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Sample records for model neurons nt2

  1. Potential for Cell-Transplant Therapy with Human Neuronal Precursors to Treat Neuropathic Pain in Models of PNS and CNS Injury: Comparison of hNT2.17 and hNT2.19 Cell Lines

    PubMed Central

    Eaton, Mary J.; Berrocal, Yerko; Wolfe, Stacey Q.

    2012-01-01

    Effective treatment of sensory neuropathies in peripheral neuropathies and spinal cord injury (SCI) is one of the most difficult problems in modern clinical practice. Cell therapy to release antinociceptive agents near the injured spinal cord is a logical next step in the development of treatment modalities. But few clinical trials, especially for chronic pain, have tested the potential of transplant of cells to treat chronic pain. Cell lines derived from the human neuronal NT2 cell line parentage, the hNT2.17 and hNT2.19 lines, which synthesize and release the neurotransmitters gamma-aminobutyric acid (GABA) and serotonin (5HT), respectively, have been used to evaluate the potential of cell-based release of antinociceptive agents near the lumbar dorsal (horn) spinal sensory cell centers to relieve neuropathic pain after PNS (partial nerve and diabetes-related injury) and CNS (spinal cord injury) damage in rat models. Both cell lines transplants potently and permanently reverse behavioral hypersensitivity without inducing tumors or other complications after grafting. Functioning as cellular minipumps for antinociception, human neuronal precursors, like these NT2-derived cell lines, would likely provide a useful adjuvant or replacement for current pharmacological treatments for neuropathic pain. PMID:22619713

  2. Expression of α5 integrin rescues fibronectin responsiveness in NT2N CNS neuronal cells

    PubMed Central

    Meland, Marit N.; Herndon, Mary E.; Stipp, Christopher S.

    2010-01-01

    The extracellular matrix protein fibronectin is implicated in neuronal regeneration in the peripheral nervous system. In the central nervous system (CNS), fibronectin is upregulated at sites of penetrating injuries and stroke; however, CNS neurons downregulate the fibronectin receptor, α5β1 integrin, during differentiation and generally respond poorly to fibronectin. NT2N CNS neuron-like cells (derived from NT2 precursor cells) have been used in pre-clinical and clinical studies for treatment of stroke and a variety of CNS injury and disease models. Here we show that, like primary CNS neurons, NT2N cells downregulate α5β1 integrin during differentiation and respond poorly to fibronectin. The poor neurite outgrowth by NT2N cells on fibronectin can be rescued by transducing NT2 precursors with a retroviral vector expressing α5 integrin under the control of the Murine Stem Cell Virus 5′ long terminal repeat. Sustained α5 integrin expression is compatible with the CNS-like neuronal differentiation of NT2N cells and does not prevent robust neurite outgrowth on other integrin ligands. Thus, α5 integrin expression in CNS neuronal precursor cells may provide a strategy for enhancing the outgrowth and survival of implanted cells in cell replacement therapies for CNS injury and disease. PMID:19598247

  3. Data for the morphometric characterization of NT2-derived postmitotic neurons

    PubMed Central

    González-Burguera, Imanol; Ricobaraza, Ana; Aretxabala, Xabier; Barrondo, Sergio; García del Caño, Gontzal; López de Jesús, Maider; Sallés, Joan

    2016-01-01

    NTERA2/D1 human teratocarcinoma progenitors induced to differentiate into postmitotic neurons by either long-term treatment with retinoic acid or short-term treatment with the nucleoside analog cytosine β-D-arabinofuranoside were subjected to morphometric analysis and compared. Our data provide a methodological and conceptual framework for future investigations aiming at distinguishing neuronal phenotypes on the basis of morphometric analysis. Data presented here are related to research concurrently published in “Highly Efficient Generation of Glutamatergic/Cholinergic NT2-Derived Postmitotic Human Neurons by Short-Term treatment with the Nucleoside Analogue Cytosine β-D-Arabinofuranoside” [1]. PMID:27158648

  4. Data for the morphometric characterization of NT2-derived postmitotic neurons.

    PubMed

    González-Burguera, Imanol; Ricobaraza, Ana; Aretxabala, Xabier; Barrondo, Sergio; García Del Caño, Gontzal; López de Jesús, Maider; Sallés, Joan

    2016-06-01

    NTERA2/D1 human teratocarcinoma progenitors induced to differentiate into postmitotic neurons by either long-term treatment with retinoic acid or short-term treatment with the nucleoside analog cytosine β-D-arabinofuranoside were subjected to morphometric analysis and compared. Our data provide a methodological and conceptual framework for future investigations aiming at distinguishing neuronal phenotypes on the basis of morphometric analysis. Data presented here are related to research concurrently published in "Highly Efficient Generation of Glutamatergic/Cholinergic NT2-Derived Postmitotic Human Neurons by Short-Term treatment with the Nucleoside Analogue Cytosine β-D-Arabinofuranoside" [1]. PMID:27158648

  5. REST-VP16 activates multiple neuronal differentiation genes in human NT2 cells.

    PubMed

    Immaneni, A; Lawinger, P; Zhao, Z; Lu, W; Rastelli, L; Morris, J H; Majumder, S

    2000-09-01

    The RE1-silencing transcription factor (REST)/neuron-restrictive silencer factor (NRSF) can repress transcription of a battery of neuronal differentiation genes in non-neuronal cells by binding to a specific consensus DNA sequence present in their regulatory regions. However, REST/NRSF(-/-) mice suggest that the absence of REST/NRSF-dependent repression alone is not sufficient for the expression of these neuronal differentiation genes and that the presence of other promoter/enhancer-specific activators is required. Here we describe the construction of a recombinant transcription factor, REST-VP16, by replacing repressor domains of REST/NRSF with the activation domain of a viral activator VP16. In transient transfection experiments, REST-VP16 was found to operate through RE1 binding site/neuron-restrictive enhancer element (RE1/NRSE), activate plasmid-encoded neuronal promoters in various mammalian cell types and activate cellular REST/NRSF target genes, even in the absence of factors that are otherwise required to activate such genes. Efficient expression of REST-VP16 through adenoviral vectors in NT2 cells, which resemble human committed neuronal progenitor cells, was found to cause activation of multiple neuronal genes that are characteristic markers for neuronal differentiation. Thus, REST-VP16 could be used as a unique tool to study neuronal differentiation pathways and neuronal diseases that arise due to the deregulation of this process. PMID:10954611

  6. Spatiotemporal changes in Cx30 and Cx43 expression during neuronal differentiation of P19 EC and NT2/D1 cells

    PubMed Central

    Wan, Carthur K; O'Carroll, Simon J; Kim, Sue-Ling; Green, Colin R; Nicholson, Louise F B

    2013-01-01

    While connexins (Cxs) are thought to be involved in differentiation, their expression and role has yet to be fully elucidated. We investigated the temporal expression of Cx30, Cx36 and Cx43 in two in vitro models of neuronal differentiation: human NT2/D1 and murine P19 cells, and the spatial localisation of Cx30 and Cx43 in these models. A temporal Cx43 downregulation was confirmed in both cell lines during RA-induced neuronal differentiation using RT-PCR (P < 0.05) preceding an increase in neuronal doublecortin protein. RT-PCR showed Cx36 was upregulated twofold in NT2/D1 cells (P < 0.05) and sixfold in P19 cells (P < 0.001) during neuronal differentiation. Cx30 exhibited a transient peak in expression midway through the timecourse of differentiation increasing threefold in NT2/D1 cells (P < 0.001) and eightfold in P19 cells (P < 0.01). Qualitative immunocytochemistry was used to examine spatiotemporal patterns of Cx protein distribution alongside neuronal differentiation markers. The temporal immunolabelling pattern was similar to that seen using RT-PCR. Cx43 was observed intracellularly and on cell surfaces, while Cx30 was seen as puncta. Spatially Cx43 was seen on doublecortin-negative cells, which may indicate Cx43 downregulation is requisite for differentiation in these models. Conversely, Cx30 puncta were observed on doublecortin-positive and -negative cells in NT2/D1 cells and examination of the Cx30 peak showed puncta also localized to nestin-positive cells, with few puncta on MAP2-positive cells. In P19 cells Cx30 was localized on clusters of cells surrounded by MAP2- and doublecortin-positive processes. The expression pattern of Cx30 indicates a role in neuronal differentiation; the nature of that role warrants future investigation. PMID:25505515

  7. Interleukin-1β stimulates macrophage inflammatory protein-1α and -1β expression in human neuronal cells (NT2-N)

    PubMed Central

    Guo, Chang-Jiang; Douglas, Steven D.; Lai, Jian-Ping; Pleasure, David E.; Li, Yuan; Williams, Marge; Bannerman, Peter; Song, Li; Ho, Wen-Zhe

    2014-01-01

    Chemokines are important mediators in immune responses and inflammatory processes of neuroimmunologic and infectious diseases. Although chemokines are expressed predominantly by cells of the immune system, neurons also express chemokines and chemokine receptors. We report herein that human neuronal cells (NT2-N) produce macrophage inflammatory protein-1α and -1β (MIP-1α and MIP-1β), which could be enhanced by interleukin (IL)-1β at both mRNA and protein levels. The addition of supernatants from human peripheral blood monocyte-derived macrophage (MDM) cultures induced MIP-1β mRNA expression in NT2-N cells. Anti-IL-1β antibody removed most, but not all, of the MDM culture supernatant-induced MIP-1β mRNA expression in NT2-N cells, suggesting that IL-1β in the MDM culture supernatants is a major factor in the induction of MIP-1β expression. Investigation of the mechanism(s) responsible for IL-1β-induced MIP-1α and -1β expression demonstrated that IL-1β activated nuclear factor kappa B (NF-κB) promoter-directed luciferase activity in NT2-N cells. Caffeic acid phenethyl ester, a potent and specific inhibitor of activation of NF-κB, not only blocked IL-1β-induced activation of the NF-κB promoter but also decreased IL-1β-induced MIP-1α and -1β expression in NT2-N cells. These data suggest that NF-κB is at least partially involved in the IL-1β-mediated action on MIP-1α and -1β in NT2-N cells. IL-1β-mediated up-regulation of β-chemokine expression may have important implications in the immuno-pathogenesis of inflammatory diseases in the CNS. PMID:12603824

  8. Non ionising radiation as a non chemical strategy in regenerative medicine: Ca(2+)-ICR "In Vitro" effect on neuronal differentiation and tumorigenicity modulation in NT2 cells.

    PubMed

    Ledda, Mario; Megiorni, Francesca; Pozzi, Deleana; Giuliani, Livio; D'Emilia, Enrico; Piccirillo, Sara; Mattei, Cristiana; Grimaldi, Settimio; Lisi, Antonella

    2013-01-01

    In regenerative medicine finding a new method for cell differentiation without pharmacological treatment or gene modification and minimal cell manipulation is a challenging goal. In this work we reported a neuronal induced differentiation and consequent reduction of tumorigenicity in NT2 human pluripotent embryonal carcinoma cells exposed to an extremely low frequency electromagnetic field (ELF-EMF), matching the cyclotron frequency corresponding to the charge/mass ratio of calcium ion (Ca(2+)-ICR). These cells, capable of differentiating into post-mitotic neurons following treatment with Retinoic Acid (RA), were placed in a solenoid and exposed for 5 weeks to Ca(2+)-ICR. The solenoid was installed in a μ-metal shielded room to avoid the effect of the geomagnetic field and obtained totally controlled and reproducible conditions. Contrast microscopy analysis reveled, in the NT2 exposed cells, an important change in shape and morphology with the outgrowth of neuritic-like structures together with a lower proliferation rate and metabolic activity alike those found in the RA treated cells. A significant up-regulation of early and late neuronal differentiation markers and a significant down-regulation of the transforming growth factor-α (TGF-α) and the fibroblast growth factor-4 (FGF-4) were also observed in the exposed cells. The decreased protein expression of the transforming gene Cripto-1 and the reduced capability of the exposed NT2 cells to form colonies in soft agar supported these last results. In conclusion, our findings demonstrate that the Ca(2+)-ICR frequency is able to induce differentiation and reduction of tumorigenicity in NT2 exposed cells suggesting a new potential therapeutic use in regenerative medicine. PMID:23585910

  9. Subarachnoid Transplant of the Human Neuronal hNT2.19 Serotonergic Cell Line Attenuates Behavioral Hypersensitivity without Affecting Motor Dysfunction after Severe Contusive Spinal Cord Injury

    PubMed Central

    Eaton, Mary J.; Widerström-Noga, Eva; Wolfe, Stacey Quintero

    2011-01-01

    Transplant of cells which make biologic agents that can modulate the sensory and motor responses after spinal cord injury (SCI) would be useful to treat pain and paralysis. To address this need for clinically useful human cells, a unique neuronal cell line that synthesizes and secretes/releases the neurotransmitter serotonin (5HT) was isolated. Hind paw tactile allodynia and thermal hyperalgesia induced by severe contusive SCI were potently reversed after lumbar subarachnoid transplant of differentiated cells, but had no effect on open field motor scores, stride length, foot rotation, base of support, or gridwalk footfall errors associated with the SCI. The sensory effects appeared 1 week after transplant and did not diminish during the 8-week course of the experiment when grafts were placed 2 weeks after SCI. Many grafted cells were still present and synthesizing 5HT at the end of the study. These data suggest that the human neuronal serotonergic hNT2.19 cells can be used as a biologic minipump for receiving SCI-related neuropathic pain, but likely requires intraspinal grafts for motor recovery. PMID:21799949

  10. A note on four-particle form factors of operators {T}_{2n}{T}_{-2n} in the sinh-Gordon model

    NASA Astrophysics Data System (ADS)

    Lashkevich, Michael; Pugai, Yaroslav

    2016-07-01

    The diagonal matrix elements < {θ }1,{θ }2| {T}2n{T}-2n| {θ }1,{θ }2> between two-particle states in the sinh-Gordon model are computed analytically for all integers n\\gt 0. This confirms the proposal [1] by Smirnov and Zamolodchikov for these matrix elements and demonstrates the effectiveness of the algebraic approach to form factors.

  11. Rapid differentiation of NT2 cells in Sertoli-NT2 cell tissue constructs grown in the rotating wall bioreactor.

    PubMed

    Saporta, Samuel; Willing, Alison E; Shamekh, Rania; Bickford, Paula; Paredes, Daniel; Cameron, Don F

    2004-12-15

    Cell replacement therapy is of great interest as a long-term treatment of neurodegenerative diseases such as Parkinson's disease (PD). We have previously shown that Sertoli cells (SC) provide neurotrophic support to transplants of dopaminergic fetal neurons and NT2N neurons, derived from the human clonal precursors cell line NTera2/D1 (NT2), which differentiate into dopaminergic NT2N neurons when exposed to retinoic acid. We have created SC-NT2 cell tissue constructs cultured in the high aspect ratio vessel (HARV) rotating wall bioreactor. Sertoli cells, NT2, and SC plus NT2 cells combined in starting ratios of 1:1, 1:2, 1:4 and 1:8 were cultured in the HARV in DMEM with 10% fetal bovine serum and 1% growth factor reduced Matrigel for 3 days, without retinoic acid. Conventional, non-HARV, cultures grown in the same culture medium were used as controls. The presence of tyrosine hydroxylase (TH) was assessed in all culture conditions. Sertoli-neuron-aggregated-cell (SNAC) tissue constructs grown at starting ratios of 1:1 to 1:4 contained a significant amount of TH after 3 days of culture in the HARV. No TH was detected in SC HARV cultures, or SC, NT2 or SC-NT2 conventional co-cultures. Quantitative stereology of immunolabled 1:4 SNAC revealed that approximately 9% of NT2 cells differentiate into TH-positive (TH+) NT2N neurons after 3 days of culture in the HARV, without retinoic acid. SNAC tissue constructs also released dopamine (DA) when stimulated with KCl, suggesting that TH-positive NT2N neurons in the SNAC adopted a functional dopaminergic phenotype. SNAC tissue constructs may be an important source of dopaminergic neurons for neuronal transplantation. PMID:15561470

  12. Non Ionising Radiation as a Non Chemical Strategy in Regenerative Medicine: Ca2+-ICR “In Vitro” Effect on Neuronal Differentiation and Tumorigenicity Modulation in NT2 Cells

    PubMed Central

    Ledda, Mario; Megiorni, Francesca; Pozzi, Deleana; Giuliani, Livio; D’Emilia, Enrico; Piccirillo, Sara; Mattei, Cristiana; Grimaldi, Settimio; Lisi, Antonella

    2013-01-01

    In regenerative medicine finding a new method for cell differentiation without pharmacological treatment or gene modification and minimal cell manipulation is a challenging goal. In this work we reported a neuronal induced differentiation and consequent reduction of tumorigenicity in NT2 human pluripotent embryonal carcinoma cells exposed to an extremely low frequency electromagnetic field (ELF-EMF), matching the cyclotron frequency corresponding to the charge/mass ratio of calcium ion (Ca2+-ICR). These cells, capable of differentiating into post-mitotic neurons following treatment with Retinoic Acid (RA), were placed in a solenoid and exposed for 5 weeks to Ca2+-ICR. The solenoid was installed in a μ-metal shielded room to avoid the effect of the geomagnetic field and obtained totally controlled and reproducible conditions. Contrast microscopy analysis reveled, in the NT2 exposed cells, an important change in shape and morphology with the outgrowth of neuritic-like structures together with a lower proliferation rate and metabolic activity alike those found in the RA treated cells. A significant up-regulation of early and late neuronal differentiation markers and a significant down-regulation of the transforming growth factor-α (TGF-α) and the fibroblast growth factor-4 (FGF-4) were also observed in the exposed cells. The decreased protein expression of the transforming gene Cripto-1 and the reduced capability of the exposed NT2 cells to form colonies in soft agar supported these last results. In conclusion, our findings demonstrate that the Ca2+-ICR frequency is able to induce differentiation and reduction of tumorigenicity in NT2 exposed cells suggesting a new potential therapeutic use in regenerative medicine. PMID:23585910

  13. Towards in vitro DT/DNT testing: Assaying chemical susceptibility in early differentiating NT2 cells.

    PubMed

    Menzner, Ann-Katrin; Abolpour Mofrad, Sepideh; Friedrich, Oliver; Gilbert, Daniel F

    2015-12-01

    Human pluripotent embryonal carcinoma (NT2) cells are increasingly considered as a suitable model for in vitro toxicity testing, e.g. developmental toxicity and neurotoxicity (DT/DNT) studies, as they undergo neuronal differentiation upon stimulation with retinoic acid (RA) and permit toxicity testing at different stages of maturation. NT2 cells have recently been reported to show specific changes in dielectric resistance profiles during differentiation which can be observed as early as 24h upon RA-stimulation. These observations suggest altered susceptibility to chemicals at an early stage of differentiation. However, chemical susceptibility of early differentiating NT cells has not yet been studied. To address this question, we have established a cell fitness screening assay based on the analysis of intracellular ATP levels and we applied the assay in a large-scale drug screening experiment in NT2 stem cells and early differentiating NT2 cells. Subsequent analysis of ranked fitness phenotypes revealed 19 chemicals with differential toxicity profile in early differentiating NT2 cells. To evaluate whether any of the identified drugs have previously been associated with DT/DNT, we conducted a literature search on the identified molecules and quantified the fraction of chemicals assigned to the FDA (Food and Drug Administration) pregnancy risk categories (PRC) N, A, B, C, D, and X in the hit list and the small molecule library. While the fractions of the categories N and B were decreased (0.81 and 0.35-fold), the classes C, D and X were increased (1.35, 1.47 and 3.27-fold) in the hit list compared to the chemical library. From these data as well as from the literature review, identifying large fractions of chemicals being directly (∼42%) and indirectly associated with DT/DNT (∼32%), we conclude that our method may be beneficial to systematic in vitro-based primary screening for developmental toxicants and neurotoxicants and we propose cell fitness screening in

  14. A Predictive In Vitro Model of the Impact of Drugs with Anticholinergic Properties on Human Neuronal and Astrocytic Systems

    PubMed Central

    Woehrling, Elizabeth K.; Parri, H. Rheinallt; Tse, Erin H. Y.; Hill, Eric J.; Maidment, Ian D.; Fox, G. Christopher; Coleman, Michael D.

    2015-01-01

    The link between off-target anticholinergic effects of medications and acute cognitive impairment in older adults requires urgent investigation. We aimed to determine whether a relevant in vitro model may aid the identification of anticholinergic responses to drugs and the prediction of anticholinergic risk during polypharmacy. In this preliminary study we employed a co-culture of human-derived neurons and astrocytes (NT2.N/A) derived from the NT2 cell line. NT2.N/A cells possess much of the functionality of mature neurons and astrocytes, key cholinergic phenotypic markers and muscarinic acetylcholine receptors (mAChRs). The cholinergic response of NT2 astrocytes to the mAChR agonist oxotremorine was examined using the fluorescent dye fluo-4 to quantitate increases in intracellular calcium [Ca2+]i. Inhibition of this response by drugs classified as severe (dicycloverine, amitriptyline), moderate (cyclobenzaprine) and possible (cimetidine) on the Anticholinergic Cognitive Burden (ACB) scale, was examined after exposure to individual and pairs of compounds. Individually, dicycloverine had the most significant effect regarding inhibition of the astrocytic cholinergic response to oxotremorine, followed by amitriptyline then cyclobenzaprine and cimetidine, in agreement with the ACB scale. In combination, dicycloverine with cyclobenzaprine had the most significant effect, followed by dicycloverine with amitriptyline. The order of potency of the drugs in combination frequently disagreed with predicted ACB scores derived from summation of the individual drug scores, suggesting current scales may underestimate the effect of polypharmacy. Overall, this NT2.N/A model may be appropriate for further investigation of adverse anticholinergic effects of multiple medications, in order to inform clinical choices of suitable drug use in the elderly. PMID:25738989

  15. Stochastic models of neuronal dynamics

    PubMed Central

    Harrison, L.M; David, O; Friston, K.J

    2005-01-01

    Cortical activity is the product of interactions among neuronal populations. Macroscopic electrophysiological phenomena are generated by these interactions. In principle, the mechanisms of these interactions afford constraints on biologically plausible models of electrophysiological responses. In other words, the macroscopic features of cortical activity can be modelled in terms of the microscopic behaviour of neurons. An evoked response potential (ERP) is the mean electrical potential measured from an electrode on the scalp, in response to some event. The purpose of this paper is to outline a population density approach to modelling ERPs. We propose a biologically plausible model of neuronal activity that enables the estimation of physiologically meaningful parameters from electrophysiological data. The model encompasses four basic characteristics of neuronal activity and organization: (i) neurons are dynamic units, (ii) driven by stochastic forces, (iii) organized into populations with similar biophysical properties and response characteristics and (iv) multiple populations interact to form functional networks. This leads to a formulation of population dynamics in terms of the Fokker–Planck equation. The solution of this equation is the temporal evolution of a probability density over state-space, representing the distribution of an ensemble of trajectories. Each trajectory corresponds to the changing state of a neuron. Measurements can be modelled by taking expectations over this density, e.g. mean membrane potential, firing rate or energy consumption per neuron. The key motivation behind our approach is that ERPs represent an average response over many neurons. This means it is sufficient to model the probability density over neurons, because this implicitly models their average state. Although the dynamics of each neuron can be highly stochastic, the dynamics of the density is not. This means we can use Bayesian inference and estimation tools that have

  16. Simple neuron models of ITD sensitive neurons

    NASA Astrophysics Data System (ADS)

    Dasika, Vasant; White, John A.; Colburn, H. Steven

    2002-05-01

    Neurons which show sensitivity to interaural time delay (ITD) exist in both mammalian medial superior olive (MSO), and bird nucleus laminaris (NL). In this study, we examine simple mathematical models of single MSO and NL cells which respond probabilistically to a pair of isolated inputs with a response probability that depends on the input interpulse interval. Inputs are either isolated pulse pairs or pairs of periodic trains, with or without random jitter added to their event times. Refractoriness is incorporated in the input description and/or in the cell model in specified simulations. We find that periodic rate-ITD shapes are shaped by three interacting factors: the cell's temporal response (described by the paired-pulse response), input frequency, and the degree of input synchrony. Paired-pulse responses are able to predict the widths of rate-ITD curves obtained from deterministic periodic input simulations. Reduced input synchrony predictably smears rate-ITD curves. Larger numbers of weaker inputs yield stronger rate-ITD modulation than a few strong inputs. Model response is compared with in vivo and in vitro MSO and NL physiological data. Comparisons with published analytical models as well as more complex and realistic physiological cell models are examined.

  17. Single neuron modeling and data assimilation in BNST neurons

    NASA Astrophysics Data System (ADS)

    Farsian, Reza

    Neurons, although tiny in size, are vastly complicated systems, which are responsible for the most basic yet essential functions of any nervous system. Even the most simple models of single neurons are usually high dimensional, nonlinear, and contain many parameters and states which are unobservable in a typical neurophysiological experiment. One of the most fundamental problems in experimental neurophysiology is the estimation of these parameters and states, since knowing their values is essential in identification, model construction, and forward prediction of biological neurons. Common methods of parameter and state estimation do not perform well for neural models due to their high dimensionality and nonlinearity. In this dissertation, two alternative approaches for parameters and state estimation of biological neurons have been demonstrated: dynamical parameter estimation (DPE) and a Markov Chain Monte Carlo (MCMC) method. The first method uses elements of chaos control and synchronization theory for parameter and state estimation. MCMC is a statistical approach which uses a path integral formulation to evaluate a mean and an error bound for these unobserved parameters and states. These methods have been applied to biological system of neurons in Bed Nucleus of Stria Termialis neurons (BNST) of rats. State and parameters of neurons in both systems were estimated, and their value were used for recreating a realistic model and predicting the behavior of the neurons successfully. The knowledge of biological parameters can ultimately provide a better understanding of the internal dynamics of a neuron in order to build robust models of neuron networks.

  18. Modeling neuronal vulnerability in ALS.

    PubMed

    Roselli, Francesco; Caroni, Pico

    2014-08-20

    Using computational models of motor neuron ion fluxes, firing properties, and energy requirements, Le Masson et al. (2014) reveal how local imbalances in energy homeostasis may self-amplify and contribute to neurodegeneration in ALS. PMID:25144872

  19. Silencing of PNPLA6, the neuropathy target esterase (NTE) codifying gene, alters neurodifferentiation of human embryonal carcinoma stem cells (NT2).

