv4.2 depletion. Tau was required, as crossing with tau knock-out mice also prevented both Kv4.2 depletion and dendritic hyperexcitability. Dendritic hyperexcitability induced by Kv4.2 deficiency exacerbated behavioral deficits and increased epileptiform activity in hAPP mice. We conclude that increased dendritic excitability, associated with changes in dendritic ion channels including Kv4.2, may contribute to neuronal dysfunction in early stages AD. PMID:25878292

Numbers And Gains Of Neurons In Winner-Take-All Networks

NASA Technical Reports Server (NTRS)

Brown, Timothy X.

1993-01-01

Report presents theoretical study of gains required in neurons to implement winner-take-all electronic neural network of given size and related question of maximum size of winner-take-all network in which neurons have specified sigmoid transfer or response function with specified gain.

Simultaneous stability and sensitivity in model cortical networks is achieved through anti-correlations between the in- and out-degree of connectivity

PubMed Central

Vasquez, Juan C.; Houweling, Arthur R.; Tiesinga, Paul

2013-01-01

Neuronal networks in rodent barrel cortex are characterized by stable low baseline firing rates. However, they are sensitive to the action potentials of single neurons as suggested by recent single-cell stimulation experiments that reported quantifiable behavioral responses in response to short spike trains elicited in single neurons. Hence, these networks are stable against internally generated fluctuations in firing rate but at the same time remain sensitive to similarly-sized externally induced perturbations. We investigated stability and sensitivity in a simple recurrent network of stochastic binary neurons and determined numerically the effects of correlation between the number of afferent (“in-degree”) and efferent (“out-degree”) connections in neurons. The key advance reported in this work is that anti-correlation between in-/out-degree distributions increased the stability of the network in comparison to networks with no correlation or positive correlations, while being able to achieve the same level of sensitivity. The experimental characterization of degree distributions is difficult because all pre-synaptic and post-synaptic neurons have to be identified and counted. We explored whether the statistics of network motifs, which requires the characterization of connections between small subsets of neurons, could be used to detect evidence for degree anti-correlations. We find that the sample frequency of the 3-neuron “ring” motif (1→2→3→1), can be used to detect degree anti-correlation for sub-networks of size 30 using about 50 samples, which is of significance because the necessary measurements are achievable experimentally in the near future. Taken together, we hypothesize that barrel cortex networks exhibit degree anti-correlations and specific network motif statistics. PMID:24223550

The Functional Networks of Prepulse Inhibition: Neuronal Connectivity Analysis Based on FDG-PET in Awake and Unrestrained Rats.

PubMed

Rohleder, Cathrin; Wiedermann, Dirk; Neumaier, Bernd; Drzezga, Alexander; Timmermann, Lars; Graf, Rudolf; Leweke, F Markus; Endepols, Heike

2016-01-01

Prepulse inhibition (PPI) is a neuropsychological process during which a weak sensory stimulus ("prepulse") attenuates the motor response ("startle reaction") to a subsequent strong startling stimulus. It is measured as a surrogate marker of sensorimotor gating in patients suffering from neuropsychological diseases such as schizophrenia, as well as in corresponding animal models. A variety of studies has shown that PPI of the acoustical startle reaction comprises three brain circuitries for: (i) startle mediation, (ii) PPI mediation, and (iii) modulation of PPI mediation. While anatomical connections and information flow in the startle and PPI mediation pathways are well known, spatial and temporal interactions of the numerous regions involved in PPI modulation are incompletely understood. We therefore combined [(18)F]fluoro-2-deoxyglucose positron-emission-tomography (FDG-PET) with PPI and resting state control paradigms in awake rats. A battery of subtractive, correlative as well as seed-based functional connectivity analyses revealed a default mode-like network (DMN) active during resting state only. Furthermore, two functional networks were observed during PPI: Metabolic activity in the lateral circuitry was positively correlated with PPI effectiveness and involved the auditory system and emotional regions. The medial network was negatively correlated with PPI effectiveness, i.e., associated with startle, and recruited a spatial/cognitive network. Our study provides evidence for two distinct neuronal networks, whose continuous interplay determines PPI effectiveness in rats, probably by either protecting the prepulse or facilitating startle processing. Discovering similar networks affected in neuropsychological disorders may help to better understand mechanisms of sensorimotor gating deficits and provide new perspectives for therapeutic strategies.

GaAs Optoelectronic Integrated-Circuit Neurons

NASA Technical Reports Server (NTRS)

Lin, Steven H.; Kim, Jae H.; Psaltis, Demetri

1992-01-01

Monolithic GaAs optoelectronic integrated circuits developed for use as artificial neurons. Neural-network computer contains planar arrays of optoelectronic neurons, and variable synaptic connections between neurons effected by diffraction of light from volume hologram in photorefractive material. Basic principles of neural-network computers explained more fully in "Optoelectronic Integrated Circuits For Neural Networks" (NPO-17652). In present circuits, devices replaced by metal/semiconductor field effect transistors (MESFET's), which consume less power.

Leader neurons in leaky integrate and fire neural network simulations.

PubMed

Zbinden, Cyrille

2011-10-01

In this paper, we highlight the topological properties of leader neurons whose existence is an experimental fact. Several experimental studies show the existence of leader neurons in population bursts of activity in 2D living neural networks (Eytan and Marom, J Neurosci 26(33):8465-8476, 2006; Eckmann et al., New J Phys 10(015011), 2008). A leader neuron is defined as a neuron which fires at the beginning of a burst (respectively network spike) more often than we expect by chance considering its mean firing rate. This means that leader neurons have some burst triggering power beyond a chance-level statistical effect. In this study, we characterize these leader neuron properties. This naturally leads us to simulate neural 2D networks. To build our simulations, we choose the leaky integrate and fire (lIF) neuron model (Gerstner and Kistler 2002; Cessac, J Math Biol 56(3):311-345, 2008), which allows fast simulations (Izhikevich, IEEE Trans Neural Netw 15(5):1063-1070, 2004; Gerstner and Naud, Science 326:379-380, 2009). The dynamics of our lIF model has got stable leader neurons in the burst population that we simulate. These leader neurons are excitatory neurons and have a low membrane potential firing threshold. Except for these two first properties, the conditions required for a neuron to be a leader neuron are difficult to identify and seem to depend on several parameters involved in the simulations themselves. However, a detailed linear analysis shows a trend of the properties required for a neuron to be a leader neuron. Our main finding is: A leader neuron sends signals to many excitatory neurons as well as to few inhibitory neurons and a leader neuron receives only signals from few other excitatory neurons. Our linear analysis exhibits five essential properties of leader neurons each with different relative importance. This means that considering a given neural network with a fixed mean number of connections per neuron, our analysis gives us a way of predicting which neuron is a good leader neuron and which is not. Our prediction formula correctly assesses leadership for at least ninety percent of neurons.

Adaptive Neurons For Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Tawel, Raoul

1990-01-01

Training time decreases dramatically. In improved mathematical model of neural-network processor, temperature of neurons (in addition to connection strengths, also called weights, of synapses) varied during supervised-learning phase of operation according to mathematical formalism and not heuristic rule. Evidence that biological neural networks also process information at neuronal level.

Intrinsic protective mechanisms of the neuron-glia network against glioma invasion.

PubMed

Iwadate, Yasuo; Fukuda, Kazumasa; Matsutani, Tomoo; Saeki, Naokatsu

2016-04-01

Gliomas arising in the brain parenchyma infiltrate into the surrounding brain and break down established complex neuron-glia networks. However, mounting evidence suggests that initially the network microenvironment of the adult central nervous system (CNS) is innately non-permissive to glioma cell invasion. The main players are inhibitory molecules in CNS myelin, as well as proteoglycans associated with astrocytes. Neural stem cells, and neurons themselves, possess inhibitory functions against neighboring tumor cells. These mechanisms have evolved to protect the established neuron-glia network, which is necessary for brain function. Greater insight into the interaction between glioma cells and the surrounding neuron-glia network is crucial for developing new therapies for treating these devastating tumors while preserving the important and complex neural functions of patients. Copyright © 2015 Elsevier Ltd. All rights reserved.

Network synchronization in hippocampal neurons.

PubMed

Penn, Yaron; Segal, Menahem; Moses, Elisha

2016-03-22

Oscillatory activity is widespread in dynamic neuronal networks. The main paradigm for the origin of periodicity consists of specialized pacemaking elements that synchronize and drive the rest of the network; however, other models exist. Here, we studied the spontaneous emergence of synchronized periodic bursting in a network of cultured dissociated neurons from rat hippocampus and cortex. Surprisingly, about 60% of all active neurons were self-sustained oscillators when disconnected, each with its own natural frequency. The individual neuron's tendency to oscillate and the corresponding oscillation frequency are controlled by its excitability. The single neuron intrinsic oscillations were blocked by riluzole, and are thus dependent on persistent sodium leak currents. Upon a gradual retrieval of connectivity, the synchrony evolves: Loose synchrony appears already at weak connectivity, with the oscillators converging to one common oscillation frequency, yet shifted in phase across the population. Further strengthening of the connectivity causes a reduction in the mean phase shifts until zero-lag is achieved, manifested by synchronous periodic network bursts. Interestingly, the frequency of network bursting matches the average of the intrinsic frequencies. Overall, the network behaves like other universal systems, where order emerges spontaneously by entrainment of independent rhythmic units. Although simplified with respect to circuitry in the brain, our results attribute a basic functional role for intrinsic single neuron excitability mechanisms in driving the network's activity and dynamics, contributing to our understanding of developing neural circuits.

Temporal coding in a silicon network of integrate-and-fire neurons.

PubMed

Liu, Shih-Chii; Douglas, Rodney

2004-09-01

Spatio-temporal processing of spike trains by neuronal networks depends on a variety of mechanisms distributed across synapses, dendrites, and somata. In natural systems, the spike trains and the processing mechanisms cohere though their common physical instantiation. This coherence is lost when the natural system is encoded for simulation on a general purpose computer. By contrast, analog VLSI circuits are, like neurons, inherently related by their real-time physics, and so, could provide a useful substrate for exploring neuronlike event-based processing. Here, we describe a hybrid analog-digital VLSI chip comprising a set of integrate-and-fire neurons and short-term dynamical synapses that can be configured into simple network architectures with some properties of neocortical neuronal circuits. We show that, despite considerable fabrication variance in the properties of individual neurons, the chip offers a viable substrate for exploring real-time spike-based processing in networks of neurons.

Causal Interrogation of Neuronal Networks and Behavior through Virally Transduced Ivermectin Receptors.

PubMed

Obenhaus, Horst A; Rozov, Andrei; Bertocchi, Ilaria; Tang, Wannan; Kirsch, Joachim; Betz, Heinrich; Sprengel, Rolf

2016-01-01

The causal interrogation of neuronal networks involved in specific behaviors requires the spatially and temporally controlled modulation of neuronal activity. For long-term manipulation of neuronal activity, chemogenetic tools provide a reasonable alternative to short-term optogenetic approaches. Here we show that virus mediated gene transfer of the ivermectin (IVM) activated glycine receptor mutant GlyRα1 (AG) can be used for the selective and reversible silencing of specific neuronal networks in mice. In the striatum, dorsal hippocampus, and olfactory bulb, GlyRα1 (AG) promoted IVM dependent effects in representative behavioral assays. Moreover, GlyRα1 (AG) mediated silencing had a strong and reversible impact on neuronal ensemble activity and c-Fos activation in the olfactory bulb. Together our results demonstrate that long-term, reversible and re-inducible neuronal silencing via GlyRα1 (AG) is a promising tool for the interrogation of network mechanisms underlying the control of behavior and memory formation.

The emergence of spontaneous activity in neuronal cultures

NASA Astrophysics Data System (ADS)

Orlandi, J. G.; Alvarez-Lacalle, E.; Teller, S.; Soriano, J.; Casademunt, J.

2013-01-01

In vitro neuronal networks of dissociated hippocampal or cortical tissues are one of the most attractive model systems for the physics and neuroscience communities. Cultured neurons grow and mature, develop axons and dendrites, and quickly connect to their neighbors to establish a spontaneously active network within a week. The resulting neuronal network is characterized by a combination of excitatory and inhibitory neurons coupled through synaptic connections that interact in a highly nonlinear manner. The nonlinear behavior emerges from the dynamics of both the neurons' spiking activity and synaptic transmission, together with biological noise. These ingredients give rise to a rich repertoire of phenomena that are still poorly understood, including the emergence and maintenance of periodic spontaneous activity, avalanches, propagation of fronts and synchronization. In this work we present an overview on the rich activity of cultured neuronal networks, and detail the minimal theoretical considerations needed to describe experimental observations.

Synchronization in neural nets

NASA Technical Reports Server (NTRS)

Vidal, Jacques J.; Haggerty, John

1988-01-01

The paper presents an artificial neural network concept (the Synchronizable Oscillator Networks) where the instants of individual firings in the form of point processes constitute the only form of information transmitted between joining neurons. In the model, neurons fire spontaneously and regularly in the absence of perturbation. When interaction is present, the scheduled firings are advanced or delayed by the firing of neighboring neurons. Networks of such neurons become global oscillators which exhibit multiple synchronizing attractors. From arbitrary initial states, energy minimization learning procedures can make the network converge to oscillatory modes that satisfy multi-dimensional constraints. Such networks can directly represent routing and scheduling problems that consist of ordering sequences of events.

Neurotechnology: a new approach for treating brain disorders.

PubMed

Robson, John A; Davenport, R John

2014-05-01

Advances in neuroscience, engineering and computer technologies are creating opportunities to connect the brain directly to devices to treat a variety of disorders, both neurological and psychiatric. They are opening a new field of neuroscience called "neurotechnology." This article reviews efforts in this area that are ongoing at Brown University and the hospitals affiliated with Brown's Alpert Medical School. Two general approaches are being used. One uses advanced electrodes to "sense" the activity of many individual neurons in the cerebral cortex and then use that activity for therapeutic purposes. The other uses various types of devices to stimulate specific networks in the brain in order to restore normal function and alleviate symptoms.

Intrinsic Neuronal Properties Switch the Mode of Information Transmission in Networks

PubMed Central

Gjorgjieva, Julijana; Mease, Rebecca A.; Moody, William J.; Fairhall, Adrienne L.

2014-01-01

Diverse ion channels and their dynamics endow single neurons with complex biophysical properties. These properties determine the heterogeneity of cell types that make up the brain, as constituents of neural circuits tuned to perform highly specific computations. How do biophysical properties of single neurons impact network function? We study a set of biophysical properties that emerge in cortical neurons during the first week of development, eventually allowing these neurons to adaptively scale the gain of their response to the amplitude of the fluctuations they encounter. During the same time period, these same neurons participate in large-scale waves of spontaneously generated electrical activity. We investigate the potential role of experimentally observed changes in intrinsic neuronal properties in determining the ability of cortical networks to propagate waves of activity. We show that such changes can strongly affect the ability of multi-layered feedforward networks to represent and transmit information on multiple timescales. With properties modeled on those observed at early stages of development, neurons are relatively insensitive to rapid fluctuations and tend to fire synchronously in response to wave-like events of large amplitude. Following developmental changes in voltage-dependent conductances, these same neurons become efficient encoders of fast input fluctuations over few layers, but lose the ability to transmit slower, population-wide input variations across many layers. Depending on the neurons' intrinsic properties, noise plays different roles in modulating neuronal input-output curves, which can dramatically impact network transmission. The developmental change in intrinsic properties supports a transformation of a networks function from the propagation of network-wide information to one in which computations are scaled to local activity. This work underscores the significance of simple changes in conductance parameters in governing how neurons represent and propagate information, and suggests a role for background synaptic noise in switching the mode of information transmission. PMID:25474701

Replicating receptive fields of simple and complex cells in primary visual cortex in a neuronal network model with temporal and population sparseness and reliability.

PubMed

Tanaka, Takuma; Aoyagi, Toshio; Kaneko, Takeshi

2012-10-01

We propose a new principle for replicating receptive field properties of neurons in the primary visual cortex. We derive a learning rule for a feedforward network, which maintains a low firing rate for the output neurons (resulting in temporal sparseness) and allows only a small subset of the neurons in the network to fire at any given time (resulting in population sparseness). Our learning rule also sets the firing rates of the output neurons at each time step to near-maximum or near-minimum levels, resulting in neuronal reliability. The learning rule is simple enough to be written in spatially and temporally local forms. After the learning stage is performed using input image patches of natural scenes, output neurons in the model network are found to exhibit simple-cell-like receptive field properties. When the output of these simple-cell-like neurons are input to another model layer using the same learning rule, the second-layer output neurons after learning become less sensitive to the phase of gratings than the simple-cell-like input neurons. In particular, some of the second-layer output neurons become completely phase invariant, owing to the convergence of the connections from first-layer neurons with similar orientation selectivity to second-layer neurons in the model network. We examine the parameter dependencies of the receptive field properties of the model neurons after learning and discuss their biological implications. We also show that the localized learning rule is consistent with experimental results concerning neuronal plasticity and can replicate the receptive fields of simple and complex cells.

Synchronization transition in neuronal networks composed of chaotic or non-chaotic oscillators.

PubMed

Xu, Kesheng; Maidana, Jean Paul; Castro, Samy; Orio, Patricio

2018-05-30

Chaotic dynamics has been shown in the dynamics of neurons and neural networks, in experimental data and numerical simulations. Theoretical studies have proposed an underlying role of chaos in neural systems. Nevertheless, whether chaotic neural oscillators make a significant contribution to network behaviour and whether the dynamical richness of neural networks is sensitive to the dynamics of isolated neurons, still remain open questions. We investigated synchronization transitions in heterogeneous neural networks of neurons connected by electrical coupling in a small world topology. The nodes in our model are oscillatory neurons that - when isolated - can exhibit either chaotic or non-chaotic behaviour, depending on conductance parameters. We found that the heterogeneity of firing rates and firing patterns make a greater contribution than chaos to the steepness of the synchronization transition curve. We also show that chaotic dynamics of the isolated neurons do not always make a visible difference in the transition to full synchrony. Moreover, macroscopic chaos is observed regardless of the dynamics nature of the neurons. However, performing a Functional Connectivity Dynamics analysis, we show that chaotic nodes can promote what is known as multi-stable behaviour, where the network dynamically switches between a number of different semi-synchronized, metastable states.

Biological conservation law as an emerging functionality in dynamical neuronal networks.

PubMed

Podobnik, Boris; Jusup, Marko; Tiganj, Zoran; Wang, Wen-Xu; Buldú, Javier M; Stanley, H Eugene

2017-11-07

Scientists strive to understand how functionalities, such as conservation laws, emerge in complex systems. Living complex systems in particular create high-ordered functionalities by pairing up low-ordered complementary processes, e.g., one process to build and the other to correct. We propose a network mechanism that demonstrates how collective statistical laws can emerge at a macro (i.e., whole-network) level even when they do not exist at a unit (i.e., network-node) level. Drawing inspiration from neuroscience, we model a highly stylized dynamical neuronal network in which neurons fire either randomly or in response to the firing of neighboring neurons. A synapse connecting two neighboring neurons strengthens when both of these neurons are excited and weakens otherwise. We demonstrate that during this interplay between the synaptic and neuronal dynamics, when the network is near a critical point, both recurrent spontaneous and stimulated phase transitions enable the phase-dependent processes to replace each other and spontaneously generate a statistical conservation law-the conservation of synaptic strength. This conservation law is an emerging functionality selected by evolution and is thus a form of biological self-organized criticality in which the key dynamical modes are collective.

Biological conservation law as an emerging functionality in dynamical neuronal networks

PubMed Central

Podobnik, Boris; Tiganj, Zoran; Wang, Wen-Xu; Buldú, Javier M.

2017-01-01

Scientists strive to understand how functionalities, such as conservation laws, emerge in complex systems. Living complex systems in particular create high-ordered functionalities by pairing up low-ordered complementary processes, e.g., one process to build and the other to correct. We propose a network mechanism that demonstrates how collective statistical laws can emerge at a macro (i.e., whole-network) level even when they do not exist at a unit (i.e., network-node) level. Drawing inspiration from neuroscience, we model a highly stylized dynamical neuronal network in which neurons fire either randomly or in response to the firing of neighboring neurons. A synapse connecting two neighboring neurons strengthens when both of these neurons are excited and weakens otherwise. We demonstrate that during this interplay between the synaptic and neuronal dynamics, when the network is near a critical point, both recurrent spontaneous and stimulated phase transitions enable the phase-dependent processes to replace each other and spontaneously generate a statistical conservation law—the conservation of synaptic strength. This conservation law is an emerging functionality selected by evolution and is thus a form of biological self-organized criticality in which the key dynamical modes are collective. PMID:29078286

Molecular changes in brain aging and Alzheimer’s disease are mirrored in experimentally silenced cortical neuron networks

PubMed Central

Gleichmann, Marc; Zhang, Yongqing; Wood, William H.; Becker, Kevin G.; Mughal, Mohamed R.; Pazin, Michael J.; van Praag, Henriette; Kobilo, Tali; Zonderman, Alan B.; Troncoso, Juan C.; Markesbery, William R.; Mattson, Mark P.

2010-01-01

Activity-dependent modulation of neuronal gene expression promotes neuronal survival and plasticity, and neuronal network activity is perturbed in aging and Alzheimer’s disease (AD). Here we show that cerebral cortical neurons respond to chronic suppression of excitability by downregulating the expression of genes and their encoded proteins involved in inhibitory transmission (GABAergic and somatostatin) and Ca2+ signaling; alterations in pathways involved in lipid metabolism and energy management are also features of silenced neuronal networks. A molecular fingerprint strikingly similar to that of diminished network activity occurs in the human brain during aging and in AD, and opposite changes occur in response to activation of N-methyl-D-aspartate (NMDA) and brain-derived neurotrophic factor (BDNF) receptors in cultured cortical neurons and in mice in response to an enriched environment or electroconvulsive shock. Our findings suggest that reduced inhibitory neurotransmission during aging and in AD may be the result of compensatory responses that, paradoxically, render the neurons vulnerable to Ca2+-mediated degeneration. PMID:20947216

Stochastic multiresonance in coupled excitable FHN neurons

NASA Astrophysics Data System (ADS)

Li, Huiyan; Sun, Xiaojuan; Xiao, Jinghua

2018-04-01

In this paper, effects of noise on Watts-Strogatz small-world neuronal networks, which are stimulated by a subthreshold signal, have been investigated. With the numerical simulations, it is surprisingly found that there exist several optimal noise intensities at which the subthreshold signal can be detected efficiently. This indicates the occurrence of stochastic multiresonance in the studied neuronal networks. Moreover, it is revealed that the occurrence of stochastic multiresonance has close relationship with the period of subthreshold signal Te and the noise-induced mean period of the neuronal networks T0. In detail, we find that noise could induce the neuronal networks to generate stochastic resonance for M times if Te is not very large and falls into the interval ( M × T 0 , ( M + 1 ) × T 0 ) with M being a positive integer. In real neuronal system, subthreshold signal detection is very meaningful. Thus, the obtained results in this paper could give some important implications on detecting subthreshold signal and propagating neuronal information in neuronal systems.

The circadian rhythm induced by the heterogeneous network structure of the suprachiasmatic nucleus

NASA Astrophysics Data System (ADS)

Gu, Changgui; Yang, Huijie

2016-05-01

In mammals, the master clock is located in the suprachiasmatic nucleus (SCN), which is composed of about 20 000 nonidentical neuronal oscillators expressing different intrinsic periods. These neurons are coupled through neurotransmitters to form a network consisting of two subgroups, i.e., a ventrolateral (VL) subgroup and a dorsomedial (DM) subgroup. The VL contains about 25% SCN neurons that receive photic input from the retina, and the DM comprises the remaining 75% SCN neurons which are coupled to the VL. The synapses from the VL to the DM are evidently denser than that from the DM to the VL, in which the VL dominates the DM. Therefore, the SCN is a heterogeneous network where the neurons of the VL are linked with a large number of SCN neurons. In the present study, we mimicked the SCN network based on Goodwin model considering four types of networks including an all-to-all network, a Newman-Watts (NW) small world network, an Erdös-Rényi (ER) random network, and a Barabási-Albert (BA) scale free network. We found that the circadian rhythm was induced in the BA, ER, and NW networks, while the circadian rhythm was absent in the all-to-all network with weak cellular coupling, where the amplitude of the circadian rhythm is largest in the BA network which is most heterogeneous in the network structure. Our finding provides an alternative explanation for the induction or enhancement of circadian rhythm by the heterogeneity of the network structure.

Artificial astrocytes improve neural network performance.

PubMed

Porto-Pazos, Ana B; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

2011-04-19

Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

Artificial Astrocytes Improve Neural Network Performance

PubMed Central

Porto-Pazos, Ana B.; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

2011-01-01

Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function. PMID:21526157

A Hox regulatory network establishes motor neuron pool identity and target-muscle connectivity.

PubMed

Dasen, Jeremy S; Tice, Bonnie C; Brenner-Morton, Susan; Jessell, Thomas M

2005-11-04

Spinal motor neurons acquire specialized "pool" identities that determine their ability to form selective connections with target muscles in the limb, but the molecular basis of this striking example of neuronal specificity has remained unclear. We show here that a Hox transcriptional regulatory network specifies motor neuron pool identity and connectivity. Two interdependent sets of Hox regulatory interactions operate within motor neurons, one assigning rostrocaudal motor pool position and a second directing motor pool diversity at a single segmental level. This Hox regulatory network directs the downstream transcriptional identity of motor neuron pools and defines the pattern of target-muscle connectivity.

[Neurotransmission in developmental disorders].