    PubMed

    Pamies, D; Bal-Price, A; Fabbri, M; Gribaldo, L; Scelfo, B; Harris, G; Collotta, A; Vilanova, E; Sogorb, M A

    2014-12-01

    Neuropathy target esterase (NTE) is a protein involved in the development of a polyneuropathy caused by exposure to certain organophosphorus compounds. In vivo and in vitro studies have also associated NTE with embryonic development since NTE null mice embryos are non-viable, and silencing the NTE-codifying gene (Pnpla6) in mouse embryonic stem cells strongly alters the differentiation of vascular and nervous systems. In this paper, human embryonal carcinoma stem cells human-derived NTera2/D1 (hNT2) are used as an in vitro neurodifferentiation model to determine whether PNPLA6 silencing is able to alter the differentiation process. In control cultures, PNPLA6 mRNA levels increased in parallel with other neuroectodermal markers during neurodifferentiation. PNPLA6 silencing with specific interference RNA reached a 97% decrease in gene expression 3days after transfection and with a maximum reduction in NTE enzymatic activity (50%), observed on day 4. Silencing PNPLA6 showed an 80% decrease in quantifiable neuronal cells after 13days in vitro (DIV) compared to controls and absence of different neuronal markers after 66DIV. Microarray data analysis of the PNPLA6-silenced cells showed alterations in several developmental processes, mainly neurogenesis and epithelium tube morphogenesis. PNPLA6 silencing also led to a reduction in electrical activity and an altered neuronal phenotype. This work is the first proof supporting the hypothesis that NTE plays a role in human early neurodevelopment using a human cell differentiation model. PMID:25255935

  20. Neuron Model with Simplified Memristive Ionic Channels

    NASA Astrophysics Data System (ADS)

    Hegab, Almoatazbellah M.; Salem, Noha M.; Radwan, Ahmed G.; Chua, Leon

    2015-06-01

    A simplified neuron model is introduced to mimic the action potential generated by the famous Hodgkin-Huxley equations by using the genetic optimization algorithm. Comparison with different neuron models is investigated, and it is confirmed that the sodium and potassium channels in our simplified neuron model are made out of memristors. In addition, the channel equations in the simplified model may be adjusted to introduce a simplified memristor model that is in accordance with the theoretical conditions of the memristive systems.

  1. Neuronal cell lines as model dorsal root ganglion neurons

    PubMed Central

    Yin, Kathleen; Baillie, Gregory J

    2016-01-01

    Background Dorsal root ganglion neuron-derived immortal cell lines including ND7/23 and F-11 cells have been used extensively as in vitro model systems of native peripheral sensory neurons. However, while it is clear that some sensory neuron-specific receptors and ion channels are present in these cell lines, a systematic comparison of the molecular targets expressed by these cell lines with those expressed in intact peripheral neurons is lacking. Results In this study, we examined the expression of RNA transcripts in the human neuroblastoma-derived cell line, SH-SY5Y, and two dorsal root ganglion hybridoma cell lines, F-11 and ND7/23, using Illumina next-generation sequencing, and compared the results with native whole murine dorsal root ganglions. The gene expression profiles of these three cell lines did not resemble any specific defined dorsal root ganglion subclass. The cell lines lacked many markers for nociceptive sensory neurons, such as the Transient receptor potential V1 gene, but expressed markers for both myelinated and unmyelinated neurons. Global gene ontology analysis on whole dorsal root ganglions and cell lines showed similar enrichment of biological process terms across all samples. Conclusions This paper provides insights into the receptor repertoire expressed in common dorsal root ganglion neuron-derived cell lines compared with whole murine dorsal root ganglions, and illustrates the limits and potentials of these cell lines as tools for neuropharmacological exploration. PMID:27130590

  2. Elevated caspase 3 activity and cytosolic cytochrome c in NT2 cybrids containing amyotrophic lateral sclerosis subject mtDNA.

    PubMed

    Shrivastava, Mohita; Subbiah, Vivekanandhan

    2016-09-01

    Apoptosis of motor neurons is an important feature in amyotrophic lateral sclerosis (ALS). A vital role of mitochondria in apoptosis and cell survival is well documented. Eventually mitochondria have shown to be an early target in the pathogenesis of ALS. On account of these facts, we investigated the involvement of mitochondrial-dependent apoptosis in ALS and control (CTR) cybrids, generated fusing human platelets with mitochondrial DNA-depleted NT2-neuroteratocarcinoma cells. After a 6 week selection process during which transferred subject mtDNA repopulated the NT2 cells and restored mitochondrial oxygen consumption, we assessed cell viability and two programmed cell death parameters, caspase 3 activity and cytosolic cytochrome c levels. Compared to the control cybrid lines (n = 5), the ALS cybrid lines (n = 10) showed 45% less XTT reduction and higher caspase 3 activity ( p < 0.05, two-way Student's t test) exhibiting lesser cell viability and execution of apoptosis. Elevated cytosolic cytochrome c levels in ALS cybrid lines (n = 8) than in CTR (n = 4) ( p < 0.05, two-way Student's t-test) indicating its mitochondrial release and initiation of apoptosis. This indicates apoptosis as one of the possible mechanisms of cell death in ALS. Our findings support the view that in ALS, subject's mitochondria are altered in non-degenerating tissues in such a way that intrinsic apoptotic pathway activity is relatively increased. PMID:26268635

  3. Fitting Neuron Models to Spike Trains

    PubMed Central

    Rossant, Cyrille; Goodman, Dan F. M.; Fontaine, Bertrand; Platkiewicz, Jonathan; Magnusson, Anna K.; Brette, Romain

    2011-01-01

    Computational modeling is increasingly used to understand the function of neural circuits in systems neuroscience. These studies require models of individual neurons with realistic input–output properties. Recently, it was found that spiking models can accurately predict the precisely timed spike trains produced by cortical neurons in response to somatically injected currents, if properly fitted. This requires fitting techniques that are efficient and flexible enough to easily test different candidate models. We present a generic solution, based on the Brian simulator (a neural network simulator in Python), which allows the user to define and fit arbitrary neuron models to electrophysiological recordings. It relies on vectorization and parallel computing techniques to achieve efficiency. We demonstrate its use on neural recordings in the barrel cortex and in the auditory brainstem, and confirm that simple adaptive spiking models can accurately predict the response of cortical neurons. Finally, we show how a complex multicompartmental model can be reduced to a simple effective spiking model. PMID:21415925

  4. Map-based models in neuronal dynamics

    NASA Astrophysics Data System (ADS)

    Ibarz, B.; Casado, J. M.; Sanjuán, M. A. F.

    2011-04-01

    Ever since the pioneering work of Hodgkin and Huxley, biological neuron models have consisted of ODEs representing the evolution of the transmembrane voltage and the dynamics of ionic conductances. It is only recently that discrete dynamical systems-also known as maps-have begun to receive attention as valid phenomenological neuron models. The present review tries to provide a coherent perspective of map-based biological neuron models, describing their dynamical properties; stressing the similarities and differences, both among them and in relation to continuous-time models; exploring their behavior in networks; and examining their wide-ranging possibilities of application in computational neuroscience.

  5. An overview of the neuron ring model

    NASA Technical Reports Server (NTRS)

    Taber, Rod

    1991-01-01

    The Neuron Ring model employs an avalanche structure with two important distinctions at the neuron level. Each neuron has two memory latches; one traps maximum neuronal activation during pattern presentation, and the other records the time of latch content change. The latches filter short term memory. In the process, they preserve length 1 snapshots of activation theory history. The model finds utility in pattern classification. Its synaptic weights are first conditioned with sample spectra. The model then receives a test or unknown signal. The objective is to identify the sample closest to the test signal. Class decision follows complete presentation of the test data. The decision maker relies exclusively on the latch contents. Presented here is an overview of the Neuron Ring at the seminar level.

  6. Animal models for motor neuron disease.

    PubMed

    Green, S L; Tolwani, R J

    1999-10-01

    Motor neuron disease is a general term applied to a broad class of neurodegenerative diseases that are characterized by fatally progressive muscular weakness, atrophy, and paralysis attributable to loss of motor neurons. At present, there is no cure for most motor neuron diseases, including amyotrophic lateral sclerosis (ALS), the most common human motor neuron disease--the cause of which remains largely unknown. Animal models of motor neuron disease (MND) have significantly contributed to the remarkable recent progress in understanding the cause, genetic factors, and pathologic mechanisms proposed for this class of human neurodegenerative disorders. Largely driven by ALS research, animal models of MND have proven their usefulness in elucidating potential causes and specific pathogenic mechanisms, and have helped to advance promising new treatments from "benchside to bedside." This review summarizes important features of selected established animal models of MND: genetically engineered mice and inherited or spontaneously occurring MND in the murine, canine, and equine species. PMID:10551448

  7. Effective Stimuli for Constructing Reliable Neuron Models

    PubMed Central

    Druckmann, Shaul; Berger, Thomas K.; Schürmann, Felix; Hill, Sean; Markram, Henry; Segev, Idan

    2011-01-01

    The rich dynamical nature of neurons poses major conceptual and technical challenges for unraveling their nonlinear membrane properties. Traditionally, various current waveforms have been injected at the soma to probe neuron dynamics, but the rationale for selecting specific stimuli has never been rigorously justified. The present experimental and theoretical study proposes a novel framework, inspired by learning theory, for objectively selecting the stimuli that best unravel the neuron's dynamics. The efficacy of stimuli is assessed in terms of their ability to constrain the parameter space of biophysically detailed conductance-based models that faithfully replicate the neuron's dynamics as attested by their ability to generalize well to the neuron's response to novel experimental stimuli. We used this framework to evaluate a variety of stimuli in different types of cortical neurons, ages and animals. Despite their simplicity, a set of stimuli consisting of step and ramp current pulses outperforms synaptic-like noisy stimuli in revealing the dynamics of these neurons. The general framework that we propose paves a new way for defining, evaluating and standardizing effective electrical probing of neurons and will thus lay the foundation for a much deeper understanding of the electrical nature of these highly sophisticated and non-linear devices and of the neuronal networks that they compose. PMID:21876663

  8. A COMPUTATIONAL MODEL OF MOTOR NEURON DEGENERATION

    PubMed Central

    Le Masson, Gwendal; Przedborski, Serge; Abbott, L.F.

    2014-01-01

    SUMMARY To explore the link between bioenergetics and motor neuron degeneration, we used a computational model in which detailed morphology and ion conductance are paired with intracellular ATP production and consumption. We found that reduced ATP availability increases the metabolic cost of a single action potential and disrupts K+/Na+ homeostasis, resulting in a chronic depolarization. The magnitude of the ATP shortage at which this ionic instability occurs depends on the morphology and intrinsic conductance characteristic of the neuron. If ATP shortage is confined to the distal part of the axon, the ensuing local ionic instability eventually spreads to the whole neuron and involves fasciculation-like spiking events. A shortage of ATP also causes a rise in intracellular calcium. Our modeling work supports the notion that mitochondrial dysfunction can account for salient features of the paralytic disorder amyotrophic lateral sclerosis, including motor neuron hyperexcitability, fasciculation, and differential vulnerability of motor neuron subpopulations. PMID:25088365

  9. Bistable behaviour in a neocortical neurone model.

    PubMed

    Delord, B; Klaassen, A J; Burnod, Y; Costalat, R; Guigon, E

    1997-03-01

    Intracellular recordings have shown that neocortical pyramidal neurones have an intrinsic capacity for regenerative firing. The cellular mechanism of this firing was investigated by computer simulations of a model neurone endowed with standard action potential and persistent sodium (gNaP) conductances. The firing mode of the neurone was determined as a function of leakage and NaP maximal conductances (gl and gNaP). The neurone had two stable states of activity (bistable) over wide range of gl and gNaP, one at the resting potential and the other in a regenerative firing mode, that could be triggered by a transient input. This model points to a cellular mechanism that may contribute to the generation and maintenance of long-lasting sustained neuronal discharges in the cerebral cortex. PMID:9141084

  10. Context-aware modeling of neuronal morphologies

    PubMed Central

    Torben-Nielsen, Benjamin; De Schutter, Erik

    2014-01-01

    Neuronal morphologies are pivotal for brain functioning: physical overlap between dendrites and axons constrain the circuit topology, and the precise shape and composition of dendrites determine the integration of inputs to produce an output signal. At the same time, morphologies are highly diverse and variant. The variance, presumably, originates from neurons developing in a densely packed brain substrate where they interact (e.g., repulsion or attraction) with other actors in this substrate. However, when studying neurons their context is never part of the analysis and they are treated as if they existed in isolation. Here we argue that to fully understand neuronal morphology and its variance it is important to consider neurons in relation to each other and to other actors in the surrounding brain substrate, i.e., their context. We propose a context-aware computational framework, NeuroMaC, in which large numbers of neurons can be grown simultaneously according to growth rules expressed in terms of interactions between the developing neuron and the surrounding brain substrate. As a proof of principle, we demonstrate that by using NeuroMaC we can generate accurate virtual morphologies of distinct classes both in isolation and as part of neuronal forests. Accuracy is validated against population statistics of experimentally reconstructed morphologies. We show that context-aware generation of neurons can explain characteristics of variation. Indeed, plausible variation is an inherent property of the morphologies generated by context-aware rules. We speculate about the applicability of this framework to investigate morphologies and circuits, to classify healthy and pathological morphologies, and to generate large quantities of morphologies for large-scale modeling. PMID:25249944

  11. Modeling of Biological Neurons using Superconducting Circuitry

    NASA Astrophysics Data System (ADS)

    Khadka, Shreeya; Svitelskiy, Oleksiy; Kaplan, Steve; Segall, Kenneth

    2014-03-01

    With the goal of understanding the collective behavior of large network of neurons, we purpose a new analog method based on superconducting Josephson junction (JJ) circuitry. Through numerical simulations, we were able to show that these JJ neurons reproduce many characteristic features of biological neurons such as action potential, firing threshold and refractory period. For preliminary testing, we have designed and fabricated a superconducting chip consisting of two coupled JJ neurons, connected to each other in a closed loop. The numerical simulations of the two synchronized coupled neurons, showed a characteristic phase-flip-bifurcation where the two neurons would fire either in-phase or out-of-phase depending on their coupling strength. Thus, we are looking for the characteristic phase-flip-bifurcation in the experiment also. If these encouraging observations find further confirmation, our JJ model will open a way for developing a fast and low power method of studying the dynamics of large neural networks. We would like to thank Zictools/WRSpice for layout and simulation, and Hypres Inc. for fabricating the chip.

  12. Modelling the magnetic signature of neuronal tissue.

    PubMed

    Blagoev, K B; Mihaila, B; Travis, B J; Alexandrov, L B; Bishop, A R; Ranken, D; Posse, S; Gasparovic, C; Mayer, A; Aine, C J; Ulbert, I; Morita, M; Müller, W; Connor, J; Halgren, E

    2007-08-01

    Neuronal communication in the brain involves electrochemical currents, which produce magnetic fields. Stimulus-evoked brain responses lead to changes in these fields and can be studied using magneto- and electro-encephalography (MEG/EEG). In this paper we model the spatiotemporal distribution of the magnetic field of a physiologically idealized but anatomically realistic neuron to assess the possibility of using magnetic resonance imaging (MRI) for directly mapping the neuronal currents in the human brain. Our results show that the magnetic field several centimeters from the centre of the neuron is well approximated by a dipole source, but the field close to the neuron is not, a finding particularly important for understanding the possible contrast mechanism underlying the use of MRI to detect and locate these currents. We discuss the importance of the spatiotemporal characteristics of the magnetic field in cortical tissue for evaluating and optimizing an experiment based on this mechanism and establish an upper bound for the expected MRI signal change due to stimulus-induced cortical response. Our simulations show that the expected change of the signal magnitude is 1.6% and its phase shift is 1 degrees . An unexpected finding of this work is that the cortical orientation with respect to the external magnetic field has little effect on the predicted MRI contrast. This encouraging result shows that magnetic resonance contrast directly based on the neuronal currents present in the cortex is theoretically a feasible imaging technique. MRI contrast generation based on neuronal currents depends on the dendritic architecture and we obtained high-resolution optical images of cortical tissue to discuss the spatial structure of the magnetic field in grey matter. PMID:17544300

  13. Towards Reproducible Descriptions of Neuronal Network Models

    PubMed Central

    Nordlie, Eilen; Gewaltig, Marc-Oliver; Plesser, Hans Ekkehard

    2009-01-01

    Progress in science depends on the effective exchange of ideas among scientists. New ideas can be assessed and criticized in a meaningful manner only if they are formulated precisely. This applies to simulation studies as well as to experiments and theories. But after more than 50 years of neuronal network simulations, we still lack a clear and common understanding of the role of computational models in neuroscience as well as established practices for describing network models in publications. This hinders the critical evaluation of network models as well as their re-use. We analyze here 14 research papers proposing neuronal network models of different complexity and find widely varying approaches to model descriptions, with regard to both the means of description and the ordering and placement of material. We further observe great variation in the graphical representation of networks and the notation used in equations. Based on our observations, we propose a good model description practice, composed of guidelines for the organization of publications, a checklist for model descriptions, templates for tables presenting model structure, and guidelines for diagrams of networks. The main purpose of this good practice is to trigger a debate about the communication of neuronal network models in a manner comprehensible to humans, as opposed to machine-readable model description languages. We believe that the good model description practice proposed here, together with a number of other recent initiatives on data-, model-, and software-sharing, may lead to a deeper and more fruitful exchange of ideas among computational neuroscientists in years to come. We further hope that work on standardized ways of describing—and thinking about—complex neuronal networks will lead the scientific community to a clearer understanding of high-level concepts in network dynamics, and will thus lead to deeper insights into the function of the brain. PMID:19662159

  14. Nonsmooth dynamics in spiking neuron models

    NASA Astrophysics Data System (ADS)

    Coombes, S.; Thul, R.; Wedgwood, K. C. A.

    2012-11-01

    Large scale studies of spiking neural networks are a key part of modern approaches to understanding the dynamics of biological neural tissue. One approach in computational neuroscience has been to consider the detailed electrophysiological properties of neurons and build vast computational compartmental models. An alternative has been to develop minimal models of spiking neurons with a reduction in the dimensionality of both parameter and variable space that facilitates more effective simulation studies. In this latter case the single neuron model of choice is often a variant of the classic integrate-and-fire model, which is described by a nonsmooth dynamical system. In this paper we review some of the more popular spiking models of this class and describe the types of spiking pattern that they can generate (ranging from tonic to burst firing). We show that a number of techniques originally developed for the study of impact oscillators are directly relevant to their analysis, particularly those for treating grazing bifurcations. Importantly we highlight one particular single neuron model, capable of generating realistic spike trains, that is both computationally cheap and analytically tractable. This is a planar nonlinear integrate-and-fire model with a piecewise linear vector field and a state dependent reset upon spiking. We call this the PWL-IF model and analyse it at both the single neuron and network level. The techniques and terminology of nonsmooth dynamical systems are used to flesh out the bifurcation structure of the single neuron model, as well as to develop the notion of Lyapunov exponents. We also show how to construct the phase response curve for this system, emphasising that techniques in mathematical neuroscience may also translate back to the field of nonsmooth dynamical systems. The stability of periodic spiking orbits is assessed using a linear stability analysis of spiking times. At the network level we consider linear coupling between voltage

  15. A drive-reinforcement model of single neuron function: An alternative to the Hebbian neuronal model

    NASA Astrophysics Data System (ADS)

    Klopf, A. Harry

    1986-08-01

    A neuronal learning mechanism is proposed that accounts for the basic animal learning phenomena that have been observed. Among the classical conditioning phenomena predicted by the neuronal model are delay conditioning, trace conditioning, simultaneous conditioning, conditioned stimulus duration and amplitude effects, unconditioned stimulus amplitude effects, interstimulus interval effects, second and higher order conditioning, conditioned inhibition, habituation and extinction, reacquisition effects, backward conditioning, blocking, overshadowing and serial compound conditioning. The proposed neuronal model and learning mechanism offer a new building block for constructing neural network-like computer arthitectures for artificial intelligence.

  16. Estimating Neuronal Ageing with Hidden Markov Models

    NASA Astrophysics Data System (ADS)

    Wang, Bing; Pham, Tuan D.

    2011-06-01

    Neuronal degeneration is widely observed in normal ageing, meanwhile the neurode-generative disease like Alzheimer's disease effects neuronal degeneration in a faster way which is considered as faster ageing. Early intervention of such disease could benefit subjects with potentials of positive clinical outcome, therefore, early detection of disease related brain structural alteration is required. In this paper, we propose a computational approach for modelling the MRI-based structure alteration with ageing using hidden Markov model. The proposed hidden Markov model based brain structural model encodes intracortical tissue/fluid distribution using discrete wavelet transformation and vector quantization. Further, it captures gray matter volume loss, which is capable of reflecting subtle intracortical changes with ageing. Experiments were carried out on healthy subjects to validate its accuracy and robustness. Results have shown its ability of predicting the brain age with prediction error of 1.98 years without training data, which shows better result than other age predition methods.

  17. Model reduction of strong-weak neurons.

    PubMed

    Du, Bosen; Sorensen, Danny; Cox, Steven J

    2014-01-01

    We consider neurons with large dendritic trees that are weakly excitable in the sense that back propagating action potentials are severly attenuated as they travel from the small, strongly excitable, spike initiation zone. In previous work we have shown that the computational size of weakly excitable cell models may be reduced by two or more orders of magnitude, and that the size of strongly excitable models may be reduced by at least one order of magnitude, without sacrificing the spatio-temporal nature of its inputs (in the sense we reproduce the cell's precise mapping of inputs to outputs). We combine the best of these two strategies via a predictor-corrector decomposition scheme and achieve a drastically reduced highly accurate model of a caricature of the neuron responsible for collision detection in the locust. PMID:25566048

  18. Ill-Posed Point Neuron Models.

    PubMed

    Nielsen, Bjørn Fredrik; Wyller, John

    2016-12-01

    We show that point-neuron models with a Heaviside firing rate function can be ill posed. More specifically, the initial-condition-to-solution map might become discontinuous in finite time. Consequently, if finite precision arithmetic is used, then it is virtually impossible to guarantee the accurate numerical solution of such models. If a smooth firing rate function is employed, then standard ODE theory implies that point-neuron models are well posed. Nevertheless, in the steep firing rate regime, the problem may become close to ill posed, and the error amplification, in finite time, can be very large. This observation is illuminated by numerical experiments. We conclude that, if a steep firing rate function is employed, then minor round-off errors can have a devastating effect on simulations, unless proper error-control schemes are used. PMID:27129667

  19. Model reduction of strong-weak neurons

    PubMed Central

    Du, Bosen; Sorensen, Danny; Cox, Steven J.

    2014-01-01

    We consider neurons with large dendritic trees that are weakly excitable in the sense that back propagating action potentials are severly attenuated as they travel from the small, strongly excitable, spike initiation zone. In previous work we have shown that the computational size of weakly excitable cell models may be reduced by two or more orders of magnitude, and that the size of strongly excitable models may be reduced by at least one order of magnitude, without sacrificing the spatio–temporal nature of its inputs (in the sense we reproduce the cell's precise mapping of inputs to outputs). We combine the best of these two strategies via a predictor-corrector decomposition scheme and achieve a drastically reduced highly accurate model of a caricature of the neuron responsible for collision detection in the locust. PMID:25566048

  20. Simple models for reading neuronal population codes.

    PubMed Central

    Seung, H S; Sompolinsky, H

    1993-01-01

    In many neural systems, sensory information is distributed throughout a population of neurons. We study simple neural network models for extracting this information. The inputs to the networks are the stochastic responses of a population of sensory neurons tuned to directional stimuli. The performance of each network model in psychophysical tasks is compared with that of the optimal maximum likelihood procedure. As a model of direction estimation in two dimensions, we consider a linear network that computes a population vector. Its performance depends on the width of the population tuning curves and is maximal for width, which increases with the level of background activity. Although for narrowly tuned neurons the performance of the population vector is significantly inferior to that of maximum likelihood estimation, the difference between the two is small when the tuning is broad. For direction discrimination, we consider two models: a perceptron with fully adaptive weights and a network made by adding an adaptive second layer to the population vector network. We calculate the error rates of these networks after exhaustive training to a particular direction. By testing on the full range of possible directions, the extent of transfer of training to novel stimuli can be calculated. It is found that for threshold linear networks the transfer of perceptual learning is nonmonotonic. Although performance deteriorates away from the training stimulus, it peaks again at an intermediate angle. This nonmonotonicity provides an important psychophysical test of these models. PMID:8248166

  1. Biophysically realistic minimal model of dopamine neuron

    NASA Astrophysics Data System (ADS)

    Oprisan, Sorinel

    2008-03-01

    We proposed and studied a new biophysically relevant computational model of dopaminergic neurons. Midbrain dopamine neurons are involved in motivation and the control of movement, and have been implicated in various pathologies such as Parkinson's disease, schizophrenia, and drug abuse. The model we developed is a single-compartment Hodgkin-Huxley (HH)-type parallel conductance membrane model. The model captures the essential mechanisms underlying the slow oscillatory potentials and plateau potential oscillations. The main currents involved are: 1) a voltage-dependent fast calcium current, 2) a small conductance potassium current that is modulated by the cytosolic concentration of calcium, and 3) a slow voltage-activated potassium current. We developed multidimensional bifurcation diagrams and extracted the effective domains of sustained oscillations. The model includes a calcium balance due to the fundamental importance of calcium influx as proved by simultaneous electrophysiological and calcium imaging procedure. Although there are significant evidences to suggest a partially electrogenic calcium pump, all previous models considered only elecrtogenic pumps. We investigated the effect of the electrogenic calcium pump on the bifurcation diagram of the model and compared our findings against the experimental results.

  2. Qualitative-Modeling-Based Silicon Neurons and Their Networks

    PubMed Central

    Kohno, Takashi; Sekikawa, Munehisa; Li, Jing; Nanami, Takuya; Aihara, Kazuyuki

    2016-01-01

    The ionic conductance models of neuronal cells can finely reproduce a wide variety of complex neuronal activities. However, the complexity of these models has prompted the development of qualitative neuron models. They are described by differential equations with a reduced number of variables and their low-dimensional polynomials, which retain the core mathematical structures. Such simple models form the foundation of a bottom-up approach in computational and theoretical neuroscience. We proposed a qualitative-modeling-based approach for designing silicon neuron circuits, in which the mathematical structures in the polynomial-based qualitative models are reproduced by differential equations with silicon-native expressions. This approach can realize low-power-consuming circuits that can be configured to realize various classes of neuronal cells. In this article, our qualitative-modeling-based silicon neuron circuits for analog and digital implementations are quickly reviewed. One of our CMOS analog silicon neuron circuits can realize a variety of neuronal activities with a power consumption less than 72 nW. The square-wave bursting mode of this circuit is explained. Another circuit can realize Class I and II neuronal activities with about 3 nW. Our digital silicon neuron circuit can also realize these classes. An auto-associative memory realized on an all-to-all connected network of these silicon neurons is also reviewed, in which the neuron class plays important roles in its performance. PMID:27378842

  3. Synchronized action of synaptically coupled chaotic model neurons.

    PubMed

    Abarbanel, H D; Huerta, R; Rabinovich, M I; Rulkov, N F; Rowat, P F; Selverston, A I

    1996-11-15

    Experimental observations of the intracellular recorded electrical activity in individual neurons show that the temporal behavior is often chaotic. We discuss both our own observations on a cell from the stomatogastric central pattern generator of lobster and earlier observations in other cells. In this paper we work with models with chaotic neurons, building on models by Hindmarsh and Rose for bursting, spiking activity in neurons. The key feature of these simplified models of neurons is the presence of coupled slow and fast subsystems. We analyze the model neurons using the same tools employed in the analysis of our experimental data. We couple two model neurons both electrotonically and electrochemically in inhibitory and excitatory fashions. In each of these cases, we demonstrate that the model neurons can synchronize in phase and out of phase depending on the strength of the coupling. For normal synaptic coupling, we have a time delay between the action of one neuron and the response of the other. We also analyze how the synchronization depends on this delay. A rich spectrum of synchronized behaviors is possible for electrically coupled neurons and for inhibitory coupling between neurons. In synchronous neurons one typically sees chaotic motion of the coupled neurons. Excitatory coupling produces essentially periodic voltage trajectories, which are also synchronized. We display and discuss these synchronized behaviors using two "distance" measures of the synchronization. PMID:8888609

  4. Stochastic resonance in models of neuronal ensembles

    NASA Astrophysics Data System (ADS)

    Chialvo, Dante R.; Longtin, André; Müautller-Gerking, Johannes

    1997-02-01

    Two recently suggested mechanisms for the neuronal encoding of sensory information involving the effect of stochastic resonance with aperiodic time-varying inputs are considered. It is shown, using theoretical arguments and numerical simulations, that the nonmonotonic behavior with increasing noise of the correlation measures used for the so-called aperiodic stochastic resonance (ASR) scenario does not rely on the cooperative effect typical of stochastic resonance in bistable and excitable systems. Rather, ASR with slowly varying signals is more properly interpreted as linearization by noise. Consequently, the broadening of the ``resonance curve'' in the multineuron stochastic resonance without tuning scenario can also be explained by this linearization. Computation of the input-output correlation as a function of both signal frequency and noise for the model system further reveals conditions where noise-induced firing with aperiodic inputs will benefit from stochastic resonance rather than linearization by noise. Thus, our study clarifies the tuning requirements for the optimal transduction of subthreshold aperiodic signals. It also shows that a single deterministic neuron can perform as well as a network when biased into a suprathreshold regime. Finally, we show that the inclusion of a refractory period in the spike-detection scheme produces a better correlation between instantaneous firing rate and input signal.