PubMed

Takeuchi, Yoshihiro

2008-11-01

Attention deficit/hyperactivity disorder (AD/HD) is a heterogeneous developmental disorder with an etiology that is not fully understood. AD/HD has been considered to occur due to a disturbance in cathecholaminergic neurotransmission, with particular emphasis on dopamine. The neurotransmission of dopamine in subcortical regions such as the basal ganglia and limbic areas is synaptic; on the other hand, dopamine neurotransmission in the frontal cortex is quite different, because there are very few dopamine transporters (DAT) in the frontal cortex that allow dopamine to diffuse away from the dopamine synapse ("volume transmission"). It is now clear that noradrenergic neurons play a key regulatory role in dopaminergic function in the frontal cortex. Furthermore, serotonergic neurons exert an inhibitory effect on midbrain dopamine cell bodies, and they have an influence on dopamine release in terminal regions. There is accumulating neurobiological evidence pointing toward a role of the serotonin system in AD/HD. The etiology of autism spectrum disorders (ASD) is still unclear, but information from genetics, neuropathology, brain imaging, and basic neuroscience has provided insights into the understanding of this developmental disorder. In addition to abnormal circuitry in specific limbic and neocortical areas of the cerebral cortex, impairments in brainstem, cerebellar, thalamic, and basal ganglia connections have been reported. Numerous studies have pointed to abnormalities in serotonin and glutamate neurotransmission. Three important aspects involved in the pathophysiology of ASD have been proposed. The first is cell migration, the second is unbalanced excitatory-inhibitory networks, and the third is synapse formation and pruning, the key factors being reelin, neurexin, and neuroligin. Serotonin is considered to play an important role in all of these aspects of the pathophysiology of ASD. Finally, I would like to emphasize that it is crucial in the field of child neurology medical examination and treatment should be based on the basic neuroscience, always taking "neurons" into consideration.

Quantitative 3D investigation of Neuronal network in mouse spinal cord model

NASA Astrophysics Data System (ADS)

Bukreeva, I.; Campi, G.; Fratini, M.; Spanò, R.; Bucci, D.; Battaglia, G.; Giove, F.; Bravin, A.; Uccelli, A.; Venturi, C.; Mastrogiacomo, M.; Cedola, A.

2017-01-01

The investigation of the neuronal network in mouse spinal cord models represents the basis for the research on neurodegenerative diseases. In this framework, the quantitative analysis of the single elements in different districts is a crucial task. However, conventional 3D imaging techniques do not have enough spatial resolution and contrast to allow for a quantitative investigation of the neuronal network. Exploiting the high coherence and the high flux of synchrotron sources, X-ray Phase-Contrast multiscale-Tomography allows for the 3D investigation of the neuronal microanatomy without any aggressive sample preparation or sectioning. We investigated healthy-mouse neuronal architecture by imaging the 3D distribution of the neuronal-network with a spatial resolution of 640 nm. The high quality of the obtained images enables a quantitative study of the neuronal structure on a subject-by-subject basis. We developed and applied a spatial statistical analysis on the motor neurons to obtain quantitative information on their 3D arrangement in the healthy-mice spinal cord. Then, we compared the obtained results with a mouse model of multiple sclerosis. Our approach paves the way to the creation of a “database” for the characterization of the neuronal network main features for a comparative investigation of neurodegenerative diseases and therapies.

Synchronous firing patterns of induced pluripotent stem cell-derived cortical neurons depend on the network structure consisting of excitatory and inhibitory neurons.

PubMed

Iida, Shoko; Shimba, Kenta; Sakai, Koji; Kotani, Kiyoshi; Jimbo, Yasuhiko

2018-06-18

The balance between glutamate-mediated excitation and GABA-mediated inhibition is critical to cortical functioning. However, the contribution of network structure consisting of the both neurons to cortical functioning has not been elucidated. We aimed to evaluate the relationship between the network structure and functional activity patterns in vitro. We used mouse induced pluripotent stem cells (iPSCs) to construct three types of neuronal populations; excitatory-rich (Exc), inhibitory-rich (Inh), and control (Cont). Then, we analyzed the activity patterns of these neuronal populations using microelectrode arrays (MEAs). Inhibitory synaptic densities differed between the three types of iPSC-derived neuronal populations, and the neurons showed spontaneously synchronized bursting activity with functional maturation for one month. Moreover, different firing patterns were observed between the three populations; Exc demonstrated the highest firing rates, including frequent, long, and dominant bursts. In contrast, Inh demonstrated the lowest firing rates and the least dominant bursts. Synchronized bursts were enhanced by disinhibition via GABA A receptor blockade. The present study, using iPSC-derived neurons and MEAs, for the first time show that synchronized bursting of cortical networks in vitro depends on the network structure consisting of excitatory and inhibitory neurons. Copyright © 2018 Elsevier Inc. All rights reserved.

Inferring Single Neuron Properties in Conductance Based Balanced Networks

PubMed Central

Pool, Román Rossi; Mato, Germán

2011-01-01

Balanced states in large networks are a usual hypothesis for explaining the variability of neural activity in cortical systems. In this regime the statistics of the inputs is characterized by static and dynamic fluctuations. The dynamic fluctuations have a Gaussian distribution. Such statistics allows to use reverse correlation methods, by recording synaptic inputs and the spike trains of ongoing spontaneous activity without any additional input. By using this method, properties of the single neuron dynamics that are masked by the balanced state can be quantified. To show the feasibility of this approach we apply it to large networks of conductance based neurons. The networks are classified as Type I or Type II according to the bifurcations which neurons of the different populations undergo near the firing onset. We also analyze mixed networks, in which each population has a mixture of different neuronal types. We determine under which conditions the intrinsic noise generated by the network can be used to apply reverse correlation methods. We find that under realistic conditions we can ascertain with low error the types of neurons present in the network. We also find that data from neurons with similar firing rates can be combined to perform covariance analysis. We compare the results of these methods (that do not requite any external input) to the standard procedure (that requires the injection of Gaussian noise into a single neuron). We find a good agreement between the two procedures. PMID:22016730

Impact of delays on the synchronization transitions of modular neuronal networks with hybrid synapses

NASA Astrophysics Data System (ADS)

Liu, Chen; Wang, Jiang; Yu, Haitao; Deng, Bin; Wei, Xile; Tsang, Kaiming; Chan, Wailok

2013-09-01

The combined effects of the information transmission delay and the ratio of the electrical and chemical synapses on the synchronization transitions in the hybrid modular neuronal network are investigated in this paper. Numerical results show that the synchronization of neuron activities can be either promoted or destroyed as the information transmission delay increases, irrespective of the probability of electrical synapses in the hybrid-synaptic network. Interestingly, when the number of the electrical synapses exceeds a certain level, further increasing its proportion can obviously enhance the spatiotemporal synchronization transitions. Moreover, the coupling strength has a significant effect on the synchronization transition. The dominated type of the synapse always has a more profound effect on the emergency of the synchronous behaviors. Furthermore, the results of the modular neuronal network structures demonstrate that excessive partitioning of the modular network may result in the dramatic detriment of neuronal synchronization. Considering that information transmission delays are inevitable in intra- and inter-neuronal networks communication, the obtained results may have important implications for the exploration of the synchronization mechanism underlying several neural system diseases such as Parkinson's Disease.

Ergodic properties of spiking neuronal networks with delayed interactions

NASA Astrophysics Data System (ADS)

Palmigiano, Agostina; Wolf, Fred

The dynamical stability of neuronal networks, and the possibility of chaotic dynamics in the brain pose profound questions to the mechanisms underlying perception. Here we advance on the tractability of large neuronal networks of exactly solvable neuronal models with delayed pulse-coupled interactions. Pulse coupled delayed systems with an infinite dimensional phase space can be studied in equivalent systems of fixed and finite degrees of freedom by introducing a delayer variable for each neuron. A Jacobian of the equivalent system can be analytically obtained, and numerically evaluated. We find that depending on the action potential onset rapidness and the level of heterogeneities, the asynchronous irregular regime characteristic of balanced state networks loses stability with increasing delays to either a slow synchronous irregular or a fast synchronous irregular state. In networks of neurons with slow action potential onset, the transition to collective oscillations leads to an increase of the exponential rate of divergence of nearby trajectories and of the entropy production rate of the chaotic dynamics. The attractor dimension, instead of increasing linearly with increasing delay as reported in many other studies, decreases until eventually the network reaches full synchrony

The application of the multi-alternative approach in active neural network models

NASA Astrophysics Data System (ADS)

Podvalny, S.; Vasiljev, E.

2017-02-01

The article refers to the construction of intelligent systems based artificial neuron networks are used. We discuss the basic properties of the non-compliance of artificial neuron networks and their biological prototypes. It is shown here that the main reason for these discrepancies is the structural immutability of the neuron network models in the learning process, that is, their passivity. Based on the modern understanding of the biological nervous system as a structured ensemble of nerve cells, it is proposed to abandon the attempts to simulate its work at the level of the elementary neurons functioning processes and proceed to the reproduction of the information structure of data storage and processing on the basis of the general enough evolutionary principles of multialternativity, i.e. the multi-level structural model, diversity and modularity. The implementation method of these principles is offered, using the faceted memory organization in the neuron network with the rearranging active structure. An example of the implementation of the active facet-type neuron network in the intellectual decision-making system in the conditions of critical events development in the electrical distribution system.

Autapse-Induced Spiral Wave in Network of Neurons under Noise

PubMed Central

Qin, Huixin; Ma, Jun; Wang, Chunni; Wu, Ying

2014-01-01

Autapse plays an important role in regulating the electric activity of neuron by feedbacking time-delayed current on the membrane of neuron. Autapses are considered in a local area of regular network of neurons to investigate the development of spatiotemporal pattern, and emergence of spiral wave is observed while it fails to grow up and occupy the network completely. It is found that spiral wave can be induced to occupy more area in the network under optimized noise on the network with periodical or no-flux boundary condition being used. The developed spiral wave with self-sustained property can regulate the collective behaviors of neurons as a pacemaker. To detect the collective behaviors, a statistical factor of synchronization is calculated to investigate the emergence of ordered state in the network. The network keeps ordered state when self-sustained spiral wave is formed under noise and autapse in local area of network, and it independent of the selection of periodical or no-flux boundary condition. The developed stable spiral wave could be helpful for memory due to the distinct self-sustained property. PMID:24967577

An integrate-and-fire model for synchronized bursting in a network of cultured cortical neurons.

PubMed

French, D A; Gruenstein, E I

2006-12-01

It has been suggested that spontaneous synchronous neuronal activity is an essential step in the formation of functional networks in the central nervous system. The key features of this type of activity consist of bursts of action potentials with associated spikes of elevated cytoplasmic calcium. These features are also observed in networks of rat cortical neurons that have been formed in culture. Experimental studies of these cultured networks have led to several hypotheses for the mechanisms underlying the observed synchronized oscillations. In this paper, bursting integrate-and-fire type mathematical models for regular spiking (RS) and intrinsic bursting (IB) neurons are introduced and incorporated through a small-world connection scheme into a two-dimensional excitatory network similar to those in the cultured network. This computer model exhibits spontaneous synchronous activity through mechanisms similar to those hypothesized for the cultured experimental networks. Traces of the membrane potential and cytoplasmic calcium from the model closely match those obtained from experiments. We also consider the impact on network behavior of the IB neurons, the geometry and the small world connection scheme.

Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks IV: structuring synaptic pathways among recurrent connections.

PubMed

Gilson, Matthieu; Burkitt, Anthony N; Grayden, David B; Thomas, Doreen A; van Hemmen, J Leo

2009-12-01

In neuronal networks, the changes of synaptic strength (or weight) performed by spike-timing-dependent plasticity (STDP) are hypothesized to give rise to functional network structure. This article investigates how this phenomenon occurs for the excitatory recurrent connections of a network with fixed input weights that is stimulated by external spike trains. We develop a theoretical framework based on the Poisson neuron model to analyze the interplay between the neuronal activity (firing rates and the spike-time correlations) and the learning dynamics, when the network is stimulated by correlated pools of homogeneous Poisson spike trains. STDP can lead to both a stabilization of all the neuron firing rates (homeostatic equilibrium) and a robust weight specialization. The pattern of specialization for the recurrent weights is determined by a relationship between the input firing-rate and correlation structures, the network topology, the STDP parameters and the synaptic response properties. We find conditions for feed-forward pathways or areas with strengthened self-feedback to emerge in an initially homogeneous recurrent network.

Autapse-induced spiral wave in network of neurons under noise.

PubMed

Qin, Huixin; Ma, Jun; Wang, Chunni; Wu, Ying

2014-01-01

Autapse plays an important role in regulating the electric activity of neuron by feedbacking time-delayed current on the membrane of neuron. Autapses are considered in a local area of regular network of neurons to investigate the development of spatiotemporal pattern, and emergence of spiral wave is observed while it fails to grow up and occupy the network completely. It is found that spiral wave can be induced to occupy more area in the network under optimized noise on the network with periodical or no-flux boundary condition being used. The developed spiral wave with self-sustained property can regulate the collective behaviors of neurons as a pacemaker. To detect the collective behaviors, a statistical factor of synchronization is calculated to investigate the emergence of ordered state in the network. The network keeps ordered state when self-sustained spiral wave is formed under noise and autapse in local area of network, and it independent of the selection of periodical or no-flux boundary condition. The developed stable spiral wave could be helpful for memory due to the distinct self-sustained property.

The effects of neuron morphology on graph theoretic measures of network connectivity: the analysis of a two-level statistical model.

PubMed

Aćimović, Jugoslava; Mäki-Marttunen, Tuomo; Linne, Marja-Leena

2015-01-01

We developed a two-level statistical model that addresses the question of how properties of neurite morphology shape the large-scale network connectivity. We adopted a low-dimensional statistical description of neurites. From the neurite model description we derived the expected number of synapses, node degree, and the effective radius, the maximal distance between two neurons expected to form at least one synapse. We related these quantities to the network connectivity described using standard measures from graph theory, such as motif counts, clustering coefficient, minimal path length, and small-world coefficient. These measures are used in a neuroscience context to study phenomena from synaptic connectivity in the small neuronal networks to large scale functional connectivity in the cortex. For these measures we provide analytical solutions that clearly relate different model properties. Neurites that sparsely cover space lead to a small effective radius. If the effective radius is small compared to the overall neuron size the obtained networks share similarities with the uniform random networks as each neuron connects to a small number of distant neurons. Large neurites with densely packed branches lead to a large effective radius. If this effective radius is large compared to the neuron size, the obtained networks have many local connections. In between these extremes, the networks maximize the variability of connection repertoires. The presented approach connects the properties of neuron morphology with large scale network properties without requiring heavy simulations with many model parameters. The two-steps procedure provides an easier interpretation of the role of each modeled parameter. The model is flexible and each of its components can be further expanded. We identified a range of model parameters that maximizes variability in network connectivity, the property that might affect network capacity to exhibit different dynamical regimes.

Effect of the heterogeneous neuron and information transmission delay on stochastic resonance of neuronal networks

NASA Astrophysics Data System (ADS)

Wang, Qingyun; Zhang, Honghui; Chen, Guanrong

2012-12-01

We study the effect of heterogeneous neuron and information transmission delay on stochastic resonance of scale-free neuronal networks. For this purpose, we introduce the heterogeneity to the specified neuron with the highest degree. It is shown that in the absence of delay, an intermediate noise level can optimally assist spike firings of collective neurons so as to achieve stochastic resonance on scale-free neuronal networks for small and intermediate αh, which plays a heterogeneous role. Maxima of stochastic resonance measure are enhanced as αh increases, which implies that the heterogeneity can improve stochastic resonance. However, as αh is beyond a certain large value, no obvious stochastic resonance can be observed. If the information transmission delay is introduced to neuronal networks, stochastic resonance is dramatically affected. In particular, the tuned information transmission delay can induce multiple stochastic resonance, which can be manifested as well-expressed maximum in the measure for stochastic resonance, appearing every multiple of one half of the subthreshold stimulus period. Furthermore, we can observe that stochastic resonance at odd multiple of one half of the subthreshold stimulus period is subharmonic, as opposed to the case of even multiple of one half of the subthreshold stimulus period. More interestingly, multiple stochastic resonance can also be improved by the suitable heterogeneous neuron. Presented results can provide good insights into the understanding of the heterogeneous neuron and information transmission delay on realistic neuronal networks.

A computational perspective on autism

PubMed Central

Rosenberg, Ari; Patterson, Jaclyn Sky; Angelaki, Dora E.

2015-01-01

Autism is a neurodevelopmental disorder that manifests as a heterogeneous set of social, cognitive, motor, and perceptual symptoms. This system-wide pervasiveness suggests that, rather than narrowly impacting individual systems such as affection or vision, autism may broadly alter neural computation. Here, we propose that alterations in nonlinear, canonical computations occurring throughout the brain may underlie the behavioral characteristics of autism. One such computation, called divisive normalization, balances a neuron’s net excitation with inhibition reflecting the overall activity of the neuronal population. Through neural network simulations, we investigate how alterations in divisive normalization may give rise to autism symptomatology. Our findings show that a reduction in the amount of inhibition that occurs through divisive normalization can account for perceptual consequences of autism, consistent with the hypothesis of an increased ratio of neural excitation to inhibition (E/I) in the disorder. These results thus establish a bridge between an E/I imbalance and behavioral data on autism that is currently absent. Interestingly, our findings implicate the context-dependent, neuronal milieu as a key factor in autism symptomatology, with autism reflecting a less “social” neuronal population. Through a broader discussion of perceptual data, we further examine how altered divisive normalization may contribute to a wide array of the disorder’s behavioral consequences. These analyses show how a computational framework can provide insights into the neural basis of autism and facilitate the generation of falsifiable hypotheses. A computational perspective on autism may help resolve debates within the field and aid in identifying physiological pathways to target in the treatment of the disorder. PMID:26170299

Probabilistic diffusion tractography and graph theory analysis reveal abnormal white matter structural connectivity networks in drug-naive boys with attention deficit/hyperactivity disorder.

PubMed

Cao, Qingjiu; Shu, Ni; An, Li; Wang, Peng; Sun, Li; Xia, Ming-Rui; Wang, Jin-Hui; Gong, Gao-Lang; Zang, Yu-Feng; Wang, Yu-Feng; He, Yong

2013-06-26

Attention-deficit/hyperactivity disorder (ADHD), which is characterized by core symptoms of inattention and hyperactivity/impulsivity, is one of the most common neurodevelopmental disorders of childhood. Neuroimaging studies have suggested that these behavioral disturbances are associated with abnormal functional connectivity among brain regions. However, the alterations in the structural connections that underlie these behavioral and functional deficits remain poorly understood. Here, we used diffusion magnetic resonance imaging and probabilistic tractography method to examine whole-brain white matter (WM) structural connectivity in 30 drug-naive boys with ADHD and 30 healthy controls. The WM networks of the human brain were constructed by estimating inter-regional connectivity probability. The topological properties of the resultant networks (e.g., small-world and network efficiency) were then analyzed using graph theoretical approaches. Nonparametric permutation tests were applied for between-group comparisons of these graphic metrics. We found that both the ADHD and control groups showed an efficient small-world organization in the whole-brain WM networks, suggesting a balance between structurally segregated and integrated connectivity patterns. However, relative to controls, patients with ADHD exhibited decreased global efficiency and increased shortest path length, with the most pronounced efficiency decreases in the left parietal, frontal, and occipital cortices. Intriguingly, the ADHD group showed decreased structural connectivity in the prefrontal-dominant circuitry and increased connectivity in the orbitofrontal-striatal circuitry, and these changes significantly correlated with the inattention and hyperactivity/impulsivity symptoms, respectively. The present study shows disrupted topological organization of large-scale WM networks in ADHD, extending our understanding of how structural disruptions of neuronal circuits underlie behavioral disturbances in patients with ADHD.

A distance constrained synaptic plasticity model of C. elegans neuronal network

NASA Astrophysics Data System (ADS)

Badhwar, Rahul; Bagler, Ganesh

2017-03-01

Brain research has been driven by enquiry for principles of brain structure organization and its control mechanisms. The neuronal wiring map of C. elegans, the only complete connectome available till date, presents an incredible opportunity to learn basic governing principles that drive structure and function of its neuronal architecture. Despite its apparently simple nervous system, C. elegans is known to possess complex functions. The nervous system forms an important underlying framework which specifies phenotypic features associated to sensation, movement, conditioning and memory. In this study, with the help of graph theoretical models, we investigated the C. elegans neuronal network to identify network features that are critical for its control. The 'driver neurons' are associated with important biological functions such as reproduction, signalling processes and anatomical structural development. We created 1D and 2D network models of C. elegans neuronal system to probe the role of features that confer controllability and small world nature. The simple 1D ring model is critically poised for the number of feed forward motifs, neuronal clustering and characteristic path-length in response to synaptic rewiring, indicating optimal rewiring. Using empirically observed distance constraint in the neuronal network as a guiding principle, we created a distance constrained synaptic plasticity model that simultaneously explains small world nature, saturation of feed forward motifs as well as observed number of driver neurons. The distance constrained model suggests optimum long distance synaptic connections as a key feature specifying control of the network.

Introduction to Focus Issue: Rhythms and Dynamic Transitions in Neurological Disease: Modeling, Computation, and Experiment

NASA Astrophysics Data System (ADS)

Kaper, Tasso J.; Kramer, Mark A.; Rotstein, Horacio G.

2013-12-01

Rhythmic neuronal oscillations across a broad range of frequencies, as well as spatiotemporal phenomena, such as waves and bumps, have been observed in various areas of the brain and proposed as critical to brain function. While there is a long and distinguished history of studying rhythms in nerve cells and neuronal networks in healthy organisms, the association and analysis of rhythms to diseases are more recent developments. Indeed, it is now thought that certain aspects of diseases of the nervous system, such as epilepsy, schizophrenia, Parkinson's, and sleep disorders, are associated with transitions or disruptions of neurological rhythms. This focus issue brings together articles presenting modeling, computational, analytical, and experimental perspectives about rhythms and dynamic transitions between them that are associated to various diseases.

Strategies for clinical approach to neurodegeneration in Amyotrophic lateral sclerosis.

PubMed

Carlesi, Cecilia; Pasquali, Livia; Piazza, Selina; Lo Gerfo, Annalisa; Caldarazzo Ienco, Elena; Alessi, Rosaria; Fornai, Francesco; Siciliano, Gabriele

2011-03-01

Amyotrophic lateral sclerosis (ALS) is a rapidly progressive and ultimately fatal neurodegenerative disorder of unknown aetiology that involves the loss of upper and lower motor neurons in the cerebral cortex, brainstem and spinal cord. Significant progress in understanding the cellular mechanisms of motor neuron degeneration in ALS has not been matched with the development of therapeutic strategies to prevent disease progression, and riluzole remains the only available therapy, with only marginal effects on disease survival. More recently alterations of mRNA processing in genetically defined forms of ALS, as those related to TDP-43 and FUS-TLS gene mutations have provided important insights into the molecular networks implicated in the disease pathogenesis. Here we review some of the recent progress in promoting therapeutic strategies for neurodegeneration.

Loss of delta catenin function in severe autism

PubMed Central

Turner, Tychele N.; Sharma, Kamal; Oh, Edwin C.; Liu, Yangfan P.; Collins, Ryan L.; Sosa, Maria X.; Auer, Dallas R.; Brand, Harrison; Sanders, Stephan J.; Moreno-De-Luca, Daniel; Pihur, Vasyl; Plona, Teri; Pike, Kristen; Soppet, Daniel R.; Smith, Michael W.; Cheung, Sau Wai; Martin, Christa Lese; State, Matthew W.; Talkowski, Michael E.; Cook, Edwin; Huganir, Richard; Katsanis, Nicholas; Chakravarti, Aravinda

2015-01-01

SUMMARY Autism is a multifactorial neurodevelopmental disorder affecting more males than females; consequently, under a multifactorial genetic hypothesis, females are affected only when they cross a higher biological threshold. We hypothesize that deleterious variants at conserved residues are enriched in severely affected patients arising from FEMFs (female-enriched multiplex families) with severe disease, enhancing the detection of key autism genes in modest numbers of cases. We show the utility of this strategy by identifying missense and dosage sequence variants in the gene encoding the adhesive junction-associated delta catenin protein (CTNND2) in FEMFs and demonstrating their loss-of-function effect by functional analyses in zebrafish embryos and cultured hippocampal neurons from wildtype and Ctnnd2 null mouse embryos. Finally, through gene expression and network analyses, we highlight a critical role for CTNND2 in neuronal development and an intimate connection to chromatin biology. Our data contribute to the understanding of the genetic architecture of autism and suggest that genetic analyses of phenotypic extremes, such as FEMFs, are of innate value in multifactorial disorders. PMID:25807484

Work-associated irritable larynx syndrome.

PubMed

Anderson, Jennifer A

2015-04-01

The purpose of this study is to review the relevant literature concerning work-associated irritable larynx syndrome (WILS), a hyperkinetic laryngeal disorder associated with occupational irritant exposure. Clinical symptoms are variable and include dysphonia, cough, dyspnoea and globus pharyngeus. WILS is a clinical diagnosis and can be difficult to differentiate from asthma. Treatment options for WILS include medical and behavioural therapy. Laryngeal-centred upper airway symptoms secondary to airborne irritants have been documented in the literature under a variety of diagnostic labels, including WILS, vocal cord dysfunction (VCD), laryngeal hypersensitivity and laryngeal neuropathy and many others. The underlying pathophysiology is as yet poorly understood; however, the clinical scenario suggests a multifactorial nature to the disorder. More recent literature indicates that central neuronal plasticity, inflammatory processes and psychological factors are all likely contributors. Possible mechanisms for WILS include central neuronal network plasticity after noxious exposure and/or viral infection, inflammation (i.e. reflux disease) and intrinsic patient factors such a psychological state. Treatment is individualized and frequently includes one or more of the following: environmental changes in the workplace, GERD therapy, behavioural/speech therapy, psychotherapy counselling and neural modifiers.

Lateral Information Processing by Spiking Neurons: A Theoretical Model of the Neural Correlate of Consciousness

PubMed Central

Ebner, Marc; Hameroff, Stuart

2011-01-01

Cognitive brain functions, for example, sensory perception, motor control and learning, are understood as computation by axonal-dendritic chemical synapses in networks of integrate-and-fire neurons. Cognitive brain functions may occur either consciously or nonconsciously (on “autopilot”). Conscious cognition is marked by gamma synchrony EEG, mediated largely by dendritic-dendritic gap junctions, sideways connections in input/integration layers. Gap-junction-connected neurons define a sub-network within a larger neural network. A theoretical model (the “conscious pilot”) suggests that as gap junctions open and close, a gamma-synchronized subnetwork, or zone moves through the brain as an executive agent, converting nonconscious “auto-pilot” cognition to consciousness, and enhancing computation by coherent processing and collective integration. In this study we implemented sideways “gap junctions” in a single-layer artificial neural network to perform figure/ground separation. The set of neurons connected through gap junctions form a reconfigurable resistive grid or sub-network zone. In the model, outgoing spikes are temporally integrated and spatially averaged using the fixed resistive grid set up by neurons of similar function which are connected through gap-junctions. This spatial average, essentially a feedback signal from the neuron's output, determines whether particular gap junctions between neurons will open or close. Neurons connected through open gap junctions synchronize their output spikes. We have tested our gap-junction-defined sub-network in a one-layer neural network on artificial retinal inputs using real-world images. Our system is able to perform figure/ground separation where the laterally connected sub-network of neurons represents a perceived object. Even though we only show results for visual stimuli, our approach should generalize to other modalities. The system demonstrates a moving sub-network zone of synchrony, within which the contents of perception are represented and contained. This mobile zone can be viewed as a model of the neural correlate of consciousness in the brain. PMID:22046178

Small Modifications to Network Topology Can Induce Stochastic Bistable Spiking Dynamics in a Balanced Cortical Model

PubMed Central

McDonnell, Mark D.; Ward, Lawrence M.

2014-01-01

Abstract Directed random graph models frequently are used successfully in modeling the population dynamics of networks of cortical neurons connected by chemical synapses. Experimental results consistently reveal that neuronal network topology is complex, however, in the sense that it differs statistically from a random network, and differs for classes of neurons that are physiologically different. This suggests that complex network models whose subnetworks have distinct topological structure may be a useful, and more biologically realistic, alternative to random networks. Here we demonstrate that the balanced excitation and inhibition frequently observed in small cortical regions can transiently disappear in otherwise standard neuronal-scale models of fluctuation-driven dynamics, solely because the random network topology was replaced by a complex clustered one, whilst not changing the in-degree of any neurons. In this network, a small subset of cells whose inhibition comes only from outside their local cluster are the cause of bistable population dynamics, where different clusters of these cells irregularly switch back and forth from a sparsely firing state to a highly active state. Transitions to the highly active state occur when a cluster of these cells spikes sufficiently often to cause strong unbalanced positive feedback to each other. Transitions back to the sparsely firing state rely on occasional large fluctuations in the amount of non-local inhibition received. Neurons in the model are homogeneous in their intrinsic dynamics and in-degrees, but differ in the abundance of various directed feedback motifs in which they participate. Our findings suggest that (i) models and simulations should take into account complex structure that varies for neuron and synapse classes; (ii) differences in the dynamics of neurons with similar intrinsic properties may be caused by their membership in distinctive local networks; (iii) it is important to identify neurons that share physiological properties and location, but differ in their connectivity. PMID:24743633

Lateral information processing by spiking neurons: a theoretical model of the neural correlate of consciousness.

PubMed

Ebner, Marc; Hameroff, Stuart

2011-01-01

Cognitive brain functions, for example, sensory perception, motor control and learning, are understood as computation by axonal-dendritic chemical synapses in networks of integrate-and-fire neurons. Cognitive brain functions may occur either consciously or nonconsciously (on "autopilot"). Conscious cognition is marked by gamma synchrony EEG, mediated largely by dendritic-dendritic gap junctions, sideways connections in input/integration layers. Gap-junction-connected neurons define a sub-network within a larger neural network. A theoretical model (the "conscious pilot") suggests that as gap junctions open and close, a gamma-synchronized subnetwork, or zone moves through the brain as an executive agent, converting nonconscious "auto-pilot" cognition to consciousness, and enhancing computation by coherent processing and collective integration. In this study we implemented sideways "gap junctions" in a single-layer artificial neural network to perform figure/ground separation. The set of neurons connected through gap junctions form a reconfigurable resistive grid or sub-network zone. In the model, outgoing spikes are temporally integrated and spatially averaged using the fixed resistive grid set up by neurons of similar function which are connected through gap-junctions. This spatial average, essentially a feedback signal from the neuron's output, determines whether particular gap junctions between neurons will open or close. Neurons connected through open gap junctions synchronize their output spikes. We have tested our gap-junction-defined sub-network in a one-layer neural network on artificial retinal inputs using real-world images. Our system is able to perform figure/ground separation where the laterally connected sub-network of neurons represents a perceived object. Even though we only show results for visual stimuli, our approach should generalize to other modalities. The system demonstrates a moving sub-network zone of synchrony, within which the contents of perception are represented and contained. This mobile zone can be viewed as a model of the neural correlate of consciousness in the brain.

[Perisylvian magnetoencephalografic impairments in patients with autism spectrum disorders].

PubMed

Palau-Baduell, M; Salvado-Salvado, B; Idiazabal-Alecha, M A; Fernandez-Teruel, A; Ortiz, T

2018-03-01

The perisylvian areas, located around the Sylvian fissure, are constituted by frontal, temporal and parietal brain regions. These are connected forming specialized neural networks and play a primary role in the development of linguistic skills and social cognition. These areas are a possible neuronal substrate of cognitive and behavioral impairments in patients with autism spectrum disorders (ASD). To locate and quantify epileptiform activity sources through magnetoencephalography in frontal perisylvian areas in children with idiopathic ASD. Sixty-eight children with idiopathic ASD were studied by magnetoencephalography. The children were classified into two groups: a group of 41 children with autistic disorder and a combined group of 27 children with Asperger syndrome and children with pervasive developmental disorder not otherwise specified. The sources of magnetoencephalografic epileptiform activity detected in the frontal perisylvian were localized and quantified. The amount of epileptiform activity in frontal perisylvian region was significantly higher in children with autistic disorder. The amount of epileptiform activity in frontal perisylvian areas differed significantly between children with autistic disorder and those with Asperger syndrome and pervasive developmental disorder not otherwise specified.

A synaptic organizing principle for cortical neuronal groups

PubMed Central

Perin, Rodrigo; Berger, Thomas K.; Markram, Henry

2011-01-01

Neuronal circuitry is often considered a clean slate that can be dynamically and arbitrarily molded by experience. However, when we investigated synaptic connectivity in groups of pyramidal neurons in the neocortex, we found that both connectivity and synaptic weights were surprisingly predictable. Synaptic weights follow very closely the number of connections in a group of neurons, saturating after only 20% of possible connections are formed between neurons in a group. When we examined the network topology of connectivity between neurons, we found that the neurons cluster into small world networks that are not scale-free, with less than 2 degrees of separation. We found a simple clustering rule where connectivity is directly proportional to the number of common neighbors, which accounts for these small world networks and accurately predicts the connection probability between any two neurons. This pyramidal neuron network clusters into multiple groups of a few dozen neurons each. The neurons composing each group are surprisingly distributed, typically more than 100 μm apart, allowing for multiple groups to be interlaced in the same space. In summary, we discovered a synaptic organizing principle that groups neurons in a manner that is common across animals and hence, independent of individual experiences. We speculate that these elementary neuronal groups are prescribed Lego-like building blocks of perception and that acquired memory relies more on combining these elementary assemblies into higher-order constructs. PMID:21383177

Synchronization in a non-uniform network of excitatory spiking neurons

NASA Astrophysics Data System (ADS)

Echeveste, Rodrigo; Gros, Claudius

Spontaneous synchronization of pulse coupled elements is ubiquitous in nature and seems to be of vital importance for life. Networks of pacemaker cells in the heart, extended populations of southeast asian fireflies, and neuronal oscillations in cortical networks, are examples of this. In the present work, a rich repertoire of dynamical states with different degrees of synchronization are found in a network of excitatory-only spiking neurons connected in a non-uniform fashion. In particular, uncorrelated and partially correlated states are found without the need for inhibitory neurons or external currents. The phase transitions between these states, as well the robustness, stability, and response of the network to external stimulus are studied.

Sensitivity of feedforward neural networks to weight errors

NASA Technical Reports Server (NTRS)

Stevenson, Maryhelen; Widrow, Bernard; Winter, Rodney

1990-01-01

An analysis is made of the sensitivity of feedforward layered networks of Adaline elements (threshold logic units) to weight errors. An approximation is derived which expresses the probability of error for an output neuron of a large network (a network with many neurons per layer) as a function of the percentage change in the weights. As would be expected, the probability of error increases with the number of layers in the network and with the percentage change in the weights. The probability of error is essentially independent of the number of weights per neuron and of the number of neurons per layer, as long as these numbers are large (on the order of 100 or more).

Effects of third trimester-equivalent ethanol exposure on Cl(-) co-transporter expression, network activity, and GABAergic transmission in the CA3 hippocampal region of neonatal rats.

PubMed

Everett, Julie C; Licón-Muñoz, Yamhilette; Valenzuela, C Fernando

2012-09-01

Fetal alcohol spectrum disorders are often associated with structural and functional hippocampal abnormalities, leading to long-lasting learning and memory deficits. The mechanisms underlying these abnormalities are not fully understood. Here, we investigated whether ethanol exposure during the 3rd trimester-equivalent period alters spontaneous network activity that is involved in neuronal circuit development in the CA3 hippocampal region. This activity is driven by GABA(A) receptors, which can have excitatory actions in developing neurons as a consequence of greater expression of the Cl(-) importer, NKCC1, with respect to expression of the Cl(-) exporter, KCC2, resulting in high [Cl(-)](i). Rat pups were exposed to ethanol vapor from postnatal day (P) 2-16 (4 h/day). Weight gain was significantly reduced in pups exposed to ethanol compared to control at P15 and 16. Brain slices were prepared immediately after the end of the 4-h exposure on P4-16 and experiments were also performed under ethanol-free conditions at the end of the exposure paradigm (P17-22). Ethanol exposure did not significantly affect expression of KCC2 or NKCC1, nor did it affect network activity in the CA3 hippocampal region. Ethanol exposure significantly decreased the frequency (at P9-11) and increased the amplitude (at P5-8 and P17-21) of GABA(A) receptor-mediated miniature postsynaptic currents. These data suggest that repeated in vivo exposure to ethanol during the 3rd trimester-equivalent period alters GABAergic transmission in the CA3 hippocampal region, an effect that could lead to abnormal circuit maturation and perhaps contribute to the pathophysiology of fetal alcohol spectrum disorders. Copyright © 2012 Elsevier Inc. All rights reserved.

On the continuous differentiability of inter-spike intervals of synaptically connected cortical spiking neurons in a neuronal network.

PubMed

Kumar, Gautam; Kothare, Mayuresh V

2013-12-01

We derive conditions for continuous differentiability of inter-spike intervals (ISIs) of spiking neurons with respect to parameters (decision variables) of an external stimulating input current that drives a recurrent network of synaptically connected neurons. The dynamical behavior of individual neurons is represented by a class of discontinuous single-neuron models. We report here that ISIs of neurons in the network are continuously differentiable with respect to decision variables if (1) a continuously differentiable trajectory of the membrane potential exists between consecutive action potentials with respect to time and decision variables and (2) the partial derivative of the membrane potential of spiking neurons with respect to time is not equal to the partial derivative of their firing threshold with respect to time at the time of action potentials. Our theoretical results are supported by showing fulfillment of these conditions for a class of known bidimensional spiking neuron models.

NeuroGrid: recording action potentials from the surface of the brain.

PubMed

Khodagholy, Dion; Gelinas, Jennifer N; Thesen, Thomas; Doyle, Werner; Devinsky, Orrin; Malliaras, George G; Buzsáki, György

2015-02-01

Recording from neural networks at the resolution of action potentials is critical for understanding how information is processed in the brain. Here, we address this challenge by developing an organic material-based, ultraconformable, biocompatible and scalable neural interface array (the 'NeuroGrid') that can record both local field potentials(LFPs) and action potentials from superficial cortical neurons without penetrating the brain surface. Spikes with features of interneurons and pyramidal cells were simultaneously acquired by multiple neighboring electrodes of the NeuroGrid, allowing for the isolation of putative single neurons in rats. Spiking activity demonstrated consistent phase modulation by ongoing brain oscillations and was stable in recordings exceeding 1 week's duration. We also recorded LFP-modulated spiking activity intraoperatively in patients undergoing epilepsy surgery. The NeuroGrid constitutes an effective method for large-scale, stable recording of neuronal spikes in concert with local population synaptic activity, enhancing comprehension of neural processes across spatiotemporal scales and potentially facilitating diagnosis and therapy for brain disorders.

A Markovian event-based framework for stochastic spiking neural networks.

PubMed

Touboul, Jonathan D; Faugeras, Olivier D

2011-11-01

In spiking neural networks, the information is conveyed by the spike times, that depend on the intrinsic dynamics of each neuron, the input they receive and on the connections between neurons. In this article we study the Markovian nature of the sequence of spike times in stochastic neural networks, and in particular the ability to deduce from a spike train the next spike time, and therefore produce a description of the network activity only based on the spike times regardless of the membrane potential process. To study this question in a rigorous manner, we introduce and study an event-based description of networks of noisy integrate-and-fire neurons, i.e. that is based on the computation of the spike times. We show that the firing times of the neurons in the networks constitute a Markov chain, whose transition probability is related to the probability distribution of the interspike interval of the neurons in the network. In the cases where the Markovian model can be developed, the transition probability is explicitly derived in such classical cases of neural networks as the linear integrate-and-fire neuron models with excitatory and inhibitory interactions, for different types of synapses, possibly featuring noisy synaptic integration, transmission delays and absolute and relative refractory period. This covers most of the cases that have been investigated in the event-based description of spiking deterministic neural networks.

N-acetyl-aspartate (NAA) as a correlate of pharmacological treatment in psychiatric disorders: a systematic review.

PubMed

Paslakis, Georgios; Träber, Frank; Roberz, Jens; Block, Wolfgang; Jessen, Frank

2014-10-01

The amino-acid N-acetyl-aspartate (NAA) is located in neurons and the concentration of NAA correlates with neuronal mitochondrial function. The signal of NAA, as measured with proton magnetic resonance spectroscopy (1H-MRS), is considered to reflect both, neuronal density and integrity of neuronal mitochondria. A reduction of the NAA concentrations has been found in several psychiatric disorders. Newer studies report reversal of decreased NAA concentration with treatment. The objective of this review is to summarize the literature on NAA changes in association with psychopharmacological treatment in psychiatric disorders (affective disorders, obsessive-compulsive disorder, schizophrenia and dementia). The majority of studies identified increased NAA concentrations in response to treatment, while a smaller number of studies did not find this effect. The NAA increase seems to be neither specific for a certain disorder nor for a specific intervention. This suggests that the reduction of NAA may represent an altered functional (metabolic) state of neurons common to different psychiatric disorders and the increase after treatment to indicate functional restoration as one general effect of interventions. Copyright © 2014 Elsevier B.V. and ECNP. All rights reserved.

Investigating the Neural Correlates of Emotion–Cognition Interaction Using an Affective Stroop Task

PubMed Central

Raschle, Nora M.; Fehlbaum, Lynn V.; Menks, Willeke M.; Euler, Felix; Sterzer, Philipp; Stadler, Christina

2017-01-01

The human brain has the capacity to integrate various sources of information and continuously adapts our behavior according to situational needs in order to allow a healthy functioning. Emotion–cognition interactions are a key example for such integrative processing. However, the neuronal correlates investigating the effects of emotion on cognition remain to be explored and replication studies are needed. Previous neuroimaging studies have indicated an involvement of emotion and cognition related brain structures including parietal and prefrontal cortices and limbic brain regions. Here, we employed whole brain event-related functional magnetic resonance imaging (fMRI) during an affective number Stroop task and aimed at replicating previous findings using an adaptation of an existing task design in 30 healthy young adults. The Stroop task is an indicator of cognitive control and enables the quantification of interference in relation to variations in cognitive load. By the use of emotional primes (negative/neutral) prior to Stroop task performance, an emotional variation is added as well. Behavioral in-scanner data showed that negative primes delayed and disrupted cognitive processing. Trials with high cognitive demand furthermore negatively influenced cognitive control mechanisms. Neuronally, the emotional primes consistently activated emotion-related brain regions (e.g., amygdala, insula, and prefrontal brain regions) while Stroop task performance lead to activations in cognition networks of the brain (prefrontal cortices, superior temporal lobe, and insula). When assessing the effect of emotion on cognition, increased cognitive demand led to decreases in neural activation in response to emotional stimuli (negative > neutral) within prefrontal cortex, amygdala, and insular cortex. Overall, these results suggest that emotional primes significantly impact cognitive performance and increasing cognitive demand leads to reduced neuronal activation in emotion related brain regions, and therefore support previous findings investigating emotion–cognition interaction in healthy adults. Moreover, emotion and cognition seem to be tightly related to each other, as indicated by shared neural networks involved in both of these processes. Emotion processing, cognitive control, and their interaction are crucial for healthy functioning and a lack thereof is related to psychiatric disorders such as, disruptive behavior disorders. Future studies may investigate the neural characteristics of children and adolescents with disruptive behavior disorders. PMID:28919871

The serotonergic anatomy of the developing human medulla oblongata: implications for pediatric disorders of homeostasis.

PubMed

Kinney, Hannah C; Broadbelt, Kevin G; Haynes, Robin L; Rognum, Ingvar J; Paterson, David S

2011-07-01

The caudal serotonergic (5-HT) system is a critical component of a medullary "homeostatic network" that regulates protective responses to metabolic stressors such as hypoxia, hypercapnia, and hyperthermia. We define anatomically the caudal 5-HT system in the human medulla as 5-HT neuronal cell bodies located in the raphé (raphé obscurus, raphé magnus, and raphé pallidus), extra-raphé (gigantocellularis, paragigantocellularis lateralis, intermediate reticular zone, lateral reticular nucleus, and nucleus subtrigeminalis), and ventral surface (arcuate nucleus). These 5-HT neurons are adjacent to all of the respiratory- and autonomic-related nuclei in the medulla where they are positioned to modulate directly the responses of these effector nuclei. In the following review, we highlight the topography and development of the caudal 5-HT system in the human fetus and infant, and its inter-relationships with nicotinic, GABAergic, and cytokine receptors. We also summarize pediatric disorders in early life which we term "developmental serotonopathies" of the caudal (as well as rostral) 5-HT domain and which are associated with homeostatic imbalances. The delineation of the development and organization of the human caudal 5-HT system provides the critical foundation for the neuropathologic elucidation of its disorders directly in the human brain. Copyright © 2011 Elsevier B.V. All rights reserved.

Network activity of mirror neurons depends on experience.

PubMed

Ushakov, Vadim L; Kartashov, Sergey I; Zavyalova, Victoria V; Bezverhiy, Denis D; Posichanyuk, Vladimir I; Terentev, Vasliliy N; Anokhin, Konstantin V

2013-03-01

In this work, the investigation of network activity of mirror neurons systems in animal brains depending on experience (existence or absence performance of the shown actions) was carried out. It carried out the research of mirror neurons network in the C57/BL6 line mice in the supervision task of swimming mice-demonstrators in Morris water maze. It showed the presence of mirror neurons systems in the motor cortex M1, M2, cingular cortex, hippocampus in mice groups, having experience of the swimming and without it. The conclusion is drawn about the possibility of the new functional network systems formation by means of mirror neurons systems and the acquisition of new knowledge through supervision by the animals in non-specific tasks.

Optimization Methods for Spiking Neurons and Networks

PubMed Central

Russell, Alexander; Orchard, Garrick; Dong, Yi; Mihalaş, Ştefan; Niebur, Ernst; Tapson, Jonathan; Etienne-Cummings, Ralph

2011-01-01

Spiking neurons and spiking neural circuits are finding uses in a multitude of tasks such as robotic locomotion control, neuroprosthetics, visual sensory processing, and audition. The desired neural output is achieved through the use of complex neuron models, or by combining multiple simple neurons into a network. In either case, a means for configuring the neuron or neural circuit is required. Manual manipulation of parameters is both time consuming and non-intuitive due to the nonlinear relationship between parameters and the neuron’s output. The complexity rises even further as the neurons are networked and the systems often become mathematically intractable. In large circuits, the desired behavior and timing of action potential trains may be known but the timing of the individual action potentials is unknown and unimportant, whereas in single neuron systems the timing of individual action potentials is critical. In this paper, we automate the process of finding parameters. To configure a single neuron we derive a maximum likelihood method for configuring a neuron model, specifically the Mihalas–Niebur Neuron. Similarly, to configure neural circuits, we show how we use genetic algorithms (GAs) to configure parameters for a network of simple integrate and fire with adaptation neurons. The GA approach is demonstrated both in software simulation and hardware implementation on a reconfigurable custom very large scale integration chip. PMID:20959265

Constructing Neuronal Network Models in Massively Parallel Environments.

PubMed

Ippen, Tammo; Eppler, Jochen M; Plesser, Hans E; Diesmann, Markus

2017-01-01

Recent advances in the development of data structures to represent spiking neuron network models enable us to exploit the complete memory of petascale computers for a single brain-scale network simulation. In this work, we investigate how well we can exploit the computing power of such supercomputers for the creation of neuronal networks. Using an established benchmark, we divide the runtime of simulation code into the phase of network construction and the phase during which the dynamical state is advanced in time. We find that on multi-core compute nodes network creation scales well with process-parallel code but exhibits a prohibitively large memory consumption. Thread-parallel network creation, in contrast, exhibits speedup only up to a small number of threads but has little overhead in terms of memory. We further observe that the algorithms creating instances of model neurons and their connections scale well for networks of ten thousand neurons, but do not show the same speedup for networks of millions of neurons. Our work uncovers that the lack of scaling of thread-parallel network creation is due to inadequate memory allocation strategies and demonstrates that thread-optimized memory allocators recover excellent scaling. An analysis of the loop order used for network construction reveals that more complex tests on the locality of operations significantly improve scaling and reduce runtime by allowing construction algorithms to step through large networks more efficiently than in existing code. The combination of these techniques increases performance by an order of magnitude and harnesses the increasingly parallel compute power of the compute nodes in high-performance clusters and supercomputers.

Constructing Neuronal Network Models in Massively Parallel Environments

PubMed Central

Ippen, Tammo; Eppler, Jochen M.; Plesser, Hans E.; Diesmann, Markus

2017-01-01

Recent advances in the development of data structures to represent spiking neuron network models enable us to exploit the complete memory of petascale computers for a single brain-scale network simulation. In this work, we investigate how well we can exploit the computing power of such supercomputers for the creation of neuronal networks. Using an established benchmark, we divide the runtime of simulation code into the phase of network construction and the phase during which the dynamical state is advanced in time. We find that on multi-core compute nodes network creation scales well with process-parallel code but exhibits a prohibitively large memory consumption. Thread-parallel network creation, in contrast, exhibits speedup only up to a small number of threads but has little overhead in terms of memory. We further observe that the algorithms creating instances of model neurons and their connections scale well for networks of ten thousand neurons, but do not show the same speedup for networks of millions of neurons. Our work uncovers that the lack of scaling of thread-parallel network creation is due to inadequate memory allocation strategies and demonstrates that thread-optimized memory allocators recover excellent scaling. An analysis of the loop order used for network construction reveals that more complex tests on the locality of operations significantly improve scaling and reduce runtime by allowing construction algorithms to step through large networks more efficiently than in existing code. The combination of these techniques increases performance by an order of magnitude and harnesses the increasingly parallel compute power of the compute nodes in high-performance clusters and supercomputers. PMID:28559808

Impaired inhibitory control of cortical synchronization in fragile X syndrome.

PubMed

Paluszkiewicz, Scott M; Olmos-Serrano, Jose Luis; Corbin, Joshua G; Huntsman, Molly M

2011-11-01

Fragile X syndrome (FXS) is a neurodevelopmental disorder characterized by severe cognitive impairments, sensory hypersensitivity, and comorbidities with autism and epilepsy. Fmr1 knockout (KO) mouse models of FXS exhibit alterations in excitatory and inhibitory neurotransmission, but it is largely unknown how aberrant function of specific neuronal subtypes contributes to these deficits. In this study we show specific inhibitory circuit dysfunction in layer II/III of somatosensory cortex of Fmr1 KO mice. We demonstrate reduced activation of somatostatin-expressing low-threshold-spiking (LTS) interneurons in response to the group I metabotropic glutamate receptor (mGluR) agonist 3,5-dihydroxyphenylglycine (DHPG) in Fmr1 KO mice, resulting in impaired synaptic inhibition. Paired recordings from pyramidal neurons revealed reductions in synchronized synaptic inhibition and coordinated spike synchrony in response to DHPG, indicating a weakened LTS interneuron network in Fmr1 KO mice. Together, these findings reveal a functional defect in a single subtype of cortical interneuron in Fmr1 KO mice. This defect is linked to altered activity of the cortical network in line with the FXS phenotype.

Synchronous behaviour in network model based on human cortico-cortical connections.

PubMed

Protachevicz, Paulo Ricardo; Borges, Rafael Ribaski; Reis, Adriane da Silva; Borges, Fernando da Silva; Iarosz, Kelly Cristina; Caldas, Ibere Luiz; Lameu, Ewandson Luiz; Macau, Elbert Einstein Nehrer; Viana, Ricardo Luiz; Sokolov, Igor M; Ferrari, Fabiano A S; Kurths, Jürgen; Batista, Antonio Marcos

2018-06-22

We consider a network topology according to the cortico-cortical connec- tion network of the human brain, where each cortical area is composed of a random network of adaptive exponential integrate-and-fire neurons. Depending on the parameters, this neuron model can exhibit spike or burst patterns. As a diagnostic tool to identify spike and burst patterns we utilise the coefficient of variation of the neuronal inter-spike interval. In our neuronal network, we verify the existence of spike and burst synchronisation in different cortical areas. Our simulations show that the network arrangement, i.e., its rich-club organisation, plays an important role in the transition of the areas from desynchronous to synchronous behaviours. © 2018 Institute of Physics and Engineering in Medicine.