  5. Chaotic neuron models and their VLSI circuit implementations.

    PubMed

    Hsu, C C; Gobovic, D; Zaghloul, M E; Szu, H H

    1996-01-01

    The design of a chaotic neuron model is proposed and implemented in a CMOS very large scale integration (VLSI) chip. The transfer function of the neuron is defined as a piecewise linear (PWL) N-shaped function. In this paper, the new concept of the baseline function is introduced. It is the mapping of the neuron state to the neuron output. It is used to control the chaotic behavior of collective neurons. The chaotic behavior is analyzed and verified by Lyapunov exponents. An analog CMOS chip was designed to implement the theory and it was fabricated through the MOSIS program. The measurement diagnoses of the chip is demonstrated. PMID:18263529

  6. Computational models of neuron-astrocyte interaction in epilepsy

    PubMed Central

    Volman, Vladislav; Bazhenov, Maxim; Sejnowski, Terrence J.

    2012-01-01

    Astrocytes actively shape the dynamics of neurons and neuronal ensembles by affecting several aspects critical to neuronal function, such as regulating synaptic plasticity, modulating neuronal excitability, and maintaining extracellular ion balance. These pathways for astrocyte-neuron interaction can also enhance the information-processing capabilities of brains, but in other circumstances may lead the brain on the road to pathological ruin. In this article, we review the existing computational models of astrocytic involvement in epileptogenesis, focusing on their relevance to existing physiological data. PMID:23060780

  7. The effects of cholinergic neuromodulation on neuronal phase-response curves of modeled cortical neurons

    PubMed Central

    Stiefel, Klaus M.; Gutkin, Boris S.; Sejnowski, Terrence J.

    2010-01-01

    The response of an oscillator to perturbations is described by its phase-response curve (PRC), which is related to the type of bifurcation leading from rest to tonic spiking. In a recent experimental study, we have shown that the type of PRC in cortical pyramidal neurons can be switched by cholinergic neuromodulation from type II (biphasic) to type I (monophasic). We explored how intrinsic mechanisms affected by acetylcholine influence the PRC using three different types of neuronal models: a theta neuron, single-compartment neurons and a multi-compartment neuron. In all of these models a decrease in the amount of a spike-frequency adaptation current was a necessary and sufficient condition for the shape of the PRC to change from biphasic (type II) to purely positive (type I). PMID:18784991

  8. Functionalized anatomical models for EM-neuron Interaction modeling.

    PubMed

    Neufeld, Esra; Cassará, Antonino Mario; Montanaro, Hazael; Kuster, Niels; Kainz, Wolfgang

    2016-06-21

    The understanding of interactions between electromagnetic (EM) fields and nerves are crucial in contexts ranging from therapeutic neurostimulation to low frequency EM exposure safety. To properly consider the impact of in vivo induced field inhomogeneity on non-linear neuronal dynamics, coupled EM-neuronal dynamics modeling is required. For that purpose, novel functionalized computable human phantoms have been developed. Their implementation and the systematic verification of the integrated anisotropic quasi-static EM solver and neuronal dynamics modeling functionality, based on the method of manufactured solutions and numerical reference data, is described. Electric and magnetic stimulation of the ulnar and sciatic nerve were modeled to help understanding a range of controversial issues related to the magnitude and optimal determination of strength-duration (SD) time constants. The results indicate the importance of considering the stimulation-specific inhomogeneous field distributions (especially at tissue interfaces), realistic models of non-linear neuronal dynamics, very short pulses, and suitable SD extrapolation models. These results and the functionalized computable phantom will influence and support the development of safe and effective neuroprosthetic devices and novel electroceuticals. Furthermore they will assist the evaluation of existing low frequency exposure standards for the entire population under all exposure conditions. PMID:27224508

  9. Functionalized anatomical models for EM-neuron Interaction modeling

    NASA Astrophysics Data System (ADS)

    Neufeld, Esra; Cassará, Antonino Mario; Montanaro, Hazael; Kuster, Niels; Kainz, Wolfgang

    2016-06-01

    The understanding of interactions between electromagnetic (EM) fields and nerves are crucial in contexts ranging from therapeutic neurostimulation to low frequency EM exposure safety. To properly consider the impact of in vivo induced field inhomogeneity on non-linear neuronal dynamics, coupled EM-neuronal dynamics modeling is required. For that purpose, novel functionalized computable human phantoms have been developed. Their implementation and the systematic verification of the integrated anisotropic quasi-static EM solver and neuronal dynamics modeling functionality, based on the method of manufactured solutions and numerical reference data, is described. Electric and magnetic stimulation of the ulnar and sciatic nerve were modeled to help understanding a range of controversial issues related to the magnitude and optimal determination of strength-duration (SD) time constants. The results indicate the importance of considering the stimulation-specific inhomogeneous field distributions (especially at tissue interfaces), realistic models of non-linear neuronal dynamics, very short pulses, and suitable SD extrapolation models. These results and the functionalized computable phantom will influence and support the development of safe and effective neuroprosthetic devices and novel electroceuticals. Furthermore they will assist the evaluation of existing low frequency exposure standards for the entire population under all exposure conditions.

  10. A simple chaotic neuron model: stochastic behavior of neural networks.

    PubMed

    Aydiner, Ekrem; Vural, Adil M; Ozcelik, Bekir; Kiymac, Kerim; Tan, Uner

    2003-05-01

    We have briefly reviewed the occurrence of the post-synaptic potentials between neurons, the relationship between EEG and neuron dynamics, as well as methods of signal analysis. We propose a simple stochastic model representing electrical activity of neuronal systems. The model is constructed using the Monte Carlo simulation technique. The results yielded EEG-like signals with their phase portraits in three-dimensional space. The Lyapunov exponent was positive, indicating chaotic behavior. The correlation of the EEG-like signals was.92, smaller than those reported by others. It was concluded that this neuron model may provide valuable clues about the dynamic behavior of neural systems. PMID:12745622

  11. Determining the Oxidation States of Manganese in NT2 Cells and Cultured Astrocytes

    SciTech Connect

    Gunter,K.; Aschner, M.; Miller, L.; Eliseev, R.; Salter, J.; Andersen, K.; Gunter, T.

    2006-01-01

    Excessive brain manganese (Mn) can produce a syndrome called 'manganism', which correlates with loss of striatal dopamine and cell death in the striatum and globus pallidus. The prevalent hypothesis for the cause of this syndrome has been oxidation of cell components by the strong oxidizing agent, Mn{sup 3+}, either formed by oxidation of intracellular Mn{sup 2+} or transported into the cell as Mn{sup 3+}. We have recently used X-ray absorption near edge structure spectroscopy (XANES) to determine the oxidation states of manganese complexes in brain and liver mitochondria and in nerve growth factor (NGF)-induced and non-induced PC12 cells. No evidence was found for stabilization or accumulation of Mn{sup 3+} complexes because of oxidation of Mn{sup 2+} by reactive oxygen species in these tissues. Here we extend these studies of manganese oxidation state to cells of brain origin, human neuroteratocarcinoma (NT2) cells and primary cultures of rat astrocytes. Again we find no evidence for stabilization or accumulation of any Mn{sup 3+} complex derived from oxidation of Mn{sup 2+} under a range of conditions.

  12. Comparing Realistic Subthalamic Nucleus Neuron Models

    NASA Astrophysics Data System (ADS)

    Njap, Felix; Claussen, Jens C.; Moser, Andreas; Hofmann, Ulrich G.

    2011-06-01

    The mechanism of action of clinically effective electrical high frequency stimulation is still under debate. However, recent evidence points at the specific activation of GABA-ergic ion channels. Using a computational approach, we analyze temporal properties of the spike trains emitted by biologically realistic neurons of the subthalamic nucleus (STN) as a function of GABA-ergic synaptic input conductances. Our contribution is based on a model proposed by Rubin and Terman and exhibits a wide variety of different firing patterns, silent, low spiking, moderate spiking and intense spiking activity. We observed that most of the cells in our network turn to silent mode when we increase the GABAA input conductance above the threshold of 3.75 mS/cm2. On the other hand, insignificant changes in firing activity are observed when the input conductance is low or close to zero. We thus reproduce Rubin's model with vanishing synaptic conductances. To quantitatively compare spike trains from the original model with the modified model at different conductance levels, we apply four different (dis)similarity measures between them. We observe that Mahalanobis distance, Victor-Purpura metric, and Interspike Interval distribution are sensitive to different firing regimes, whereas Mutual Information seems undiscriminative for these functional changes.

  13. Multistability in a neuron model with extracellular potassium dynamics

    NASA Astrophysics Data System (ADS)

    Wu, Xing-Xing; Shuai, J. W.

    2012-06-01

    Experiments show a primary role of extracellular potassium concentrations in neuronal hyperexcitability and in the generation of epileptiform bursting and depolarization blocks without synaptic mechanisms. We adopt a physiologically relevant hippocampal CA1 neuron model in a zero-calcium condition to better understand the function of extracellular potassium in neuronal seizurelike activities. The model neuron is surrounded by interstitial space in which potassium ions are able to accumulate. Potassium currents, Na+-K+ pumps, glial buffering, and ion diffusion are regulatory mechanisms of extracellular potassium. We also consider a reduced model with a fixed potassium concentration. The bifurcation structure and spiking frequency of the two models are studied. We show that, besides hyperexcitability and bursting pattern modulation, the potassium dynamics can induce not only bistability but also tristability of different firing patterns. Our results reveal the emergence of the complex behavior of multistability due to the dynamical [K+]o modulation on neuronal activities.

  14. A modular analog neuron-model for research and teaching.

    PubMed

    Koch, U T; Brunner, M

    1988-01-01

    An analog electronic neuron for modeling neuronal networks and for teaching purposes is presented. The circuitry represents a new approach, since ionic channels are all modeled by fast relay switches in combination with resistors. Thus, the synapse design includes nonlinear PSP superposition and reversal potentials. The spike is modeled with Na+ and K+ currents. Detailed circuit diagrams and examples of performance are given. Besides its use in network research, the model has been successfully used over 3 years of teaching practice. PMID:3196775

  15. SIMPEL: circuit model for photonic spike processing laser neurons.

    PubMed

    Shastri, Bhavin J; Nahmias, Mitchell A; Tait, Alexander N; Wu, Ben; Prucnal, Paul R

    2015-03-23

    We propose an equivalent circuit model for photonic spike processing laser neurons with an embedded saturable absorber—a simulation model for photonic excitable lasers (SIMPEL). We show that by mapping the laser neuron rate equations into a circuit model, SPICE analysis can be used as an efficient and accurate engine for numerical calculations, capable of generalization to a variety of different types of laser neurons with saturable absorber found in literature. The development of this model parallels the Hodgkin-Huxley model of neuron biophysics, a circuit framework which brought efficiency, modularity, and generalizability to the study of neural dynamics. We employ the model to study various signal-processing effects such as excitability with excitatory and inhibitory pulses, binary all-or-nothing response, and bistable dynamics. PMID:25837141

  16. Measuring neuronal branching patterns using model-based approach.

    PubMed

    Luczak, Artur

    2010-01-01

    Neurons have complex branching systems which allow them to communicate with thousands of other neurons. Thus understanding neuronal geometry is clearly important for determining connectivity within the network and how this shapes neuronal function. One of the difficulties in uncovering relationships between neuronal shape and its function is the problem of quantifying complex neuronal geometry. Even by using multiple measures such as: dendritic length, distribution of segments, direction of branches, etc, a description of three dimensional neuronal embedding remains incomplete. To help alleviate this problem, here we propose a new measure, a shape diffusiveness index (SDI), to quantify spatial relations between branches at the local and global scale. It was shown that growth of neuronal trees can be modeled by using diffusion limited aggregation (DLA) process. By measuring "how easy" it is to reproduce the analyzed shape by using the DLA algorithm it can be measured how "diffusive" is that shape. Intuitively, "diffusiveness" measures how tree-like is a given shape. For example shapes like an oak tree will have high values of SDI. This measure is capturing an important feature of dendritic tree geometry, which is difficult to assess with other measures. This approach also presents a paradigm shift from well-defined deterministic measures to model-based measures, which estimate how well a model with specific properties can account for features of analyzed shape. PMID:21079752

  17. Modeling schizophrenia using hiPSC neurons

    PubMed Central

    Brennand, Kristen; Simone, Anthony; Jou, Jessica; Gelboin-Burkhart, Chelsea; Tran, Ngoc; Sangar, Sarah; Li, Yan; Mu, Yangling; Chen, Gong; Yu, Diana; McCarthy, Shane; Sebat, Jonathan; Gage, Fred H.

    2012-01-01

    SUMMARY Schizophrenia (SCZD) is a debilitating neurological disorder with a world-wide prevalence of 1%; there is a strong genetic component, with an estimated heritability of 80–85%1. Though postmortem studies have revealed reduced brain volume, cell size, spine density and abnormal neural distribution in the prefrontal cortex and hippocampus of SCZD brain tissue2 and neuropharmacological studies have implicated dopaminergic, glutamatergic and GABAergic activity in SCZD3, the cell types affected in SCZD and the molecular mechanisms underlying the disease state remain unclear. To elucidate the cellular and molecular defects of SCZD, we directly reprogrammed fibroblasts from SCZD patients into human induced pluripotent stem cells (hiPSCs) and subsequently differentiated these disorder-specific hiPSCs into neurons (SI Fig. 1). SCZD hiPSC neurons showed diminished neuronal connectivity in conjunction with decreased neurite number, PSD95-protein levels and glutamate receptor expression. Gene expression profiles of SCZD hiPSC neurons identified altered expression of many components of the cAMP and WNT signaling pathways. Key cellular and molecular elements of the SCZD phenotype were ameliorated following treatment of SCZD hiPSC neurons with the antipsychotic Loxapine. To date, hiPSC neuronal pathology has only been demonstrated in diseases characterized by both the loss of function of a single gene product and rapid disease progression in early childhood4–6. We now report hiPSC neuronal phenotypes and gene expression changes associated with SCZD, a complex genetic psychiatric disorder (SI Table 1). PMID:21490598

  18. Small is beautiful: models of small neuronal networks.

    PubMed

    Lamb, Damon G; Calabrese, Ronald L

    2012-08-01

    Modeling has contributed a great deal to our understanding of how individual neurons and neuronal networks function. In this review, we focus on models of the small neuronal networks of invertebrates, especially rhythmically active CPG networks. Models have elucidated many aspects of these networks, from identifying key interacting membrane properties to pointing out gaps in our understanding, for example missing neurons. Even the complex CPGs of vertebrates, such as those that underlie respiration, have been reduced to small network models to great effect. Modeling of these networks spans from simplified models, which are amenable to mathematical analyses, to very complicated biophysical models. Some researchers have now adopted a population approach, where they generate and analyze many related models that differ in a few to several judiciously chosen free parameters; often these parameters show variability across animals and thus justify the approach. Models of small neuronal networks will continue to expand and refine our understanding of how neuronal networks in all animals program motor output, process sensory information and learn. PMID:22364687

  19. Small is beautiful: models of small neuronal networks

    PubMed Central

    Lamb, Damon G; Calabrese, Ronald L

    2013-01-01

    Modeling has contributed a great deal to our understanding of how individual neurons and neuronal networks function. In this review, we focus on models of the small neuronal networks of invertebrates, especially rhythmically active CPG networks. Models have elucidated many aspects of these networks, from identifying key interacting membrane properties to pointing out gaps in our understanding, for example missing neurons. Even the complex CPGs of vertebrates, such as those that underlie respiration, have been reduced to small network models to great effect. Modeling of these networks spans from simplified models, which are amenable to mathematical analyses, to very complicated biophysical models. Some researchers have now adopted a population approach, where they generate and analyze many related models that differ in a few to several judiciously chosen free parameters; often these parameters show variability across animals and thus justify the approach. Models of small neuronal networks will continue to expand and refine our understanding of how neuronal networks in all animals program motor output, process sensory information and learn. PMID:22364687

  20. How to bake a brain: yeast as a model neuron.

    PubMed

    Sarto-Jackson, Isabella; Tomaska, Lubomir

    2016-05-01

    More than 30 years ago Dan Koshland published an inspirational essay presenting the bacterium as a model neuron (Koshland, Trends Neurosci 6:133-137, 1983). In the article he argued that there are several similarities between neurons and bacterial cells in "how signals are processed within a cell or how this processing machinery can be modified to produce plasticity". He then explored the bacterial chemosensory system to emphasize its attributes that are analogous to information processing in neurons. In this review, we wish to expand Koshland's original idea by adding the yeast cell to the list of useful models of a neuron. The fact that yeasts and neurons are specialized versions of the eukaryotic cell sharing all principal components sets the stage for a grand evolutionary tinkering where these components are employed in qualitatively different tasks, but following analogous molecular logic. By way of example, we argue that evolutionarily conserved key components involved in polarization processes (from budding or mating in Saccharomyces cervisiae to neurite outgrowth or spinogenesis in neurons) are shared between yeast and neurons. This orthologous conservation of modules makes S. cervisiae an excellent model organism to investigate neurobiological questions. We substantiate this claim by providing examples of yeast models used for studying neurological diseases. PMID:26782173

  1. A Neuron-Based Model of Sleep-Wake Cycles

    NASA Astrophysics Data System (ADS)

    Postnova, Svetlana; Peters, Achim; Braun, Hans

    2008-03-01

    In recent years it was discovered that a neuropeptide orexin/hypocretin plays a main role in sleep processes. This peptide is produced by the neurons in the lateral hypothalamus, which project to almost all brain areas. We present a computational model of sleep-wake cycles, which is based on the Hodgkin-Huxley type neurons and considers reciprocal glutaminergic projections between the lateral hypothalamus and the prefrontal cortex. Orexin is released as a neuromodulator and is required to keep the neurons firing, which corresponds to the wake state. When orexin is depleted the neurons are getting silent as observed in the sleep state. They can be reactivated by the circadian signal from the suprachiasmatic nucleus and/or external stimuli (alarm clock). Orexin projections to the thalamocortical neurons also can account for their transition from tonic firing activity during wakefulness to synchronized burst discharges during sleep.

  2. A Statistical Model for In Vivo Neuronal Dynamics

    PubMed Central

    Surace, Simone Carlo; Pfister, Jean-Pascal

    2015-01-01

    Single neuron models have a long tradition in computational neuroscience. Detailed biophysical models such as the Hodgkin-Huxley model as well as simplified neuron models such as the class of integrate-and-fire models relate the input current to the membrane potential of the neuron. Those types of models have been extensively fitted to in vitro data where the input current is controlled. Those models are however of little use when it comes to characterize intracellular in vivo recordings since the input to the neuron is not known. Here we propose a novel single neuron model that characterizes the statistical properties of in vivo recordings. More specifically, we propose a stochastic process where the subthreshold membrane potential follows a Gaussian process and the spike emission intensity depends nonlinearly on the membrane potential as well as the spiking history. We first show that the model has a rich dynamical repertoire since it can capture arbitrary subthreshold autocovariance functions, firing-rate adaptations as well as arbitrary shapes of the action potential. We then show that this model can be efficiently fitted to data without overfitting. We finally show that this model can be used to characterize and therefore precisely compare various intracellular in vivo recordings from different animals and experimental conditions. PMID:26571371

  3. Neuronal trauma model: in search of Thanatos.

    PubMed

    Cole, Kasie; Perez-Polo, J Regino

    2004-11-01

    Trauma to the nervous system triggers responses that include oxidative stress due to the generation of reactive oxygen species (ROS). DNA is a major macromolecular target of ROS, and ROS-induced DNA strand breaks activate poly(ADP-ribose)polymerase-1 (PARP-1). Upon activation PARP-1 uses NAD(+) as a substrate to catalyze the transfer of ADP-ribose subunits to a host of nuclear proteins. In the face of extensive DNA strand breaks, PARP-1 activation can lead to depletion of intracellular NAD(P)(H) pools, large decreases in ATP, that threaten cell survival. Accordingly, inhibition of PARP-1 activity after acute oxidative injury has been shown to increase cell survival. When NGF-differentiated PC12 cells, an in vitro neuronal model, are exposed to H(2)O(2) there is increased synthesis of poly ADP-ribose and decreases in intracellular NAD(P)(H) and ATP. Addition of the chemical PARP inhibitor 3-aminobenzamide (AB) prior to H(2)O(2) exposure blocks the synthesis of poly ADP-ribose and maintains intracellular NAD(P)(H) and ATP levels. H(2)O(2) injury is characterized by an immediate, necrotic cell death 2h after injury and a delayed apoptotic-like death 12-24h after injury. This apoptotic-like death is characterized by apoptotic membrane changes and apoptotic DNA fragmentation but is not associated with measurable caspase-3 activity. AB delays cell death beyond 24h and increases cell survival by approximately 25%. This protective effect is accompanied by significantly decreased necrosis and the apoptotic-like death associated with H(2)O(2) exposure. AB also restores caspase-3 which can be attributed to the activation of the upstream activator of caspase-3, caspase-9. Thus, the maintenance of intracellular ATP levels associated with PARP-1 inhibition shifts cell death from necrosis to apoptosis and from apoptosis to cell survival. Furthermore, the shift from necrosis to apoptosis may be explained, in part, by an energy-dependent activation of caspase-9. PMID:15465278

  4. Virtual NEURON: a strategy for merged biochemical and electrophysiological modeling.

    PubMed

    Brown, Sherry-Ann; Moraru, Ion I; Schaff, James C; Loew, Leslie M

    2011-10-01

    Because of its highly branched dendrite, the Purkinje neuron requires significant computational resources if coupled electrical and biochemical activity are to be simulated. To address this challenge, we developed a scheme for reducing the geometric complexity; while preserving the essential features of activity in both the soma and a remote dendritic spine. We merged our previously published biochemical model of calcium dynamics and lipid signaling in the Purkinje neuron, developed in the Virtual Cell modeling and simulation environment, with an electrophysiological model based on a Purkinje neuron model available in NEURON. A novel reduction method was applied to the Purkinje neuron geometry to obtain a model with fewer compartments that is tractable in Virtual Cell. Most of the dendritic tree was subject to reduction, but we retained the neuron's explicit electrical and geometric features along a specified path from spine to soma. Further, unlike previous simplification methods, the dendrites that branch off along the preserved explicit path are retained as reduced branches. We conserved axial resistivity and adjusted passive properties and active channel conductances for the reduction in surface area, and cytosolic calcium for the reduction in volume. Rallpacks are used to validate the reduction algorithm and show that it can be generalized to other complex neuronal geometries. For the Purkinje cell, we found that current injections at the soma were able to produce similar trains of action potentials and membrane potential propagation in the full and reduced models in NEURON; the reduced model produces identical spiking patterns in NEURON and Virtual Cell. Importantly, our reduced model can simulate communication between the soma and a distal spine; an alpha function applied at the spine to represent synaptic stimulation gave similar results in the full and reduced models for potential changes associated with both the spine and the soma. Finally, we combined

  5. An experimental electronic model for a neuronal cell

    NASA Astrophysics Data System (ADS)

    Campos-Cantón, I.; Rangel-López, A.; Martel-Gallegos, G.; Zarazúa, S.; Vertiz-Hérnandez, A.

    2014-04-01

    Over the last two decades, the study of information transmission in living beings has acquired great relevance, because it regulates and conducts the functioning of all of the organs in the body. In information transmission pathways, the neuron plays an important role in that it receives, transmits, and processes electrical signals from different parts of the human body; these signals are transmitted as electrical impulses called action potentials, and they transmit information from one neuron to another. In this work, and with the aim of developing experiments for teaching biological processes, we implemented an electronic circuit of the neuron cell device and its mathematical model based on piecewise linear functions.

  6. Genesis and Control of bursting activity in a neuronal model

    NASA Astrophysics Data System (ADS)

    Cymbalyuk, Gennady

    2005-11-01

    Neurons are observed in one of four fundamental activity modes: silence, sub-threshold oscillations, tonic spiking, and bursting. Neurons exhibit various activity regimes and regime transitions that reflect their complement of ionic channels and modulatory state. The leech presents unique opportunities for experimental and theoretical studies on the dynamics of neuronal activity. The central pattern generator controlling the leech's heartbeat contains identified pairs of mutually inhibitory neurons. Bursting activity of neurons is an oscillatory activity consisting of intervals of repetitive spiking separated by intervals of quiescence. It has been observed in neurons under normal and pathological conditions. Neurons which are capable of generating bursting activity endogenously play an important role in motor control and other brain functions. Burst duration, interburst interval and spike frequency are crucial temporal characteristics of bursting activity and thus have to be regulated. Application of the bifurcation theory of dynamical systems suggests new mechanism of how bursting activity can be generated by neurons and how burst duration can be regulated. Here we describe two mechanisms for the transition between tonic spiking and bursting. First mechanism describes a smooth, continuous and reversible transition from tonic spiking into bursting in a model neuron. The burst duration increases with no bound as 1/(a-a0)^1/2, where a0 is a parameter determining the transition. The characteristic features of this mechanism are that (a) the burst duration can be made arbitrarily long while (b) inter-burst interval does not depend on the parameter. The second mechanism is concerned with bi-stability where simultaneous tonic spiking and bursting activities co-exist in a neuron. The mechanism is based on a saddle-node periodic orbit bifurcation with non-central homoclinic orbits. This bifurcation describes a transition between three qualitatively different types of

  7. Inflection, canards and excitability threshold in neuronal models.

    PubMed

    Desroches, M; Krupa, M; Rodrigues, S

    2013-10-01

    A technique is presented, based on the differential geometry of planar curves, to evaluate the excitability threshold of neuronal models. The aim is to determine regions of the phase plane where solutions to the model equations have zero local curvature, thereby defining a zero-curvature (inflection) set that discerns between sub-threshold and spiking electrical activity. This transition can arise through a Hopf bifurcation, via the so-called canard explosion that happens in an exponentially small parameter variation, and this is typical for a large class of planar neuronal models (FitzHugh-Nagumo, reduced Hodgkin-Huxley), namely, type II neurons (resonators). This transition can also correspond to the crossing of the stable manifold of a saddle equilibrium, in the case of type I neurons (integrators). We compute inflection sets and study how well they approximate the excitability threshold of these neuron models, that is, both in the canard and in the non-canard regime, using tools from invariant manifold theory and singularity theory. With the latter, we investigate the topological changes that inflection sets undergo upon parameter variation. Finally, we show that the concept of inflection set gives a good approximation of the threshold in both the so-called resonator and integrator neuronal cases. PMID:22945512

  8. An electromechanical model of neuronal dynamics using Hamilton's principle

    PubMed Central

    Drapaca, Corina S.