Unsupervised discrimination of patterns in spiking neural networks with excitatory and inhibitory synaptic plasticity

PubMed Central

Srinivasa, Narayan; Cho, Youngkwan

2014-01-01

A spiking neural network model is described for learning to discriminate among spatial patterns in an unsupervised manner. The network anatomy consists of source neurons that are activated by external inputs, a reservoir that resembles a generic cortical layer with an excitatory-inhibitory (EI) network and a sink layer of neurons for readout. Synaptic plasticity in the form of STDP is imposed on all the excitatory and inhibitory synapses at all times. While long-term excitatory STDP enables sparse and efficient learning of the salient features in inputs, inhibitory STDP enables this learning to be stable by establishing a balance between excitatory and inhibitory currents at each neuron in the network. The synaptic weights between source and reservoir neurons form a basis set for the input patterns. The neural trajectories generated in the reservoir due to input stimulation and lateral connections between reservoir neurons can be readout by the sink layer neurons. This activity is used for adaptation of synapses between reservoir and sink layer neurons. A new measure called the discriminability index (DI) is introduced to compute if the network can discriminate between old patterns already presented in an initial training session. The DI is also used to compute if the network adapts to new patterns without losing its ability to discriminate among old patterns. The final outcome is that the network is able to correctly discriminate between all patterns—both old and new. This result holds as long as inhibitory synapses employ STDP to continuously enable current balance in the network. The results suggest a possible direction for future investigation into how spiking neural networks could address the stability-plasticity question despite having continuous synaptic plasticity. PMID:25566045

Unsupervised discrimination of patterns in spiking neural networks with excitatory and inhibitory synaptic plasticity.

PubMed

Srinivasa, Narayan; Cho, Youngkwan

2014-01-01

A spiking neural network model is described for learning to discriminate among spatial patterns in an unsupervised manner. The network anatomy consists of source neurons that are activated by external inputs, a reservoir that resembles a generic cortical layer with an excitatory-inhibitory (EI) network and a sink layer of neurons for readout. Synaptic plasticity in the form of STDP is imposed on all the excitatory and inhibitory synapses at all times. While long-term excitatory STDP enables sparse and efficient learning of the salient features in inputs, inhibitory STDP enables this learning to be stable by establishing a balance between excitatory and inhibitory currents at each neuron in the network. The synaptic weights between source and reservoir neurons form a basis set for the input patterns. The neural trajectories generated in the reservoir due to input stimulation and lateral connections between reservoir neurons can be readout by the sink layer neurons. This activity is used for adaptation of synapses between reservoir and sink layer neurons. A new measure called the discriminability index (DI) is introduced to compute if the network can discriminate between old patterns already presented in an initial training session. The DI is also used to compute if the network adapts to new patterns without losing its ability to discriminate among old patterns. The final outcome is that the network is able to correctly discriminate between all patterns-both old and new. This result holds as long as inhibitory synapses employ STDP to continuously enable current balance in the network. The results suggest a possible direction for future investigation into how spiking neural networks could address the stability-plasticity question despite having continuous synaptic plasticity.

Dcx Re-expression Reduces Subcortical Band Heterotopia and Seizure Threshold in an Animal Model of Neuronal Migration Disorder

PubMed Central

Manent, Jean-Bernard; Wang, Yu; Chang, YoonJeung; Paramasivam, Murugan; LoTurco, Joseph J

2009-01-01

Disorders of neuronal migration can lead to malformations of the cerebral neocortex that greatly increase the risk of seizures. It remains untested whether malformations caused by disorders in neuronal migration can be reduced by reactivating cellular migration, and whether such repair can decrease seizure risk. Here we show, in a rat model of subcortical band heterotopia (SBH) generated by in utero RNAi of Dcx, that aberrantly positioned neurons can be stimulated to migrate by re-expressing Dcx after birth. Re-starting migration in this way both reduces neocortical malformations and restores neuronal patterning. We find further that the capacity to reduce SBH has a critical period in early postnatal development. Moreover, intervention after birth reduces convulsant-induced seizure threshold to levels similar to that of malformation-free controls. These results suggest that disorders of neuronal migration may be eventually treatable by re-engaging developmental programs both to reduce the size of cortical malformations and to reduce seizure risk. PMID:19098909

Hopf bifurcation of an (n + 1) -neuron bidirectional associative memory neural network model with delays.

PubMed

Xiao, Min; Zheng, Wei Xing; Cao, Jinde

2013-01-01

Recent studies on Hopf bifurcations of neural networks with delays are confined to simplified neural network models consisting of only two, three, four, five, or six neurons. It is well known that neural networks are complex and large-scale nonlinear dynamical systems, so the dynamics of the delayed neural networks are very rich and complicated. Although discussing the dynamics of networks with a few neurons may help us to understand large-scale networks, there are inevitably some complicated problems that may be overlooked if simplified networks are carried over to large-scale networks. In this paper, a general delayed bidirectional associative memory neural network model with n + 1 neurons is considered. By analyzing the associated characteristic equation, the local stability of the trivial steady state is examined, and then the existence of the Hopf bifurcation at the trivial steady state is established. By applying the normal form theory and the center manifold reduction, explicit formulae are derived to determine the direction and stability of the bifurcating periodic solution. Furthermore, the paper highlights situations where the Hopf bifurcations are particularly critical, in the sense that the amplitude and the period of oscillations are very sensitive to errors due to tolerances in the implementation of neuron interconnections. It is shown that the sensitivity is crucially dependent on the delay and also significantly influenced by the feature of the number of neurons. Numerical simulations are carried out to illustrate the main results.

Altered motor network activation and functional connectivity in adult Tourette's syndrome.

PubMed

Werner, Cornelius J; Stöcker, Tony; Kellermann, Thilo; Bath, Jessica; Beldoch, Margarete; Schneider, Frank; Wegener, Hans Peter; Shah, Jon N; Neuner, Irene

2011-11-01

Tourette's syndrome (TS) is a developmental neuropsychiatric disorder characterized by motor and vocal tics as well as psychiatric comorbidities. Disturbances of the fronto-striatal-thalamic pathways responsible for motor control and impulse inhibition have been previously described in other studies. Although differences in motor performance are well recognized, imaging data elucidating the neuronal correlates are scarce. Here, we examined 19 adult TS patients (13 men, aged 22-52 years, mean = 34.3 years) and 18 age- and sex-matched controls (13 men, aged 24-57 years, mean = 37.6 years) in a functional magnetic resonance imaging study at 1.5 T. We corrected for possible confounds introduced by tics, motion, and brain-structural differences as well as age, sex, comorbidities, and medication. Patients and controls were asked to perform a sequential finger-tapping task using their right, left, and both hands, respectively. Task performance was monitored by simultaneous MR-compatible video recording. Although behavioral data obtained during scanning did not show significant differences across groups, we observed differential neuronal activation patterns depending on both handedness (dominant vs. nondominant) and tapping frequency in frontal, parietal, and subcortical areas. When controlling for open motor performance, a failure of deactivation in easier task conditions was found in the subgenual cingulate cortex in the TS patients. In addition, performance-related functional connectivity of lower- and higher-order motor networks differed between patients and controls. In summary, although open performance was comparable, patients showed different neuronal networks and connectivity patterns when performing increasingly demanding tasks, further illustrating the impact of the disease on the motor system. Copyright © 2011 Wiley-Liss, Inc.

Functional Neurosurgery in the Treatment of Severe Obsessive Compulsive Disorder and Major Depression: Overview of Disease Circuits and Therapeutic Targeting for the Clinician

PubMed Central

Shah, Dhwani B.; Pesiridou, Angeliki; Baltuch, Gordon H.; Malone, Donald A.; O’Reardon, John P.

2008-01-01

Over the past 20 years, there has been a concerted effort to expand our understanding of the neural circuitry involved in the pathogenesis of psychiatric disorders. Distinct neuronal circuits and networks have been implicated in obsessive compulsive disorder (OCD) and major depressive disorder (MDD) involving feedback loops between the cortex, striatum, and thalamus. When neurosurgery is used as a therapeutic tool in severe OCD and MDD, the goal is to modulate specific targets or nodes within these networks in an effort to produce symptom relief. Currently, four lesioning neurosurgical procedures are utilized for treatment refractory OCD and MDD: cingulotomy, capsulotomy, subcaudate tractotomy, and limbic leucotomy. Deep brain stimulation (DBS) is a novel neurosurgical approach that has some distinct advantages over lesioning procedures. With DBS, the desired clinical effect can be achieved by reversible, high frequency stimulation in a nucleus or at a node in the circuit without the need to produce an irreversible lesion. Recent trials of deep brain stimulation in both OCD and MDD at several neuroanatomical targets have reported promising early results in highly refractory patients and with a good safety profile. Future definitive trials in MDD and OCD are envisaged. PMID:19727257

Interplay between population firing stability and single neuron dynamics in hippocampal networks

PubMed Central

Slomowitz, Edden; Styr, Boaz; Vertkin, Irena; Milshtein-Parush, Hila; Nelken, Israel; Slutsky, Michael; Slutsky, Inna

2015-01-01

Neuronal circuits' ability to maintain the delicate balance between stability and flexibility in changing environments is critical for normal neuronal functioning. However, to what extent individual neurons and neuronal populations maintain internal firing properties remains largely unknown. In this study, we show that distributions of spontaneous population firing rates and synchrony are subject to accurate homeostatic control following increase of synaptic inhibition in cultured hippocampal networks. Reduction in firing rate triggered synaptic and intrinsic adaptive responses operating as global homeostatic mechanisms to maintain firing macro-stability, without achieving local homeostasis at the single-neuron level. Adaptive mechanisms, while stabilizing population firing properties, reduced short-term facilitation essential for synaptic discrimination of input patterns. Thus, invariant ongoing population dynamics emerge from intrinsically unstable activity patterns of individual neurons and synapses. The observed differences in the precision of homeostatic control at different spatial scales challenge cell-autonomous theory of network homeostasis and suggest the existence of network-wide regulation rules. DOI: http://dx.doi.org/10.7554/eLife.04378.001 PMID:25556699

Synchronization properties of networks of electrically coupled neurons in the presence of noise and heterogeneities.

PubMed

Ostojic, Srdjan; Brunel, Nicolas; Hakim, Vincent

2009-06-01

We investigate how synchrony can be generated or induced in networks of electrically coupled integrate-and-fire neurons subject to noisy and heterogeneous inputs. Using analytical tools, we find that in a network under constant external inputs, synchrony can appear via a Hopf bifurcation from the asynchronous state to an oscillatory state. In a homogeneous net work, in the oscillatory state all neurons fire in synchrony, while in a heterogeneous network synchrony is looser, many neurons skipping cycles of the oscillation. If the transmission of action potentials via the electrical synapses is effectively excitatory, the Hopf bifurcation is supercritical, while effectively inhibitory transmission due to pronounced hyperpolarization leads to a subcritical bifurcation. In the latter case, the network exhibits bistability between an asynchronous state and an oscillatory state where all the neurons fire in synchrony. Finally we show that for time-varying external inputs, electrical coupling enhances the synchronization in an asynchronous network via a resonance at the firing-rate frequency.

Neuronal networks: flip-flops in the brain.

PubMed

McCormick, David A

2005-04-26

Neuronal activity can rapidly flip-flop between stable states. Although these semi-stable states can be generated through interactions of neuronal networks, it is now known that they can also occur in vivo through intrinsic ionic currents.

Matrix stiffness modulates formation and activity of neuronal networks of controlled architectures.

PubMed

Lantoine, Joséphine; Grevesse, Thomas; Villers, Agnès; Delhaye, Geoffrey; Mestdagh, Camille; Versaevel, Marie; Mohammed, Danahe; Bruyère, Céline; Alaimo, Laura; Lacour, Stéphanie P; Ris, Laurence; Gabriele, Sylvain

2016-05-01

The ability to construct easily in vitro networks of primary neurons organized with imposed topologies is required for neural tissue engineering as well as for the development of neuronal interfaces with desirable characteristics. However, accumulating evidence suggests that the mechanical properties of the culture matrix can modulate important neuronal functions such as growth, extension, branching and activity. Here we designed robust and reproducible laminin-polylysine grid micropatterns on cell culture substrates that have similar biochemical properties but a 100-fold difference in Young's modulus to investigate the role of the matrix rigidity on the formation and activity of cortical neuronal networks. We found that cell bodies of primary cortical neurons gradually accumulate in circular islands, whereas axonal extensions spread on linear tracks to connect circular islands. Our findings indicate that migration of cortical neurons is enhanced on soft substrates, leading to a faster formation of neuronal networks. Furthermore, the pre-synaptic density was two times higher on stiff substrates and consistently the number of action potentials and miniature synaptic currents was enhanced on stiff substrates. Taken together, our results provide compelling evidence to indicate that matrix stiffness is a key parameter to modulate the growth dynamics, synaptic density and electrophysiological activity of cortical neuronal networks, thus providing useful information on scaffold design for neural tissue engineering. Copyright © 2016 Elsevier Ltd. All rights reserved.

Comparison of the dynamics of neural interactions between current-based and conductance-based integrate-and-fire recurrent networks

PubMed Central

Cavallari, Stefano; Panzeri, Stefano; Mazzoni, Alberto

2014-01-01

Models of networks of Leaky Integrate-and-Fire (LIF) neurons are a widely used tool for theoretical investigations of brain function. These models have been used both with current- and conductance-based synapses. However, the differences in the dynamics expressed by these two approaches have been so far mainly studied at the single neuron level. To investigate how these synaptic models affect network activity, we compared the single neuron and neural population dynamics of conductance-based networks (COBNs) and current-based networks (CUBNs) of LIF neurons. These networks were endowed with sparse excitatory and inhibitory recurrent connections, and were tested in conditions including both low- and high-conductance states. We developed a novel procedure to obtain comparable networks by properly tuning the synaptic parameters not shared by the models. The so defined comparable networks displayed an excellent and robust match of first order statistics (average single neuron firing rates and average frequency spectrum of network activity). However, these comparable networks showed profound differences in the second order statistics of neural population interactions and in the modulation of these properties by external inputs. The correlation between inhibitory and excitatory synaptic currents and the cross-neuron correlation between synaptic inputs, membrane potentials and spike trains were stronger and more stimulus-modulated in the COBN. Because of these properties, the spike train correlation carried more information about the strength of the input in the COBN, although the firing rates were equally informative in both network models. Moreover, the network activity of COBN showed stronger synchronization in the gamma band, and spectral information about the input higher and spread over a broader range of frequencies. These results suggest that the second order statistics of network dynamics depend strongly on the choice of synaptic model. PMID:24634645

Comparison of the dynamics of neural interactions between current-based and conductance-based integrate-and-fire recurrent networks.

PubMed

Cavallari, Stefano; Panzeri, Stefano; Mazzoni, Alberto

2014-01-01

Models of networks of Leaky Integrate-and-Fire (LIF) neurons are a widely used tool for theoretical investigations of brain function. These models have been used both with current- and conductance-based synapses. However, the differences in the dynamics expressed by these two approaches have been so far mainly studied at the single neuron level. To investigate how these synaptic models affect network activity, we compared the single neuron and neural population dynamics of conductance-based networks (COBNs) and current-based networks (CUBNs) of LIF neurons. These networks were endowed with sparse excitatory and inhibitory recurrent connections, and were tested in conditions including both low- and high-conductance states. We developed a novel procedure to obtain comparable networks by properly tuning the synaptic parameters not shared by the models. The so defined comparable networks displayed an excellent and robust match of first order statistics (average single neuron firing rates and average frequency spectrum of network activity). However, these comparable networks showed profound differences in the second order statistics of neural population interactions and in the modulation of these properties by external inputs. The correlation between inhibitory and excitatory synaptic currents and the cross-neuron correlation between synaptic inputs, membrane potentials and spike trains were stronger and more stimulus-modulated in the COBN. Because of these properties, the spike train correlation carried more information about the strength of the input in the COBN, although the firing rates were equally informative in both network models. Moreover, the network activity of COBN showed stronger synchronization in the gamma band, and spectral information about the input higher and spread over a broader range of frequencies. These results suggest that the second order statistics of network dynamics depend strongly on the choice of synaptic model.

Bifurcations of large networks of two-dimensional integrate and fire neurons.

PubMed

Nicola, Wilten; Campbell, Sue Ann

2013-08-01

Recently, a class of two-dimensional integrate and fire models has been used to faithfully model spiking neurons. This class includes the Izhikevich model, the adaptive exponential integrate and fire model, and the quartic integrate and fire model. The bifurcation types for the individual neurons have been thoroughly analyzed by Touboul (SIAM J Appl Math 68(4):1045-1079, 2008). However, when the models are coupled together to form networks, the networks can display bifurcations that an uncoupled oscillator cannot. For example, the networks can transition from firing with a constant rate to burst firing. This paper introduces a technique to reduce a full network of this class of neurons to a mean field model, in the form of a system of switching ordinary differential equations. The reduction uses population density methods and a quasi-steady state approximation to arrive at the mean field system. Reduced models are derived for networks with different topologies and different model neurons with biologically derived parameters. The mean field equations are able to qualitatively and quantitatively describe the bifurcations that the full networks display. Extensions and higher order approximations are discussed.

Balanced excitation and inhibition are required for high-capacity, noise-robust neuronal selectivity

PubMed Central

Abbott, L. F.; Sompolinsky, Haim

2017-01-01

Neurons and networks in the cerebral cortex must operate reliably despite multiple sources of noise. To evaluate the impact of both input and output noise, we determine the robustness of single-neuron stimulus selective responses, as well as the robustness of attractor states of networks of neurons performing memory tasks. We find that robustness to output noise requires synaptic connections to be in a balanced regime in which excitation and inhibition are strong and largely cancel each other. We evaluate the conditions required for this regime to exist and determine the properties of networks operating within it. A plausible synaptic plasticity rule for learning that balances weight configurations is presented. Our theory predicts an optimal ratio of the number of excitatory and inhibitory synapses for maximizing the encoding capacity of balanced networks for given statistics of afferent activations. Previous work has shown that balanced networks amplify spatiotemporal variability and account for observed asynchronous irregular states. Here we present a distinct type of balanced network that amplifies small changes in the impinging signals and emerges automatically from learning to perform neuronal and network functions robustly. PMID:29042519

An artificial network model for estimating the network structure underlying partially observed neuronal signals.

PubMed

Komatsu, Misako; Namikawa, Jun; Chao, Zenas C; Nagasaka, Yasuo; Fujii, Naotaka; Nakamura, Kiyohiko; Tani, Jun

2014-01-01

Many previous studies have proposed methods for quantifying neuronal interactions. However, these methods evaluated the interactions between recorded signals in an isolated network. In this study, we present a novel approach for estimating interactions between observed neuronal signals by theorizing that those signals are observed from only a part of the network that also includes unobserved structures. We propose a variant of the recurrent network model that consists of both observable and unobservable units. The observable units represent recorded neuronal activity, and the unobservable units are introduced to represent activity from unobserved structures in the network. The network structures are characterized by connective weights, i.e., the interaction intensities between individual units, which are estimated from recorded signals. We applied this model to multi-channel brain signals recorded from monkeys, and obtained robust network structures with physiological relevance. Furthermore, the network exhibited common features that portrayed cortical dynamics as inversely correlated interactions between excitatory and inhibitory populations of neurons, which are consistent with the previous view of cortical local circuits. Our results suggest that the novel concept of incorporating an unobserved structure into network estimations has theoretical advantages and could provide insights into brain dynamics beyond what can be directly observed. Copyright © 2014 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

Neuromodulation: Selected approaches and challenges

PubMed Central

Parpura, Vladimir; Silva, Gabriel A.; Tass, Peter A.; Bennet, Kevin E.; Meyyappan, Meyya; Koehne, Jessica; Lee, Kendall H.; Andrews, Russell J.

2012-01-01

The brain operates through complex interactions in the flow of information and signal processing within neural networks. The “wiring” of such networks, being neuronal or glial, can physically and/or functionally go rogue in various pathological states. Neuromodulation, as a multidisciplinary venture, attempts to correct such faulty nets. In this review, selected approaches and challenges in neuromoduation are discussed. The use of water-dispersible carbon nanotubes have proven effective in modulation of neurite outgrowth in culture as well as in aiding regeneration after spinal cord injury in vivo. Studying neural circuits using computational biology and analytical engineering approaches brings to light geometrical mapping of dynamics within neural networks, much needed information for stimulation interventions in medical practice. Indeed, sophisticated desynchronization approaches used for brain stimulation have been successful in coaxing “misfiring” neuronal circuits to resume productive firing patterns in various human disorders. Devices have been developed for the real time measurement of various neurotransmitters as well as electrical activity in the human brain during electrical deep brain stimulation. Such devices can establish the dynamics of electrochemical changes in the brain during stimulation. With increasing application of nanomaterials in devices for electrical and chemical recording and stimulating in the brain, the era of cellular, and even intracellular, precision neuromodulation will soon be upon us. PMID:23190025

Single-Neuron NMDA Receptor Phenotype Influences Neuronal Rewiring and Reintegration following Traumatic Injury

PubMed Central

Patel, Tapan P.; Ventre, Scott C.; Geddes-Klein, Donna; Singh, Pallab K.

2014-01-01

Alterations in the activity of neural circuits are a common consequence of traumatic brain injury (TBI), but the relationship between single-neuron properties and the aggregate network behavior is not well understood. We recently reported that the GluN2B-containing NMDA receptors (NMDARs) are key in mediating mechanical forces during TBI, and that TBI produces a complex change in the functional connectivity of neuronal networks. Here, we evaluated whether cell-to-cell heterogeneity in the connectivity and aggregate contribution of GluN2B receptors to [Ca2+]i before injury influenced the functional rewiring, spontaneous activity, and network plasticity following injury using primary rat cortical dissociated neurons. We found that the functional connectivity of a neuron to its neighbors, combined with the relative influx of calcium through distinct NMDAR subtypes, together contributed to the individual neuronal response to trauma. Specifically, individual neurons whose [Ca2+]i oscillations were largely due to GluN2B NMDAR activation lost many of their functional targets 1 h following injury. In comparison, neurons with large GluN2A contribution or neurons with high functional connectivity both independently protected against injury-induced loss in connectivity. Mechanistically, we found that traumatic injury resulted in increased uncorrelated network activity, an effect linked to reduction of the voltage-sensitive Mg2+ block of GluN2B-containing NMDARs. This uncorrelated activation of GluN2B subtypes after injury significantly limited the potential for network remodeling in response to a plasticity stimulus. Together, our data suggest that two single-cell characteristics, the aggregate contribution of NMDAR subtypes and the number of functional connections, influence network structure following traumatic injury. PMID:24647941

Acetylated tau destabilizes the cytoskeleton in the axon initial segment and is mislocalized to the somatodendritic compartment.

PubMed

Sohn, Peter Dongmin; Tracy, Tara E; Son, Hye-In; Zhou, Yungui; Leite, Renata E P; Miller, Bruce L; Seeley, William W; Grinberg, Lea T; Gan, Li

2016-06-29

Neurons are highly polarized cells in which asymmetric axonal-dendritic distribution of proteins is crucial for neuronal function. Loss of polarized distribution of the axonal protein tau is an early sign of Alzheimer's disease (AD) and other neurodegenerative disorders. The cytoskeletal network in the axon initial segment (AIS) forms a barrier between the axon and the somatodentritic compartment, contributing to axonal retention of tau. Although perturbation of the AIS cytoskeleton has been implicated in neurological disorders, the molecular triggers and functional consequence of AIS perturbation are incompletely understood. Here we report that tau acetylation and consequent destabilization of the AIS cytoskeleton promote the somatodendritic mislocalization of tau. AIS cytoskeletal proteins, including ankyrin G and βIV-spectrin, were downregulated in AD brains and negatively correlated with an increase in tau acetylated at K274 and K281. AIS proteins were also diminished in transgenic mice expressing tauK274/281Q, a tau mutant that mimics K274 and K281 acetylation. In primary neuronal cultures, the tauK274/281Q mutant caused hyperdynamic microtubules (MTs) in the AIS, shown by live-imaging of MT mobility and fluorescence recovery after photobleaching. Using photoconvertible tau constructs, we found that axonal tauK274/281Q was missorted into the somatodendritic compartment. Stabilizing MTs with epothilone D to restore the cytoskeletal barrier in the AIS prevented tau mislocalization in primary neuronal cultures. Together, these findings demonstrate that tau acetylation contributes to the pathogenesis of neurodegenerative disease by compromising the cytoskeletal sorting machinery in the AIS.

Possible neuronal mechanisms of sleep disturbances in patients with autism spectrum disorders and attention-deficit/hyperactivity disorder.

PubMed

Kohyama, Jun

2016-12-01

The most common form of sleep disturbance among both patients with autism spectrum disorders and patients with attention-deficit/hyperactivity disorder is sleep-onset insomnia, but the neuronal mechanisms underlying it have yet to be elucidated and no specific treatment strategy has been proposed. This means that many caregivers struggle to manage this problem on a daily basis. This paper presents a hypothesis about the neuronal mechanisms underlying insomnia in patients with autism spectrum disorders and attention-deficit/hyperactivity disorder based on recent clinical and basic research. It is proposed that three neuronal mechanisms (increased orexinergic system activity, reduced 5-hydroxytryptamine and melatonergic system activity, rapid eye movement sleep reduction) are involved in insomnia in both autism spectrum disorders and attention-deficit/hyperactivity disorder. This suggests that antagonists against the orexin receptors may have beneficial effects on insomnia in patients with autism spectrum disorders or attention-deficit/hyperactivity disorder. To the best of the author's knowledge there has been no research into the effects of this agent on insomnia in these patient groups. Large, controlled trials should be carried out. Copyright © 2016 Elsevier Ltd. All rights reserved.

Autism and psychosis expressions diametrically modulate the right temporoparietal junction.