    2015-01-01

    Damage of the brain may be caused by mechanical loads such as penetration, blunt force, shock loading from blast, and by chemical imbalances due to neurological diseases and aging that trigger not only neuronal degeneration but also changes in the mechanical properties of brain tissue. An understanding of the interconnected nature of the electro-chemo-mechanical processes that result in brain damage and ultimately loss of functionality is currently lacking. While modern mathematical models that focus on how to link brain mechanics to its biochemistry are essential in enhancing our understanding of brain science, the lack of experimental data required by these models as well as the complexity of the corresponding computations render these models hard to use in clinical applications. In this paper we propose a unified variational framework for the modeling of neuronal electromechanics. We introduce a constrained Lagrangian formulation that takes into account Newton's law of motion of a linear viscoelastic Kelvin–Voigt solid-state neuron as well as the classic Hodgkin–Huxley equations of the electronic neuron. The system of differential equations describing neuronal electromechanics is obtained by applying Hamilton's principle. Numerical simulations of possible damage dynamics in neurons will be presented. PMID:26236195

  9. Visual Attention Model Based on Statistical Properties of Neuron Responses

    PubMed Central

    Duan, Haibin; Wang, Xiaohua

    2015-01-01

    Visual attention is a mechanism of the visual system that can select relevant objects from a specific scene. Interactions among neurons in multiple cortical areas are considered to be involved in attentional allocation. However, the characteristics of the encoded features and neuron responses in those attention related cortices are indefinite. Therefore, further investigations carried out in this study aim at demonstrating that unusual regions arousing more attention generally cause particular neuron responses. We suppose that visual saliency is obtained on the basis of neuron responses to contexts in natural scenes. A bottom-up visual attention model is proposed based on the self-information of neuron responses to test and verify the hypothesis. Four different color spaces are adopted and a novel entropy-based combination scheme is designed to make full use of color information. Valuable regions are highlighted while redundant backgrounds are suppressed in the saliency maps obtained by the proposed model. Comparative results reveal that the proposed model outperforms several state-of-the-art models. This study provides insights into the neuron responses based saliency detection and may underlie the neural mechanism of early visual cortices for bottom-up visual attention. PMID:25747859

  10. A neuronal model of vowel normalization and representation.

    PubMed

    Sussman, H M

    1986-05-01

    A speculative neuronal model for vowel normalization and representation is offered. The neurophysiological basis for the premise is the "combination-sensitive" neuron recently documented in the auditory cortex of the mustached bat (N. Suga, W. E. O'Neill, K. Kujirai, and T. Manabe, 1983, Journal of Neurophysiology, 49, 1573-1627). These neurons are specialized to respond to either precise frequency, amplitude, or time differentials between specific harmonic components of the pulse-echo pair comprising the biosonar signal of the bat. Such multiple frequency comparisons lie at the heart of human vowel perception and categorization. A representative vowel normalization algorithm is used to illustrate the operational principles of the neuronal model in accomplishing both normalization and categorization in early infancy. The neurological precursors to a phonemic vocalic system is described based on the neurobiological events characterizing regressive neurogenesis. PMID:3013360

  11. Tunable neuromimetic integrated system for emulating cortical neuron models.

    PubMed

    Grassia, Filippo; Buhry, Laure; Lévi, Timothée; Tomas, Jean; Destexhe, Alain; Saïghi, Sylvain

    2011-01-01

    Nowadays, many software solutions are currently available for simulating neuron models. Less conventional than software-based systems, hardware-based solutions generally combine digital and analog forms of computation. In previous work, we designed several neuromimetic chips, including the Galway chip that we used for this paper. These silicon neurons are based on the Hodgkin-Huxley formalism and they are optimized for reproducing a large variety of neuron behaviors thanks to tunable parameters. Due to process variation and device mismatch in analog chips, we use a full-custom fitting method in voltage-clamp mode to tune our neuromimetic integrated circuits. By comparing them with experimental electrophysiological data of these cells, we show that the circuits can reproduce the main firing features of cortical cell types. In this paper, we present the experimental measurements of our system which mimic the four most prominent biological cells: fast spiking, regular spiking, intrinsically bursting, and low-threshold spiking neurons into analog neuromimetic integrated circuit dedicated to cortical neuron simulations. This hardware and software platform will allow to improve the hybrid technique, also called "dynamic-clamp," that consists of connecting artificial and biological neurons to study the function of neuronal circuits. PMID:22163213

  12. Modeling pain in vitro using nociceptor neurons reprogrammed from fibroblasts

    PubMed Central

    Wainger, Brian J.; Buttermore, Elizabeth D.; Oliveira, Julia T.; Mellin, Cassidy; Lee, Seungkyu; Saber, Wardiya Afshar; Wang, Amy; Ichida, Justin K.; Chiu, Isaac M.; Barrett, Lee; Huebner, Eric A.; Bilgin, Canan; Tsujimoto, Naomi; Brenneis, Christian; Kapur, Kush; Rubin, Lee L.; Eggan, Kevin; Woolf, Clifford J.

    2015-01-01

    Reprogramming somatic cells from one cell fate to another can generate specific neurons suitable for disease modeling. To maximize the utility of patient-derived neurons, they must model not only disease-relevant cell classes but also the diversity of neuronal subtypes found in vivo and the pathophysiological changes that underlie specific clinical diseases. Here, we identify five transcription factors that reprogram mouse and human fibroblasts into noxious stimulus-detecting (nociceptor) neurons that recapitulate the expression of quintessential nociceptor-specific functional receptors and channels found in adult mouse nociceptor neurons as well as native subtype diversity. Moreover, the derived nociceptor neurons exhibit TrpV1 sensitization to the inflammatory mediator prostaglandin E2 and the chemotherapeutic drug oxaliplatin, modeling the inherent mechanisms underlying inflammatory pain hypersensitivity and painful chemotherapy-induced neuropathy. Using fibroblasts from patients with familial dysautonomia (hereditary sensory and autonomic neuropathy type III), we show that the technique can reveal novel aspects of human disease phenotypes in vitro. PMID:25420066

  13. Virtual NEURON: a strategy for merged biochemical and electrophysiological modeling

    PubMed Central

    Brown, Sherry-Ann; Moraru, Ion I.; Schaff, James C.

    2011-01-01

    Because of its highly branched dendrite, the Purkinje neuron requires significant computational resources if coupled electrical and biochemical activity are to be simulated. To address this challenge, we developed a scheme for reducing the geometric complexity; while preserving the essential features of activity in both the soma and a remote dendritic spine. We merged our previously published biochemical model of calcium dynamics and lipid signaling in the Purkinje neuron, developed in the Virtual Cell modeling and simulation environment, with an electrophysiological model based on a Purkinje neuron model available in NEURON. A novel reduction method was applied to the Purkinje neuron geometry to obtain a model with fewer compartments that is tractable in Virtual Cell. Most of the dendritic tree was subject to reduction, but we retained the neuron’s explicit electrical and geometric features along a specified path from spine to soma. Further, unlike previous simplification methods, the dendrites that branch off along the preserved explicit path are retained as reduced branches. We conserved axial resistivity and adjusted passive properties and active channel conductances for the reduction in surface area, and cytosolic calcium for the reduction in volume. Rallpacks are used to validate the reduction algorithm and show that it can be generalized to other complex neuronal geometries. For the Purkinje cell, we found that current injections at the soma were able to produce similar trains of action potentials and membrane potential propagation in the full and reduced models in NEURON; the reduced model produces identical spiking patterns in NEURON and Virtual Cell. Importantly, our reduced model can simulate communication between the soma and a distal spine; an alpha function applied at the spine to represent synaptic stimulation gave similar results in the full and reduced models for potential changes associated with both the spine and the soma. Finally, we combined

  14. Exploring the nonlinear dynamics of a physiologically viable model neuron

    SciTech Connect

    Lindner, J.F.; Ditto, W.L.

    1996-06-01

    We describe efforts underway to explore the nonlinear dynamics of the Pinsky-Rinzel model neuron. Via computer simulations, we seek to discover nonlinear phenomena in this physiologically accurate model, thereby complementing ongoing and future experiments. Here we describe the model in detail and analyze it using tools of nonlinear dynamics to demonstrate nontrivial behaviors. {copyright} {ital 1996 American Institute of Physics.}

  15. Mitochondrial bioenergetics and neuronal survival modelled in primary neuronal culture and isolated nerve terminals.

    PubMed

    Nicholls, David G; Brand, Martin D; Gerencser, Akos A

    2015-04-01

    Mitochondria play multiple roles in the maintenance of neuronal function under physiological and pathological conditions. In addition to ATP generation, they can act as major short-term calcium sinks and can both generate, and be damaged by, reactive oxygen species. Two complementary preparations have been extensively employed to investigate in situ neuronal mitochondrial bioenergetics, primary neuronal cultures and acutely isolated nerve terminals, synaptosomes. A major focus of the cell culture preparation has been the investigation of glutamate excitotoxicity. Oxidative phosphorylation, calcium transport and reactive oxygen species play complex interlocking roles in the life and death of the glutamate exposed neuron. Synaptosomes may be isolated from specific brain regions at any developmental stage and therefore provide a valuable ex vivo approach in studying mouse models. Recent advances have allowed synaptosomal bioenergetics to be studied on a microgram scale, and, in combination with approaches to correct for functional and transmitter heterogeneity, have allowed hypotheses concerning presynaptic mitochondrial dysfunction to be tested on a variety of genetic models of neurodegenerative disorders. PMID:25172197

  16. Mathematical modeling of subthreshold resonant properties in pyloric dilator neurons.

    PubMed

    Vazifehkhah Ghaffari, Babak; Kouhnavard, Mojgan; Aihara, Takeshi; Kitajima, Tatsuo

    2015-01-01

    Various types of neurons exhibit subthreshold resonance oscillation (preferred frequency response) to fluctuating sinusoidal input currents. This phenomenon is well known to influence the synaptic plasticity and frequency of neural network oscillation. This study evaluates the resonant properties of pacemaker pyloric dilator (PD) neurons in the central pattern generator network through mathematical modeling. From the pharmacological point of view, calcium currents cannot be blocked in PD neurons without removing the calcium-dependent potassium current. Thus, the effects of calcium (I(Ca)) and calcium-dependent potassium (I(KCa)) currents on resonant properties remain unclear. By taking advantage of Hodgkin-Huxley-type model of neuron and its equivalent RLC circuit, we examine the effects of changing resting membrane potential and those ionic currents on the resonance. Results show that changing the resting membrane potential influences the amplitude and frequency of resonance so that the strength of resonance (Q-value) increases by both depolarization and hyperpolarization of the resting membrane potential. Moreover, hyperpolarization-activated inward current (I(h)) and I(Ca) (in association with I(KCa)) are dominant factors on resonant properties at hyperpolarized and depolarized potentials, respectively. Through mathematical analysis, results indicate that I h and I(KCa) affect the resonant properties of PD neurons. However, I(Ca) only has an amplifying effect on the resonance amplitude of these neurons. PMID:25960999

  17. Mathematical Modeling of Subthreshold Resonant Properties in Pyloric Dilator Neurons

    PubMed Central

    Vazifehkhah Ghaffari, Babak; Kouhnavard, Mojgan; Aihara, Takeshi; Kitajima, Tatsuo

    2015-01-01

    Various types of neurons exhibit subthreshold resonance oscillation (preferred frequency response) to fluctuating sinusoidal input currents. This phenomenon is well known to influence the synaptic plasticity and frequency of neural network oscillation. This study evaluates the resonant properties of pacemaker pyloric dilator (PD) neurons in the central pattern generator network through mathematical modeling. From the pharmacological point of view, calcium currents cannot be blocked in PD neurons without removing the calcium-dependent potassium current. Thus, the effects of calcium (ICa) and calcium-dependent potassium (IKCa) currents on resonant properties remain unclear. By taking advantage of Hodgkin-Huxley-type model of neuron and its equivalent RLC circuit, we examine the effects of changing resting membrane potential and those ionic currents on the resonance. Results show that changing the resting membrane potential influences the amplitude and frequency of resonance so that the strength of resonance (Q-value) increases by both depolarization and hyperpolarization of the resting membrane potential. Moreover, hyperpolarization-activated inward current (Ih) and ICa (in association with IKCa) are dominant factors on resonant properties at hyperpolarized and depolarized potentials, respectively. Through mathematical analysis, results indicate that Ih and IKCa affect the resonant properties of PD neurons. However, ICa only has an amplifying effect on the resonance amplitude of these neurons. PMID:25960999

  18. The neuronal ceroid lipofuscinoses: Opportunities from model systems.

    PubMed

    Faller, Kiterie M E; Gutierrez-Quintana, Rodrigo; Mohammed, Alamin; Rahim, Ahad A; Tuxworth, Richard I; Wager, Kim; Bond, Michael

    2015-10-01

    The neuronal ceroid lipofuscinoses are a group of severe and progressive neurodegenerative disorders, generally with childhood onset. Despite the fact that these diseases remain fatal, significant breakthroughs have been made in our understanding of the genetics that underpin these conditions. This understanding has allowed the development of a broad range of models to study disease processes, and to develop new therapeutic approaches. Such models have contributed significantly to our knowledge of these conditions. In this review we will focus on the advantages of each individual model, describe some of the contributions the models have made to our understanding of the broader disease biology and highlight new techniques and approaches relevant to the study and potential treatment of the neuronal ceroid lipofuscinoses. This article is part of a Special Issue entitled: "Current Research on the Neuronal Ceroid Lipofuscinoses (Batten Disease)". PMID:25937302

  19. Spiking neuron model for temporal sequence recognition.

    PubMed

    Byrnes, Sean; Burkitt, Anthony N; Grayden, David B; Meffin, Hamish

    2010-01-01

    A biologically inspired neuronal network that stores and recognizes temporal sequences of symbols is described. Each symbol is represented by excitatory input to distinct groups of neurons (symbol pools). Unambiguous storage of multiple sequences with common subsequences is ensured by partitioning each symbol pool into subpools that respond only when the current symbol has been preceded by a particular sequence of symbols. We describe synaptic structure and neural dynamics that permit the selective activation of subpools by the correct sequence. Symbols may have varying durations of the order of hundreds of milliseconds. Physiologically plausible plasticity mechanisms operate on a time scale of tens of milliseconds; an interaction of the excitatory input with periodic global inhibition bridges this gap so that neural events representing successive symbols occur on this much faster timescale. The network is shown to store multiple overlapping sequences of events. It is robust to variation in symbol duration, it is scalable, and its performance degrades gracefully with perturbation of its parameters. PMID:19842991

  20. A Developmental Approach to Predicting Neuronal Connectivity from Small Biological Datasets: A Gradient-Based Neuron Growth Model

    PubMed Central

    Borisyuk, Roman; Azad, Abul Kalam al; Conte, Deborah; Roberts, Alan; Soffe, Stephen R.

    2014-01-01

    Relating structure and function of neuronal circuits is a challenging problem. It requires demonstrating how dynamical patterns of spiking activity lead to functions like cognitive behaviour and identifying the neurons and connections that lead to appropriate activity of a circuit. We apply a “developmental approach” to define the connectome of a simple nervous system, where connections between neurons are not prescribed but appear as a result of neuron growth. A gradient based mathematical model of two-dimensional axon growth from rows of undifferentiated neurons is derived for the different types of neurons in the brainstem and spinal cord of young tadpoles of the frog Xenopus. Model parameters define a two-dimensional CNS growth environment with three gradient cues and the specific responsiveness of the axons of each neuron type to these cues. The model is described by a nonlinear system of three difference equations; it includes a random variable, and takes specific neuron characteristics into account. Anatomical measurements are first used to position cell bodies in rows and define axon origins. Then a generalization procedure allows information on the axons of individual neurons from small anatomical datasets to be used to generate larger artificial datasets. To specify parameters in the axon growth model we use a stochastic optimization procedure, derive a cost function and find the optimal parameters for each type of neuron. Our biologically realistic model of axon growth starts from axon outgrowth from the cell body and generates multiple axons for each different neuron type with statistical properties matching those of real axons. We illustrate how the axon growth model works for neurons with axons which grow to the same and the opposite side of the CNS. We then show how, by adding a simple specification for dendrite morphology, our model “developmental approach” allows us to generate biologically-realistic connectomes. PMID:24586794

  1. Reconstruction of neuronal input through modeling single-neuron dynamics and computations

    NASA Astrophysics Data System (ADS)

    Qin, Qing; Wang, Jiang; Yu, Haitao; Deng, Bin; Chan, Wai-lok

    2016-06-01

    Mathematical models provide a mathematical description of neuron activity, which can better understand and quantify neural computations and corresponding biophysical mechanisms evoked by stimulus. In this paper, based on the output spike train evoked by the acupuncture mechanical stimulus, we present two different levels of models to describe the input-output system to achieve the reconstruction of neuronal input. The reconstruction process is divided into two steps: First, considering the neuronal spiking event as a Gamma stochastic process. The scale parameter and the shape parameter of Gamma process are, respectively, defined as two spiking characteristics, which are estimated by a state-space method. Then, leaky integrate-and-fire (LIF) model is used to mimic the response system and the estimated spiking characteristics are transformed into two temporal input parameters of LIF model, through two conversion formulas. We test this reconstruction method by three different groups of simulation data. All three groups of estimates reconstruct input parameters with fairly high accuracy. We then use this reconstruction method to estimate the non-measurable acupuncture input parameters. Results show that under three different frequencies of acupuncture stimulus conditions, estimated input parameters have an obvious difference. The higher the frequency of the acupuncture stimulus is, the higher the accuracy of reconstruction is.

  2. Reconstruction of neuronal input through modeling single-neuron dynamics and computations.

    PubMed

    Qin, Qing; Wang, Jiang; Yu, Haitao; Deng, Bin; Chan, Wai-Lok

    2016-06-01

    Mathematical models provide a mathematical description of neuron activity, which can better understand and quantify neural computations and corresponding biophysical mechanisms evoked by stimulus. In this paper, based on the output spike train evoked by the acupuncture mechanical stimulus, we present two different levels of models to describe the input-output system to achieve the reconstruction of neuronal input. The reconstruction process is divided into two steps: First, considering the neuronal spiking event as a Gamma stochastic process. The scale parameter and the shape parameter of Gamma process are, respectively, defined as two spiking characteristics, which are estimated by a state-space method. Then, leaky integrate-and-fire (LIF) model is used to mimic the response system and the estimated spiking characteristics are transformed into two temporal input parameters of LIF model, through two conversion formulas. We test this reconstruction method by three different groups of simulation data. All three groups of estimates reconstruct input parameters with fairly high accuracy. We then use this reconstruction method to estimate the non-measurable acupuncture input parameters. Results show that under three different frequencies of acupuncture stimulus conditions, estimated input parameters have an obvious difference. The higher the frequency of the acupuncture stimulus is, the higher the accuracy of reconstruction is. PMID:27368786

  3. Parameter space of the Rulkov chaotic neuron model

    NASA Astrophysics Data System (ADS)

    Wang, Caixia; Cao, Hongjun

    2014-06-01

    The parameter space of the two dimensional Rulkov chaotic neuron model is taken into account by using the qualitative analysis, the co-dimension 2 bifurcation, the center manifold theorem, and the normal form. The goal is intended to clarify analytically different dynamics and firing regimes of a single neuron in a two dimensional parameter space. Our research demonstrates the origin that there exist very rich nonlinear dynamics and complex biological firing regimes lies in different domains and their boundary curves in the two dimensional parameter plane. We present the parameter domains of fixed points, the saddle-node bifurcation, the supercritical/subcritical Neimark-Sacker bifurcation, stability conditions of non hyperbolic fixed points and quasiperiodic solutions. Based on these parameter domains, it is easy to know that the Rulkov chaotic neuron model can produce what kinds of firing regimes as well as their transition mechanisms. These results are very useful for building-up a large-scale neuron network with different biological functional roles and cognitive activities, especially in establishing some specific neuron network models of neurological diseases.

  4. Minimal attractors in digraph system models of neuronal networks

    NASA Astrophysics Data System (ADS)

    Just, Winfried; Ahn, Sungwoo; Terman, David

    2008-12-01

    We study a class of discrete dynamical systems models of neuronal networks. In these models, each neuron is represented by a finite number of states and there are rules for how a neuron transitions from one state to another. In particular, the rules determine when a neuron fires and how this affects the state of other neurons. In an earlier paper [D. Terman, S. Ahn, X. Wang, W. Just, Reducing neuronal networks to discrete dynamics, Physica D 237 (2008) 324-338], we demonstrate that a general class of excitatory-inhibitory networks can, in fact, be rigorously reduced to the discrete model. In the present paper, we analyze how the connectivity of the network influences the dynamics of the discrete model. For randomly connected networks, we find two major phase transitions. If the connection probability is above the second but below the first phase transition, then starting in a generic initial state, most but not all cells will fire at all times along the trajectory as soon as they reach the end of their refractory period. Above the first phase transition, this will be true for all cells in a typical initial state; thus most states will belong to a minimal attractor of oscillatory behavior (in a sense that is defined precisely in the paper). The exact positions of the phase transitions depend on intrinsic properties of the cells including the lengths of the cells’ refractory periods and the thresholds for firing. Existence of these phase transitions is both rigorously proved for sufficiently large networks and corroborated by numerical experiments on networks of moderate size.

  5. A Neuronal Network Model for Pitch Selectivity and Representation

    PubMed Central

    Huang, Chengcheng; Rinzel, John

    2016-01-01

    Pitch is a perceptual correlate of periodicity. Sounds with distinct spectra can elicit the same pitch. Despite the importance of pitch perception, understanding the cellular mechanism of pitch perception is still a major challenge and a mechanistic model of pitch is lacking. A multi-stage neuronal network model is developed for pitch frequency estimation using biophysically-based, high-resolution coincidence detector neurons. The neuronal units respond only to highly coincident input among convergent auditory nerve fibers across frequency channels. Their selectivity for only very fast rising slopes of convergent input enables these slope-detectors to distinguish the most prominent coincidences in multi-peaked input time courses. Pitch can then be estimated from the first-order interspike intervals of the slope-detectors. The regular firing pattern of the slope-detector neurons are similar for sounds sharing the same pitch despite the distinct timbres. The decoded pitch strengths also correlate well with the salience of pitch perception as reported by human listeners. Therefore, our model can serve as a neural representation for pitch. Our model performs successfully in estimating the pitch of missing fundamental complexes and reproducing the pitch variation with respect to the frequency shift of inharmonic complexes. It also accounts for the phase sensitivity of pitch perception in the cases of Schroeder phase, alternating phase and random phase relationships. Moreover, our model can also be applied to stochastic sound stimuli, iterated-ripple-noise, and account for their multiple pitch perceptions. PMID:27378900

  6. Rate of neuronal fallout in a transsynaptic cerebellar model.

    PubMed

    Triarhou, L C

    1998-10-01

    Quantitative analyses of transsynaptic granule cell death subsequent to the genetically determined degeneration of Purkinje cells in the cerebellum of pcd/pcd mutant mice show that granule neuron fallout follows a typical mathematical pattern of elemental decay. Biological and theoretical connotations are discussed in light of the empirical observations and a simulation model. PMID:9865853

  7. Modeling of Auditory Neuron Response Thresholds with Cochlear Implants.

    PubMed

    Venail, Frederic; Mura, Thibault; Akkari, Mohamed; Mathiolon, Caroline; Menjot de Champfleur, Sophie; Piron, Jean Pierre; Sicard, Marielle; Sterkers-Artieres, Françoise; Mondain, Michel; Uziel, Alain

    2015-01-01

    The quality of the prosthetic-neural interface is a critical point for cochlear implant efficiency. It depends not only on technical and anatomical factors such as electrode position into the cochlea (depth and scalar placement), electrode impedance, and distance between the electrode and the stimulated auditory neurons, but also on the number of functional auditory neurons. The efficiency of electrical stimulation can be assessed by the measurement of e-CAP in cochlear implant users. In the present study, we modeled the activation of auditory neurons in cochlear implant recipients (nucleus device). The electrical response, measured using auto-NRT (neural responses telemetry) algorithm, has been analyzed using multivariate regression with cubic splines in order to take into account the variations of insertion depth of electrodes amongst subjects as well as the other technical and anatomical factors listed above. NRT thresholds depend on the electrode squared impedance (β = -0.11 ± 0.02, P < 0.01), the scalar placement of the electrodes (β = -8.50 ± 1.97, P < 0.01), and the depth of insertion calculated as the characteristic frequency of auditory neurons (CNF). Distribution of NRT residues according to CNF could provide a proxy of auditory neurons functioning in implanted cochleas. PMID:26236725

  8. Silicon modeling of the Mihalaş-Niebur neuron.

    PubMed

    Folowosele, Fopefolu; Hamilton, Tara Julia; Etienne-Cummings, Ralph

    2011-12-01

    There are a number of spiking and bursting neuron models with varying levels of complexity, ranging from the simple integrate-and-fire model to the more complex Hodgkin-Huxley model. The simpler models tend to be easily implemented in silicon but yet not biologically plausible. Conversely, the more complex models tend to occupy a large area although they are more biologically plausible. In this paper, we present the 0.5 μm complementary metal-oxide-semiconductor (CMOS) implementation of the Mihalaş-Niebur neuron model--a generalized model of the leaky integrate-and-fire neuron with adaptive threshold--that is able to produce most of the known spiking and bursting patterns that have been observed in biology. Our implementation modifies the original proposed model, making it more amenable to CMOS implementation and more biologically plausible. All but one of the spiking properties--tonic spiking, class 1 spiking, phasic spiking, hyperpolarized spiking, rebound spiking, spike frequency adaptation, accommodation, threshold variability, integrator and input bistability--are demonstrated in this model. PMID:21990331

  9. Neuronal modelling of baroreflex response to orthostatic stress

    NASA Astrophysics Data System (ADS)

    Samin, Azfar

    The accelerations experienced in aerial combat can cause pilot loss of consciousness (GLOC) due to a critical reduction in cerebral blood circulation. The development of smart protective equipment requires understanding of how the brain processes blood pressure (BP) information in response to acceleration. We present a biologically plausible model of the Baroreflex to investigate the neural correlates of short-term BP control under acceleration or orthostatic stress. The neuronal network model, which employs an integrate-and-fire representation of a biological neuron, comprises the sensory, motor, and the central neural processing areas that form the Baroreflex. Our modelling strategy is to test hypotheses relating to the encoding mechanisms of multiple sensory inputs to the nucleus tractus solitarius (NTS), the site of central neural processing. The goal is to run simulations and reproduce model responses that are consistent with the variety of available experimental data. Model construction and connectivity are inspired by the available anatomical and neurophysiological evidence that points to a barotopic organization in the NTS, and the presence of frequency-dependent synaptic depression, which provides a mechanism for generating non-linear local responses in NTS neurons that result in quantifiable dynamic global baroreflex responses. The entire physiological range of BP and rate of change of BP variables is encoded in a palisade of NTS neurons in that the spike responses approximate Gaussian 'tuning' curves. An adapting weighted-average decoding scheme computes the motor responses and a compensatory signal regulates the heart rate (HR). Model simulations suggest that: (1) the NTS neurons can encode the hydrostatic pressure difference between two vertically separated sensory receptor regions at +Gz, and use changes in that difference for the regulation of HR; (2) even though NTS neurons do not fire with a cardiac rhythm seen in the afferents, pulse

  10. Stimulus Coding and Synchrony in Stochastic Neuron Models

    NASA Astrophysics Data System (ADS)

    Cieniak, Jakub

    A stochastic leaky integrate-and-fire neuron model was implemented in this study to simulate the spiking activity of the electrosensory "P-unit" receptor neurons of the weakly electric fish Apteronotus leptorhynchus. In the context of sensory coding, these cells have been previously shown to respond in experiment to natural random narrowband signals with either a linear or nonlinear coding scheme, depending on the intrinsic firing rate of the cell in the absence of external stimulation. It was hypothesised in this study that this duality is due to the relation of the stimulus to the neuron's excitation threshold. This hypothesis was validated with the model by lowering the threshold of the neuron or increasing its intrinsic noise, or randomness, either of which made the relation between firing rate and input strength more linear. Furthermore, synchronous P-unit firing to a common input also plays a role in decoding the stimulus at deeper levels of the neural pathways. Synchronisation and desynchronisation between multiple model responses for different types of natural communication signals were shown to agree with experimental observations. A novel result of resonance-induced synchrony enhancement of P-units to certain communication frequencies was also found.

  11. Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data

    PubMed Central

    Lynch, Eoin P.; Houghton, Conor J.

    2015-01-01

    Spiking neuron models can accurately predict the response of neurons to somatically injected currents if the model parameters are carefully tuned. Predicting the response of in-vivo neurons responding to natural stimuli presents a far more challenging modeling problem. In this study, an algorithm is presented for parameter estimation of spiking neuron models. The algorithm is a hybrid evolutionary algorithm which uses a spike train metric as a fitness function. We apply this to parameter discovery in modeling two experimental data sets with spiking neurons; in-vitro current injection responses from a regular spiking pyramidal neuron are modeled using spiking neurons and in-vivo extracellular auditory data is modeled using a two stage model consisting of a stimulus filter and spiking neuron model. PMID:25941485

  12. On the capabilities and computational costs of neuron models.

    PubMed

    Skocik, Michael J; Long, Lyle N

    2014-08-01

    We review the Hodgkin-Huxley, Izhikevich, and leaky integrate-and-fire neuron models in regular spiking modes solved with the forward Euler, fourth-order Runge-Kutta, and exponential Euler methods and determine the necessary time steps and corresponding computational costs required to make the solutions accurate. We conclude that the leaky integrate-and-fire needs the least number of computations, and that the Hodgkin-Huxley and Izhikevich models are comparable in computational cost. PMID:25050945

  13. Study on dynamic characteristics' change of hippocampal neuron reduced models caused by the Alzheimer's disease.

    PubMed

    Peng, Yueping; Wang, Jue; Zheng, Chongxun

    2016-01-01

    In the paper, based on the electrophysiological experimental data, the Hippocampal neuron reduced model under the pathology condition of Alzheimer's disease (AD) has been built by modifying parameters' values. The reduced neuron model's dynamic characteristics under effect of AD are comparatively studied. Under direct current stimulation, compared with the normal neuron model, the AD neuron model's dynamic characteristics have obviously been changed. The neuron model under the AD condition undergoes supercritical Andronov-Hopf bifurcation from the rest state to the continuous discharge state. It is different from the neuron model under the normal condition, which undergoes saddle-node bifurcation. So, the neuron model changes into a resonator with monostable state from an integrator with bistable state under AD's action. The research reveals the neuron model's dynamic characteristics' changing under effect of AD, and provides some theoretic basis for AD research by neurodynamics theory. PMID:26998957

  14. The Volterra-Wiener approach in neuronal modeling.

    PubMed

    Mitsis, Georgios D

    2011-01-01

    Systems identification is being used increasingly in quantitative neurophysiology, including the auditory, visual and somatosensory systems. In this context, the Volterra-Wiener approach, which is an important branch of nonlinear systems identification, has met with considerable success in neuronal systems modeling, as these systems often exhibit complex nonlinear behavior. The Volterra-Wiener approach provides a comprehensive data-driven framework that does not place any a priori assumptions on the system structure. Therefore, it can approximate highly complex nonlinear mappings provided that experimental protocols are carefully designed in order to meet the requirements of the corresponding estimation procedure. In the present paper, we present a brief overview of Volterra-Wiener models and methodologies for their estimation as they relate to modeling neuronal systems. We also examine a specific example from a mechanoreceptor system. PMID:22255685

  15. Neuronal and brain morphological changes in animal models of schizophrenia.

    PubMed

    Flores, Gonzalo; Morales-Medina, Julio César; Diaz, Alfonso

    2016-03-15

    Schizophrenia, a severe and debilitating disorder with a high social burden, affects 1% of the adult world population. Available therapies are unable to treat all the symptoms, and result in strong side effects. For this reason, numerous animal models have been generated to elucidate the pathophysiology of this disorder. All these models present neuronal remodeling and abnormalities in spine stability. It is well known that the complexity in dendritic arborization determines the number of receptive synaptic contacts. Also the loss of dendritic spines and arbor stability are strongly associated with schizophrenia. This review evaluates changes in spine density and dendritic arborization in animal models of schizophrenia. By understanding these changes, pharmacological treatments can be designed to target specific neural systems to attenuate neuronal remodeling and associated behavioral deficits. PMID:26738967

  16. The Neuroid: A novel and simplified neuron-model.

    PubMed

    Arguello, E; Silva, R; Castillo, C; Huerta, M

    2012-01-01

    In this paper we introduce a novel computational neuron-model, the Neuroid, which is based on three basic operations that are carried out by nerve cells to process the incoming information, such as comparison, and frequency pulse modulation-demodulation. The model was implemented using LabVIEW 10.0, in order to assign to each of these operations, an execution block (Virtual Instrument). The results of its implementation showed a very similar behavior to that exhibited by real neurons. Furthermore, due to its simplicity and low computational requirements, it is expected that the Neuroid can be used to create several software models of biological neural systems, either for research or teaching purposes. PMID:23366121

  17. Computational modeling of optogenetic neuronal excitation under complex illumination conditions using a Matlab-Neuron interface (Conference Presentation)

    NASA Astrophysics Data System (ADS)

    Yona, Guy; Weissler, Yonatan; Meitav, Nizan; Guzi, Eliran; Rifold, Dafna D.; Kahn, Itamar; Shoham, Shy

    2016-03-01

    Optogenetics has in recent years become a central tool in neuroscience research. Creating a realistic model of optogenetic neuronal excitation is of crucial importance for controlling the activation levels of various neuronal populations in different depths, predicting experimental results and designing the optical systems. However, current approaches to modeling light propagation through rodents' brain tissue suffer from major shortcomings and comprehensive modeling of local illumination levels together with other important factors governing excitation (i.e., cellular morphology, channel dynamics and expression), are still lacking. To address this challenge we introduce a new simulation tool for optogenetic neuronal excitation under complex and realistic illumination conditions that implements a detailed physical model for light scattering (in MATLAB) together with neuron morphology and channelrhodopsin-2 model (in NEURON). These two disparate simulation environments were interconnected using a newly developed generic interface termed 'NeuroLab'. Applying this method, we show that in a layer-V cortical neuron, the relative contribution of the apical dendrites to neuronal excitation is considerably greater than that of the soma or basal dendrites, when illuminated from the surface.

  18. Colored noise and memory effects on formal spiking neuron models

    NASA Astrophysics Data System (ADS)

    da Silva, L. A.; Vilela, R. D.

    2015-06-01

    Simplified neuronal models capture the essence of the electrical activity of a generic neuron, besides being more interesting from the computational point of view when compared to higher-dimensional models such as the Hodgkin-Huxley one. In this work, we propose a generalized resonate-and-fire model described by a generalized Langevin equation that takes into account memory effects and colored noise. We perform a comprehensive numerical analysis to study the dynamics and the point process statistics of the proposed model, highlighting interesting new features such as (i) nonmonotonic behavior (emergence of peak structures, enhanced by the choice of colored noise characteristic time scale) of the coefficient of variation (CV) as a function of memory characteristic time scale, (ii) colored noise-induced shift in the CV, and (iii) emergence and suppression of multimodality in the interspike interval (ISI) distribution due to memory-induced subthreshold oscillations. Moreover, in the noise-induced spike regime, we study how memory and colored noise affect the coherence resonance (CR) phenomenon. We found that for sufficiently long memory, not only is CR suppressed but also the minimum of the CV-versus-noise intensity curve that characterizes the presence of CR may be replaced by a maximum. The aforementioned features allow to interpret the interplay between memory and colored noise as an effective control mechanism to neuronal variability. Since both variability and nontrivial temporal patterns in the ISI distribution are ubiquitous in biological cells, we hope the present model can be useful in modeling real aspects of neurons.

  19. Modeling Neuronal Response to Simultaneous and Sequential Multi-Site Synaptic Stimulation

    NASA Astrophysics Data System (ADS)

    Johnston, David; Mekhail, Simon Peter; Go, Mary Ann; Daria, Vincent R.

    The flow of information in the brain theorizes that each neuron in a network receives synaptic inputs and sends off its processed signals to neighboring neurons. Here, we model these synaptic inputs to understand how each neuron processes these inputs and transmits neurotransmitters to neighboring neurons. We use the NEURON simulation package to stimulate a neuron at multiple synaptic locations along its dendritic tree. Accumulation of multiple synaptic inputs causes changes in the neuron's membrane potential leading to firing of an action potential. Our simulations show that simultaneous synaptic stimulation approaches firing of an action potential at lesser inputs compared to sequential stimulation at multiple sites distributed along several dendritic branches.

  20. Translating network models to parallel hardware in NEURON

    PubMed Central

    Hines, M.L.; Carnevale, N.T.

    2008-01-01

    The increasing complexity of network models poses a growing computational burden. At the same time, computational neuroscientists are finding it easier to access parallel hardware, such as multiprocessor personal computers, workstation clusters, and massively parallel supercomputers. The practical question is how to move a working network model from a single processor to parallel hardware. Here we show how to make this transition for models implemented with NEURON, in such a way that the final result will run and produce numerically identical results on either serial or parallel hardware. This allows users to develop and debug models on readily available local resources, then run their code without modification on a parallel supercomputer. PMID:17997162

  1. Optical computing based on neuronal models

    NASA Astrophysics Data System (ADS)

    Farhat, Nabil H.

    1987-10-01

    Ever since the fit between what neural net models can offer (collective, iterative, nonlinear, robust, and fault-tolerant approach to information processing) and the inherent capabilities of optics (parallelism and massive interconnectivity) was first pointed out and the first optical associative memory demonstrated in 1985, work and interest in neuromorphic optical signal processing has been growing steadily. For example, work in optical associative memories is currently being conducted at several academic institutions (e.g., California Institute of Technology, University of Colorado, University of California-San Diego, Stanford University, University of Rochester, and the author's own institution the University of Pennsylvania) and at several industrial and governmental laboratories (e.g., Hughes Research Laboratories - Malibu, the Naval Research Laboratory, and the Jet Propulsion Laboratory). In these efforts, in addition to the vector matrix multiplication with thresholding and feedback scheme utilized in early implementations, an arsenal of sophisticated optical tools such as holographic storage, phase conjugate optics, and wavefront modulation and mixing are being drawn on to realize associative memory functions.

  2. Stochastic resonance in neuron models: Endogenous stimulation revisited

    NASA Astrophysics Data System (ADS)

    Plesser, Hans E.; Geisel, Theo

    2001-03-01

    The paradigm of stochastic resonance (SR)-the idea that signal detection and transmission may benefit from noise-has met with great interest in both physics and the neurosciences. We investigate here the consequences of reducing the dynamics of a periodically driven neuron to a renewal process (stimulation with reset or endogenous stimulation). This greatly simplifies the mathematical analysis, but we show that stochastic resonance as reported earlier occurs in this model only as a consequence of the reduced dynamics.

  3. Analysis of Chaotic Resonance in Izhikevich Neuron Model.

    PubMed

    Nobukawa, Sou; Nishimura, Haruhiko; Yamanishi, Teruya; Liu, Jian-Qin

    2015-01-01

    In stochastic resonance (SR), the presence of noise helps a nonlinear system amplify a weak (sub-threshold) signal. Chaotic resonance (CR) is a phenomenon similar to SR but without stochastic noise, which has been observed in neural systems. However, no study to date has investigated and compared the characteristics and performance of the signal responses of a spiking neural system in some chaotic states in CR. In this paper, we focus on the Izhikevich neuron model, which can reproduce major spike patterns that have been experimentally observed. We examine and classify the chaotic characteristics of this model by using Lyapunov exponents with a saltation matrix and Poincaré section methods in order to address the measurement challenge posed by the state-dependent jump in the resetting process. We found the existence of two distinctive states, a chaotic state involving primarily turbulent movement and an intermittent chaotic state. In order to assess the signal responses of CR in these classified states, we introduced an extended Izhikevich neuron model by considering weak periodic signals, and defined the cycle histogram of neuron spikes as well as the corresponding mutual correlation and information. Through computer simulations, we confirmed that both chaotic states in CR can sensitively respond to weak signals. Moreover, we found that the intermittent chaotic state exhibited a prompter response than the chaotic state with primarily turbulent movement. PMID:26422140

  4. Analysis of Chaotic Resonance in Izhikevich Neuron Model

    PubMed Central

    Nobukawa, Sou; Nishimura, Haruhiko; Yamanishi, Teruya; Liu, Jian-Qin

    2015-01-01

    In stochastic resonance (SR), the presence of noise helps a nonlinear system amplify a weak (sub-threshold) signal. Chaotic resonance (CR) is a phenomenon similar to SR but without stochastic noise, which has been observed in neural systems. However, no study to date has investigated and compared the characteristics and performance of the signal responses of a spiking neural system in some chaotic states in CR. In this paper, we focus on the Izhikevich neuron model, which can reproduce major spike patterns that have been experimentally observed. We examine and classify the chaotic characteristics of this model by using Lyapunov exponents with a saltation matrix and Poincaré section methods in order to address the measurement challenge posed by the state-dependent jump in the resetting process. We found the existence of two distinctive states, a chaotic state involving primarily turbulent movement and an intermittent chaotic state. In order to assess the signal responses of CR in these classified states, we introduced an extended Izhikevich neuron model by considering weak periodic signals, and defined the cycle histogram of neuron spikes as well as the corresponding mutual correlation and information. Through computer simulations, we confirmed that both chaotic states in CR can sensitively respond to weak signals. Moreover, we found that the intermittent chaotic state exhibited a prompter response than the chaotic state with primarily turbulent movement. PMID:26422140

  5. Response characteristics of a low-dimensional model neuron.

    PubMed

    Cartling, B

    1996-11-15

    It is shown that a low-dimensional model neuron with a response time constant smaller than the membrane time constant closely reproduces the activity and excitability behavior of a detailed conductance-based model of Hodgkin-Huxley type. The fast response of the activity variable also makes it possible to reduce the model to a one-dimensional model, in particular for typical conditions. As an example, the reduction to a single-variable model from a multivariable conductance-based model of a neocortical pyramidal cell with somatic input is demonstrated. The conditions for avoiding a spurious damped oscillatory response to a constant input are derived, and it is shown that a limit-cycle response cannot occur. The capability of the low-dimensional model to approximate higher-dimensional models accurately makes it useful for describing complex dynamics of nets of interconnected neurons. The simplicity of the model facilitates analytic studies, elucidations of neurocomputational mechanisms, and applications to large-scale systems. PMID:8888611

  6. Statistics of a neuron model driven by asymmetric colored noise

    NASA Astrophysics Data System (ADS)

    Müller-Hansen, Finn; Droste, Felix; Lindner, Benjamin

    2015-02-01

    Irregular firing of neurons can be modeled as a stochastic process. Here we study the perfect integrate-and-fire neuron driven by dichotomous noise, a Markovian process that jumps between two states (i.e., possesses a non-Gaussian statistics) and exhibits nonvanishing temporal correlations (i.e., represents a colored noise). Specifically, we consider asymmetric dichotomous noise with two different transition rates. Using a first-passage-time formulation, we derive exact expressions for the probability density and the serial correlation coefficient of the interspike interval (time interval between two subsequent neural action potentials) and the power spectrum of the spike train. Furthermore, we extend the model by including additional Gaussian white noise, and we give approximations for the interspike interval (ISI) statistics in this case. Numerical simulations are used to validate the exact analytical results for pure dichotomous noise, and to test the approximations of the ISI statistics when Gaussian white noise is included. The results may help to understand how correlations and asymmetry of noise and signals in nerve cells shape neuronal firing statistics.

  7. TAL Effector-Nucleotide Targeter (TALE-NT) 2.0: tools for TAL effector design and target prediction

    PubMed Central

    Doyle, Erin L.; Booher, Nicholas J.; Standage, Daniel S.; Voytas, Daniel F.; Brendel, Volker P.; VanDyk, John K.; Bogdanove, Adam J.

    2012-01-01

    Transcription activator-like (TAL) effectors are repeat-containing proteins used by plant pathogenic bacteria to manipulate host gene expression. Repeats are polymorphic and individually specify single nucleotides in the DNA target, with some degeneracy. A TAL effector-nucleotide binding code that links repeat type to specified nucleotide enables prediction of genomic binding sites for TAL effectors and customization of TAL effectors for use in DNA targeting, in particular as custom transcription factors for engineered gene regulation and as site-specific nucleases for genome editing. We have developed a suite of web-based tools called TAL Effector-Nucleotide Targeter 2.0 (TALE-NT 2.0; https://boglab.plp.iastate.edu/) that enables design of custom TAL effector repeat arrays for desired targets and prediction of TAL effector binding sites, ranked by likelihood, in a genome, promoterome or other sequence of interest. Search parameters can be set by the user to work with any TAL effector or TAL effector nuclease architecture. Applications range from designing highly specific DNA targeting tools and identifying potential off-target sites to predicting effector targets important in plant disease. PMID:22693217

  8. Realistic modeling of neurons and networks: towards brain simulation

    PubMed Central

    D’Angelo, Egidio; Solinas, Sergio; Garrido, Jesus; Casellato, Claudia; Pedrocchi, Alessandra; Mapelli, Jonathan; Gandolfi, Daniela; Prestori, Francesca

    Summary Realistic modeling is a new advanced methodology for investigating brain functions. Realistic modeling is based on a detailed biophysical description of neurons and synapses, which can be integrated into microcircuits. The latter can, in turn, be further integrated to form large-scale brain networks and eventually to reconstruct complex brain systems. Here we provide a review of the realistic simulation strategy and use the cerebellar network as an example. This network has been carefully investigated at molecular and cellular level and has been the object of intense theoretical investigation. The cerebellum is thought to lie at the core of the forward controller operations of the brain and to implement timing and sensory prediction functions. The cerebellum is well described and provides a challenging field in which one of the most advanced realistic microcircuit models has been generated. We illustrate how these models can be elaborated and embedded into robotic control systems to gain insight into how the cellular properties of cerebellar neurons emerge in integrated behaviors. Realistic network modeling opens up new perspectives for the investigation of brain pathologies and for the neurorobotic field. PMID:24139652

  9. Motor neurones in culture as a model to study ALS.

    PubMed

    Silani, V; Braga, M; Ciammola, A; Cardin, V; Scarlato, G

    2000-03-01

    Defining the basis of the selective cell vulnerability of motor neurones (MN) represents the key issue in amyotrophic lateral sclerosis (ALS), and tissue culture models are the ideal system for the identification of the MN specific features at the single cell level. Neurone-astrocyte metabolic interactions, which have a critical role in MN through glutamatergic toxicity, have been mostly defined in vitro. Ca++ metabolism, which appears to play a critical role in inducing MN loss in ALS, has been successfully studied using in vitro cell models. Furthermore, primary cultures demonstrated that apoptotic or necrotic death of neurones after injury depends upon the cell energetic status. Superoxide dismutase- (SOD-1) mutations were successfully expressed in cultured rodent MNs, providing a critical assay to sequence the molecular processes responsible for MN degeneration due to the identified genetic defect. The recent identification of genes that separate humans from apes further increases the value of the human in vitro models to better understand specific human cellular properties. Purified human MNs and astrocytes can today be obtained from the human embryonic spinal cord anterior horns. Interactions at the single cell level can be dissected using the cDNA amplification techniques. The effects of molecules affecting MN survival, neurite extension, and metabolism can easily be defined in vitro, gaining a critical mass of information of immediate clinical application in the treatment of patients affected by ALS. Understanding the properties of human MNs in vitro represents today a significant and critical tool that can easily be reached after extension of the available knowledge from non-primate to human research. Human MN culture studies can greatly contribute to identifying the primitive critical cellular events responsible for the MN degeneration observed in ALS and to gaining crucial information on new therapeutical agents. PMID:10795884

  10. Multiple dynamical resonances in a discrete neuronal model

    SciTech Connect

    Jiang Yu

    2005-05-01

    The conditions for the occurrence of different multiple resonances in an excitable neuron model are analyzed numerically. It is shown that the excitable system may display both stochastic and coherence resonance, in response to periodic stimuli in the presence of different intensities of additive and parametric noises. It is found that double coherence resonances may take place in the low-amplitude oscillation regimes, and coherence resonance may persists even in the weak oscillatory regimes for control parameters slightly larger than the Hopf bifurcation point, where the system is in the incipient stage of large-amplitude excitation regime.

  11. A human pluripotent carcinoma stem cell-based model for in vitro developmental neurotoxicity testing: effects of methylmercury, lead and aluminum evaluated by gene expression studies.

    PubMed

    Laurenza, Incoronata; Pallocca, Giorgia; Mennecozzi, Milena; Scelfo, Bibiana; Pamies, David; Bal-Price, Anna

    2013-11-01

    The major advantage of the neuronal cell culture models derived from human stem cells is their ability to replicate the crucial stages of neurodevelopment such as the commitment of human stem cells to the neuronal lineage and their subsequent stages of differentiation into neuronal and glial-like cell. In these studies we used mixed neuronal/glial culture derived from the NTERA-2 (NT-2) cell line, which has been established from human pluripotent testicular embryonal carcinoma cells. After characterization of the different stages of cell differentiation into neuronal- and glial-like phenotype toxicity studies were performed to evaluate whether this model would be suitable for developmental neurotoxicity studies. The cells were exposed during the differentiation process to non-cytotoxic concentrations of methylmercury chloride, lead chloride and aluminum nitrate for two weeks. The toxicity was then evaluated by measuring the mRNA levels of cell specific markers (neuronal and glial). The results obtained suggest that lead chloride and aluminum nitrate at low concentrations were toxic primarily to astrocytes and at the higher concentrations it also induced neurotoxicity. In contrast, MetHgCl was toxic for both cell types, neuronal and glial, as mRNA specific for astrocytes and neuronal markers were affected. The results obtained suggest that a neuronal mixed culture derived from human NT2 precursor cells is a suitable model for developmental neurotoxicity studies and gene expression could be used as a sensitive endpoint for initial screening of potential neurotoxic compounds. PMID:23501475

  12. Minimal Conductance-Based Model of Auditory Coincidence Detector Neurons

    PubMed Central

    Ashida, Go; Funabiki, Kazuo; Kretzberg, Jutta

    2015-01-01

    Sound localization is a fundamental sensory function of a wide variety of animals. The interaural time difference (ITD), an important cue for sound localization, is computed in the auditory brainstem. In our previous modeling study, we introduced a two-compartment Hodgkin-Huxley type model to investigate how cellular and synaptic specializations may contribute to precise ITD computation of the barn owl's auditory coincidence detector neuron. Although our model successfully reproduced fundamental physiological properties observed in vivo, it was unsuitable for mathematical analyses and large scale simulations because of a number of nonlinear variables. In the present study, we reduce our former model into three types of conductance-based integrate-and-fire (IF) models. We test their electrophysiological properties using data from published in vivo and in vitro studies. Their robustness to parameter changes and computational efficiencies are also examined. Our numerical results suggest that the single-compartment active IF model is superior to other reduced models in terms of physiological reproducibility and computational performance. This model will allow future theoretical studies that use more rigorous mathematical analysis and network simulations. PMID:25844803

  13. 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

  14. Extending the mirror neuron system model, I. Audible actions and invisible grasps.

    PubMed

    Bonaiuto, James; Rosta, Edina; Arbib, Michael

    2007-01-01

    The paper introduces mirror neuron system II (MNS2), a new version of the MNS model (Oztop and Arbib in Biol Cybern 87 (2):116-140, 2002) of action recognition learning by mirror neurons of the macaque brain. The new model uses a recurrent architecture that is biologically more plausible than that of the original model. Moreover, MNS2 extends the capacity of the model to address data on audio-visual mirror neurons and on the response of mirror neurons when the target object was recently visible but is currently hidden. PMID:17028884

  15. Mapping Generative Models onto a Network of Digital Spiking Neurons.

    PubMed

    Pedroni, Bruno U; Das, Srinjoy; Arthur, John V; Merolla, Paul A; Jackson, Bryan L; Modha, Dharmendra S; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

    2016-08-01

    Stochastic neural networks such as Restricted Boltzmann Machines (RBMs) have been successfully used in applications ranging from speech recognition to image classification, and are particularly interesting because of their potential for generative tasks. Inference and learning in these algorithms use a Markov Chain Monte Carlo procedure called Gibbs sampling, where a logistic function forms the kernel of this sampler. On the other side of the spectrum, neuromorphic systems have shown great promise for low-power and parallelized cognitive computing, but lack well-suited applications and automation procedures. In this work, we propose a systematic method for bridging the RBM algorithm and digital neuromorphic systems, with a generative pattern completion task as proof of concept. For this, we first propose a method of producing the Gibbs sampler using bio-inspired digital noisy integrate-and-fire neurons. Next, we describe the process of mapping generative RBMs trained offline onto the IBM TrueNorth neurosynaptic processor-a low-power digital neuromorphic VLSI substrate. Mapping these algorithms onto neuromorphic hardware presents unique challenges in network connectivity and weight and bias quantization, which, in turn, require architectural and design strategies for the physical realization. Generative performance is analyzed to validate the neuromorphic requirements and to best select the neuron parameters for the model. Lastly, we describe a design automation procedure which achieves optimal resource usage, accounting for the novel hardware adaptations. This work represents the first implementation of generative RBM inference on a neuromorphic VLSI substrate. PMID:27214915