PubMed

Abu-Akel, Ahmad M; Apperly, Ian A; Wood, Stephen J; Hansen, Peter C

2017-10-01

The mentalizing network is atypically activated in autism and schizophrenia spectrum disorders. While these disorders are considered diagnostically independent, expressions of both can co-occur in the same individual. We examined the concurrent effect of autism traits and psychosis proneness on the activity of the mentalizing network in 24 neurotypical adults while performing a social competitive game. Activations were observed in the paracingulate cortex and the right temporoparietal junction (rTPJ). Autism traits and psychosis proneness did not modulate activity within the paracingulate or the dorsal component of the rTPJ. However, diametric modulations of autism traits and psychosis proneness were observed in the posterior (rvpTPJ) and anterior (rvaTPJ) subdivisions of the ventral rTPJ, which respectively constitute core regions within the mentalizing and attention-reorienting networks. Within the rvpTPJ, increasing autism tendencies decreased activity, and increasing psychosis proneness increased activity. This effect was reversed within the rvaTPJ. We suggest that this results from an interaction between regions responsible for higher level social cognitive processing (rvpTPJ) and regions responsible for domain-general attentional processes (rvaTPJ). The observed diametric modulation of autism tendencies and psychosis proneness of neuronal activity within the mentalizing network highlights the importance of assessing both autism and psychosis expressions within the individual.

Modelling Feedback Excitation, Pacemaker Properties and Sensory Switching of Electrically Coupled Brainstem Neurons Controlling Rhythmic Activity

PubMed Central

Hull, Michael J.; Soffe, Stephen R.; Willshaw, David J.; Roberts, Alan

2016-01-01

What cellular and network properties allow reliable neuronal rhythm generation or firing that can be started and stopped by brief synaptic inputs? We investigate rhythmic activity in an electrically-coupled population of brainstem neurons driving swimming locomotion in young frog tadpoles, and how activity is switched on and off by brief sensory stimulation. We build a computational model of 30 electrically-coupled conditional pacemaker neurons on one side of the tadpole hindbrain and spinal cord. Based on experimental estimates for neuron properties, population sizes, synapse strengths and connections, we show that: long-lasting, mutual, glutamatergic excitation between the neurons allows the network to sustain rhythmic pacemaker firing at swimming frequencies following brief synaptic excitation; activity persists but rhythm breaks down without electrical coupling; NMDA voltage-dependency doubles the range of synaptic feedback strengths generating sustained rhythm. The network can be switched on and off at short latency by brief synaptic excitation and inhibition. We demonstrate that a population of generic Hodgkin-Huxley type neurons coupled by glutamatergic excitatory feedback can generate sustained asynchronous firing switched on and off synaptically. We conclude that networks of neurons with NMDAR mediated feedback excitation can generate self-sustained activity following brief synaptic excitation. The frequency of activity is limited by the kinetics of the neuron membrane channels and can be stopped by brief inhibitory input. Network activity can be rhythmic at lower frequencies if the neurons are electrically coupled. Our key finding is that excitatory synaptic feedback within a population of neurons can produce switchable, stable, sustained firing without synaptic inhibition. PMID:26824331

Simultaneous multi-patch-clamp and extracellular-array recordings: Single neuron reflects network activity.

PubMed

Vardi, Roni; Goldental, Amir; Sardi, Shira; Sheinin, Anton; Kanter, Ido

2016-11-08

The increasing number of recording electrodes enhances the capability of capturing the network's cooperative activity, however, using too many monitors might alter the properties of the measured neural network and induce noise. Using a technique that merges simultaneous multi-patch-clamp and multi-electrode array recordings of neural networks in-vitro, we show that the membrane potential of a single neuron is a reliable and super-sensitive probe for monitoring such cooperative activities and their detailed rhythms. Specifically, the membrane potential and the spiking activity of a single neuron are either highly correlated or highly anti-correlated with the time-dependent macroscopic activity of the entire network. This surprising observation also sheds light on the cooperative origin of neuronal burst in cultured networks. Our findings present an alternative flexible approach to the technique based on a massive tiling of networks by large-scale arrays of electrodes to monitor their activity.

Channel Noise-Enhanced Synchronization Transitions Induced by Time Delay in Adaptive Neuronal Networks with Spike-Timing-Dependent Plasticity

NASA Astrophysics Data System (ADS)

Xie, Huijuan; Gong, Yubing; Wang, Baoying

In this paper, we numerically study the effect of channel noise on synchronization transitions induced by time delay in adaptive scale-free Hodgkin-Huxley neuronal networks with spike-timing-dependent plasticity (STDP). It is found that synchronization transitions by time delay vary as channel noise intensity is changed and become most pronounced when channel noise intensity is optimal. This phenomenon depends on STDP and network average degree, and it can be either enhanced or suppressed as network average degree increases depending on channel noise intensity. These results show that there are optimal channel noise and network average degree that can enhance the synchronization transitions by time delay in the adaptive neuronal networks. These findings could be helpful for better understanding of the regulation effect of channel noise on synchronization of neuronal networks. They could find potential implications for information transmission in neural systems.

Cooperation-Controlled Learning for Explicit Class Structure in Self-Organizing Maps

PubMed Central

Kamimura, Ryotaro

2014-01-01

We attempt to demonstrate the effectiveness of multiple points of view toward neural networks. By restricting ourselves to two points of view of a neuron, we propose a new type of information-theoretic method called “cooperation-controlled learning.” In this method, individual and collective neurons are distinguished from one another, and we suppose that the characteristics of individual and collective neurons are different. To implement individual and collective neurons, we prepare two networks, namely, cooperative and uncooperative networks. The roles of these networks and the roles of individual and collective neurons are controlled by the cooperation parameter. As the parameter is increased, the role of cooperative networks becomes more important in learning, and the characteristics of collective neurons become more dominant. On the other hand, when the parameter is small, individual neurons play a more important role. We applied the method to the automobile and housing data from the machine learning database and examined whether explicit class boundaries could be obtained. Experimental results showed that cooperation-controlled learning, in particular taking into account information on input units, could be used to produce clearer class structure than conventional self-organizing maps. PMID:25309950

Bayesian Inference and Online Learning in Poisson Neuronal Networks.

PubMed

Huang, Yanping; Rao, Rajesh P N

2016-08-01

Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.

Collective Dynamics for Heterogeneous Networks of Theta Neurons

NASA Astrophysics Data System (ADS)

Luke, Tanushree

Collective behavior in neural networks has often been used as an indicator of communication between different brain areas. These collective synchronization and desynchronization patterns are also considered an important feature in understanding normal and abnormal brain function. To understand the emergence of these collective patterns, I create an analytic model that identifies all such macroscopic steady-states attainable by a network of Type-I neurons. This network, whose basic unit is the model "theta'' neuron, contains a mixture of excitable and spiking neurons coupled via a smooth pulse-like synapse. Applying the Ott-Antonsen reduction method in the thermodynamic limit, I obtain a low-dimensional evolution equation that describes the asymptotic dynamics of the macroscopic mean field of the network. This model can be used as the basis in understanding more complicated neuronal networks when additional dynamical features are included. From this reduced dynamical equation for the mean field, I show that the network exhibits three collective attracting steady-states. The first two are equilibrium states that both reflect partial synchronization in the network, whereas the third is a limit cycle in which the degree of network synchronization oscillates in time. In addition to a comprehensive identification of all possible attracting macro-states, this analytic model permits a complete bifurcation analysis of the collective behavior of the network with respect to three key network features: the degree of excitability of the neurons, the heterogeneity of the population, and the overall coupling strength. The network typically tends towards the two macroscopic equilibrium states when the neuron's intrinsic dynamics and the network interactions reinforce each other. In contrast, the limit cycle state, bifurcations, and multistability tend to occur when there is competition between these network features. I also outline here an extension of the above model where the neurons' excitability now varies in time sinuosoidally, thus simulating a parabolic bursting network. This time-varying excitability can lead to the emergence of macroscopic chaos and multistability in the collective behavior of the network. Finally, I expand the single population model described above to examine a two-population neuronal network where each population has its own unique mixture of excitable and spiking neurons, as well as its own coupling strength (either excitatory or inhibitory in nature). Specifically, I consider the situation where the first population is only allowed to influence the second population without any feedback, thus effectively creating a feed-forward "driver-response" system. In this special arrangement, the driver's asymptotic macroscopic dynamics are fully explored in the comprehensive analysis of the single population. Then, in the presence of an influence from the driver, the modified dynamics of the second population, which now acts as a response population, can also be fully analyzed. As in the time-varying model, these modifications give rise to richer dynamics to the response population than those found from the single population formalism, including multi-periodicity and chaos.

Synchronization in a noise-driven developing neural network

NASA Astrophysics Data System (ADS)

Lin, I.-H.; Wu, R.-K.; Chen, C.-M.

2011-11-01

We use computer simulations to investigate the structural and dynamical properties of a developing neural network whose activity is driven by noise. Structurally, the constructed neural networks in our simulations exhibit the small-world properties that have been observed in several neural networks. The dynamical change of neuronal membrane potential is described by the Hodgkin-Huxley model, and two types of learning rules, including spike-timing-dependent plasticity (STDP) and inverse STDP, are considered to restructure the synaptic strength between neurons. Clustered synchronized firing (SF) of the network is observed when the network connectivity (number of connections/maximal connections) is about 0.75, in which the firing rate of neurons is only half of the network frequency. At the connectivity of 0.86, all neurons fire synchronously at the network frequency. The network SF frequency increases logarithmically with the culturing time of a growing network and decreases exponentially with the delay time in signal transmission. These conclusions are consistent with experimental observations. The phase diagrams of SF in a developing network are investigated for both learning rules.

A Reevaluation of the Vestibulo-Ocular Reflex: New Ideas of its Purpose, Properties, Neural Substrate, and Disorders

NASA Technical Reports Server (NTRS)

Leigh, R. John; Brandt, Thomas

1992-01-01

Conventional views of the Vestibulo-Ocular Reflex (VOR) have emphasized testing with caloric stimuli and by passively rotating patients at low frequencies in a chair. The properties of the VOR tested under these conditions differ from the performance of this reflex during the natural function for which it evolved-locomotion. Only the VOR (and not visually mediated eye movements) can cope with the high-frequency angular and linear perturbations of the head that occur during locomotion; this is achieved by generating eye movements at short latency (less than 16 msec). Interpretation of vestibular testing is enhanced by the realization that, although the di- and trisynaptic components of the VOR are essential for this short-latency response, the overall accuracy and plasticity of the VOR depend upon a distributed, parallel network of neurons involving the vestibular nuclei. Neurons in this network variously encode inputs from the labyrinthine semicircular canals and otoliths, as well as from the visual and somatosensory systems. The central vestibular pathways branch to contact vestibular cortex (for perception) and the spinal cord (for control of posture). Thus, the vestibular nuclei basically coordinate the stabilization of gaze and posture, and contribute to the perception of verticality and self-motion. Consequently, brainstem disorders that disrupt the VOR cause not just only nystagmus, but also instability of posture (eg, increased fore-aft sway in patients with downbeat nystagmus) and disturbance of spatial orientation (eg, tilt of the subjective visual vertical in Wallenberg's syndrome).

Hebbian based learning with winner-take-all for spiking neural networks

NASA Astrophysics Data System (ADS)

Gupta, Ankur; Long, Lyle

2009-03-01

Learning methods for spiking neural networks are not as well developed as the traditional neural networks that widely use back-propagation training. We propose and implement a Hebbian based learning method with winner-take-all competition for spiking neural networks. This approach is spike time dependent which makes it naturally well suited for a network of spiking neurons. Homeostasis with Hebbian learning is implemented which ensures stability and quicker learning. Homeostasis implies that the net sum of incoming weights associated with a neuron remains the same. Winner-take-all is also implemented for competitive learning between output neurons. We implemented this learning rule on a biologically based vision processing system that we are developing, and use layers of leaky integrate and fire neurons. The network when presented with 4 bars (or Gabor filters) of different orientation learns to recognize the bar orientations (or Gabor filters). After training, each output neuron learns to recognize a bar at specific orientation and responds by firing more vigorously to that bar and less vigorously to others. These neurons are found to have bell shaped tuning curves and are similar to the simple cells experimentally observed by Hubel and Wiesel in the striate cortex of cat and monkey.

Oxygen matters: tissue culture oxygen levels affect mitochondrial function and structure as well as responses to HIV viroproteins.

PubMed

Tiede, L M; Cook, E A; Morsey, B; Fox, H S

2011-12-22

Mitochondrial dysfunction is implicated in a majority of neurodegenerative disorders and much study of neurodegenerative disease is done on cultured neurons. In traditional tissue culture, the oxygen level that cells experience is dramatically higher (21%) than in vivo conditions (1-11%). These differences can alter experimental results, especially, pertaining to mitochondria and oxidative metabolism. Our results show that primary neurons cultured at physiological oxygen levels found in the brain showed higher polarization, lower rates of ROS production, larger mitochondrial networks, greater cytoplasmic fractions of mitochondria and larger mitochondrial perimeters than those cultured at higher oxygen levels. Although neurons cultured in either physiological oxygen or atmospheric oxygen exhibit significant increases in mitochondrial reactive oxygen species (ROS) production when treated with the human immunodeficiency virus (HIV) virotoxin trans-activator of transcription, mitochondria of neurons cultured at physiological oxygen underwent depolarization with dramatically increased cell death, whereas those cultured at atmospheric oxygen became hyperpolarized with no increase in cell death. Studies with a second HIV virotoxin, negative regulation factor (Nef), revealed that Nef treatment also increased mitochondrial ROS production for both the oxygen conditions, but resulted in mitochondrial depolarization and increased death only in neurons cultured in physiological oxygen. These results indicate a role for oxidative metabolism in a mechanism of neurotoxicity during HIV infection and demonstrate the importance of choosing the correct, physiological, culture oxygen in mitochondrial studies performed in neurons.

Alterations in neuronal activity in basal ganglia-thalamocortical circuits in the parkinsonian state

PubMed Central

Galvan, Adriana; Devergnas, Annaelle; Wichmann, Thomas

2015-01-01

In patients with Parkinson’s disease and in animal models of this disorder, neurons in the basal ganglia and related regions in thalamus and cortex show changes that can be recorded by using electrophysiologic single-cell recording techniques, including altered firing rates and patterns, pathologic oscillatory activity and increased inter-neuronal synchronization. In addition, changes in synaptic potentials or in the joint spiking activities of populations of neurons can be monitored as alterations in local field potentials (LFPs), electroencephalograms (EEGs) or electrocorticograms (ECoGs). Most of the mentioned electrophysiologic changes are probably related to the degeneration of diencephalic dopaminergic neurons, leading to dopamine loss in the striatum and other basal ganglia nuclei, although degeneration of non-dopaminergic cell groups may also have a role. The altered electrical activity of the basal ganglia and associated nuclei may contribute to some of the motor signs of the disease. We here review the current knowledge of the electrophysiologic changes at the single cell level, the level of local populations of neural elements, and the level of the entire basal ganglia-thalamocortical network in parkinsonism, and discuss the possible use of this information to optimize treatment approaches to Parkinson’s disease, such as deep brain stimulation (DBS) therapy. PMID:25698937

Cellular manganese content is developmentally regulated in human dopaminergic neurons

NASA Astrophysics Data System (ADS)

Kumar, Kevin K.; Lowe, Edward W., Jr.; Aboud, Asad A.; Neely, M. Diana; Redha, Rey; Bauer, Joshua A.; Odak, Mihir; Weaver, C. David; Meiler, Jens; Aschner, Michael; Bowman, Aaron B.

2014-10-01

Manganese (Mn) is both an essential biological cofactor and neurotoxicant. Disruption of Mn biology in the basal ganglia has been implicated in the pathogenesis of neurodegenerative disorders, such as parkinsonism and Huntington's disease. Handling of other essential metals (e.g. iron and zinc) occurs via complex intracellular signaling networks that link metal detection and transport systems. However, beyond several non-selective transporters, little is known about the intracellular processes regulating neuronal Mn homeostasis. We hypothesized that small molecules that modulate intracellular Mn could provide insight into cell-level Mn regulatory mechanisms. We performed a high throughput screen of 40,167 small molecules for modifiers of cellular Mn content in a mouse striatal neuron cell line. Following stringent validation assays and chemical informatics, we obtained a chemical `toolbox' of 41 small molecules with diverse structure-activity relationships that can alter intracellular Mn levels under biologically relevant Mn exposures. We utilized this toolbox to test for differential regulation of Mn handling in human floor-plate lineage dopaminergic neurons, a lineage especially vulnerable to environmental Mn exposure. We report differential Mn accumulation between developmental stages and stage-specific differences in the Mn-altering activity of individual small molecules. This work demonstrates cell-level regulation of Mn content across neuronal differentiation.

Metabolic and hemodynamic events following changes in neuronal activity: current hypotheses, theoretical predictions and in vivo NMR experimental findings

PubMed Central

Mangia, Silvia; Giove, Federico; Tkáč, Ivan; Logothetis, Nikos K.; Henry, Pierre-Gilles; Olman, Cheryl A.; Maraviglia, Bruno; Di Salle, Francesco; Uğurbil, Kâmil

2009-01-01

Unraveling the energy metabolism and the hemodynamic outcomes of excitatory and inhibitory neuronal activity is critical not only for our basic understanding of overall brain function, but also for the understanding of many brain disorders. Methodologies of magnetic resonance spectroscopy (MRS) and magnetic resonance imaging (MRI) are powerful tools for the non-invasive investigation of brain metabolism and physiology. However, the temporal and spatial resolution of in vivo MRS and MRI is not suitable to provide direct evidence for hypotheses that involve metabolic compartmentalization between different cell types, or to untangle the complex neuronal micro-circuitry which results in changes of electrical activity. This review aims at describing how the current models of brain metabolism, mainly built on the basis of in vitro evidence, relate to experimental findings recently obtained in vivo by 1H MRS, 13C MRS and MRI. The hypotheses related to the role of different metabolic substrates, the metabolic neuron-glia interactions, along with the available theoretical predictions of the energy budget of neurotransmission, will be discussed. In addition, the cellular and network mechanisms that characterize different types of increased and suppressed neuronal activity will be considered within the sensitivity-constraints of MRS and MRI. PMID:19002199

Inference of neuronal network spike dynamics and topology from calcium imaging data

PubMed Central

Lütcke, Henry; Gerhard, Felipe; Zenke, Friedemann; Gerstner, Wulfram; Helmchen, Fritjof

2013-01-01

Two-photon calcium imaging enables functional analysis of neuronal circuits by inferring action potential (AP) occurrence (“spike trains”) from cellular fluorescence signals. It remains unclear how experimental parameters such as signal-to-noise ratio (SNR) and acquisition rate affect spike inference and whether additional information about network structure can be extracted. Here we present a simulation framework for quantitatively assessing how well spike dynamics and network topology can be inferred from noisy calcium imaging data. For simulated AP-evoked calcium transients in neocortical pyramidal cells, we analyzed the quality of spike inference as a function of SNR and data acquisition rate using a recently introduced peeling algorithm. Given experimentally attainable values of SNR and acquisition rate, neural spike trains could be reconstructed accurately and with up to millisecond precision. We then applied statistical neuronal network models to explore how remaining uncertainties in spike inference affect estimates of network connectivity and topological features of network organization. We define the experimental conditions suitable for inferring whether the network has a scale-free structure and determine how well hub neurons can be identified. Our findings provide a benchmark for future calcium imaging studies that aim to reliably infer neuronal network properties. PMID:24399936

Autaptic effects on synchrony of neurons coupled by electrical synapses

NASA Astrophysics Data System (ADS)

Kim, Youngtae

2017-07-01

In this paper, we numerically study the effects of a special synapse known as autapse on synchronization of population of Morris-Lecar (ML) neurons coupled by electrical synapses. Several configurations of the ML neuronal populations such as a pair or a ring or a globally coupled network with and without autapses are examined. While most of the papers on the autaptic effects on synchronization have used networks of neurons of same spiking rate, we use the network of neurons of different spiking rates. We find that the optimal autaptic coupling strength and the autaptic time delay enhance synchronization in our neural networks. We use the phase response curve analysis to explain the enhanced synchronization by autapses. Our findings reveal the important relationship between the intraneuronal feedback loop and the interneuronal coupling.

Phase-locking and bistability in neuronal networks with synaptic depression

NASA Astrophysics Data System (ADS)

Akcay, Zeynep; Huang, Xinxian; Nadim, Farzan; Bose, Amitabha

2018-02-01

We consider a recurrent network of two oscillatory neurons that are coupled with inhibitory synapses. We use the phase response curves of the neurons and the properties of short-term synaptic depression to define Poincaré maps for the activity of the network. The fixed points of these maps correspond to phase-locked modes of the network. Using these maps, we analyze the conditions that allow short-term synaptic depression to lead to the existence of bistable phase-locked, periodic solutions. We show that bistability arises when either the phase response curve of the neuron or the short-term depression profile changes steeply enough. The results apply to any Type I oscillator and we illustrate our findings using the Quadratic Integrate-and-Fire and Morris-Lecar neuron models.

An NV-Diamond Magnetic Imager for Neuroscience

NASA Astrophysics Data System (ADS)

Turner, Matthew; Schloss, Jennifer; Bauch, Erik; Hart, Connor; Walsworth, Ronald

2017-04-01

We present recent progress towards imaging time-varying magnetic fields from neurons using nitrogen-vacancy centers in diamond. The diamond neuron imager is noninvasive, label-free, and achieves single-cell resolution and state-of-the-art broadband sensitivity. By imaging magnetic fields from injected currents in mammalian neurons, we will map functional neuronal network connections and illuminate biophysical properties of neurons invisible to traditional electrophysiology. Furthermore, through enhancing magnetometer sensitivity, we aim to demonstrate real-time imaging of action potentials from networks of mammalian neurons.

Network Receptive Field Modeling Reveals Extensive Integration and Multi-feature Selectivity in Auditory Cortical Neurons.

PubMed

Harper, Nicol S; Schoppe, Oliver; Willmore, Ben D B; Cui, Zhanfeng; Schnupp, Jan W H; King, Andrew J

2016-11-01

Cortical sensory neurons are commonly characterized using the receptive field, the linear dependence of their response on the stimulus. In primary auditory cortex neurons can be characterized by their spectrotemporal receptive fields, the spectral and temporal features of a sound that linearly drive a neuron. However, receptive fields do not capture the fact that the response of a cortical neuron results from the complex nonlinear network in which it is embedded. By fitting a nonlinear feedforward network model (a network receptive field) to cortical responses to natural sounds, we reveal that primary auditory cortical neurons are sensitive over a substantially larger spectrotemporal domain than is seen in their standard spectrotemporal receptive fields. Furthermore, the network receptive field, a parsimonious network consisting of 1-7 sub-receptive fields that interact nonlinearly, consistently better predicts neural responses to auditory stimuli than the standard receptive fields. The network receptive field reveals separate excitatory and inhibitory sub-fields with different nonlinear properties, and interaction of the sub-fields gives rise to important operations such as gain control and conjunctive feature detection. The conjunctive effects, where neurons respond only if several specific features are present together, enable increased selectivity for particular complex spectrotemporal structures, and may constitute an important stage in sound recognition. In conclusion, we demonstrate that fitting auditory cortical neural responses with feedforward network models expands on simple linear receptive field models in a manner that yields substantially improved predictive power and reveals key nonlinear aspects of cortical processing, while remaining easy to interpret in a physiological context.

Network Receptive Field Modeling Reveals Extensive Integration and Multi-feature Selectivity in Auditory Cortical Neurons

PubMed Central

Willmore, Ben D. B.; Cui, Zhanfeng; Schnupp, Jan W. H.; King, Andrew J.

2016-01-01

Cortical sensory neurons are commonly characterized using the receptive field, the linear dependence of their response on the stimulus. In primary auditory cortex neurons can be characterized by their spectrotemporal receptive fields, the spectral and temporal features of a sound that linearly drive a neuron. However, receptive fields do not capture the fact that the response of a cortical neuron results from the complex nonlinear network in which it is embedded. By fitting a nonlinear feedforward network model (a network receptive field) to cortical responses to natural sounds, we reveal that primary auditory cortical neurons are sensitive over a substantially larger spectrotemporal domain than is seen in their standard spectrotemporal receptive fields. Furthermore, the network receptive field, a parsimonious network consisting of 1–7 sub-receptive fields that interact nonlinearly, consistently better predicts neural responses to auditory stimuli than the standard receptive fields. The network receptive field reveals separate excitatory and inhibitory sub-fields with different nonlinear properties, and interaction of the sub-fields gives rise to important operations such as gain control and conjunctive feature detection. The conjunctive effects, where neurons respond only if several specific features are present together, enable increased selectivity for particular complex spectrotemporal structures, and may constitute an important stage in sound recognition. In conclusion, we demonstrate that fitting auditory cortical neural responses with feedforward network models expands on simple linear receptive field models in a manner that yields substantially improved predictive power and reveals key nonlinear aspects of cortical processing, while remaining easy to interpret in a physiological context. PMID:27835647

Resting-state activity in development and maintenance of normal brain function.

PubMed

Pizoli, Carolyn E; Shah, Manish N; Snyder, Abraham Z; Shimony, Joshua S; Limbrick, David D; Raichle, Marcus E; Schlaggar, Bradley L; Smyth, Matthew D

2011-07-12

One of the most intriguing recent discoveries concerning brain function is that intrinsic neuronal activity manifests as spontaneous fluctuations of the blood oxygen level-dependent (BOLD) functional MRI signal. These BOLD fluctuations exhibit temporal synchrony within widely distributed brain regions known as resting-state networks. Resting-state networks are present in the waking state, during sleep, and under general anesthesia, suggesting that spontaneous neuronal activity plays a fundamental role in brain function. Despite its ubiquitous presence, the physiological role of correlated, spontaneous neuronal activity remains poorly understood. One hypothesis is that this activity is critical for the development of synaptic connections and maintenance of synaptic homeostasis. We had a unique opportunity to test this hypothesis in a 5-y-old boy with severe epileptic encephalopathy. The child developed marked neurologic dysfunction in association with a seizure disorder, resulting in a 1-y period of behavioral regression and progressive loss of developmental milestones. His EEG showed a markedly abnormal pattern of high-amplitude, disorganized slow activity with frequent generalized and multifocal epileptiform discharges. Resting-state functional connectivity MRI showed reduced BOLD fluctuations and a pervasive lack of normal connectivity. The child underwent successful corpus callosotomy surgery for treatment of drop seizures. Postoperatively, the patient's behavior returned to baseline, and he resumed development of new skills. The waking EEG revealed a normal background, and functional connectivity MRI demonstrated restoration of functional connectivity architecture. These results provide evidence that intrinsic, coherent neuronal signaling may be essential to the development and maintenance of the brain's functional organization.