  16. Neurons compute internal models of the physical laws of motion.

    PubMed

    Angelaki, Dora E; Shaikh, Aasef G; Green, Andrea M; Dickman, J David

    2004-07-29

    A critical step in self-motion perception and spatial awareness is the integration of motion cues from multiple sensory organs that individually do not provide an accurate representation of the physical world. One of the best-studied sensory ambiguities is found in visual processing, and arises because of the inherent uncertainty in detecting the motion direction of an untextured contour moving within a small aperture. A similar sensory ambiguity arises in identifying the actual motion associated with linear accelerations sensed by the otolith organs in the inner ear. These internal linear accelerometers respond identically during translational motion (for example, running forward) and gravitational accelerations experienced as we reorient the head relative to gravity (that is, head tilt). Using new stimulus combinations, we identify here cerebellar and brainstem motion-sensitive neurons that compute a solution to the inertial motion detection problem. We show that the firing rates of these populations of neurons reflect the computations necessary to construct an internal model representation of the physical equations of motion. PMID:15282606

  17. Parametric Anatomical Modeling: a method for modeling the anatomical layout of neurons and their projections.

    PubMed

    Pyka, Martin; Klatt, Sebastian; Cheng, Sen

    2014-01-01

    Computational models of neural networks can be based on a variety of different parameters. These parameters include, for example, the 3d shape of neuron layers, the neurons' spatial projection patterns, spiking dynamics and neurotransmitter systems. While many well-developed approaches are available to model, for example, the spiking dynamics, there is a lack of approaches for modeling the anatomical layout of neurons and their projections. We present a new method, called Parametric Anatomical Modeling (PAM), to fill this gap. PAM can be used to derive network connectivities and conduction delays from anatomical data, such as the position and shape of the neuronal layers and the dendritic and axonal projection patterns. Within the PAM framework, several mapping techniques between layers can account for a large variety of connection properties between pre- and post-synaptic neuron layers. PAM is implemented as a Python tool and integrated in the 3d modeling software Blender. We demonstrate on a 3d model of the hippocampal formation how PAM can help reveal complex properties of the synaptic connectivity and conduction delays, properties that might be relevant to uncover the function of the hippocampus. Based on these analyses, two experimentally testable predictions arose: (i) the number of neurons and the spread of connections is heterogeneously distributed across the main anatomical axes, (ii) the distribution of connection lengths in CA3-CA1 differ qualitatively from those between DG-CA3 and CA3-CA3. Models created by PAM can also serve as an educational tool to visualize the 3d connectivity of brain regions. The low-dimensional, but yet biologically plausible, parameter space renders PAM suitable to analyse allometric and evolutionary factors in networks and to model the complexity of real networks with comparatively little effort. PMID:25309338

  18. Parametric Anatomical Modeling: a method for modeling the anatomical layout of neurons and their projections

    PubMed Central

    Pyka, Martin; Klatt, Sebastian; Cheng, Sen

    2014-01-01

    Computational models of neural networks can be based on a variety of different parameters. These parameters include, for example, the 3d shape of neuron layers, the neurons' spatial projection patterns, spiking dynamics and neurotransmitter systems. While many well-developed approaches are available to model, for example, the spiking dynamics, there is a lack of approaches for modeling the anatomical layout of neurons and their projections. We present a new method, called Parametric Anatomical Modeling (PAM), to fill this gap. PAM can be used to derive network connectivities and conduction delays from anatomical data, such as the position and shape of the neuronal layers and the dendritic and axonal projection patterns. Within the PAM framework, several mapping techniques between layers can account for a large variety of connection properties between pre- and post-synaptic neuron layers. PAM is implemented as a Python tool and integrated in the 3d modeling software Blender. We demonstrate on a 3d model of the hippocampal formation how PAM can help reveal complex properties of the synaptic connectivity and conduction delays, properties that might be relevant to uncover the function of the hippocampus. Based on these analyses, two experimentally testable predictions arose: (i) the number of neurons and the spread of connections is heterogeneously distributed across the main anatomical axes, (ii) the distribution of connection lengths in CA3-CA1 differ qualitatively from those between DG-CA3 and CA3-CA3. Models created by PAM can also serve as an educational tool to visualize the 3d connectivity of brain regions. The low-dimensional, but yet biologically plausible, parameter space renders PAM suitable to analyse allometric and evolutionary factors in networks and to model the complexity of real networks with comparatively little effort. PMID:25309338

  19. Neurons derived from transplanted neural stem cells restore disrupted neuronal circuitry in a mouse model of spinal cord injury

    PubMed Central

    Abematsu, Masahiko; Tsujimura, Keita; Yamano, Mariko; Saito, Michiko; Kohno, Kenji; Kohyama, Jun; Namihira, Masakazu; Komiya, Setsuro; Nakashima, Kinichi

    2010-01-01

    The body’s capacity to restore damaged neural networks in the injured CNS is severely limited. Although various treatment regimens can partially alleviate spinal cord injury (SCI), the mechanisms responsible for symptomatic improvement remain elusive. Here, using a mouse model of SCI, we have shown that transplantation of neural stem cells (NSCs) together with administration of valproic acid (VPA), a known antiepileptic and histone deacetylase inhibitor, dramatically enhanced the restoration of hind limb function. VPA treatment promoted the differentiation of transplanted NSCs into neurons rather than glial cells. Transsynaptic anterograde corticospinal tract tracing revealed that transplant-derived neurons reconstructed broken neuronal circuits, and electron microscopic analysis revealed that the transplant-derived neurons both received and sent synaptic connections to endogenous neurons. Ablation of the transplanted cells abolished the recovery of hind limb motor function, confirming that NSC transplantation directly contributed to restored motor function. These findings raise the possibility that epigenetic status in transplanted NSCs can be manipulated to provide effective treatment for SCI. PMID:20714104

  20. A Caenorhabditis elegans model for epithelial–neuronal transdifferentiation

    PubMed Central

    Jarriault, Sophie; Schwab, Yannick; Greenwald, Iva

    2008-01-01

    Understanding transdifferentiation—the conversion of one differentiated cell type into another—is important from both basic science and clinical perspectives. In Caenorhabditis elegans, an epithelial cell named Y is initially part of the rectum but later appears to withdraw, migrate, and then become a motor neuron named PDA. Here, we show that this represents a bona fide transdifferentiation event: Y has epithelial hallmarks without detectable neural characteristics, and PDA has no residual epithelial characteristics. Using available mutants and laser microsurgery, we found that transdifferentiation does not depend on fusion with a neighboring cell or require migration of Y away from the rectum, that other rectal epithelial cells are not competent to transdifferentiate, and that transdifferentiation requires the EGL-5 and SEM-4 transcription factors and LIN-12/Notch signaling. Our results establish Y-to-PDA transdifferentiation as a genetically tractable model for deciphering the mechanisms underlying cellular plasticity in vivo. PMID:18308937

  1. Dynamic characteristics of a simple bursting neuron model

    NASA Astrophysics Data System (ADS)

    Nakajima, Koji; Sato, Shigeo; Hayakawa, Yoshihiro

    We present a simple neuron model that shows a rich property in spite of the simple structure derived from the simplification of the Hindmarsh-Rose, the Morris-Lecar, and the Hodgkin-Huxley models. The model is a typical example whose characteristics can be discussed through the concept of potential with active areas. A potential function is able to provide a global landscape for dynamics of a model, and the dynamics is explained in connection with the disposition of the active areas on the potential, and hence we are able to discuss the global dynamic behaviors and the common properties among these realistic models. The obtained outputs are broadly classified as simple oscillations, spiking, bursting, and chaotic oscillations. The bursting outputs are classified as with spike undershoot and without spike undershoot, and the bursts without spike undershoot are classified as with tapered and without tapered. We show the parameter dependence of these outputs and discuss the connection between these outputs and the potential with active areas.

  2. A model based rule for selecting spiking thresholds in neuron models.

    PubMed

    Mikkelsen, Frederik Riis

    2016-06-01

    Determining excitability thresholds in neuronal models is of high interest due to its applicability in separating spiking from non-spiking phases of neuronal membrane potential processes. However, excitability thresholds are known to depend on various auxiliary variables, including any conductance or gating variables. Such dependences pose as a double-edged sword; they are natural consequences of the complexity of the model, but proves difficult to apply in practice, since gating variables are rarely measured. In this paper a technique for finding excitability thresholds, based on the local behaviour of the flow in dynamical systems, is presented. The technique incorporates the dynamics of the auxiliary variables, yet only produces thresholds for the membrane potential. The method is applied to several classical neuron models and the threshold's dependence upon external parameters is studied, along with a general evaluation of the technique. PMID:27106187

  3. A Computational Model for the Loss of Neuronal Organization in Microcolumns

    PubMed Central

    Henderson, Maxwell; Urbanc, Brigita; Cruz, Luis

    2014-01-01

    A population of neurons in the cerebral cortex of humans and other mammals organize themselves into vertical microcolumns perpendicular to the pial surface. Anatomical changes to these microcolumns have been correlated with neurological diseases and normal aging; in particular, in area 46 of the rhesus monkey brain, the strength of microcolumns was shown to decrease with age. These changes can be caused by alterations in the spatial distribution of the neurons in microcolumns and/or neuronal loss. Using a three-dimensional computational model of neuronal arrangements derived from thin tissue sections and validated in brain tissue from rhesus monkeys, we show that neuronal loss is inconsistent with the findings in aged individuals. In contrast, a model of simple random neuronal displacements, constrained in magnitude by restorative harmonic forces, is consistent with observed changes and provides mechanistic insights into the age-induced loss of microcolumnar structure. Connection of the model to normal aging and disease are discussed. PMID:24853752

  4. Simple Cortical and Thalamic Neuron Models for Digital Arithmetic Circuit Implementation

    PubMed Central

    Nanami, Takuya; Kohno, Takashi

    2016-01-01

    Trade-off between reproducibility of neuronal activities and computational efficiency is one of crucial subjects in computational neuroscience and neuromorphic engineering. A wide variety of neuronal models have been studied from different viewpoints. The digital spiking silicon neuron (DSSN) model is a qualitative model that focuses on efficient implementation by digital arithmetic circuits. We expanded the DSSN model and found appropriate parameter sets with which it reproduces the dynamical behaviors of the ionic-conductance models of four classes of cortical and thalamic neurons. We first developed a four-variable model by reducing the number of variables in the ionic-conductance models and elucidated its mathematical structures using bifurcation analysis. Then, expanded DSSN models were constructed that reproduce these mathematical structures and capture the characteristic behavior of each neuron class. We confirmed that statistics of the neuronal spike sequences are similar in the DSSN and the ionic-conductance models. Computational cost of the DSSN model is larger than that of the recent sophisticated Integrate-and-Fire-based models, but smaller than the ionic-conductance models. This model is intended to provide another meeting point for above trade-off that satisfies the demand for large-scale neuronal network simulation with closer-to-biology models. PMID:27242397

  5. Phase Sensitivity and Entrainment in a Modeled Bursting Neuron

    PubMed Central

    Demir, S. S.; Butera, R. J.; DeFranceschi, A. A.; Clark, J. W.; Byrne, J. H.

    1997-01-01

    A model of neuron R15 in Aplysia was used to study the mechanisms determining the phase-response curve (PRC) of the cell in response to both extrinsic current pulses and modeled synaptic input and to compare entrainment predictions from PRCs with those from actual simulations. Over the range of stimulus parameters studied, the PRCs of the model exhibited minimal dependence upon stimulus amplitude, and a strong dependence upon stimulus duration. State-space analysis of the effect of transient current pulses provided several important insights into the relationship between the PRC and the underlying dynamics of the model, such as a correlation between the prestimulus concentration of Ca2+ and the poststimulus phase of the oscillation. The system nullclines were also found to provide well-defined limits upon the perturbatory extent of a hyperpolarizing input. These results demonstrated that experimentally applied current pulses are sufficient to determine the shape of the PRC in response to a synaptic input, provided that the duration of the current pulse is of a duration similar to that of the evoked synaptic current. Furthermore, we found that predictions of phase-locked 1:m entrainment from PRCs were valid, even when the duration of the periodically applied pulses were a significant portion of the control limit cycle. ImagesFIGURE 5FIGURE 7FIGURE 8 PMID:9017188

  6. Continuum modeling of neuronal cell under blast loading

    PubMed Central

    Jérusalem, Antoine; Dao, Ming

    2012-01-01

    Traumatic brain injuries have recently been put under the spotlight as one of the most important causes of accidental brain dysfunctions. Significant experimental and modeling efforts are thus ongoing to study the associated biological, mechanical and physical mechanisms. In the field of cell mechanics, progresses are also being made at the experimental and modeling levels to better characterize many of the cell functions such as differentiation, growth, migration and death, among others. The work presented here aims at bridging both efforts by proposing a continuum model of neuronal cell submitted to blast loading. In this approach, cytoplasm, nucleus and membrane (plus cortex) are differentiated in a representative cell geometry, and different material constitutive models are adequately chosen for each one. The material parameters are calibrated against published experimental work of cell nanoindentation at multiple rates. The final cell model is ultimately subjected to blast loading within a complete fluid-structure interaction computational framework. The results are compared to the nanoindentation simulation and the specific effects of the blast wave on the pressure and shear levels at the interfaces are identified. As a conclusion, the presented model successfully captures some of the intrinsic intracellular phenomena occurring during its deformation under blast loading and potentially leading to cell damage. It suggests more particularly the localization of damage at the nucleus membrane similarly to what has already been observed at the overall cell membrane. This degree of damage is additionally predicted to be worsened by a longer blast positive phase duration. As a conclusion, the proposed model ultimately provides a new three dimensional computational tool to evaluate intracellular damage during blast loading. PMID:22562014

  7. Continuum modeling of a neuronal cell under blast loading.

    PubMed

    Jérusalem, Antoine; Dao, Ming

    2012-09-01

    Traumatic brain injuries have recently been put under the spotlight as one of the most important causes of accidental brain dysfunctions. Significant experimental and modeling efforts are thus underway to study the associated biological, mechanical and physical mechanisms. In the field of cell mechanics, progress is also being made at the experimental and modeling levels to better characterize many of the cell functions, including differentiation, growth, migration and death. The work presented here aims to bridge both efforts by proposing a continuum model of a neuronal cell submitted to blast loading. In this approach, the cytoplasm, nucleus and membrane (plus cortex) are differentiated in a representative cell geometry, and different suitable material constitutive models are chosen for each one. The material parameters are calibrated against published experimental work on cell nanoindentation at multiple rates. The final cell model is ultimately subjected to blast loading within a complete computational framework of fluid-structure interaction. The results are compared to the nanoindentation simulation, and the specific effects of the blast wave on the pressure and shear levels at the interfaces are identified. As a conclusion, the presented model successfully captures some of the intrinsic intracellular phenomena occurring during the cellular deformation under blast loading that potentially lead to cell damage. It suggests, more particularly, that the localization of damage at the nucleus membrane is similar to what has already been observed at the overall cell membrane. This degree of damage is additionally predicted to be worsened by a longer blast positive phase duration. In conclusion, the proposed model ultimately provides a new three-dimensional computational tool to evaluate intracellular damage during blast loading. PMID:22562014

  8. Block-Based Neural Networks with Pulsed Neuron Model

    NASA Astrophysics Data System (ADS)

    Iguchi, Syota; Koakutsu, Seiichi; Okamoto, Takashi; Hirata, Hironori

    In recent years, the study of hardware implementation of Neural Networks (NN) has been getting more important. In particular, Block-Based Neural Networks (BBNN) which are one of NN have been attracted attention. However, the conventional BBNN are analogue NN (ANN). The digital hardware implementation of ANN is very difficult, because the input and output signals are represented as analogue values. Pulsed Neural Networks (PNN) which adopt a pulsed neuron (PN) model instead of the AN model have been proposed in order to solve this problem. The input and output signals of PNN are represented as a series of pulses, and thus the digital hardware implementation of PNN becomes easy. In this paper, we propose Block-Based Pulsed Neural Networks (BBPNN) introducing the PN model into BBNN in order to faciliate the implementation of NN on digital hardware. We use particle swarm optimization (PSO) for optimization of weights of BBPNN, because PSO can produce a globally optimum solution of nonlinear continuous optimization problems in practicable calculation time by high accuracy. To evaluate the proposed BBPNN, we apply them to XOR problem and autonomous mobile robot control problems. Computational experiments indicate that the proposed BBPNN and the conventional BBNN can produce about the same results.

  9. Efficient estimation of detailed single-neuron models.

    PubMed

    Huys, Quentin J M; Ahrens, Misha B; Paninski, Liam

    2006-08-01

    Biophysically accurate multicompartmental models of individual neurons have significantly advanced our understanding of the input-output function of single cells. These models depend on a large number of parameters that are difficult to estimate. In practice, they are often hand-tuned to match measured physiological behaviors, thus raising questions of identifiability and interpretability. We propose a statistical approach to the automatic estimation of various biologically relevant parameters, including 1) the distribution of channel densities, 2) the spatiotemporal pattern of synaptic input, and 3) axial resistances across extended dendrites. Recent experimental advances, notably in voltage-sensitive imaging, motivate us to assume access to: i) the spatiotemporal voltage signal in the dendrite and ii) an approximate description of the channel kinetics of interest. We show here that, given i and ii, parameters 1-3 can be inferred simultaneously by nonnegative linear regression; that this optimization problem possesses a unique solution and is guaranteed to converge despite the large number of parameters and their complex nonlinear interaction; and that standard optimization algorithms efficiently reach this optimum with modest computational and data requirements. We demonstrate that the method leads to accurate estimations on a wide variety of challenging model data sets that include up to about 10(4) parameters (roughly two orders of magnitude more than previously feasible) and describe how the method gives insights into the functional interaction of groups of channels. PMID:16624998

  10. Basic neuron model electrical equivalent circuit: an undergraduate laboratory exercise.

    PubMed

    Dabrowski, Katie M; Castaño, Diego J; Tartar, Jaime L

    2013-01-01

    We developed a hands-on laboratory exercise for undergraduate students in which they can build and manipulate a neuron equivalent circuit. This exercise uses electrical circuit components that resemble neuron components and are easy to construct. We describe the methods for creating the equivalent circuit and how to observe different neuron properties through altering the structure of the equivalent circuit. We explain how this hands-on laboratory activity allows for the better understanding of this fundamental neuroscience concept. At the conclusion of this laboratory exercise, undergraduate students will be able to apply the principles of Ohm's law, cable theory with regards to neurons, and understand the functions of resistance and capacitance in a neuron. PMID:24319391

  11. Human in vitro reporter model of neuronal development and early differentiation processes

    PubMed Central

    Couillard-Despres, Sebastien; Quehl, Eike; Altendorfer, Katrin; Karl, Claudia; Ploetz, Sonja; Bogdahn, Ulrich; Winkler, Juergen; Aigner, Ludwig

    2008-01-01

    Background During developmental and adult neurogenesis, doublecortin is an early neuronal marker expressed when neural stem cells assume a neuronal cell fate. To understand mechanisms involved in early processes of neuronal fate decision, we investigated cell lines for their capacity to induce expression of doublecortin upon neuronal differentiation and develop in vitro reporter models using doublecortin promoter sequences. Results Among various cell lines investigated, the human teratocarcinoma cell line NTERA-2 was found to fulfill our criteria. Following induction of differentiation using retinoic acid treatment, we observed a 16-fold increase in doublecortin mRNA expression, as well as strong induction of doublecortin polypeptide expression. The acquisition of a neuronal precursor phenotype was also substantiated by the establishment of a multipolar neuronal morphology and expression of additional neuronal markers, such as Map2, βIII-tubulin and neuron-specific enolase. Moreover, stable transfection in NTERA-2 cells of reporter constructs encoding fluorescent or luminescent genes under the control of the doublecortin promoter allowed us to directly detect induction of neuronal differentiation in cell culture, such as following retinoic acid treatment or mouse Ngn2 transient overexpression. Conclusion Induction of doublecortin expression in differentiating NTERA-2 cells suggests that these cells accurately recapitulate some of the very early events of neuronal determination. Hence, the use of reporter genes under the control of the doublecortin promoter in NTERA-2 cells will help us to investigate factors involved early in the course of neuronal differentiation processes. Moreover the ease to detect the induction of a neuronal program in this model will permit to perform high throughput screening for compounds acting on the early neuronal differentiation mechanisms. PMID:18312642

  12. Characterization of Kiss1 neurons using transgenic mouse models

    PubMed Central

    Cravo, Roberta M.; Margatho, Lisandra O.; Osborne-Lawrence, Sherri; Donato, José; Atkin, Stan; Bookout, Angie L.; Rovinsky, Sherry; Frazão, Renata; Lee, Charlotte E.; Gautron, Laurent; Zigman, Jeffrey M.; Elias, Carol F.

    2010-01-01

    Humans and mice with loss-of-function mutations of the genes encoding kisspeptins (Kiss1) or kisspeptin receptors (Kiss1r) are infertile due to hypogonadotropic hypogonadism. Within the hypothalamus, Kiss1 mRNA is expressed in the anteroventral periventricular nucleus (AVPV) and the arcuate nucleus (Arc). In order to better study the different populations of kisspeptin cells we generated Kiss1-Cre transgenic mice. We obtained one line with Cre activity specifically within Kiss1 neurons (line J2-4), as assessed by generating mice with Cre-dependent expression of green fluorescent protein or β-galactosidase. Also, we demonstrated Kiss1 expression in the cerebral cortex and confirmed previous data showing Kiss1 mRNA in the medial nucleus of amygdala and anterodorsal preoptic nucleus. Kiss1 neurons were more concentrated towards the caudal levels of the Arc and higher leptin-responsivity was observed in the most caudal population of Arc Kiss1 neurons. No evidence for direct action of leptin in AVPV Kiss1 neurons was observed. Melanocortin fibers innervated subsets of Kiss1 neurons of the preoptic area and Arc, and both populations expressed MC4R. Specifically in the preoptic area, 18–28% of Kiss1 neurons expressed MC4R. In the Arc, 90% of Kiss1 neurons were glutamatergic, 50% of which also were GABAergic. In the AVPV, 20% of Kiss1 neurons were glutamatergic whereas 75% were GABAergic. The differences observed between the Kiss1 neurons in the preoptic area and the Arc likely represent neuronal evidence for their differential roles in metabolism and reproduction. PMID:21093546

  13. Use of human induced pluripotent stem cell-derived neurons as a model for Cerebral Toxoplasmosis.

    PubMed

    Tanaka, Naomi; Ashour, Danah; Dratz, Edward; Halonen, Sandra

    2016-01-01

    Toxoplasma gondii is a ubiquitous protozoan parasite with approximately one-third of the worlds' population chronically infected. In chronically infected individuals, the parasite resides primarily in cysts within neurons in the central nervous system. The chronic infection in immunocompetent individuals has been considered to be asymptomatic but increasing evidence indicates the chronic infection can lead to neuropsychiatric disorders such as Schizophrenia, prenatal depression and suicidal thoughts. A better understanding of the mechanism(s) by which the parasite exerts effects on human behavior is limited due to lack of suitable human neuronal models. In this paper, we report the use of human neurons derived from normal cord blood CD34+ cells generated via genetic reprogramming, as an in vitro model for the study T. gondii in neurons. This culture method resulted in a relatively pure monolayer of induced human neuronal-like cells that stained positive for neuronal markers, MAP2, NFL, NFH and NeuN. These induced human neuronal-like cells (iHNs) were efficiently infected by the Prugniad strain of the parasite and supported replication of the tachyzoite stage and development of the cyst stage. Infected iHNs could be maintained through 5 days of infection, allowing for formation of large cysts. This induced human neuronal model represents a novel culture method to study both tachyzoite and bradyzoite stages of T. gondii in human neurons. PMID:27083472

  14. Hypoxanthine deregulates genes involved in early neuronal development. Implications in Lesch-Nyhan disease pathogenesis.

    PubMed

    Torres, R J; Puig, J G

    2015-11-01

    Neurological manifestations in Lesch-Nyhan disease (LND) are attributed to the effect of hypoxanthine-guanine phosphoribosyltransferase (HPRT) deficiency on the nervous system development. HPRT deficiency causes the excretion of increased amounts of hypoxanthine into the extracellular medium and we hypothesized that HPRT deficiency related to hypoxanthine excess may then lead, directly or indirectly, to transcriptional aberrations in a variety of genes essential for the function and development of striatal progenitor cells. We have examined the effect of hypoxanthine excess on the differentiation of neurons in the well-established human NTERA-2 cl.D1 (NT2/D1) embryonic carcinoma neurogenesis model. NT2/D1 cells differentiate along neuroectodermal lineages after exposure to retinoic acid (RA). Hypoxanthine effects on RA-differentiation were examined by the changes on the expression of various transcription factor genes essential to neuronal differentiation and by the changes in tyrosine hydroxylase (TH), dopamine, adenosine and serotonin receptors (DRD, ADORA, HTR). We report that hypoxanthine excess deregulate WNT4, from Wnt/β-catenin pathway, and engrailed homeobox 1 gene and increased TH and dopamine DRD1, adenosine ADORA2A and serotonin HTR7 receptors, whose over expression characterize early neuro-developmental processes. PMID:25940910

  15. The Hopfield Model as the Minimum Noise Model for Neurons with Variable Firing Strength

    NASA Astrophysics Data System (ADS)

    Bassias, K. V.; Frank, B.

    A “non-standard Hopfield” network of neurons with variable maximum firing strength ai is studied and a noise analysis is employed. It is shown that the requirement of minimum noise forces the solution with ai=1, ∀i, namely, the standard Hopfield model. The minimum noise is calculated.