Excitement and synchronization of small-world neuronal networks with short-term synaptic plasticity.

PubMed

Han, Fang; Wiercigroch, Marian; Fang, Jian-An; Wang, Zhijie

2011-10-01

Excitement and synchronization of electrically and chemically coupled Newman-Watts (NW) small-world neuronal networks with a short-term synaptic plasticity described by a modified Oja learning rule are investigated. For each type of neuronal network, the variation properties of synaptic weights are examined first. Then the effects of the learning rate, the coupling strength and the shortcut-adding probability on excitement and synchronization of the neuronal network are studied. It is shown that the synaptic learning suppresses the over-excitement, helps synchronization for the electrically coupled network but impairs synchronization for the chemically coupled one. Both the introduction of shortcuts and the increase of the coupling strength improve synchronization and they are helpful in increasing the excitement for the chemically coupled network, but have little effect on the excitement of the electrically coupled one.

Convergent neuromodulation onto a network neuron can have divergent effects at the network level.

PubMed

Kintos, Nickolas; Nusbaum, Michael P; Nadim, Farzan

2016-04-01

Different neuromodulators often target the same ion channel. When such modulators act on different neuron types, this convergent action can enable a rhythmic network to produce distinct outputs. Less clear are the functional consequences when two neuromodulators influence the same ion channel in the same neuron. We examine the consequences of this seeming redundancy using a mathematical model of the crab gastric mill (chewing) network. This network is activated in vitro by the projection neuron MCN1, which elicits a half-center bursting oscillation between the reciprocally-inhibitory neurons LG and Int1. We focus on two neuropeptides which modulate this network, including a MCN1 neurotransmitter and the hormone crustacean cardioactive peptide (CCAP). Both activate the same voltage-gated current (I MI ) in the LG neuron. However, I MI-MCN1 , resulting from MCN1 released neuropeptide, has phasic dynamics in its maximal conductance due to LG presynaptic inhibition of MCN1, while I MI-CCAP retains the same maximal conductance in both phases of the gastric mill rhythm. Separation of time scales allows us to produce a 2D model from which phase plane analysis shows that, as in the biological system, I MI-MCN1 and I MI-CCAP primarily influence the durations of opposing phases of this rhythm. Furthermore, I MI-MCN1 influences the rhythmic output in a manner similar to the Int1-to-LG synapse, whereas I MI-CCAP has an influence similar to the LG-to-Int1 synapse. These results show that distinct neuromodulators which target the same voltage-gated ion channel in the same network neuron can nevertheless produce distinct effects at the network level, providing divergent neuromodulator actions on network activity.

Convergent neuromodulation onto a network neuron can have divergent effects at the network level

PubMed Central

Kintos, Nickolas; Nusbaum, Michael P.; Nadim, Farzan

2016-01-01

Different neuromodulators often target the same ion channel. When such modulators act on different neuron types, this convergent action can enable a rhythmic network to produce distinct outputs. Less clear are the functional consequences when two neuromodulators influence the same ion channel in the same neuron. We examine the consequences of this seeming redundancy using a mathematical model of the crab gastric mill (chewing) network. This network is activated in vitro by the projection neuron MCN1, which elicits a half-center bursting oscillation between the reciprocally-inhibitory neurons LG and Int1. We focus on two neuropeptides which modulate this network, including a MCN1 neurotransmitter and the hormone crustacean cardioactive peptide (CCAP). Both activate the same voltage-gated current (IMI) in the LG neuron. However, IMI-MCN1, resulting from MCN1 released neuropeptide, has phasic dynamics in its maximal conductance due to LG presynaptic inhibition of MCN1, while IMI-CCAP retains the same maximal conductance in both phases of the gastric mill rhythm. Separation of time scales allows us to produce a 2D model from which phase plane analysis shows that, as in the biological system, IMI-MCN1 and IMI-CCAP primarily influence the durations of opposing phases of this rhythm. Furthermore, IMI-MCN1 influences the rhythmic output in a manner similar to the Int1-to-LG synapse, whereas IMI-CCAP has an influence similar to the LG-to-Int1 synapse. These results show that distinct neuromodulators which target the same voltage-gated ion channel in the same network neuron can nevertheless produce distinct effects at the network level, providing divergent neuromodulator actions on network activity. PMID:26798029

Obtaining Arbitrary Prescribed Mean Field Dynamics for Recurrently Coupled Networks of Type-I Spiking Neurons with Analytically Determined Weights

PubMed Central

Nicola, Wilten; Tripp, Bryan; Scott, Matthew

2016-01-01

A fundamental question in computational neuroscience is how to connect a network of spiking neurons to produce desired macroscopic or mean field dynamics. One possible approach is through the Neural Engineering Framework (NEF). The NEF approach requires quantities called decoders which are solved through an optimization problem requiring large matrix inversion. Here, we show how a decoder can be obtained analytically for type I and certain type II firing rates as a function of the heterogeneity of its associated neuron. These decoders generate approximants for functions that converge to the desired function in mean-squared error like 1/N, where N is the number of neurons in the network. We refer to these decoders as scale-invariant decoders due to their structure. These decoders generate weights for a network of neurons through the NEF formula for weights. These weights force the spiking network to have arbitrary and prescribed mean field dynamics. The weights generated with scale-invariant decoders all lie on low dimensional hypersurfaces asymptotically. We demonstrate the applicability of these scale-invariant decoders and weight surfaces by constructing networks of spiking theta neurons that replicate the dynamics of various well known dynamical systems such as the neural integrator, Van der Pol system and the Lorenz system. As these decoders are analytically determined and non-unique, the weights are also analytically determined and non-unique. We discuss the implications for measured weights of neuronal networks. PMID:26973503

Obtaining Arbitrary Prescribed Mean Field Dynamics for Recurrently Coupled Networks of Type-I Spiking Neurons with Analytically Determined Weights.

PubMed

Nicola, Wilten; Tripp, Bryan; Scott, Matthew

2016-01-01

A fundamental question in computational neuroscience is how to connect a network of spiking neurons to produce desired macroscopic or mean field dynamics. One possible approach is through the Neural Engineering Framework (NEF). The NEF approach requires quantities called decoders which are solved through an optimization problem requiring large matrix inversion. Here, we show how a decoder can be obtained analytically for type I and certain type II firing rates as a function of the heterogeneity of its associated neuron. These decoders generate approximants for functions that converge to the desired function in mean-squared error like 1/N, where N is the number of neurons in the network. We refer to these decoders as scale-invariant decoders due to their structure. These decoders generate weights for a network of neurons through the NEF formula for weights. These weights force the spiking network to have arbitrary and prescribed mean field dynamics. The weights generated with scale-invariant decoders all lie on low dimensional hypersurfaces asymptotically. We demonstrate the applicability of these scale-invariant decoders and weight surfaces by constructing networks of spiking theta neurons that replicate the dynamics of various well known dynamical systems such as the neural integrator, Van der Pol system and the Lorenz system. As these decoders are analytically determined and non-unique, the weights are also analytically determined and non-unique. We discuss the implications for measured weights of neuronal networks.

A microfluidic platform for controlled biochemical stimulation of twin neuronal networks.

PubMed

Biffi, Emilia; Piraino, Francesco; Pedrocchi, Alessandra; Fiore, Gianfranco B; Ferrigno, Giancarlo; Redaelli, Alberto; Menegon, Andrea; Rasponi, Marco

2012-06-01

Spatially and temporally resolved delivery of soluble factors is a key feature for pharmacological applications. In this framework, microfluidics coupled to multisite electrophysiology offers great advantages in neuropharmacology and toxicology. In this work, a microfluidic device for biochemical stimulation of neuronal networks was developed. A micro-chamber for cell culturing, previously developed and tested for long term neuronal growth by our group, was provided with a thin wall, which partially divided the cell culture region in two sub-compartments. The device was reversibly coupled to a flat micro electrode array and used to culture primary neurons in the same microenvironment. We demonstrated that the two fluidically connected compartments were able to originate two parallel neuronal networks with similar electrophysiological activity but functionally independent. Furthermore, the device allowed to connect the outlet port to a syringe pump and to transform the static culture chamber in a perfused one. At 14 days invitro, sub-networks were independently stimulated with a test molecule, tetrodotoxin, a neurotoxin known to block action potentials, by means of continuous delivery. Electrical activity recordings proved the ability of the device configuration to selectively stimulate each neuronal network individually. The proposed microfluidic approach represents an innovative methodology to perform biological, pharmacological, and electrophysiological experiments on neuronal networks. Indeed, it allows for controlled delivery of substances to cells, and it overcomes the limitations due to standard drug stimulation techniques. Finally, the twin network configuration reduces biological variability, which has important outcomes on pharmacological and drug screening.

A new approach to applying feedforward neural networks to the prediction of musculoskeletal disorder risk.

PubMed

Chen, C L; Kaber, D B; Dempsey, P G

2000-06-01

A new and improved method to feedforward neural network (FNN) development for application to data classification problems, such as the prediction of levels of low-back disorder (LBD) risk associated with industrial jobs, is presented. Background on FNN development for data classification is provided along with discussions of previous research and neighborhood (local) solution search methods for hard combinatorial problems. An analytical study is presented which compared prediction accuracy of a FNN based on an error-back propagation (EBP) algorithm with the accuracy of a FNN developed by considering results of local solution search (simulated annealing) for classifying industrial jobs as posing low or high risk for LBDs. The comparison demonstrated superior performance of the FNN generated using the new method. The architecture of this FNN included fewer input (predictor) variables and hidden neurons than the FNN developed based on the EBP algorithm. Independent variable selection methods and the phenomenon of 'overfitting' in FNN (and statistical model) generation for data classification are discussed. The results are supportive of the use of the new approach to FNN development for applications to musculoskeletal disorders and risk forecasting in other domains.

Respiratory pathophysiology: sleep-related breathing disorders

PubMed Central

Schäfer, Thorsten

2006-01-01

A widespread network of respiratory-related neurons within the brainstem controls the regular respiratory cycle, which is dependent upon unspecific and specific drives like hypoxia or hypercapnia. This respiratory network and its respiratory drives are subjects to typical changes during the transition from wakefulness to sleep and within the various sleep states, which favor a destabilization of breathing during sleep. There is also a respiratory-related innervation of the dilating and stiffening pharyngeal muscles as well as a local reflex control of the basic tone of upper airway muscles, both of which are influenced by the different states of wakefulness and sleep. These sleep-related changes cause an increase in upper airway resistance during sleep. In healthy subjects, however, these features during sleep are almost completely compensated and the gas exchange is hardly hindered. However, in the case of illness, severe disordered breathing, disturbed gas exchange and interrupted sleep may occur. The central hypoventilation syndrome, central apnea-hypopnea syndromes, as well as the obstructive sleep apnea syndrome belong to these diseases. Because of the intense research, we have a detailed picture of the pathophysiological mechanisms of the origin and the maintenance of sleep-related breathing disorders. PMID:22073070

Effects of Morphology Constraint on Electrophysiological Properties of Cortical Neurons

NASA Astrophysics Data System (ADS)

Zhu, Geng; Du, Liping; Jin, Lei; Offenhäusser, Andreas

2016-04-01

There is growing interest in engineering nerve cells in vitro to control architecture and connectivity of cultured neuronal networks or to build neuronal networks with predictable computational function. Pattern technologies, such as micro-contact printing, have been developed to design ordered neuronal networks. However, electrophysiological characteristics of the single patterned neuron haven’t been reported. Here, micro-contact printing, using polyolefine polymer (POP) stamps with high resolution, was employed to grow cortical neurons in a designed structure. The results demonstrated that the morphology of patterned neurons was well constrained, and the number of dendrites was decreased to be about 2. Our electrophysiological results showed that alterations of dendritic morphology affected firing patterns of neurons and neural excitability. When stimulated by current, though both patterned and un-patterned neurons presented regular spiking, the dynamics and strength of the response were different. The un-patterned neurons exhibited a monotonically increasing firing frequency in response to injected current, while the patterned neurons first exhibited frequency increase and then a slow decrease. Our findings indicate that the decrease in dendritic complexity of cortical neurons will influence their electrophysiological characteristics and alter their information processing activity, which could be considered when designing neuronal circuitries.

[Brain imaging in autism spectrum disorders. A review].

PubMed

Dziobek, I; Köhne, S

2011-05-01

In the past two decades, an increasing number of functional and structural brain imaging studies has provided insights into the neurobiological basis of autism spectrum disorders (ASD). This article summarizes pertinent functional brain imaging studies addressing the neuronal underpinnings of ASD symptomatology (impairments in social interaction and communication, repetitive and restrictive behavior) and associated neuropsychological deficits (theory of mind, executive functions, central coherence), complemented by relevant structural imaging findings. The results of these studies show that although cognitive functions in ASD are generally mediated by the same brain regions as in typically developed individuals, the degree and especially the patterns of brain activation often differ. Therefore, a hypothesis of aberrant network connectivity has increasingly been favored over one of focal brain dysfunction.

The transfer and transformation of collective network information in gene-matched networks.

PubMed

Kitsukawa, Takashi; Yagi, Takeshi

2015-10-09

Networks, such as the human society network, social and professional networks, and biological system networks, contain vast amounts of information. Information signals in networks are distributed over nodes and transmitted through intricately wired links, making the transfer and transformation of such information difficult to follow. Here we introduce a novel method for describing network information and its transfer using a model network, the Gene-matched network (GMN), in which nodes (neurons) possess attributes (genes). In the GMN, nodes are connected according to their expression of common genes. Because neurons have multiple genes, the GMN is cluster-rich. We show that, in the GMN, information transfer and transformation were controlled systematically, according to the activity level of the network. Furthermore, information transfer and transformation could be traced numerically with a vector using genes expressed in the activated neurons, the active-gene array, which was used to assess the relative activity among overlapping neuronal groups. Interestingly, this coding style closely resembles the cell-assembly neural coding theory. The method introduced here could be applied to many real-world networks, since many systems, including human society and various biological systems, can be represented as a network of this type.

Interfering of the Reelin/ApoER2/PSD95 Signaling Axis Reactivates Dendritogenesis of Mature Hippocampal Neurons.

PubMed

Ampuero, Estibaliz; Jury, Nur; Härtel, Steffen; Marzolo, María-Paz; van Zundert, Brigitte

2017-05-01

Reelin, an extracellular glycoprotein secreted in embryonic and adult brain, participates in neuronal migration and neuronal plasticity. Extensive evidence shows that reelin via activation of the ApoER2 and VLDLR receptors promotes dendrite and spine formation during early development. Further evidence suggests that reelin signaling is needed to maintain a stable architecture in mature neurons, but, direct evidence is lacking. During activity-dependent maturation of the neuronal circuitry, the synaptic protein PSD95 is inserted into the postsynaptic membrane to induce structural refinement and stability of spines and dendrites. Given that ApoER2 interacts with PSD95, we tested if reelin signaling interference in adult neurons reactivates the dendritic architecture. Unlike findings in developing cultures, the presently obtained in vitro and in vivo data show, for the first time, that reelin signaling interference robustly increase dendritogenesis and reduce spine density in mature hippocampal neurons. In particular, the expression of a mutant ApoER2 form (ApoER2-tailless), which is unable to interact with PSD95 and hence cannot transduce reelin signaling, resulted in robust dendritogenesis in mature hippocampal neurons in vitro. These results indicate that reelin/ApoER2/PSD95 signaling is important for neuronal structure maintenance in mature neurons. Mechanistically, obtained immunofluorescent data indicate that reelin signaling impairment reduced synaptic PSD95 levels, consequently leading to synaptic re-insertion of NR2B-NMDARs. Our findings underscore the importance of reelin in maintaining adult network stability and reveal a new mode for reactivating dendritogenesis in neurological disorders where dendritic arbor complexity is limited, such as in depression, Alzheimer's disease, and stroke. J. Cell. Physiol. 232: 1187-1199, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Berberine induces neuronal differentiation through inhibition of cancer stemness and epithelial-mesenchymal transition in neuroblastoma cells.

PubMed

Naveen, C R; Gaikwad, Sagar; Agrawal-Rajput, Reena

2016-06-15

Berberine, a plant alkaloid, has been used since many years for treatment of gastrointestinal disorders. It also shows promising medicinal use against metabolic disorders, neurodegenerative disorders and cancer; however its efficacy in neuroblastoma (NB) is poorly explored. EMT is important in cancer stemness and metastasis resulting in failure to differentiate; thus targeting EMT and related pathways can have clinical benefits. Potential of berberine was investigated for (i) neuronal differentiation and cancer stemness inhibition, (ii) underlying molecular mechanisms regulating cancer-stemness and (iii) EMT reversal. Using neuro2a (N2a) neuroblastoma cells (NB); we investigated effect of berberine on neuronal differentiation, cancer-stemness, EMT and underlying signalling by immunofluorescence, RT-PCR, Western blot. High glucose-induced TGF-β mediated EMT model was used to test EMT reversal potential by Western blot and RT-PCR. STRING analysis was done to determine and validate functional protein-interaction networks. We demonstrate berberine induces neuronal differentiation accompanying increased neuronal differentiation markers like MAP2, β-III tubulin and NCAM; generated neurons were viable. Berberine attenuated cancer stemness markers CD133, β-catenin, n-myc, sox2, notch2 and nestin. Berberine potentiated G0/G1 cell cycle arrest by inhibiting proliferation, cyclin dependent kinases and cyclins resulting in apoptosis through increased bax/bcl-2 ratio. Restoration of tumor suppressor proteins, p27 and p53, indicate promising anti-cancer property. The induction of NCAM and reduction in its polysialylation indicates anti-migratory potential which is supported by down regulation of MMP-2/9. It increased epithelial marker laminin and smad and increased Hsp70 levels also suggest its protective role. Molecular insights revealed that berberine regulates EMT via downregulation of PI3/Akt and Ras-Raf-ERK signalling and subsequent upregulation of p38-MAPK. TGF-β secretion from N2a cells was potentiated by high glucose and negatively regulated by berberine through modulation of TGF-β receptors II and III. Berberine reverted mesenchymal markers, vimentin and fibronectin, with restoration of epithelial marker E-cadherin, highlighting the role of berberine in reversal of EMT. Collectively, the study demonstrates prospective use of berberine against neuroblastoma as elucidated through inhibition of fundamental characteristics of cancer stem cells: tumorigenicity and failure to differentiation and instigates reversal in the EMT. Copyright © 2016 Elsevier GmbH. All rights reserved.

Population equations for degree-heterogenous neural networks

NASA Astrophysics Data System (ADS)

Kähne, M.; Sokolov, I. M.; Rüdiger, S.

2017-11-01

We develop a statistical framework for studying recurrent networks with broad distributions of the number of synaptic links per neuron. We treat each group of neurons with equal input degree as one population and derive a system of equations determining the population-averaged firing rates. The derivation rests on an assumption of a large number of neurons and, additionally, an assumption of a large number of synapses per neuron. For the case of binary neurons, analytical solutions can be constructed, which correspond to steps in the activity versus degree space. We apply this theory to networks with degree-correlated topology and show that complex, multi-stable regimes can result for increasing correlations. Our work is motivated by the recent finding of subnetworks of highly active neurons and the fact that these neurons tend to be connected to each other with higher probability.

Multistability, local pattern formation, and global collective firing in a small-world network of nonleaky integrate-and-fire neurons.

PubMed

Rothkegel, Alexander; Lehnertz, Klaus

2009-03-01

We investigate numerically the collective dynamical behavior of pulse-coupled nonleaky integrate-and-fire neurons that are arranged on a two-dimensional small-world network. To ensure ongoing activity, we impose a probability for spontaneous firing for each neuron. We study network dynamics evolving from different sets of initial conditions in dependence on coupling strength and rewiring probability. Besides a homogeneous equilibrium state for low coupling strength, we observe different local patterns including cyclic waves, spiral waves, and turbulentlike patterns, which-depending on network parameters-interfere with the global collective firing of the neurons. We attribute the various network dynamics to distinct regimes in the parameter space. For the same network parameters different network dynamics can be observed depending on the set of initial conditions only. Such a multistable behavior and the interplay between local pattern formation and global collective firing may be attributable to the spatiotemporal dynamics of biological networks.

Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models.

PubMed

Mazzoni, Alberto; Lindén, Henrik; Cuntz, Hermann; Lansner, Anders; Panzeri, Stefano; Einevoll, Gaute T

2015-12-01

Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best "LFP proxy", we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with "ground-truth" LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo.

Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models

PubMed Central

Cuntz, Hermann; Lansner, Anders; Panzeri, Stefano; Einevoll, Gaute T.

2015-01-01

Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best “LFP proxy”, we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with “ground-truth” LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo. PMID:26657024

Beyond good and evil: A putative continuum-sorting hypothesis for the functional role of proBDNF/BDNF-propeptide/mBDNF in antidepressant treatment.

PubMed

Diniz, Cassiano R A F; Casarotto, Plinio C; Resstel, Leonardo; Joca, Sâmia R L

2018-04-04

Depression and posttraumatic stress disorder are assumed to be maladaptive responses to stress and antidepressants are thought to counteract such responses by increasing BDNF (brain-derived neurotrophic factor) levels. BDNF acts through TrkB (tropomyosin-related receptor kinase B) and plays a central role in neuroplasticity. In contrast, both precursor proBDNF and BDNF propeptide (another metabolic product from proBDNF cleavage) have a high affinity to p75 receptor (p75R) and usually convey apoptosis and neuronal shrinkage. Although BDNF and proBDNF/propeptide apparently act in opposite ways, neuronal turnover and remodeling might be a final common way that both act to promote more effective neuronal networking, avoiding neuronal redundancy and the misleading effects of environmental contingencies. This review aims to provide a brief overview about the BDNF functional role in antidepressant action and about p75R and TrkB signaling to introduce the "continuum-sorting hypothesis." The resulting hypothesis suggests that both BDNF/proBDNF and BDNF/propeptide act as protagonists to fine-tune antidepressant-dependent neuroplasticity in crucial brain structures to modulate behavioral responses to stress. Copyright © 2018 Elsevier Ltd. All rights reserved.

Possible Effects of Synaptic Imbalances on Oligodendrocyte–Axonic Interactions in Schizophrenia: A Hypothetical Model

PubMed Central

Mitterauer, Bernhard J.; Kofler-Westergren, Birgitta

2011-01-01

A model of glial–neuronal interactions is proposed that could be explanatory for the demyelination identified in brains with schizophrenia. It is based on two hypotheses: (1) that glia–neuron systems are functionally viable and important for normal brain function, and (2) that disruption of this postulated function disturbs the glial categorization function, as shown by formal analysis. According to this model, in schizophrenia receptors on astrocytes in glial–neuronal synaptic units are not functional, loosing their modulatory influence on synaptic neurotransmission. Hence, an unconstrained neurotransmission flux occurs that hyperactivates the axon and floods the cognate receptors of neurotransmitters on oligodendrocytes. The excess of neurotransmitters may have a toxic effect on oligodendrocytes and myelin, causing demyelination. In parallel, an increasing impairment of axons may disconnect neuronal networks. It is formally shown how oligodendrocytes normally categorize axonic information processing via their processes. Demyelination decomposes the oligodendrocyte–axonic system making it incapable to generate categories of information. This incoherence may be responsible for symptoms of disorganization in schizophrenia, such as thought disorder, inappropriate affect and incommunicable motor behavior. In parallel, the loss of oligodendrocytes affects gap junctions in the panglial syncytium, presumably responsible for memory impairment in schizophrenia. PMID:21647404

Analysis of connectivity map: Control to glutamate injured and phenobarbital treated neuronal network

NASA Astrophysics Data System (ADS)

Kamal, Hassan; Kanhirodan, Rajan; Srinivas, Kalyan V.; Sikdar, Sujit K.

2010-04-01

We study the responses of a cultured neural network when it is exposed to epileptogenesis glutamate injury causing epilepsy and subsequent treatment with phenobarbital by constructing connectivity map of neurons using correlation matrix. This study is particularly useful in understanding the pharmaceutical drug induced changes in the neuronal network properties with insights into changes at the systems biology level.

Mirror neuron activity associated with social impairments but not age in autism spectrum disorder.

PubMed

Enticott, Peter G; Kennedy, Hayley A; Rinehart, Nicole J; Tonge, Bruce J; Bradshaw, John L; Taffe, John R; Daskalakis, Zafiris J; Fitzgerald, Paul B

2012-03-01

The neurobiology of autism spectrum disorder (ASD) is not particularly well understood, and biomedical treatment approaches are therefore extremely limited. A prominent explanatory model suggests that social-relating symptoms may arise from dysfunction within the mirror neuron system, while a recent neuroimaging study suggests that these impairments in ASD might reduce with age. Participants with autism spectrum disorder (i.e., DSM-IV autistic disorder or Asperger's disorder) (n = 34) and matched control subjects (n = 36) completed a transcranial magnetic stimulation study in which corticospinal excitability was assessed during the observation of hand gestures. Regression analyses revealed that the ASD group presented with significantly reduced corticospinal excitability during the observation of a transitive hand gesture (relative to observation of a static hand) (p < .05), which indicates reduced putative mirror neuron system activity within ventral premotor cortex/inferior frontal gyrus. Among the ASD group, there was also a negative association between putative mirror neuron activity and self-reported social-relating impairments, but there was no indication that mirror neuron impairments in ASD decrease with age. These data provide general support for the mirror neuron hypothesis of autism; researchers now must clarify the precise functional significance of mirror neurons to truly understand their role in the neuropathophysiology of ASD and to determine whether they should be used as targets for the treatment of ASD.

The formation mechanism of defects, spiral wave in the network of neurons.

PubMed

Wu, Xinyi; Ma, Jun

2013-01-01

A regular network of neurons is constructed by using the Morris-Lecar (ML) neuron with the ion channels being considered, and the potential mechnism of the formation of a spiral wave is investigated in detail. Several spiral waves are initiated by blocking the target wave with artificial defects and/or partial blocking (poisoning) in ion channels. Furthermore, possible conditions for spiral wave formation and the effect of partial channel blocking are discussed completely. Our results are summarized as follows. 1) The emergence of a target wave depends on the transmembrane currents with diversity, which mapped from the external forcing current and this kind of diversity is associated with spatial heterogeneity in the media. 2) Distinct spiral wave could be induced to occupy the network when the target wave is broken by partially blocking the ion channels of a fraction of neurons (local poisoned area), and these generated spiral waves are similar with the spiral waves induced by artificial defects. It is confirmed that partial channel blocking of some neurons in the network could play a similar role in breaking a target wave as do artificial defects; 3) Channel noise and additive Gaussian white noise are also considered, and it is confirmed that spiral waves are also induced in the network in the presence of noise. According to the results mentioned above, we conclude that appropriate poisoning in ion channels of neurons in the network acts as 'defects' on the evolution of the spatiotemporal pattern, and accounts for the emergence of a spiral wave in the network of neurons. These results could be helpful to understand the potential cause of the formation and development of spiral waves in the cortex of a neuronal system.

The Formation Mechanism of Defects, Spiral Wave in the Network of Neurons

PubMed Central

Wu, Xinyi; Ma, Jun

2013-01-01

A regular network of neurons is constructed by using the Morris-Lecar (ML) neuron with the ion channels being considered, and the potential mechnism of the formation of a spiral wave is investigated in detail. Several spiral waves are initiated by blocking the target wave with artificial defects and/or partial blocking (poisoning) in ion channels. Furthermore, possible conditions for spiral wave formation and the effect of partial channel blocking are discussed completely. Our results are summarized as follows. 1) The emergence of a target wave depends on the transmembrane currents with diversity, which mapped from the external forcing current and this kind of diversity is associated with spatial heterogeneity in the media. 2) Distinct spiral wave could be induced to occupy the network when the target wave is broken by partially blocking the ion channels of a fraction of neurons (local poisoned area), and these generated spiral waves are similar with the spiral waves induced by artificial defects. It is confirmed that partial channel blocking of some neurons in the network could play a similar role in breaking a target wave as do artificial defects; 3) Channel noise and additive Gaussian white noise are also considered, and it is confirmed that spiral waves are also induced in the network in the presence of noise. According to the results mentioned above, we conclude that appropriate poisoning in ion channels of neurons in the network acts as ‘defects’ on the evolution of the spatiotemporal pattern, and accounts for the emergence of a spiral wave in the network of neurons. These results could be helpful to understand the potential cause of the formation and development of spiral waves in the cortex of a neuronal system. PMID:23383179

Fast numerical methods for simulating large-scale integrate-and-fire neuronal networks.

PubMed

Rangan, Aaditya V; Cai, David

2007-02-01

We discuss numerical methods for simulating large-scale, integrate-and-fire (I&F) neuronal networks. Important elements in our numerical methods are (i) a neurophysiologically inspired integrating factor which casts the solution as a numerically tractable integral equation, and allows us to obtain stable and accurate individual neuronal trajectories (i.e., voltage and conductance time-courses) even when the I&F neuronal equations are stiff, such as in strongly fluctuating, high-conductance states; (ii) an iterated process of spike-spike corrections within groups of strongly coupled neurons to account for spike-spike interactions within a single large numerical time-step; and (iii) a clustering procedure of firing events in the network to take advantage of localized architectures, such as spatial scales of strong local interactions, which are often present in large-scale computational models-for example, those of the primary visual cortex. (We note that the spike-spike corrections in our methods are more involved than the correction of single neuron spike-time via a polynomial interpolation as in the modified Runge-Kutta methods commonly used in simulations of I&F neuronal networks.) Our methods can evolve networks with relatively strong local interactions in an asymptotically optimal way such that each neuron fires approximately once in [Formula: see text] operations, where N is the number of neurons in the system. We note that quantifications used in computational modeling are often statistical, since measurements in a real experiment to characterize physiological systems are typically statistical, such as firing rate, interspike interval distributions, and spike-triggered voltage distributions. We emphasize that it takes much less computational effort to resolve statistical properties of certain I&F neuronal networks than to fully resolve trajectories of each and every neuron within the system. For networks operating in realistic dynamical regimes, such as strongly fluctuating, high-conductance states, our methods are designed to achieve statistical accuracy when very large time-steps are used. Moreover, our methods can also achieve trajectory-wise accuracy when small time-steps are used.

Effects of Calcium Spikes in the Layer 5 Pyramidal Neuron on Coincidence Detection and Activity Propagation

PubMed Central

Chua, Yansong; Morrison, Abigail

2016-01-01

The role of dendritic spiking mechanisms in neural processing is so far poorly understood. To investigate the role of calcium spikes in the functional properties of the single neuron and recurrent networks, we investigated a three compartment neuron model of the layer 5 pyramidal neuron with calcium dynamics in the distal compartment. By performing single neuron simulations with noisy synaptic input and occasional large coincident input at either just the distal compartment or at both somatic and distal compartments, we show that the presence of calcium spikes confers a substantial advantage for coincidence detection in the former case and a lesser advantage in the latter. We further show that the experimentally observed critical frequency phenomenon, in which action potentials triggered by stimuli near the soma above a certain frequency trigger a calcium spike at distal dendrites, leading to further somatic depolarization, is not exhibited by a neuron receiving realistically noisy synaptic input, and so is unlikely to be a necessary component of coincidence detection. We next investigate the effect of calcium spikes in propagation of spiking activities in a feed-forward network (FFN) embedded in a balanced recurrent network. The excitatory neurons in the network are again connected to either just the distal, or both somatic and distal compartments. With purely distal connectivity, activity propagation is stable and distinguishable for a large range of recurrent synaptic strengths if the feed-forward connections are sufficiently strong, but propagation does not occur in the absence of calcium spikes. When connections are made to both the somatic and the distal compartments, activity propagation is achieved for neurons with active calcium dynamics at a much smaller number of neurons per pool, compared to a network of passive neurons, but quickly becomes unstable as the strength of recurrent synapses increases. Activity propagation at higher scaling factors can be stabilized by increasing network inhibition or introducing short term depression in the excitatory synapses, but the signal to noise ratio remains low. Our results demonstrate that the interaction of synchrony with dendritic spiking mechanisms can have profound consequences for the dynamics on the single neuron and network level. PMID:27499740

Effects of Calcium Spikes in the Layer 5 Pyramidal Neuron on Coincidence Detection and Activity Propagation.

PubMed

Chua, Yansong; Morrison, Abigail

2016-01-01

The role of dendritic spiking mechanisms in neural processing is so far poorly understood. To investigate the role of calcium spikes in the functional properties of the single neuron and recurrent networks, we investigated a three compartment neuron model of the layer 5 pyramidal neuron with calcium dynamics in the distal compartment. By performing single neuron simulations with noisy synaptic input and occasional large coincident input at either just the distal compartment or at both somatic and distal compartments, we show that the presence of calcium spikes confers a substantial advantage for coincidence detection in the former case and a lesser advantage in the latter. We further show that the experimentally observed critical frequency phenomenon, in which action potentials triggered by stimuli near the soma above a certain frequency trigger a calcium spike at distal dendrites, leading to further somatic depolarization, is not exhibited by a neuron receiving realistically noisy synaptic input, and so is unlikely to be a necessary component of coincidence detection. We next investigate the effect of calcium spikes in propagation of spiking activities in a feed-forward network (FFN) embedded in a balanced recurrent network. The excitatory neurons in the network are again connected to either just the distal, or both somatic and distal compartments. With purely distal connectivity, activity propagation is stable and distinguishable for a large range of recurrent synaptic strengths if the feed-forward connections are sufficiently strong, but propagation does not occur in the absence of calcium spikes. When connections are made to both the somatic and the distal compartments, activity propagation is achieved for neurons with active calcium dynamics at a much smaller number of neurons per pool, compared to a network of passive neurons, but quickly becomes unstable as the strength of recurrent synapses increases. Activity propagation at higher scaling factors can be stabilized by increasing network inhibition or introducing short term depression in the excitatory synapses, but the signal to noise ratio remains low. Our results demonstrate that the interaction of synchrony with dendritic spiking mechanisms can have profound consequences for the dynamics on the single neuron and network level.

Nitric Oxide Exerts Basal and Insulin-Dependent Anorexigenic Actions in POMC Hypothalamic Neurons

PubMed Central

Wellhauser, Leigh; Chalmers, Jennifer A.

2016-01-01

The arcuate nucleus of the hypothalamus represents a key center for the control of appetite and feeding through the regulation of 2 key neuronal populations, notably agouti-related peptide/neuropeptide Y and proopimelanocortin (POMC)/cocaine- and amphetamine-regulated transcript neurons. Altered regulation of these neuronal networks, in particular the dysfunction of POMC neurons upon high-fat consumption, is a major pathogenic mechanism involved in the development of obesity and type 2 diabetes mellitus. Efforts are underway to preserve the integrity or enhance the functionality of POMC neurons in order to prevent or treat these metabolic diseases. Here, we report for the first time that the nitric oxide (NO−) donor, sodium nitroprusside (SNP) mediates anorexigenic actions in both hypothalamic tissue and hypothalamic-derived cell models by mediating the up-regulation of POMC levels. SNP increased POMC mRNA in a dose-dependent manner and enhanced α-melanocortin-secreting hormone production and secretion in mHypoA-POMC/GFP-2 cells. SNP also enhanced insulin-driven POMC expression likely by inhibiting the deacetylase activity of sirtuin 1. Furthermore, SNP enhanced insulin-dependent POMC expression, likely by reducing the transcriptional repression of Foxo1 on the POMC gene. Prolonged SNP exposure prevented the development of insulin resistance. Taken together, the NO− donor SNP enhances the anorexigenic potential of POMC neurons by promoting its transcriptional expression independent and in cooperation with insulin. Thus, increasing cellular NO− levels represents a hormone-independent method of promoting anorexigenic output from the existing POMC neuronal populations and may be advantageous in the fight against these prevalent disorders. PMID:26930171

Multiple mechanisms switch an electrically coupled, synaptically inhibited neuron between competing rhythmic oscillators.

PubMed

Gutierrez, Gabrielle J; O'Leary, Timothy; Marder, Eve

2013-03-06

Rhythmic oscillations are common features of nervous systems. One of the fundamental questions posed by these rhythms is how individual neurons or groups of neurons are recruited into different network oscillations. We modeled competing fast and slow oscillators connected to a hub neuron with electrical and inhibitory synapses. We explore the patterns of coordination shown in the network as a function of the electrical coupling and inhibitory synapse strengths with the help of a novel visualization method that we call the "parameterscape." The hub neuron can be switched between the fast and slow oscillators by multiple network mechanisms, indicating that a given change in network state can be achieved by degenerate cellular mechanisms. These results have importance for interpreting experiments employing optogenetic, genetic, and pharmacological manipulations to understand circuit dynamics. Copyright © 2013 Elsevier Inc. All rights reserved.

Introduction to Focus Issue: Rhythms and Dynamic Transitions in Neurological Disease: Modeling, Computation, and Experiment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kaper, Tasso J., E-mail: tasso@bu.edu; Kramer, Mark A., E-mail: mak@bu.edu; Rotstein, Horacio G., E-mail: horacio@njit.edu

Rhythmic neuronal oscillations across a broad range of frequencies, as well as spatiotemporal phenomena, such as waves and bumps, have been observed in various areas of the brain and proposed as critical to brain function. While there is a long and distinguished history of studying rhythms in nerve cells and neuronal networks in healthy organisms, the association and analysis of rhythms to diseases are more recent developments. Indeed, it is now thought that certain aspects of diseases of the nervous system, such as epilepsy, schizophrenia, Parkinson's, and sleep disorders, are associated with transitions or disruptions of neurological rhythms. This focusmore » issue brings together articles presenting modeling, computational, analytical, and experimental perspectives about rhythms and dynamic transitions between them that are associated to various diseases.« less

Therapeutic deep brain stimulation reduces cortical phase-amplitude coupling in Parkinson's disease

PubMed Central

de Hemptinne, Coralie; Swann, Nicole; Ostrem, Jill L.; Ryapolova-Webb, Elena S.; Luciano, Marta San; Galifianakis, Nicholas; Starr, Philip A.

2015-01-01

Deep brain stimulation (DBS) is increasingly applied to the treatment of brain disorders, but its mechanism of action remains unknown. Here, we evaluate the effect of basal ganglia DBS on cortical function using invasive cortical recordings in Parkinson's disease (PD) patients undergoing DBS implantation surgery. In the primary motor cortex of PD patients neuronal population spiking is excessively synchronized to the phase of network oscillations. This manifests in brain surface recordings as exaggerated coupling between the phase of the β rhythm and the amplitude of broadband activity. We show that acute therapeutic DBS reversibly reduces phase-amplitude interactions over a similar time course as reduction in parkinsonian motor signs. We propose that DBS of the basal ganglia improves cortical function by alleviating excessive β phase locking of motor cortex neurons. PMID:25867121

Development and function of human cerebral cortex neural networks from pluripotent stem cells in vitro

PubMed Central

Kirwan, Peter; Turner-Bridger, Benita; Peter, Manuel; Momoh, Ayiba; Arambepola, Devika; Robinson, Hugh P. C.; Livesey, Frederick J.

2015-01-01

A key aspect of nervous system development, including that of the cerebral cortex, is the formation of higher-order neural networks. Developing neural networks undergo several phases with distinct activity patterns in vivo, which are thought to prune and fine-tune network connectivity. We report here that human pluripotent stem cell (hPSC)-derived cerebral cortex neurons form large-scale networks that reflect those found in the developing cerebral cortex in vivo. Synchronised oscillatory networks develop in a highly stereotyped pattern over several weeks in culture. An initial phase of increasing frequency of oscillations is followed by a phase of decreasing frequency, before giving rise to non-synchronous, ordered activity patterns. hPSC-derived cortical neural networks are excitatory, driven by activation of AMPA- and NMDA-type glutamate receptors, and can undergo NMDA-receptor-mediated plasticity. Investigating single neuron connectivity within PSC-derived cultures, using rabies-based trans-synaptic tracing, we found two broad classes of neuronal connectivity: most neurons have small numbers (<10) of presynaptic inputs, whereas a small set of hub-like neurons have large numbers of synaptic connections (>40). These data demonstrate that the formation of hPSC-derived cortical networks mimics in vivo cortical network development and function, demonstrating the utility of in vitro systems for mechanistic studies of human forebrain neural network biology. PMID:26395144

Development and function of human cerebral cortex neural networks from pluripotent stem cells in vitro.

PubMed

Kirwan, Peter; Turner-Bridger, Benita; Peter, Manuel; Momoh, Ayiba; Arambepola, Devika; Robinson, Hugh P C; Livesey, Frederick J

2015-09-15

A key aspect of nervous system development, including that of the cerebral cortex, is the formation of higher-order neural networks. Developing neural networks undergo several phases with distinct activity patterns in vivo, which are thought to prune and fine-tune network connectivity. We report here that human pluripotent stem cell (hPSC)-derived cerebral cortex neurons form large-scale networks that reflect those found in the developing cerebral cortex in vivo. Synchronised oscillatory networks develop in a highly stereotyped pattern over several weeks in culture. An initial phase of increasing frequency of oscillations is followed by a phase of decreasing frequency, before giving rise to non-synchronous, ordered activity patterns. hPSC-derived cortical neural networks are excitatory, driven by activation of AMPA- and NMDA-type glutamate receptors, and can undergo NMDA-receptor-mediated plasticity. Investigating single neuron connectivity within PSC-derived cultures, using rabies-based trans-synaptic tracing, we found two broad classes of neuronal connectivity: most neurons have small numbers (<10) of presynaptic inputs, whereas a small set of hub-like neurons have large numbers of synaptic connections (>40). These data demonstrate that the formation of hPSC-derived cortical networks mimics in vivo cortical network development and function, demonstrating the utility of in vitro systems for mechanistic studies of human forebrain neural network biology. © 2015. Published by The Company of Biologists Ltd.

Macroscopic self-oscillations and aging transition in a network of synaptically coupled quadratic integrate-and-fire neurons.

PubMed

Ratas, Irmantas; Pyragas, Kestutis

2016-09-01

We analyze the dynamics of a large network of coupled quadratic integrate-and-fire neurons, which represent the canonical model for class I neurons near the spiking threshold. The network is heterogeneous in that it includes both inherently spiking and excitable neurons. The coupling is global via synapses that take into account the finite width of synaptic pulses. Using a recently developed reduction method based on the Lorentzian ansatz, we derive a closed system of equations for the neuron's firing rate and the mean membrane potential, which are exact in the infinite-size limit. The bifurcation analysis of the reduced equations reveals a rich scenario of asymptotic behavior, the most interesting of which is the macroscopic limit-cycle oscillations. It is shown that the finite width of synaptic pulses is a necessary condition for the existence of such oscillations. The robustness of the oscillations against aging damage, which transforms spiking neurons into nonspiking neurons, is analyzed. The validity of the reduced equations is confirmed by comparing their solutions with the solutions of microscopic equations for the finite-size networks.

The Dynamics of Networks of Identical Theta Neurons.

PubMed

Laing, Carlo R

2018-02-05

We consider finite and infinite all-to-all coupled networks of identical theta neurons. Two types of synaptic interactions are investigated: instantaneous and delayed (via first-order synaptic processing). Extensive use is made of the Watanabe/Strogatz (WS) ansatz for reducing the dimension of networks of identical sinusoidally-coupled oscillators. As well as the degeneracy associated with the constants of motion of the WS ansatz, we also find continuous families of solutions for instantaneously coupled neurons, resulting from the reversibility of the reduced model and the form of the synaptic input. We also investigate a number of similar related models. We conclude that the dynamics of networks of all-to-all coupled identical neurons can be surprisingly complicated.

Complex Rotation Quantum Dynamic Neural Networks (CRQDNN) using Complex Quantum Neuron (CQN): Applications to time series prediction.

PubMed

Cui, Yiqian; Shi, Junyou; Wang, Zili

2015-11-01

Quantum Neural Networks (QNN) models have attracted great attention since it innovates a new neural computing manner based on quantum entanglement. However, the existing QNN models are mainly based on the real quantum operations, and the potential of quantum entanglement is not fully exploited. In this paper, we proposes a novel quantum neuron model called Complex Quantum Neuron (CQN) that realizes a deep quantum entanglement. Also, a novel hybrid networks model Complex Rotation Quantum Dynamic Neural Networks (CRQDNN) is proposed based on Complex Quantum Neuron (CQN). CRQDNN is a three layer model with both CQN and classical neurons. An infinite impulse response (IIR) filter is embedded in the Networks model to enable the memory function to process time series inputs. The Levenberg-Marquardt (LM) algorithm is used for fast parameter learning. The networks model is developed to conduct time series predictions. Two application studies are done in this paper, including the chaotic time series prediction and electronic remaining useful life (RUL) prediction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Effects of spike-time-dependent plasticity on the stochastic resonance of small-world neuronal networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yu, Haitao; Guo, Xinmeng; Wang, Jiang, E-mail: jiangwang@tju.edu.cn

2014-09-01

The phenomenon of stochastic resonance in Newman-Watts small-world neuronal networks is investigated when the strength of synaptic connections between neurons is adaptively adjusted by spike-time-dependent plasticity (STDP). It is shown that irrespective of the synaptic connectivity is fixed or adaptive, the phenomenon of stochastic resonance occurs. The efficiency of network stochastic resonance can be largely enhanced by STDP in the coupling process. Particularly, the resonance for adaptive coupling can reach a much larger value than that for fixed one when the noise intensity is small or intermediate. STDP with dominant depression and small temporal window ratio is more efficient formore » the transmission of weak external signal in small-world neuronal networks. In addition, we demonstrate that the effect of stochastic resonance can be further improved via fine-tuning of the average coupling strength of the adaptive network. Furthermore, the small-world topology can significantly affect stochastic resonance of excitable neuronal networks. It is found that there exists an optimal probability of adding links by which the noise-induced transmission of weak periodic signal peaks.« less

Local and global synchronization transitions induced by time delays in small-world neuronal networks with chemical synapses.

PubMed

Yu, Haitao; Wang, Jiang; Du, Jiwei; Deng, Bin; Wei, Xile

2015-02-01

Effects of time delay on the local and global synchronization in small-world neuronal networks with chemical synapses are investigated in this paper. Numerical results show that, for both excitatory and inhibitory coupling types, the information transmission delay can always induce synchronization transitions of spiking neurons in small-world networks. In particular, regions of in-phase and out-of-phase synchronization of connected neurons emerge intermittently as the synaptic delay increases. For excitatory coupling, all transitions to spiking synchronization occur approximately at integer multiples of the firing period of individual neurons; while for inhibitory coupling, these transitions appear at the odd multiples of the half of the firing period of neurons. More importantly, the local synchronization transition is more profound than the global synchronization transition, depending on the type of coupling synapse. For excitatory synapses, the local in-phase synchronization observed for some values of the delay also occur at a global scale; while for inhibitory ones, this synchronization, observed at the local scale, disappears at a global scale. Furthermore, the small-world structure can also affect the phase synchronization of neuronal networks. It is demonstrated that increasing the rewiring probability can always improve the global synchronization of neuronal activity, but has little effect on the local synchronization of neighboring neurons.

One-to-one neuron-electrode interfacing.

PubMed

Greenbaum, Alon; Anava, Sarit; Ayali, Amir; Shein, Mark; David-Pur, Moshe; Ben-Jacob, Eshel; Hanein, Yael

2009-09-15

The question of neuronal network development and organization is a principle one, which is closely related to aspects of neuronal and network form-function interactions. In-vitro two-dimensional neuronal cultures have proved to be an attractive and successful model for the study of these questions. Research is constraint however by the search for techniques aimed at culturing stable networks, whose electrical activity can be reliably and consistently monitored. A simple approach to form small interconnected neuronal circuits while achieving one-to-one neuron-electrode interfacing is presented. Locust neurons were cultured on a novel bio-chip consisting of carbon-nanotube multi-electrode-arrays. The cells self-organized to position themselves in close proximity to the bio-chip electrodes. The organization of the cells on the electrodes was analyzed using time lapse microscopy, fluorescence imaging and scanning electron microscopy. Electrical recordings from well identified cells is presented and discussed. The unique properties of the bio-chip and the specific neuron-nanotube interactions, together with the use of relatively large insect ganglion cells, allowed long-term stabilization (as long as 10 days) of predefined neural network topology as well as high fidelity electrical recording of individual neuron firing. This novel preparation opens ample opportunity for future investigation into key neurobiological questions and principles.

Rich-Club Organization in Effective Connectivity among Cortical Neurons.

PubMed

Nigam, Sunny; Shimono, Masanori; Ito, Shinya; Yeh, Fang-Chin; Timme, Nicholas; Myroshnychenko, Maxym; Lapish, Christopher C; Tosi, Zachary; Hottowy, Pawel; Smith, Wesley C; Masmanidis, Sotiris C; Litke, Alan M; Sporns, Olaf; Beggs, John M

2016-01-20

The performance of complex networks, like the brain, depends on how effectively their elements communicate. Despite the importance of communication, it is virtually unknown how information is transferred in local cortical networks, consisting of hundreds of closely spaced neurons. To address this, it is important to record simultaneously from hundreds of neurons at a spacing that matches typical axonal connection distances, and at a temporal resolution that matches synaptic delays. We used a 512-electrode array (60 μm spacing) to record spontaneous activity at 20 kHz from up to 500 neurons simultaneously in slice cultures of mouse somatosensory cortex for 1 h at a time. We applied a previously validated version of transfer entropy to quantify information transfer. Similar to in vivo reports, we found an approximately lognormal distribution of firing rates. Pairwise information transfer strengths also were nearly lognormally distributed, similar to reports of synaptic strengths. Some neurons transferred and received much more information than others, which is consistent with previous predictions. Neurons with the highest outgoing and incoming information transfer were more strongly connected to each other than chance, thus forming a "rich club." We found similar results in networks recorded in vivo from rodent cortex, suggesting the generality of these findings. A rich-club structure has been found previously in large-scale human brain networks and is thought to facilitate communication between cortical regions. The discovery of a small, but information-rich, subset of neurons within cortical regions suggests that this population will play a vital role in communication, learning, and memory. Significance statement: Many studies have focused on communication networks between cortical brain regions. In contrast, very few studies have examined communication networks within a cortical region. This is the first study to combine such a large number of neurons (several hundred at a time) with such high temporal resolution (so we can know the direction of communication between neurons) for mapping networks within cortex. We found that information was not transferred equally through all neurons. Instead, ∼70% of the information passed through only 20% of the neurons. Network models suggest that this highly concentrated pattern of information transfer would be both efficient and robust to damage. Therefore, this work may help in understanding how the cortex processes information and responds to neurodegenerative diseases. Copyright © 2016 Nigam et al.

Rich-Club Organization in Effective Connectivity among Cortical Neurons

PubMed Central

Shimono, Masanori; Ito, Shinya; Yeh, Fang-Chin; Timme, Nicholas; Myroshnychenko, Maxym; Lapish, Christopher C.; Tosi, Zachary; Hottowy, Pawel; Smith, Wesley C.; Masmanidis, Sotiris C.; Litke, Alan M.; Sporns, Olaf; Beggs, John M.