  16. Genomic and Phenotypic Alterations of the Neuronal-Like Cells Derived from Human Embryonal Carcinoma Stem Cells (NT2) Caused by Exposure to Organophosphorus Compounds Paraoxon and Mipafox

    PubMed Central

    Pamies, David; Sogorb, Miguel A.; Fabbri, Marco; Gribaldo, Laura; Collotta, Angelo; Scelfo, Bibiana; Vilanova, Eugenio; Harris, Georgina; Bal-Price, Anna

    2014-01-01

    Historically, only few chemicals have been identified as neurodevelopmental toxicants, however, concern remains, and has recently increased, based upon the association between chemical exposures and increased developmental disorders. Diminution in motor speed and latency has been reported in preschool children from agricultural communities. Organophosphorus compounds (OPs) are pesticides due to their acute insecticidal effects mediated by the inhibition of acetylcholinesterase, although other esterases as neuropathy target esterase (NTE) can also be inhibited. Other neurological and neurodevelopmental toxic effects with unknown targets have been reported after chronic exposure to OPs in vivo. We studied the initial stages of retinoic acid acid-triggered differentiation of pluripotent cells towards neural progenitors derived from human embryonal carcinoma stem cells to determine if neuropathic OP, mipafox, and non-neuropathic OP, paraoxon, are able to alter differentiation of neural precursor cells in vitro. Exposure to 1 μM paraoxon (non-cytotoxic concentrations) altered the expression of different genes involved in signaling pathways related to chromatin assembly and nucleosome integrity. Conversely, exposure to 5 μM mipafox, a known inhibitor of NTE activity, showed no significant changes on gene expression. We conclude that 1 μM paraoxon could affect the initial stage of in vitro neurodifferentiation possibly due to a teratogenic effect, while the absence of transcriptional alterations by mipafox exposure did not allow us to conclude a possible effect on neurodifferentiation pathways at the tested concentration. PMID:24413757

  17. Automatic parameter estimation of multicompartmental neuron models via minimization of trace error with control adjustment

    PubMed Central

    Goeritz, Marie L.; Marder, Eve

    2014-01-01

    We describe a new technique to fit conductance-based neuron models to intracellular voltage traces from isolated biological neurons. The biological neurons are recorded in current-clamp with pink (1/f) noise injected to perturb the activity of the neuron. The new algorithm finds a set of parameters that allows a multicompartmental model neuron to match the recorded voltage trace. Attempting to match a recorded voltage trace directly has a well-known problem: mismatch in the timing of action potentials between biological and model neuron is inevitable and results in poor phenomenological match between the model and data. Our approach avoids this by applying a weak control adjustment to the model to promote alignment during the fitting procedure. This approach is closely related to the control theoretic concept of a Luenberger observer. We tested this approach on synthetic data and on data recorded from an anterior gastric receptor neuron from the stomatogastric ganglion of the crab Cancer borealis. To test the flexibility of this approach, the synthetic data were constructed with conductance models that were different from the ones used in the fitting model. For both synthetic and biological data, the resultant models had good spike-timing accuracy. PMID:25008414

  18. Bistability, switches and working memory in a two-neuron inhibitory-feedback model.

    PubMed

    Kirillov, A B; Myre, C D; Woodward, D J

    1993-01-01

    It was reported earlier that an inhibitory-feedback network inspired by neostriatal circuitry may exhibit a bistable character and spontaneous switching phenomenon within the neuronal activity. In the presence of noise and external excitation, a few local neurons switch "on" and generate streams of impulses while other neurons remain quiescent. In time, the existing "on" neurons spontaneously switch "off" and other neurons switch "on". In this paper we examine the nature of the bistability and switching phenomenon using a simple model consisting of two mutually inhibitory neurons. For nonspiking neuron model, described by a system of nonlinear differential equations, we present a simple bifurcation analysis, which follows the birth and annihilation of two stable fixed points when model parameters are varied. We show that both nonspiking and spiking models may have two stable states, but only spiking neurons exhibit switching. The mechanism of switching for model spiking neurons, described by an equivalent RC circuit with a number of currents, is analyzed using computer simulations. It is shown that switching can be described by a two-state Markov chain with one parameter, which depends on the set of model physiological parameters, such as duration of afterhyperpolarization (AHP), maximum and the time duration of inhibitory post-synaptic potentials (IPSP's) and amplitude of the neuron noise input. "On" and "off" states of the model can be rapidly changed by localized excitatory input and the network then sustains the pattern of "on" and "off" states. That is, such a network can be used as a programmable memory device.(ABSTRACT TRUNCATED AT 250 WORDS) PMID:8476984

  19. Modeling neuron-glia interactions: from parametric model to neuromorphic hardware.

    PubMed

    Ghaderi, Viviane S; Allam, Sushmita L; Ambert, N; Bouteiller, J-M C; Choma, J; Berger, T W

    2011-01-01

    Recent experimental evidence suggests that glial cells are more than just supporting cells to neurons - they play an active role in signal transmission in the brain. We herein propose to investigate the importance of these mechanisms and model neuron-glia interactions at synapses using three approaches: A parametric model that takes into account the underlying mechanisms of the physiological system, a non-parametric model that extracts its input-output properties, and an ultra-low power, fast processing, neuromorphic hardware model. We use the EONS (Elementary Objects of the Nervous System) platform, a highly elaborate synaptic modeling platform to investigate the influence of astrocytic glutamate transporters on postsynaptic responses in the detailed micro-environment of a tri-partite synapse. The simulation results obtained using EONS are then used to build a non-parametric model that captures the essential features of glutamate dynamics. The structure of the non-parametric model we use is specifically designed for efficient hardware implementation using ultra-low power subthreshold CMOS building blocks. The utilization of the approach described allows us to build large-scale models of neuron/glial interaction and consequently provide useful insights on glial modulation during normal and pathological neural function. PMID:22255113

  20. A neuron model of stochastic resonance using rectangular pulse trains.

    PubMed

    Danziger, Zachary; Grill, Warren M

    2015-02-01

    Stochastic resonance (SR) is the enhanced representation of a weak input signal by the addition of an optimal level of broadband noise to a nonlinear (threshold) system. Since its discovery in the 1980s the domain of input signals shown to be applicable to SR has greatly expanded, from strictly periodic inputs to now nearly any aperiodic forcing function. The perturbations (noise) used to generate SR have also expanded, from white noise to now colored noise or vibrational forcing. This study demonstrates that a new class of perturbations can achieve SR, namely, series of stochastically generated biphasic pulse trains. Using these pulse trains as 'noise' we show that a Hodgkin Huxley model neuron exhibits SR behavior when detecting weak input signals. This result is of particular interest to neuroscience because nearly all artificial neural stimulation is implemented with square current or voltage pulses rather than broadband noise, and this new method may facilitate the translation of the performance gains achievable through SR to neural prosthetics. PMID:25186655

  1. A Neuron Model of Stochastic Resonance Using Rectangular Pulse Trains

    PubMed Central

    Danziger, Zachary; Grill, Warren M

    2014-01-01

    Stochastic resonance (SR) is the enhanced representation of a weak input signal by the addition of an optimal level of broadband noise to a nonlinear (threshold) system. Since its discovery in the 1980s the domain of input signals shown to be applicable to SR has greatly expanded, from strictly periodic inputs to now nearly any aperiodic forcing function. The perturbations (noise) used to generate SR have also expanded, from white noise to now colored noise or vibrational forcing. This study demonstrates that a new class of perturbations can achieve SR, namely, series of stochastically generated biphasic pulse trains. Using these pulse trains as ‘noise’ we show that a Hodgkin Huxley model neuron exhibits SR behavior when detecting weak input signals. This result is of particular interest to neuroscience because nearly all artificial neural stimulation is implemented with square current or voltage pulses rather than broadband noise, and this new method may facilitate the translation of the performance gains achievable through SR to neural prosthetics. PMID:25186655

  2. Graph structure modeling for multi-neuronal spike data

    NASA Astrophysics Data System (ADS)

    Akaho, Shotaro; Higuchi, Sho; Iwasaki, Taishi; Hino, Hideitsu; Tatsuno, Masami; Murata, Noboru

    2016-03-01

    We propose a method to extract connectivity between neurons for extracellularly recorded multiple spike trains. The method removes pseudo-correlation caused by propagation of information along an indirect pathway, and is also robust against the influence from unobserved neurons. The estimation algorithm consists of iterations of a simple matrix inversion, which is scalable to large data sets. The performance is examined by synthetic spike data.

  3. Multiscale modeling of brain dynamics: from single neurons and networks to mathematical tools.

    PubMed

    Siettos, Constantinos; Starke, Jens

    2016-09-01

    The extreme complexity of the brain naturally requires mathematical modeling approaches on a large variety of scales; the spectrum ranges from single neuron dynamics over the behavior of groups of neurons to neuronal network activity. Thus, the connection between the microscopic scale (single neuron activity) to macroscopic behavior (emergent behavior of the collective dynamics) and vice versa is a key to understand the brain in its complexity. In this work, we attempt a review of a wide range of approaches, ranging from the modeling of single neuron dynamics to machine learning. The models include biophysical as well as data-driven phenomenological models. The discussed models include Hodgkin-Huxley, FitzHugh-Nagumo, coupled oscillators (Kuramoto oscillators, Rössler oscillators, and the Hindmarsh-Rose neuron), Integrate and Fire, networks of neurons, and neural field equations. In addition to the mathematical models, important mathematical methods in multiscale modeling and reconstruction of the causal connectivity are sketched. The methods include linear and nonlinear tools from statistics, data analysis, and time series analysis up to differential equations, dynamical systems, and bifurcation theory, including Granger causal connectivity analysis, phase synchronization connectivity analysis, principal component analysis (PCA), independent component analysis (ICA), and manifold learning algorithms such as ISOMAP, and diffusion maps and equation-free techniques. WIREs Syst Biol Med 2016, 8:438-458. doi: 10.1002/wsbm.1348 For further resources related to this article, please visit the WIREs website. PMID:27340949

  4. A Modeling Approach on Why Simple Central Pattern Generators Are Built of Irregular Neurons

    PubMed Central

    Reyes, Marcelo Bussotti; Carelli, Pedro Valadão; Sartorelli, José Carlos; Pinto, Reynaldo Daniel

    2015-01-01

    The crustacean pyloric Central Pattern Generator (CPG) is a nervous circuit that endogenously provides periodic motor patterns. Even after about 40 years of intensive studies, the rhythm genesis is still not rigorously understood in this CPG, mainly because it is made of neurons with irregular intrinsic activity. Using mathematical models we addressed the question of using a network of irregularly behaving elements to generate periodic oscillations, and we show some advantages of using non-periodic neurons with intrinsic behavior in the transition from bursting to tonic spiking (as found in biological pyloric CPGs) as building components. We studied two- and three-neuron model CPGs built either with Hindmarsh-Rose or with conductance-based Hodgkin-Huxley-like model neurons. By changing a model’s parameter we could span the neuron’s intrinsic dynamical behavior from slow periodic bursting to fast tonic spiking, passing through a transition where irregular bursting was observed. Two-neuron CPG, half center oscillator (HCO), was obtained for each intrinsic behavior of the neurons by coupling them with mutual symmetric synaptic inhibition. Most of these HCOs presented regular antiphasic bursting activity and the changes of the bursting frequencies was studied as a function of the inhibitory synaptic strength. Among all HCOs, those made of intrinsic irregular neurons presented a wider burst frequency range while keeping a reliable regular oscillatory (bursting) behavior. HCOs of periodic neurons tended to be either hard to change their behavior with synaptic strength variations (slow periodic burster neurons) or unable to perform a physiologically meaningful rhythm (fast tonic spiking neurons). Moreover, 3-neuron CPGs with connectivity and output similar to those of the pyloric CPG presented the same results. PMID:25799556

  5. Serial correlation in neural spike trains: Experimental evidence, stochastic modeling, and single neuron variability

    NASA Astrophysics Data System (ADS)

    Farkhooi, Farzad; Strube-Bloss, Martin F.; Nawrot, Martin P.

    2009-02-01

    The activity of spiking neurons is frequently described by renewal point process models that assume the statistical independence and identical distribution of the intervals between action potentials. However, the assumption of independent intervals must be questioned for many different types of neurons. We review experimental studies that reported the feature of a negative serial correlation of neighboring intervals, commonly observed in neurons in the sensory periphery as well as in central neurons, notably in the mammalian cortex. In our experiments we observed the same short-lived negative serial dependence of intervals in the spontaneous activity of mushroom body extrinsic neurons in the honeybee. To model serial interval correlations of arbitrary lags, we suggest a family of autoregressive point processes. Its marginal interval distribution is described by the generalized gamma model, which includes as special cases the log-normal and gamma distributions, which have been widely used to characterize regular spiking neurons. In numeric simulations we investigated how serial correlation affects the variance of the neural spike count. We show that the experimentally confirmed negative correlation reduces single-neuron variability, as quantified by the Fano factor, by up to 50%, which favors the transmission of a rate code. We argue that the feature of a negative serial correlation is likely to be common to the class of spike-frequency-adapting neurons and that it might have been largely overlooked in extracellular single-unit recordings due to spike sorting errors.

  6. Serial correlation in neural spike trains: experimental evidence, stochastic modeling, and single neuron variability.

    PubMed

    Farkhooi, Farzad; Strube-Bloss, Martin F; Nawrot, Martin P

    2009-02-01

    The activity of spiking neurons is frequently described by renewal point process models that assume the statistical independence and identical distribution of the intervals between action potentials. However, the assumption of independent intervals must be questioned for many different types of neurons. We review experimental studies that reported the feature of a negative serial correlation of neighboring intervals, commonly observed in neurons in the sensory periphery as well as in central neurons, notably in the mammalian cortex. In our experiments we observed the same short-lived negative serial dependence of intervals in the spontaneous activity of mushroom body extrinsic neurons in the honeybee. To model serial interval correlations of arbitrary lags, we suggest a family of autoregressive point processes. Its marginal interval distribution is described by the generalized gamma model, which includes as special cases the log-normal and gamma distributions, which have been widely used to characterize regular spiking neurons. In numeric simulations we investigated how serial correlation affects the variance of the neural spike count. We show that the experimentally confirmed negative correlation reduces single-neuron variability, as quantified by the Fano factor, by up to 50%, which favors the transmission of a rate code. We argue that the feature of a negative serial correlation is likely to be common to the class of spike-frequency-adapting neurons and that it might have been largely overlooked in extracellular single-unit recordings due to spike sorting errors. PMID:19391776

  7. Optimizing neuronal differentiation from induced pluripotent stem cells to model ASD

    PubMed Central

    Kim, Dae-Sung; Ross, P. Joel; Zaslavsky, Kirill; Ellis, James

    2014-01-01

    Autism spectrum disorder (ASD) is an early-onset neurodevelopmental disorder characterized by deficits in social communication, and restricted and repetitive patterns of behavior. Despite its high prevalence, discovery of pathophysiological mechanisms underlying ASD has lagged due to a lack of appropriate model systems. Recent advances in induced pluripotent stem cell (iPSC) technology and neural differentiation techniques allow for detailed functional analyses of neurons generated from living individuals with ASD. Refinement of cortical neuron differentiation methods from iPSCs will enable mechanistic studies of specific neuronal subpopulations that may be preferentially impaired in ASD. In this review, we summarize recent accomplishments in differentiation of cortical neurons from human pluripotent stems cells and efforts to establish in vitro model systems to study ASD using personalized neurons. PMID:24782713

  8. Building a Large-Scale Computational Model of a Cortical Neuronal Network

    NASA Astrophysics Data System (ADS)

    Zemanová, Lucia; Zhou, Changsong; Kurths, Jürgen

    We introduce the general framework of the large-scale neuronal model used in the 5th Helmholtz Summer School — Complex Brain Networks. The main aim is to build a universal large-scale model of a cortical neuronal network, structured as a network of networks, which is flexible enough to implement different kinds of topology and neuronal models and which exhibits behavior in various dynamical regimes. First, we describe important biological aspects of brain topology and use them in the construction of a large-scale cortical network. Second, the general dynamical model is presented together with explanations of the major dynamical properties of neurons. Finally, we discuss the implementation of the model into parallel code and its possible modifications and improvements.

  9. Reliability of neuronal information conveyed by unreliable neuristor-based leaky integrate-and-fire neurons: a model study

    PubMed Central

    Lim, Hyungkwang; Kornijcuk, Vladimir; Seok, Jun Yeong; Kim, Seong Keun; Kim, Inho; Hwang, Cheol Seong; Jeong, Doo Seok

    2015-01-01

    We conducted simulations on the neuronal behavior of neuristor-based leaky integrate-and-fire (NLIF) neurons. The phase-plane analysis on the NLIF neuron highlights its spiking dynamics – determined by two nullclines conditional on the variables on the plane. Particular emphasis was placed on the operational noise arising from the variability of the threshold switching behavior in the neuron on each switching event. As a consequence, we found that the NLIF neuron exhibits a Poisson-like noise in spiking, delimiting the reliability of the information conveyed by individual NLIF neurons. To highlight neuronal information coding at a higher level, a population of noisy NLIF neurons was analyzed in regard to probability of successful information decoding given the Poisson-like noise of each neuron. The result demonstrates highly probable success in decoding in spite of large variability – due to the variability of the threshold switching behavior – of individual neurons. PMID:25966658

  10. Nonlinear Modeling of Dynamic Interactions within Neuronal Ensembles using Principal Dynamic Modes

    PubMed Central

    Marmarelis, V. Z.; Shin, D. C.; Song, D.; Hampson, R. E.; Deadwyler, S.; Berger, T. W.

    2012-01-01

    A methodology for nonlinear modeling of multi-input multi-output (MIMO) neuronal systems is presented that utilizes the concept of Principal Dynamic Modes (PDM). The efficacy of this new methodology is demonstrated in the study of the dynamic interactions between neuronal ensembles in the Pre-Frontal Cortex (PFC) of a behaving non-human primate (NHP) performing a Delayed Match-to-Sample task. Recorded spike trains from Layer-2 and Layer-5 neurons were viewed as the “inputs” and “outputs”, respectively, of a putative MIMO system/model that quantifies the dynamic transformation of multi-unit neuronal activity between Layer-2 and Layer-5 of the PFC. Model prediction performance was evaluated by means of computed Receiver Operating Characteristic (ROC) curves. The PDM-based approach seeks to reduce the complexity of MIMO models of neuronal ensembles in order to enable the practicable modeling of large-scale neural systems incorporating hundreds or thousands of neurons, which is emerging as a preeminent issue in the study of neural function. The “scaling-up” issue has attained critical importance as multi-electrode recordings are increasingly used to probe neural systems and advance our understanding of integrated neural function. The initial results indicate that the PDM-based modeling methodology may greatly reduce the complexity of the MIMO model without significant degradation of performance. Furthermore, the PDM-based approach offers the prospect of improved biological/physiological interpretation of the obtained MIMO models. PMID:23011343

  11. Channel-noise-induced stochastic facilitation in an auditory brainstem neuron model

    NASA Astrophysics Data System (ADS)

    Schmerl, Brett A.; McDonnell, Mark D.

    2013-11-01

    Neuronal membrane potentials fluctuate stochastically due to conductance changes caused by random transitions between the open and closed states of ion channels. Although it has previously been shown that channel noise can nontrivially affect neuronal dynamics, it is unknown whether ion-channel noise is strong enough to act as a noise source for hypothesized noise-enhanced information processing in real neuronal systems, i.e., “stochastic facilitation”. Here we demonstrate that biophysical models of channel noise can give rise to two kinds of recently discovered stochastic facilitation effects in a Hodgkin-Huxley-like model of auditory brainstem neurons. The first, known as slope-based stochastic resonance (SBSR), enables phasic neurons to emit action potentials that can encode the slope of inputs that vary slowly relative to key time constants in the model. The second, known as inverse stochastic resonance (ISR), occurs in tonically firing neurons when small levels of noise inhibit tonic firing and replace it with burstlike dynamics. Consistent with previous work, we conclude that channel noise can provide significant variability in firing dynamics, even for large numbers of channels. Moreover, our results show that possible associated computational benefits may occur due to channel noise in neurons of the auditory brainstem. This holds whether the firing dynamics in the model are phasic (SBSR can occur due to channel noise) or tonic (ISR can occur due to channel noise).

  12. Motor neuron pathology and behavioral alterations at late stages in a SMA mouse model.

    PubMed

    Fulceri, Federica; Bartalucci, Alessia; Paparelli, Silvio; Pasquali, Livia; Biagioni, Francesca; Ferrucci, Michela; Ruffoli, Riccardo; Fornai, Francesco

    2012-03-01

    Spinal muscular atrophy (SMA) is a neurogenetic autosomal recessive disorder characterized by degeneration of lower motor neurons. The validation of appropriate animal models is key in fostering SMA research. Recent studies set up an animal model showing long survival and slow disease progression. This model is knocked out for mouse SMN (Smn(-/-)) gene and carries a human mutation of the SMN1 gene (SMN1A2G), along with human SMN2 gene. In the present study we used this knock out double transgenic mouse model (SMN2(+/+); Smn(-/-); SMN1A2G(+/-)) to characterize the spinal cord pathology along with motor deficit at prolonged survival times. In particular, motor neuron loss was established stereologically (44.77%) after motor deficit reached a steady state. At this stage, spared motor neurons showed significant cell body enlargement. Moreover, similar to what was described in patients affected by SMA we found neuronal heterotopy (almost 4% of total motor neurons) in the anterior white matter. The delayed disease progression was likely to maintain fair motor activity despite a dramatic loss of large motor neurons. This provides a wonderful tool to probe novel drugs finely tuning the survival of motor neurons. In fact, small therapeutic effects protracted over considerable time intervals (even more than a year) are expected to be magnified. PMID:22306031

  13. [Hardware Implementation of Numerical Simulation Function of Hodgkin-Huxley Model Neurons Action Potential Based on Field Programmable Gate Array].

    PubMed

    Wang, Jinlong; Lu, Mai; Hu, Yanwen; Chen, Xiaoqiang; Pan, Qiangqiang

    2015-12-01

    Neuron is the basic unit of the biological neural system. The Hodgkin-Huxley (HH) model is one of the most realistic neuron models on the electrophysiological characteristic description of neuron. Hardware implementation of neuron could provide new research ideas to clinical treatment of spinal cord injury, bionics and artificial intelligence. Based on the HH model neuron and the DSP Builder technology, in the present study, a single HH model neuron hardware implementation was completed in Field Programmable Gate Array (FPGA). The neuron implemented in FPGA was stimulated by different types of current, the action potential response characteristics were analyzed, and the correlation coefficient between numerical simulation result and hardware implementation result were calculated. The results showed that neuronal action potential response of FPGA was highly consistent with numerical simulation result. This work lays the foundation for hardware implementation of neural network. PMID:27079105

  14. Modeling Dravet syndrome using induced pluripotent stem cells (iPSCs) and directly converted neurons.

    PubMed

    Jiao, Jiao; Yang, Yuanyuan; Shi, Yiwu; Chen, Jiayu; Gao, Rui; Fan, Yong; Yao, Hui; Liao, Weiping; Sun, Xiao-Fang; Gao, Shaorong

    2013-11-01

    Severe myoclonic epilepsy of infancy (SMEI, also known as Dravet syndrome) and genetic epilepsy with febrile seizures plus (mild febrile seizures) can both arise due to mutations of SCN1A, the gene encoding alpha 1 pore-forming subunit of the Nav1.1 voltage-gated sodium channel. Owing to the inaccessibility of patient brain neurons, the precise mechanism of mild febrile seizures and SMEI remains elusive, and there is no effective pharmacotherapy. Induced pluripotent stem cells (iPSCs) and induced neurons (iNs) have been successfully generated from patients and applied for modeling various neuronal diseases. In this study, we established iPSC lines from one SMEI patient and one mild febrile seizures patient, respectively. Functional glutamatergic neurons were subsequently differentiated from these iPSCs. Electrophysiological analysis of patient iPSC-derived glutamatergic neurons revealed a hyperexcitable state of enlarged and persistent sodium channel activation, more intensive evoked action potentials and typical epileptic spontaneous action potentials. In consistent with the severity of the symptoms, the hyperexcitability of the neurons derived from SMEI patient was more serious than that of mild febrile seizures patient. Furthermore, the hyperexcitability of the neurons can be alleviated by treatment with phenytoin, a conventional antiepileptic drug. In parallel, iNs were directly converted from patient fibroblasts which also showed a delayed inactivation of sodium channels. Our results demonstrate that both iPSC-derived neurons and iNs from mild febrile seizures and SMEI patients exhibited a hyperexcitable state. More importantly, patient iPSC-derived neurons can recapitulate the neuronal pathophysiology and respond to drug treatment, indicating that these neurons can be potentially used for screening appropriate drugs for personalized therapies. PMID:23773995

  15. Exogenous erythropoietin provides neuroprotection of grafted dopamine neurons in a rodent model of Parkinson's disease.

    PubMed

    Kanaan, Nicholas M; Collier, Timothy J; Marchionini, Deanna M; McGuire, Susan O; Fleming, Matthew F; Sortwell, Caryl E

    2006-01-12

    Parkinson's disease (PD) is a neurodegenerative disease marked by severe loss of dopamine (DA) neurons in the nigrostriatal system, which results in depletion of striatal DA. Transplantation of embryonic ventral mesencephalic (VM) DA neurons into the striatum is a currently explored experimental treatment aimed at replacing lost DA in the nigrostriatal system, but is plagued with poor survival (5-20%) of implanted neurons. Here, we tested the ability of erythropoietin (Epo) to provide neuroprotection for embryonic day 14 (E14) VM DA neurons. Epo was tested in vitro for the ability to augment tyrosine hydroxylase-immunoreactive (TH-ir) neuron survival under normal cell culture conditions. In vitro, Epo did not increase the number of TH-ir neurons when administered at the time of plating the E14 VM cells in culture. We also tested the efficacy of Epo to enhance E14 VM transplants in vivo. Rats unilaterally lesioned with 6-hydroxydopamine received transplants that were incubated in Epo. Treatment with Epo produced significant increases in TH-ir neuron number, soma size, and staining intensity. Animals receiving Epo-treated grafts exhibited significantly accelerated functional improvements and significantly greater overall improvements from rotational asymmetry compared to control grafted rats. These data indicate that the survival of embryonic mesencephalic TH-ir neurons is increased when Epo is administered with grafted cells in a rodent model of PD. As direct neurotrophic effects of Epo were not observed in vitro, the mechanism of Epo neuroprotection remains to be elucidated. PMID:16368081

  16. An optogenetic mouse model of rett syndrome targeting on catecholaminergic neurons.

    PubMed

    Zhang, Shuang; Johnson, Christopher M; Cui, Ningren; Xing, Hao; Zhong, Weiwei; Wu, Yang; Jiang, Chun

    2016-10-01

    Rett syndrome (RTT) is a neurodevelopmental disorder affecting multiple functions, including the norepinephrine (NE) system. In the CNS, NE is produced mostly by neurons in the locus coeruleus (LC), where defects in intrinsic neuronal properties, NE biosynthetic enzymes, neuronal CO2 sensitivity, and synaptic currents have been reported in mouse models of RTT. LC neurons in methyl-CpG-binding protein 2 gene (Mecp2) null mice show a high rate of spontaneous firing, although whether such hyperexcitability might increase or decrease the NE release from synapses is unknown. To activate the NEergic axonal terminals selectively, we generated an optogenetic mouse model of RTT in which NEergic neuronal excitability can be manipulated with light. Using commercially available mouse breeders, we produced a new strain of double-transgenic mice with Mecp2 knockout and channelrhodopsin (ChR) knockin in catecholaminergic neurons. Several RTT-like phenotypes were found in the tyrosine hydroxylase (TH)-ChR-Mecp2(-/Y) mice, including hypoactivity, low body weight, hindlimb clasping, and breathing disorders. In brain slices, optostimulation produced depolarization and an increase in the firing rate of LC neurons from TH-ChR control mice. In TH-ChR control mice, optostimulation of presynaptic NEergic neurons augmented the firing rate of hypoglossal neurons (HNs), which was blocked by the α-adrenoceptor antagonist phentolamine. Such optostimulation of NEergic terminals had almost no effect on HNs from two or three TH-ChR-Mecp2(-/Y) mice, indicating that excessive excitation of presynaptic neurons does not benefit NEergic modulation in mice with Mecp2 disruption. These results also demonstrate the feasibility of generating double-transgenic mice for studies of RTT with commercially available mice, which are inexpensive, labor/time efficient, and promising for cell-specific stimulation. © 2016 Wiley Periodicals, Inc. PMID:27317352

  17. Zinc Deficiency Induces Apoptosis via Mitochondrial p53- and Caspase-Dependent Pathways in Human Neuronal Precursor Cells

    ERIC Educational Resources Information Center

    Seth, Rohit; Corniola, Rikki S.; Gower-Winter, Shannon D.; Morgan, Thomas J., Jr.; Bishop, Brian; Levenson, Cathy W.