2016-01-01

The performance of complex networks, like the brain, depends on how effectively their elements communicate. Despite the importance of communication, it is virtually unknown how information is transferred in local cortical networks, consisting of hundreds of closely spaced neurons. To address this, it is important to record simultaneously from hundreds of neurons at a spacing that matches typical axonal connection distances, and at a temporal resolution that matches synaptic delays. We used a 512-electrode array (60 μm spacing) to record spontaneous activity at 20 kHz from up to 500 neurons simultaneously in slice cultures of mouse somatosensory cortex for 1 h at a time. We applied a previously validated version of transfer entropy to quantify information transfer. Similar to in vivo reports, we found an approximately lognormal distribution of firing rates. Pairwise information transfer strengths also were nearly lognormally distributed, similar to reports of synaptic strengths. Some neurons transferred and received much more information than others, which is consistent with previous predictions. Neurons with the highest outgoing and incoming information transfer were more strongly connected to each other than chance, thus forming a “rich club.” We found similar results in networks recorded in vivo from rodent cortex, suggesting the generality of these findings. A rich-club structure has been found previously in large-scale human brain networks and is thought to facilitate communication between cortical regions. The discovery of a small, but information-rich, subset of neurons within cortical regions suggests that this population will play a vital role in communication, learning, and memory. SIGNIFICANCE STATEMENT Many studies have focused on communication networks between cortical brain regions. In contrast, very few studies have examined communication networks within a cortical region. This is the first study to combine such a large number of neurons (several hundred at a time) with such high temporal resolution (so we can know the direction of communication between neurons) for mapping networks within cortex. We found that information was not transferred equally through all neurons. Instead, ∼70% of the information passed through only 20% of the neurons. Network models suggest that this highly concentrated pattern of information transfer would be both efficient and robust to damage. Therefore, this work may help in understanding how the cortex processes information and responds to neurodegenerative diseases. PMID:26791200

Simultaneous multi-patch-clamp and extracellular-array recordings: Single neuron reflects network activity

NASA Astrophysics Data System (ADS)

Vardi, Roni; Goldental, Amir; Sardi, Shira; Sheinin, Anton; Kanter, Ido

2016-11-01

The increasing number of recording electrodes enhances the capability of capturing the network’s cooperative activity, however, using too many monitors might alter the properties of the measured neural network and induce noise. Using a technique that merges simultaneous multi-patch-clamp and multi-electrode array recordings of neural networks in-vitro, we show that the membrane potential of a single neuron is a reliable and super-sensitive probe for monitoring such cooperative activities and their detailed rhythms. Specifically, the membrane potential and the spiking activity of a single neuron are either highly correlated or highly anti-correlated with the time-dependent macroscopic activity of the entire network. This surprising observation also sheds light on the cooperative origin of neuronal burst in cultured networks. Our findings present an alternative flexible approach to the technique based on a massive tiling of networks by large-scale arrays of electrodes to monitor their activity.

Simultaneous multi-patch-clamp and extracellular-array recordings: Single neuron reflects network activity

PubMed Central

Vardi, Roni; Goldental, Amir; Sardi, Shira; Sheinin, Anton; Kanter, Ido

2016-01-01

The increasing number of recording electrodes enhances the capability of capturing the network’s cooperative activity, however, using too many monitors might alter the properties of the measured neural network and induce noise. Using a technique that merges simultaneous multi-patch-clamp and multi-electrode array recordings of neural networks in-vitro, we show that the membrane potential of a single neuron is a reliable and super-sensitive probe for monitoring such cooperative activities and their detailed rhythms. Specifically, the membrane potential and the spiking activity of a single neuron are either highly correlated or highly anti-correlated with the time-dependent macroscopic activity of the entire network. This surprising observation also sheds light on the cooperative origin of neuronal burst in cultured networks. Our findings present an alternative flexible approach to the technique based on a massive tiling of networks by large-scale arrays of electrodes to monitor their activity. PMID:27824075

Developing neuronal networks: Self-organized criticality predicts the future

NASA Astrophysics Data System (ADS)

Pu, Jiangbo; Gong, Hui; Li, Xiangning; Luo, Qingming

2013-01-01

Self-organized criticality emerged in neural activity is one of the key concepts to describe the formation and the function of developing neuronal networks. The relationship between critical dynamics and neural development is both theoretically and experimentally appealing. However, whereas it is well-known that cortical networks exhibit a rich repertoire of activity patterns at different stages during in vitro maturation, dynamical activity patterns through the entire neural development still remains unclear. Here we show that a series of metastable network states emerged in the developing and ``aging'' process of hippocampal networks cultured from dissociated rat neurons. The unidirectional sequence of state transitions could be only observed in networks showing power-law scaling of distributed neuronal avalanches. Our data suggest that self-organized criticality may guide spontaneous activity into a sequential succession of homeostatically-regulated transient patterns during development, which may help to predict the tendency of neural development at early ages in the future.

Differential responses to lithium in hyperexcitable neurons from patients with bipolar disorder.

PubMed

Mertens, Jerome; Wang, Qiu-Wen; Kim, Yongsung; Yu, Diana X; Pham, Son; Yang, Bo; Zheng, Yi; Diffenderfer, Kenneth E; Zhang, Jian; Soltani, Sheila; Eames, Tameji; Schafer, Simon T; Boyer, Leah; Marchetto, Maria C; Nurnberger, John I; Calabrese, Joseph R; Ødegaard, Ketil J; McCarthy, Michael J; Zandi, Peter P; Alda, Martin; Alba, Martin; Nievergelt, Caroline M; Mi, Shuangli; Brennand, Kristen J; Kelsoe, John R; Gage, Fred H; Yao, Jun

2015-11-05

Bipolar disorder is a complex neuropsychiatric disorder that is characterized by intermittent episodes of mania and depression; without treatment, 15% of patients commit suicide. Hence, it has been ranked by the World Health Organization as a top disorder of morbidity and lost productivity. Previous neuropathological studies have revealed a series of alterations in the brains of patients with bipolar disorder or animal models, such as reduced glial cell number in the prefrontal cortex of patients, upregulated activities of the protein kinase A and C pathways and changes in neurotransmission. However, the roles and causation of these changes in bipolar disorder have been too complex to exactly determine the pathology of the disease. Furthermore, although some patients show remarkable improvement with lithium treatment for yet unknown reasons, others are refractory to lithium treatment. Therefore, developing an accurate and powerful biological model for bipolar disorder has been a challenge. The introduction of induced pluripotent stem-cell (iPSC) technology has provided a new approach. Here we have developed an iPSC model for human bipolar disorder and investigated the cellular phenotypes of hippocampal dentate gyrus-like neurons derived from iPSCs of patients with bipolar disorder. Guided by RNA sequencing expression profiling, we have detected mitochondrial abnormalities in young neurons from patients with bipolar disorder by using mitochondrial assays; in addition, using both patch-clamp recording and somatic Ca(2+) imaging, we have observed hyperactive action-potential firing. This hyperexcitability phenotype of young neurons in bipolar disorder was selectively reversed by lithium treatment only in neurons derived from patients who also responded to lithium treatment. Therefore, hyperexcitability is one early endophenotype of bipolar disorder, and our model of iPSCs in this disease might be useful in developing new therapies and drugs aimed at its clinical treatment.

A patterned recombinant human IgM guides neurite outgrowth of CNS neurons

PubMed Central

Xu, Xiaohua; Wittenberg, Nathan J.; Jordan, Luke R.; Kumar, Shailabh; Watzlawik, Jens O.; Warrington, Arthur E.; Oh, Sang-Hyun; Rodriguez, Moses

2013-01-01

Matrix molecules convey biochemical and physical guiding signals to neurons in the central nervous system (CNS) and shape the trajectory of neuronal fibers that constitute neural networks. We have developed recombinant human IgMs that bind to epitopes on neural cells, with the aim of treating neurological diseases. Here we test the hypothesis that recombinant human IgMs (rHIgM) can guide neurite outgrowth of CNS neurons. Microcontact printing was employed to pattern rHIgM12 and rHIgM22, antibodies that were bioengineered to have variable regions capable of binding to neurons or oligodendrocytes, respectively. rHIgM12 promoted neuronal attachment and guided outgrowth of neurites from hippocampal neurons. Processes from spinal neurons followed grid patterns of rHIgM12 and formed a physical network. Comparison between rHIgM12 and rHIgM22 suggested the biochemistry that facilitates anchoring the neuronal surfaces is a prerequisite for the function of IgM, and spatial properties cooperate in guiding the assembly of neuronal networks. PMID:23881231

Where the thoughts dwell: the physiology of neuronal-glial "diffuse neural net".

PubMed

Verkhratsky, Alexei; Parpura, Vladimir; Rodríguez, José J

2011-01-07

The mechanisms underlying the production of thoughts by exceedingly complex cellular networks that construct the human brain constitute the most challenging problem of natural sciences. Our understanding of the brain function is very much shaped by the neuronal doctrine that assumes that neuronal networks represent the only substrate for cognition. These neuronal networks however are embedded into much larger and probably more complex network formed by neuroglia. The latter, although being electrically silent, employ many different mechanisms for intercellular signalling. It appears that astrocytes can control synaptic networks and in such a capacity they may represent an integral component of the computational power of the brain rather than being just brain "connective tissue". The fundamental question of whether neuroglia is involved in cognition and information processing remains, however, open. Indeed, a remarkable increase in the number of glial cells that distinguishes the human brain can be simply a result of exceedingly high specialisation of the neuronal networks, which delegated all matters of survival and maintenance to the neuroglia. At the same time potential power of analogue processing offered by internally connected glial networks may represent the alternative mechanism involved in cognition. Copyright © 2010 Elsevier B.V. All rights reserved.

Correction of respiratory disorders in a mouse model of Rett syndrome

PubMed Central

Abdala, Ana P. L.; Dutschmann, Mathias; Bissonnette, John M.; Paton, Julian F. R.

2010-01-01

Rett syndrome (RTT) is an autism spectrum disorder caused by mutations in the X-linked gene that encodes the transcription factor methyl-CpG-binding protein 2 (MeCP2). A major debilitating phenotype in affected females is frequent apneas, and heterozygous Mecp2-deficient female mice mimic the human respiratory disorder. GABA defects have been demonstrated in the brainstem of Mecp2-deficient mice. Here, using an intact respiratory network, we show that apnea in RTT mice is characterized by excessive excitatory activity in expiratory cranial and spinal nerves. Augmenting GABA markedly improves the respiratory phenotype. In addition, a serotonin 1a receptor agonist that depresses expiratory neuron activity also reduces apnea, corrects the irregular breathing pattern, and prolongs survival in MeCP2 null males. Combining a GABA reuptake blocker with a serotonin 1a agonist in heterozygous females completely corrects their respiratory defects. The results indicate that GABA and serotonin 1a receptor activity are candidates for treatment of the respiratory disorders in Rett syndrome. PMID:20921395

Layer-specific optogenetic activation of pyramidal neurons causes beta–gamma entrainment of neonatal networks

PubMed Central

Bitzenhofer, Sebastian H; Ahlbeck, Joachim; Wolff, Amy; Wiegert, J. Simon; Gee, Christine E.; Oertner, Thomas G.; Hanganu-Opatz, Ileana L.

2017-01-01

Coordinated activity patterns in the developing brain may contribute to the wiring of neuronal circuits underlying future behavioural requirements. However, causal evidence for this hypothesis has been difficult to obtain owing to the absence of tools for selective manipulation of oscillations during early development. We established a protocol that combines optogenetics with electrophysiological recordings from neonatal mice in vivo to elucidate the substrate of early network oscillations in the prefrontal cortex. We show that light-induced activation of layer II/III pyramidal neurons that are transfected by in utero electroporation with a high-efficiency channelrhodopsin drives frequency-specific spiking and boosts network oscillations within beta–gamma frequency range. By contrast, activation of layer V/VI pyramidal neurons causes nonspecific network activation. Thus, entrainment of neonatal prefrontal networks in fast rhythms relies on the activation of layer II/III pyramidal neurons. This approach used here may be useful for further interrogation of developing circuits, and their behavioural readout. PMID:28216627

Collective behavior of large-scale neural networks with GPU acceleration.

PubMed

Qu, Jingyi; Wang, Rubin

2017-12-01

In this paper, the collective behaviors of a small-world neuronal network motivated by the anatomy of a mammalian cortex based on both Izhikevich model and Rulkov model are studied. The Izhikevich model can not only reproduce the rich behaviors of biological neurons but also has only two equations and one nonlinear term. Rulkov model is in the form of difference equations that generate a sequence of membrane potential samples in discrete moments of time to improve computational efficiency. These two models are suitable for the construction of large scale neural networks. By varying some key parameters, such as the connection probability and the number of nearest neighbor of each node, the coupled neurons will exhibit types of temporal and spatial characteristics. It is demonstrated that the implementation of GPU can achieve more and more acceleration than CPU with the increasing of neuron number and iterations. These two small-world network models and GPU acceleration give us a new opportunity to reproduce the real biological network containing a large number of neurons.

A mixed-signal implementation of a polychronous spiking neural network with delay adaptation

PubMed Central

Wang, Runchun M.; Hamilton, Tara J.; Tapson, Jonathan C.; van Schaik, André

2014-01-01

We present a mixed-signal implementation of a re-configurable polychronous spiking neural network capable of storing and recalling spatio-temporal patterns. The proposed neural network contains one neuron array and one axon array. Spike Timing Dependent Delay Plasticity is used to fine-tune delays and add dynamics to the network. In our mixed-signal implementation, the neurons and axons have been implemented as both analog and digital circuits. The system thus consists of one FPGA, containing the digital neuron array and the digital axon array, and one analog IC containing the analog neuron array and the analog axon array. The system can be easily configured to use different combinations of each. We present and discuss the experimental results of all combinations of the analog and digital axon arrays and the analog and digital neuron arrays. The test results show that the proposed neural network is capable of successfully recalling more than 85% of stored patterns using both analog and digital circuits. PMID:24672422

A mixed-signal implementation of a polychronous spiking neural network with delay adaptation.

PubMed

Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan C; van Schaik, André

2014-01-01

We present a mixed-signal implementation of a re-configurable polychronous spiking neural network capable of storing and recalling spatio-temporal patterns. The proposed neural network contains one neuron array and one axon array. Spike Timing Dependent Delay Plasticity is used to fine-tune delays and add dynamics to the network. In our mixed-signal implementation, the neurons and axons have been implemented as both analog and digital circuits. The system thus consists of one FPGA, containing the digital neuron array and the digital axon array, and one analog IC containing the analog neuron array and the analog axon array. The system can be easily configured to use different combinations of each. We present and discuss the experimental results of all combinations of the analog and digital axon arrays and the analog and digital neuron arrays. The test results show that the proposed neural network is capable of successfully recalling more than 85% of stored patterns using both analog and digital circuits.

Functional model of biological neural networks.

PubMed

Lo, James Ting-Ho

2010-12-01

A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.

The immunoglobulin-like genetic predetermination of the brain: the protocadherins, blueprint of the neuronal network

NASA Astrophysics Data System (ADS)

Hilschmann, N.; Barnikol, H. U.; Barnikol-Watanabe, S.; Götz, H.; Kratzin, H.; Thinnes, F. P.

2001-01-01

The morphogenesis of the brain is governed by synaptogenesis. Synaptogenesis in turn is determined by cell adhesion molecules, which bridge the synaptic cleft and, by homophilic contact, decide which neurons are connected and which are not. Because of their enormous diversification in specificities, protocadherins (pcdhα, pcdhβ, pcdhγ), a new class of cadherins, play a decisive role. Surprisingly, the genetic control of the protocadherins is very similar to that of the immunoglobulins. There are three sets of variable (V) genes followed by a corresponding constant (C) gene. Applying the rules of the immunoglobulin genes to the protocadherin genes leads, despite of this similarity, to quite different results in the central nervous system. The lymphocyte expresses one single receptor molecule specifically directed against an outside stimulus. In contrast, there are three specific recognition sites in each neuron, each expressing a different protocadherin. In this way, 4,950 different neurons arising from one stem cell form a neuronal network, in which homophilic contacts can be formed in 52 layers, permitting an enormous number of different connections and restraints between neurons. This network is one module of the central computer of the brain. Since the V-genes are generated during evolution and V-gene translocation during embryogenesis, outside stimuli have no influence on this network. The network is an inborn property of the protocadherin genes. Every circuit produced, as well as learning and memory, has to be based on this genetically predetermined network. This network is so universal that it can cope with everything, even the unexpected. In this respect the neuronal network resembles the recognition sites of the immunoglobulins.

Role of Ongoing, Intrinsic Activity of Neuronal Populations for Quantitative Neuroimaging of Functional Magnetic Resonance Imaging–Based Networks

PubMed Central

Herman, Peter; Sanganahalli, Basavaraju G.; Coman, Daniel; Blumenfeld, Hal; Rothman, Douglas L.

2011-01-01

Abstract A primary objective in neuroscience is to determine how neuronal populations process information within networks. In humans and animal models, functional magnetic resonance imaging (fMRI) is gaining increasing popularity for network mapping. Although neuroimaging with fMRI—conducted with or without tasks—is actively discovering new brain networks, current fMRI data analysis schemes disregard the importance of the total neuronal activity in a region. In task fMRI experiments, the baseline is differenced away to disclose areas of small evoked changes in the blood oxygenation level-dependent (BOLD) signal. In resting-state fMRI experiments, the spotlight is on regions revealed by correlations of tiny fluctuations in the baseline (or spontaneous) BOLD signal. Interpretation of fMRI-based networks is obscured further, because the BOLD signal indirectly reflects neuronal activity, and difference/correlation maps are thresholded. Since the small changes of BOLD signal typically observed in cognitive fMRI experiments represent a minimal fraction of the total energy/activity in a given area, the relevance of fMRI-based networks is uncertain, because the majority of neuronal energy/activity is ignored. Thus, another alternative for quantitative neuroimaging of fMRI-based networks is a perspective in which the activity of a neuronal population is accounted for by the demanded oxidative energy (CMRO2). In this article, we argue that network mapping can be improved by including neuronal energy/activity of both the information about baseline and small differences/fluctuations of BOLD signal. Thus, total energy/activity information can be obtained through use of calibrated fMRI to quantify differences of ΔCMRO2 and through resting-state positron emission tomography/magnetic resonance spectroscopy measurements for average CMRO2. PMID:22433047

A three-dimensional neural spheroid model for capillary-like network formation.

PubMed

Boutin, Molly E; Kramer, Liana L; Livi, Liane L; Brown, Tyler; Moore, Christopher; Hoffman-Kim, Diane

2018-04-01

In vitro three-dimensional neural spheroid models have an in vivo-like cell density, and have the potential to reduce animal usage and increase experimental throughput. The aim of this study was to establish a spheroid model to study the formation of capillary-like networks in a three-dimensional environment that incorporates both neuronal and glial cell types, and does not require exogenous vasculogenic growth factors. We created self-assembled, scaffold-free cellular spheroids using primary-derived postnatal rodent cortex as a cell source. The interactions between relevant neural cell types, basement membrane proteins, and endothelial cells were characterized by immunohistochemistry. Transmission electron microscopy was used to determine if endothelial network structures had lumens. Endothelial cells within cortical spheroids assembled into capillary-like networks with lumens. Networks were surrounded by basement membrane proteins, including laminin, fibronectin and collagen IV, as well as key neurovascular cell types. Existing in vitro models of the cortical neurovascular environment study monolayers of endothelial cells, either on transwell inserts or coating cellular spheroids. These models are not well suited to study vasculogenesis, a process hallmarked by endothelial cell cord formation and subsequent lumenization. The neural spheroid is a new model to study the formation of endothelial cell capillary-like structures in vitro within a high cell density three-dimensional environment that contains both neuronal and glial populations. This model can be applied to investigate vascular assembly in healthy or disease states, such as stroke, traumatic brain injury, or neurodegenerative disorders. Copyright © 2017 Elsevier B.V. All rights reserved.

Introduction to Concepts in Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Niebur, Dagmar

1995-01-01

This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.

Emergent properties of interacting populations of spiking neurons.

PubMed

Cardanobile, Stefano; Rotter, Stefan

2011-01-01

Dynamic neuronal networks are a key paradigm of increasing importance in brain research, concerned with the functional analysis of biological neuronal networks and, at the same time, with the synthesis of artificial brain-like systems. In this context, neuronal network models serve as mathematical tools to understand the function of brains, but they might as well develop into future tools for enhancing certain functions of our nervous system. Here, we present and discuss our recent achievements in developing multiplicative point processes into a viable mathematical framework for spiking network modeling. The perspective is that the dynamic behavior of these neuronal networks is faithfully reflected by a set of non-linear rate equations, describing all interactions on the population level. These equations are similar in structure to Lotka-Volterra equations, well known by their use in modeling predator-prey relations in population biology, but abundant applications to economic theory have also been described. We present a number of biologically relevant examples for spiking network function, which can be studied with the help of the aforementioned correspondence between spike trains and specific systems of non-linear coupled ordinary differential equations. We claim that, enabled by the use of multiplicative point processes, we can make essential contributions to a more thorough understanding of the dynamical properties of interacting neuronal populations.

Caged Neuron MEA: A system for long-term investigation of cultured neural network connectivity

PubMed Central

Erickson, Jonathan; Tooker, Angela; Tai, Y-C.; Pine, Jerome

2008-01-01

Traditional techniques for investigating cultured neural networks, such as the patch clamp and multi-electrode array, are limited by: 1) the number of identified cells which can be simultaneously electrically contacted, 2) the length of time for which cells can be studied, and 3) the lack of one-to-one neuron-to-electrode specificity. Here, we present a new device—the caged neuron multi-electrode array—which overcomes these limitations. This micro-machined device consists of an array of neurocages which mechanically trap a neuron near an extracellular electrode. While the cell body is trapped, the axon and dendrites can freely grow into the surrounding area to form a network. The electrode is bi-directional, capable of both stimulating and recording action potentials. This system is non-invasive, so that all constituent neurons of a network can be studied over its lifetime with stable one-to-one neuron-to-electrode correspondence. Proof-of-concept experiments are described to illustrate that functional networks form in a neurochip system of 16 cages in a 4×4 array, and that suprathreshold connectivity can be fully mapped over several weeks. The neurochip opens a new domain in neurobiology for studying small cultured neural networks. PMID:18775453

Emergent Properties of Interacting Populations of Spiking Neurons

PubMed Central

Cardanobile, Stefano; Rotter, Stefan

2011-01-01

Dynamic neuronal networks are a key paradigm of increasing importance in brain research, concerned with the functional analysis of biological neuronal networks and, at the same time, with the synthesis of artificial brain-like systems. In this context, neuronal network models serve as mathematical tools to understand the function of brains, but they might as well develop into future tools for enhancing certain functions of our nervous system. Here, we present and discuss our recent achievements in developing multiplicative point processes into a viable mathematical framework for spiking network modeling. The perspective is that the dynamic behavior of these neuronal networks is faithfully reflected by a set of non-linear rate equations, describing all interactions on the population level. These equations are similar in structure to Lotka-Volterra equations, well known by their use in modeling predator-prey relations in population biology, but abundant applications to economic theory have also been described. We present a number of biologically relevant examples for spiking network function, which can be studied with the help of the aforementioned correspondence between spike trains and specific systems of non-linear coupled ordinary differential equations. We claim that, enabled by the use of multiplicative point processes, we can make essential contributions to a more thorough understanding of the dynamical properties of interacting neuronal populations. PMID:22207844

Immunization with neuronal nicotinic acetylcholine receptor induces neurological autoimmune disease

PubMed Central

Lennon, Vanda A.; Ermilov, Leonid G.; Szurszewski, Joseph H.; Vernino, Steven

2003-01-01

Neuronal nicotinic AChRs (nAChRs) are implicated in the pathogenesis of diverse neurological disorders and in the regulation of small-cell lung carcinoma growth. Twelve subunits have been identified in vertebrates, and mutations of one are recognized in a rare form of human epilepsy. Mice with genetically manipulated neuronal nAChR subunits exhibit behavioral or autonomic phenotypes. Here, we report the first model of an acquired neuronal nAChR disorder and evidence for its pertinence to paraneoplastic neurological autoimmunity. Rabbits immunized once with recombinant α3 subunit (residues 1–205) develop profound gastrointestinal hypomotility, dilated pupils with impaired light response, and grossly distended bladders. As in patients with idiopathic and paraneoplastic autoimmune autonomic neuropathy, the severity parallels serum levels of ganglionic nAChR autoantibody. Failure of neurotransmission through abdominal sympathetic ganglia, with retention of neuronal viability, confirms that the disorder is a postsynaptic channelopathy. In addition, we found ganglionic nAChR protein in small-cell carcinoma lines, identifying this cancer as a potential initiator of ganglionic nAChR autoimmunity. The data support our hypothesis that immune responses driven by distinct neuronal nAChR subtypes expressed in small-cell carcinomas account for several lung cancer–related paraneoplastic disorders affecting cholinergic systems, including autoimmune autonomic neuropathy, seizures, dementia, and movement disorders. PMID:12639997

Axon and dendrite geography predict the specificity of synaptic connections in a functioning spinal cord network.

PubMed

Li, Wen-Chang; Cooke, Tom; Sautois, Bart; Soffe, Stephen R; Borisyuk, Roman; Roberts, Alan

2007-09-10

How specific are the synaptic connections formed as neuronal networks develop and can simple rules account for the formation of functioning circuits? These questions are assessed in the spinal circuits controlling swimming in hatchling frog tadpoles. This is possible because detailed information is now available on the identity and synaptic connections of the main types of neuron. The probabilities of synapses between 7 types of identified spinal neuron were measured directly by making electrical recordings from 500 pairs of neurons. For the same neuron types, the dorso-ventral distributions of axons and dendrites were measured and then used to calculate the probabilities that axons would encounter particular dendrites and so potentially form synaptic connections. Surprisingly, synapses were found between all types of neuron but contact probabilities could be predicted simply by the anatomical overlap of their axons and dendrites. These results suggested that synapse formation may not require axons to recognise specific, correct dendrites. To test the plausibility of simpler hypotheses, we first made computational models that were able to generate longitudinal axon growth paths and reproduce the axon distribution patterns and synaptic contact probabilities found in the spinal cord. To test if probabilistic rules could produce functioning spinal networks, we then made realistic computational models of spinal cord neurons, giving them established cell-specific properties and connecting them into networks using the contact probabilities we had determined. A majority of these networks produced robust swimming activity. Simple factors such as morphogen gradients controlling dorso-ventral soma, dendrite and axon positions may sufficiently constrain the synaptic connections made between different types of neuron as the spinal cord first develops and allow functional networks to form. Our analysis implies that detailed cellular recognition between spinal neuron types may not be necessary for the reliable formation of functional networks to generate early behaviour like swimming.