    2015-01-01

    Previous studies have shown that zinc deficiency leads to apoptosis of neuronal precursor cells in vivo and in vitro. In addition to the role of p53 as a nuclear transcription factor in zinc deficient cultured human neuronal precursors (NT-2), we have now identified the translocation of phosphorylated p53 to the mitochondria and p53-dependent…

  18. Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons

    PubMed Central

    Buesing, Lars; Bill, Johannes; Nessler, Bernhard; Maass, Wolfgang

    2011-01-01

    The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons. PMID:22096452

  19. Hippocampal adaptive response following extensive neuronal loss in an inducible transgenic mouse model.

    PubMed

    Myczek, Kristoffer; Yeung, Stephen T; Castello, Nicholas; Baglietto-Vargas, David; LaFerla, Frank M

    2014-01-01

    Neuronal loss is a common component of a variety of neurodegenerative disorders (including Alzheimer's, Parkinson's, and Huntington's disease) and brain traumas (stroke, epilepsy, and traumatic brain injury). One brain region that commonly exhibits neuronal loss in several neurodegenerative disorders is the hippocampus, an area of the brain critical for the formation and retrieval of memories. Long-lasting and sometimes unrecoverable deficits caused by neuronal loss present a unique challenge for clinicians and for researchers who attempt to model these traumas in animals. Can these deficits be recovered, and if so, is the brain capable of regeneration following neuronal loss? To address this significant question, we utilized the innovative CaM/Tet-DT(A) mouse model that selectively induces neuronal ablation. We found that we are able to inflict a consistent and significant lesion to the hippocampus, resulting in hippocampally-dependent behavioral deficits and a long-lasting upregulation in neurogenesis, suggesting that this process might be a critical part of hippocampal recovery. In addition, we provide novel evidence of angiogenic and vasculature changes following hippocampal neuronal loss in CaM/Tet-DTA mice. We posit that angiogenesis may be an important factor that promotes neurogenic upregulation following hippocampal neuronal loss, and both factors, angiogenesis and neurogenesis, can contribute to the adaptive response of the brain for behavioral recovery. PMID:25184527

  20. A model for the characterization of the spatial properties in vestibular neurons

    NASA Technical Reports Server (NTRS)

    Angelaki, D. E.; Bush, G. A.; Perachio, A. A.

    1992-01-01

    Quantitative study of the static and dynamic response properties of some otolith-sensitive neurons has been difficult in the past partly because their responses to different linear acceleration vectors exhibited no "null" plane and a dependence of phase on stimulus orientation. The theoretical formulation of the response ellipse provides a quantitative way to estimate the spatio-temporal properties of such neurons. Its semi-major axis gives the direction of the polarization vector (i.e., direction of maximal sensitivity) and it estimates the neuronal response for stimulation along that direction. In addition, the semi-minor axis of the ellipse provides an estimate of the neuron's maximal sensitivity in the "null" plane. In this paper, extracellular recordings from otolith-sensitive vestibular nuclei neurons in decerebrate rats were used to demonstrate the practical application of the method. The experimentally observed gain and phase dependence on the orientation angle of the acceleration vector in a head-horizontal plane was described and satisfactorily fit by the response ellipse model. In addition, the model satisfactorily fits neuronal responses in three-dimensions and unequivocally demonstrates that the response ellipse formulation is the general approach to describe quantitatively the spatial properties of vestibular neurons.

  1. Is the Langevin phase equation an efficient model for oscillating neurons?

    NASA Astrophysics Data System (ADS)

    Ota, Keisuke; Tsunoda, Takamasa; Omori, Toshiaki; Watanabe, Shigeo; Miyakawa, Hiroyoshi; Okada, Masato; Aonishi, Toru

    2009-12-01

    The Langevin phase model is an important canonical model for capturing coherent oscillations of neural populations. However, little attention has been given to verifying its applicability. In this paper, we demonstrate that the Langevin phase equation is an efficient model for neural oscillators by using the machine learning method in two steps: (a) Learning of the Langevin phase model. We estimated the parameters of the Langevin phase equation, i.e., a phase response curve and the intensity of white noise from physiological data measured in the hippocampal CA1 pyramidal neurons. (b) Test of the estimated model. We verified whether a Fokker-Planck equation derived from the Langevin phase equation with the estimated parameters could capture the stochastic oscillatory behavior of the same neurons disturbed by periodic perturbations. The estimated model could predict the neural behavior, so we can say that the Langevin phase equation is an efficient model for oscillating neurons.

  2. 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

  3. Modelling Feedback Excitation, Pacemaker Properties and Sensory Switching of Electrically Coupled Brainstem Neurons Controlling Rhythmic Activity.

    PubMed

    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

  4. Frequent Release of Low Amounts of Herpes Simplex Virus From Neurons: Results of a Mathematical Model

    PubMed Central

    Schiffer, Joshua T.; Abu-Raddad, Laith; Mark, Karen E.; Zhu, Jia; Selke, Stacy; Magaret, Amalia; Wald, Anna; Corey, Lawrence

    2010-01-01

    Herpes Simplex Virus-2 (HSV-2) is a sexually transmitted infection that is the leading cause of genital ulcers worldwide. Infection is life long and is characterized by repeated asymptomatic and symptomatic shedding episodes of virus that are initiated when virus is released from neurons into the genital tract. The pattern of HSV-2 release from neurons into the genital tract is poorly understood. We fit a mathematical model of HSV-2 pathogenesis to curves generated from daily quantification of HSV in mucosal swabs performed from patients with herpetic genital ulcers. We used virologic parameters derived from model fitting for stochastic model simulations. These simulations reproduced previously documented estimates for shedding frequency, and herpetic lesion diameter and frequency. The most realistic model output occurred when we assumed minimal amounts of daily neuronal virus introduction. In our simulations, small changes in average total quantity of HSV-2 released from neurons influenced detectable shedding frequency, while changes in frequency of neuronal HSV-2 release had little effect. Frequent HSV-2 shedding episodes in humans are explained by nearly constant release of small numbers of viruses from neurons that terminate in the genital tract. PMID:20161655

  5. Motor neuron degeneration in a mouse model of seipinopathy

    PubMed Central

    Guo, J; Qiu, W; Soh, S L Y; Wei, S; Radda, G K; Ong, W-Y; Pang, Z P; Han, W

    2013-01-01

    Heterozygosity for missense mutations (N88S/S90L) in BSCL2 (Berardinelli–Seip congenital lipodystrophy type 2)/Seipin is associated with a broad spectrum of motoneuron diseases. To understand the underlying mechanisms how the mutations lead to motor neuropathy, we generated transgenic mice with neuron-specific expression of wild-type (tgWT) or N88S/S90L mutant (tgMT) human Seipin. Transgenes led to the broad expression of WT or mutant Seipin in the brain and spinal cord. TgMT, but not tgWT, mice exhibited late-onset altered locomotor activities and gait abnormalities that recapitulate symptoms of seipinopathy patients. We found loss of alpha motor neurons in tgMT spinal cord. Mild endoreticular stress was present in both tgMT and tgWT neurons; however, only tgMT mice exhibited protein aggregates and disrupted Golgi apparatus. Furthermore, autophagosomes were significantly increased, along with elevated light chain 3 (LC3)-II level in tgMT spinal cord, consistent with the activation of autophagy pathway in response to mutant Seipin expression and protein aggregation. These results suggest that induction of autophagy pathway is involved in the cellular response to mutant Seipin in seipinopathy and that motoneuron loss is a key pathogenic process underlying the development of locomotor abnormalities. PMID:23470542

  6. Motor neuron degeneration in a mouse model of seipinopathy.

    PubMed

    Guo, J; Qiu, W; Soh, S L Y; Wei, S; Radda, G K; Ong, W-Y; Pang, Z P; Han, W

    2013-01-01

    Heterozygosity for missense mutations (N88S/S90L) in BSCL2 (Berardinelli-Seip congenital lipodystrophy type 2)/Seipin is associated with a broad spectrum of motoneuron diseases. To understand the underlying mechanisms how the mutations lead to motor neuropathy, we generated transgenic mice with neuron-specific expression of wild-type (tgWT) or N88S/S90L mutant (tgMT) human Seipin. Transgenes led to the broad expression of WT or mutant Seipin in the brain and spinal cord. TgMT, but not tgWT, mice exhibited late-onset altered locomotor activities and gait abnormalities that recapitulate symptoms of seipinopathy patients. We found loss of alpha motor neurons in tgMT spinal cord. Mild endoreticular stress was present in both tgMT and tgWT neurons; however, only tgMT mice exhibited protein aggregates and disrupted Golgi apparatus. Furthermore, autophagosomes were significantly increased, along with elevated light chain 3 (LC3)-II level in tgMT spinal cord, consistent with the activation of autophagy pathway in response to mutant Seipin expression and protein aggregation. These results suggest that induction of autophagy pathway is involved in the cellular response to mutant Seipin in seipinopathy and that motoneuron loss is a key pathogenic process underlying the development of locomotor abnormalities. PMID:23470542

  7. Using Strahler's analysis to reduce up to 200-fold the run time of realistic neuron models

    NASA Astrophysics Data System (ADS)

    Marasco, Addolorata; Limongiello, Alessandro; Migliore, Michele

    2013-10-01

    The cellular mechanisms underlying higher brain functions/dysfunctions are extremely difficult to investigate experimentally, and detailed neuron models have proven to be a very useful tool to help these kind of investigations. However, realistic neuronal networks of sizes appropriate to study brain functions present the major problem of requiring a prohibitively high computational resources. Here, building on our previous work, we present a general reduction method based on Strahler's analysis of neuron morphologies. We show that, without any fitting or tuning procedures, it is possible to map any morphologically and biophysically accurate neuron model into an equivalent reduced version. Using this method for Purkinje cells, we demonstrate how run times can be reduced up to 200-fold, while accurately taking into account the effects of arbitrarily located and activated synaptic inputs.

  8. From “Directed Differentiation” to “Neuronal Induction”: Modeling Neuropsychiatric Disease

    PubMed Central

    Ho, Seok-Man; Topol, Aaron; Brennand, Kristen J

    2015-01-01

    Aberrant behavior and function of neurons are believed to be the primary causes of most neurological diseases and psychiatric disorders. Human postmortem samples have limited availability and, while they provide clues to the state of the brain after a prolonged illness, they offer limited insight into the factors contributing to disease onset. Conversely, animal models cannot recapitulate the polygenic origins of neuropsychiatric disease. Novel methods, such as somatic cell reprogramming, deliver nearly limitless numbers of pathogenic human neurons for the study of the mechanism of neuropsychiatric disease initiation and progression. First, this article reviews the advent of human induced pluripotent stem cell (hiPSC) technology and introduces two major methods, “directed differentiation” and “neuronal induction,” by which it is now possible to generate neurons for modeling neuropsychiatric disease. Second, it discusses the recent applications, and the limitations, of these technologies to in vitro studies of psychiatric disorders. PMID:26045654

  9. Kilohertz Frequency Deep Brain Stimulation Is Ineffective at Regularizing the Firing of Model Thalamic Neurons

    PubMed Central

    Couto, João; Grill, Warren M.

    2016-01-01

    Deep brain stimulation (DBS) is an established therapy for movement disorders, including tremor, dystonia, and Parkinson's disease, but the mechanisms of action are not well understood. Symptom suppression by DBS typically requires stimulation frequencies ≥100 Hz, but when the frequency is increased above ~2 kHz, the effectiveness in tremor suppression declines (Benabid et al., 1991). We sought to test the hypothesis that the decline in efficacy at high frequencies is associated with desynchronization of the activity generated within a population of stimulated neurons. Regularization of neuronal firing is strongly correlated with tremor suppression by DBS, and desynchronization would disrupt the regularization of neuronal activity. We implemented computational models of CNS axons with either deterministic or stochastic membrane dynamics, and quantified the response of populations of model nerve fibers to extracellular stimulation at different frequencies and amplitudes. As stimulation frequency was increased from 2 to 80 Hz the regularity of neuronal firing increased (as assessed with direct estimates of entropy), in accord with the clinical effects on tremor of increasing stimulation frequency (Kuncel et al., 2006). Further, at frequencies between 80 and 500 Hz, increasing the stimulation amplitude (i.e., the proportion of neurons activated by the stimulus) increased the regularity of neuronal activity across the population, in accord with the clinical effects on tremor of stimulation amplitude (Kuncel et al., 2007). However, at stimulation frequencies above 1 kHz the regularity of neuronal firing declined due to irregular patterns of action potential generation and conduction block. The reductions in neuronal regularity that occurred at high frequencies paralleled the previously reported decline in tremor reduction and may be responsible for the loss of efficacy of DBS at very high frequencies. This analysis provides further support for the hypothesis that

  10. Two-population model for medial temporal lobe neurons: The vast majority are almost silent

    NASA Astrophysics Data System (ADS)

    Magyar, Andrew; Collins, John

    2015-07-01

    Recordings in the human medial temporal lobe have found many neurons that respond to pictures (and related stimuli) of just one particular person of those presented. It has been proposed that these are concept cells, responding to just a single concept. However, a direct experimental test of the concept cell idea appears impossible, because it would need the measurement of the response of each cell to enormous numbers of other stimuli. Here we propose a new statistical method for analysis of the data that gives a more powerful way to analyze how close data are to the concept-cell idea. Central to the model is the neuronal sparsity, defined as the total fraction of stimuli that elicit an above-threshold response in the neuron. The model exploits the large number of sampled neurons to give sensitivity to situations where the average response sparsity is much less than one response for the number of presented stimuli. We show that a conventional model where a single sparsity is postulated for all neurons gives an extremely poor fit to the data. In contrast, a model with two dramatically different populations gives an excellent fit to data from the hippocampus and entorhinal cortex. In the hippocampus, one population has 7% of the cells with a 2.6% sparsity. But a much larger fraction (93%) respond to only 0.1% of the stimuli. This can result in an extreme bias in the responsiveness of reported neurons compared with a typical neuron. Finally, we show how to allow for the fact that some identified units correspond to multiple neurons and find that our conclusions at the neural level are quantitatively changed but strengthened, with an even stronger difference between the two populations.

  11. A flexible, interactive software tool for fitting the parameters of neuronal models.

    PubMed

    Friedrich, Péter; Vella, Michael; Gulyás, Attila I; Freund, Tamás F; Káli, Szabolcs

    2014-01-01

    The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool. PMID

  12. A flexible, interactive software tool for fitting the parameters of neuronal models

    PubMed Central

    Friedrich, Péter; Vella, Michael; Gulyás, Attila I.; Freund, Tamás F.; Káli, Szabolcs

    2014-01-01

    The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool. PMID

  13. Physiological characterization of vestibular efferent brainstem neurons using a transgenic mouse model.

    PubMed

    Leijon, Sara; Magnusson, Anna K

    2014-01-01

    The functional role of efferent innervation of the vestibular end-organs in the inner ear remains elusive. This study provides the first physiological characterization of the cholinergic vestibular efferent (VE) neurons in the brainstem by utilizing a transgenic mouse model, expressing eGFP under a choline-acetyltransferase (ChAT)-locus spanning promoter in combination with targeted patch clamp recordings. The intrinsic electrical properties of the eGFP-positive VE neurons were compared to the properties of the lateral olivocochlear (LOC) brainstem neurons, which gives rise to efferent innervation of the cochlea. Both VE and the LOC neurons were marked by their negative resting membrane potential <-75 mV and their passive responses in the hyperpolarizing range. In contrast, the response properties of VE and LOC neurons differed significantly in the depolarizing range. When injected with positive currents, VE neurons fired action potentials faithfully to the onset of depolarization followed by sparse firing with long inter-spike intervals. This response gave rise to a low response gain. The LOC neurons, conversely, responded with a characteristic delayed tonic firing upon depolarizing stimuli, giving rise to higher response gain than the VE neurons. Depolarization triggered large TEA insensitive outward currents with fast inactivation kinetics, indicating A-type potassium currents, in both the inner ear-projecting neuronal types. Immunohistochemistry confirmed expression of Kv4.3 and 4.2 ion channel subunits in both the VE and LOC neurons. The difference in spiking responses to depolarization is related to a two-fold impact of these transient outward currents on somatic integration in the LOC neurons compared to in VE neurons. It is speculated that the physiological properties of the VE neurons might be compatible with a wide-spread control over motion and gravity sensation in the inner ear, providing likewise feed-back amplification of abrupt and strong phasic

  14. Reduced motor neuron excitability is an important contributor to weakness in a rat model of sepsis.

    PubMed

    Nardelli, Paul; Vincent, Jacob A; Powers, Randall; Cope, Tim C; Rich, Mark M

    2016-08-01

    The mechanisms by which sepsis triggers intensive care unit acquired weakness (ICUAW) remain unclear. We previously identified difficulty with motor unit recruitment in patients as a novel contributor to ICUAW. To study the mechanism underlying poor recruitment of motor units we used the rat cecal ligation and puncture model of sepsis. We identified striking dysfunction of alpha motor neurons during repetitive firing. Firing was more erratic, and often intermittent. Our data raised the possibility that reduced excitability of motor neurons was a significant contributor to weakness induced by sepsis. In this study we quantified the contribution of reduced motor neuron excitability and compared its magnitude to the contributions of myopathy, neuropathy and failure of neuromuscular transmission. We injected constant depolarizing current pulses (5s) into the soma of alpha motor neurons in the lumbosacral spinal cord of anesthetized rats to trigger repetitive firing. In response to constant depolarization, motor neurons in untreated control rats fired at steady and continuous firing rates and generated smooth and sustained tetanic motor unit force as expected. In contrast, following induction of sepsis, motor neurons were often unable to sustain firing throughout the 5s current injection such that force production was reduced. Even when firing, motor neurons from septic rats fired erratically and discontinuously, leading to irregular production of motor unit force. Both fast and slow type motor neurons had similar disruption of excitability. We followed rats after recovery from sepsis to determine the time course of resolution of the defect in motor neuron excitability. By one week, rats appeared to have recovered from sepsis as they had no piloerection and appeared to be in no distress. The defects in motor neuron repetitive firing were still striking at 2weeks and, although improved, were present at one month. We infer that rats suffered from weakness due to reduced

  15. Thalamic neuron models encode stimulus information by burst-size modulation

    PubMed Central

    Elijah, Daniel H.; Samengo, Inés; Montemurro, Marcelo A.

    2015-01-01

    Thalamic neurons have been long assumed to fire in tonic mode during perceptive states, and in burst mode during sleep and unconsciousness. However, recent evidence suggests that bursts may also be relevant in the encoding of sensory information. Here, we explore the neural code of such thalamic bursts. In order to assess whether the burst code is generic or whether it depends on the detailed properties of each bursting neuron, we analyzed two neuron models incorporating different levels of biological detail. One of the models contained no information of the biophysical processes entailed in spike generation, and described neuron activity at a phenomenological level. The second model represented the evolution of the individual ionic conductances involved in spiking and bursting, and required a large number of parameters. We analyzed the models' input selectivity using reverse correlation methods and information theory. We found that n-spike bursts from both models transmit information by modulating their spike count in response to changes to instantaneous input features, such as slope, phase, amplitude, etc. The stimulus feature that is most efficiently encoded by bursts, however, need not coincide with one of such classical features. We therefore searched for the optimal feature among all those that could be expressed as a linear transformation of the time-dependent input current. We found that bursting neurons transmitted 6 times more information about such more general features. The relevant events in the stimulus were located in a time window spanning ~100 ms before and ~20 ms after burst onset. Most importantly, the neural code employed by the simple and the biologically realistic models was largely the same, implying that the simple thalamic neuron model contains the essential ingredients that account for the computational properties of the thalamic burst code. Thus, our results suggest the n-spike burst code is a general property of thalamic neurons. PMID

  16. Thalamic neuron models encode stimulus information by burst-size modulation.

    PubMed

    Elijah, Daniel H; Samengo, Inés; Montemurro, Marcelo A

    2015-01-01

    Thalamic neurons have been long assumed to fire in tonic mode during perceptive states, and in burst mode during sleep and unconsciousness. However, recent evidence suggests that bursts may also be relevant in the encoding of sensory information. Here, we explore the neural code of such thalamic bursts. In order to assess whether the burst code is generic or whether it depends on the detailed properties of each bursting neuron, we analyzed two neuron models incorporating different levels of biological detail. One of the models contained no information of the biophysical processes entailed in spike generation, and described neuron activity at a phenomenological level. The second model represented the evolution of the individual ionic conductances involved in spiking and bursting, and required a large number of parameters. We analyzed the models' input selectivity using reverse correlation methods and information theory. We found that n-spike bursts from both models transmit information by modulating their spike count in response to changes to instantaneous input features, such as slope, phase, amplitude, etc. The stimulus feature that is most efficiently encoded by bursts, however, need not coincide with one of such classical features. We therefore searched for the optimal feature among all those that could be expressed as a linear transformation of the time-dependent input current. We found that bursting neurons transmitted 6 times more information about such more general features. The relevant events in the stimulus were located in a time window spanning ~100 ms before and ~20 ms after burst onset. Most importantly, the neural code employed by the simple and the biologically realistic models was largely the same, implying that the simple thalamic neuron model contains the essential ingredients that account for the computational properties of the thalamic burst code. Thus, our results suggest the n-spike burst code is a general property of thalamic neurons. PMID

  17. A generalized Leaky Integrate-and-Fire neuron model with fast implementation method.

    PubMed

    Wang, Zhenzhong; Guo, Lilin; Adjouadi, Malek

    2014-08-01

    This study introduces a new Generalized Leaky Integrate-and-Fire (GLIF) neuron model with variable leaking resistor and bias current in order to reproduce accurately the membrane voltage dynamics of a biological neuron. The accuracy of this model is ensured by adjusting its parameters to the statistical properties of the Hodgkin-Huxley model outputs; while the speed is enhanced by introducing a Generalized Exponential Moving Average method that converts the parameterized kernel functions into pre-calculated lookup tables based on an analytic solution of the dynamic equations of the GLIF model. PMID:24875788

  18. Modelling the firing pattern of bullfrog vestibular neurons responding to naturalistic stimuli

    NASA Technical Reports Server (NTRS)

    Paulin, M. G.; Hoffman, L. F.

    1999-01-01

    We have developed a neural system identification method for fitting models to stimulus-response data, where the response is a spike train. The method involves using a general nonlinear optimisation procedure to fit models in the time domain. We have applied the method to model bullfrog semicircular canal afferent neuron responses during naturalistic, broad-band head rotations. These neurons respond in diverse ways, but a simple four parameter class of models elegantly accounts for the various types of responses observed. c1999 Elsevier Science B.V. All rights reserved.

  19. 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

  20. Computational Modeling of Seizure Dynamics Using Coupled Neuronal Networks: Factors Shaping Epileptiform Activity

    PubMed Central

    Naze, Sebastien; Bernard, Christophe; Jirsa, Viktor

    2015-01-01

    Epileptic seizure dynamics span multiple scales in space and time. Understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. Mathematical models have been developed to reproduce seizure dynamics across scales ranging from the single neuron to the neural population. In this study, we develop a network model of spiking neurons and systematically investigate the conditions, under which the network displays the emergent dynamic behaviors known from the Epileptor, which is a well-investigated abstract model of epileptic neural activity. This approach allows us to study the biophysical parameters and variables leading to epileptiform discharges at cellular and network levels. Our network model is composed of two neuronal populations, characterized by fast excitatory bursting neurons and regular spiking inhibitory neurons, embedded in a common extracellular environment represented by a slow variable. By systematically analyzing the parameter landscape offered by the simulation framework, we reproduce typical sequences of neural activity observed during status epilepticus. We find that exogenous fluctuations from extracellular environment and electro-tonic couplings play a major role in the progression of the seizure, which supports previous studies and further validates our model. We also investigate the influence of chemical synaptic coupling in the generation of spontaneous seizure-like events. Our results argue towards a temporal shift of typical spike waves with fast discharges as synaptic strengths are varied. We demonstrate that spike waves, including interictal spikes, are generated primarily by inhibitory neurons, whereas fast discharges during the wave part are due to excitatory neurons. Simulated traces are compared with in vivo experimental data from rodents at different stages of the disorder. We draw the conclusion that slow

  1. A model explaining synchronization of neuron bioelectric frequency under weak alternating low frequency magnetic field

    NASA Astrophysics Data System (ADS)

    del Moral, A.; Azanza, María J.

    2015-03-01

    A biomagnetic-electrical model is presented that explains rather well the experimentally observed synchronization of the bioelectric potential firing rate ("frequency"), f, of single unit neurons of Helix aspersa mollusc under the application of extremely low frequency (ELF) weak alternating (AC) magnetic fields (MF). The proposed model incorporates to our widely experimentally tested model of superdiamagnetism (SD) and Ca2+ Coulomb explosion (CE) from lipid (LP) bilayer membrane (SD-CE model), the electrical quadrupolar long range interaction between the bilayer LP membranes of synchronized neuron pairs, not considered before. The quadrupolar interaction is capable of explaining well the observed synchronization. Actual extension of our SD-CE-model shows that the neuron firing frequency field, B, dependence becomes not modified, but the bioelectric frequency is decreased and its spontaneous temperature, T, dependence is modified. A comparison of the model with synchronization experimental results of pair of neurons under weak (B0 ≅0.2-15 mT) AC-MF of frequency fM=50 Hz is reported. From the deduced size of synchronized LP clusters under B, is suggested the formation of small neuron networks via the membrane lipid correlation.

  2. Neuroanatomical and Functional Characterization of CRF Neurons of the Amygdala using a Novel Transgenic Mouse Model

    PubMed Central

    De Francesco, Pablo N.; Valdivia, Spring; Cabral, Agustina; Reynaldo, Mirta; Raingo, Jesica; Sakata, Ichiro; Osborne-Lawrence, Sherri; Zigman, Jeffrey M.; Perelló, Mario

    2015-01-01

    The corticotrophin releasing factor (CRF)-producing neurons of the amygdala have been implicated in behavioral and physiological responses associated with fear, anxiety, stress, food intake and reward. To overcome the difficulties in identifying CRF neurons within the amygdala, a novel transgenic mouse line, in which the humanized Renilla reniformis green fluorescent protein (hrGFP) is under the control of the CRF promoter (CRF-hrGFP mice), was developed. First, the CRF-hrGFP mouse model was validated and the localization of CRF neurons within the amygdala was systematically mapped. Amygdalar hrGFP-expressing neurons were located primarily in the interstitial nucleus of the posterior limb of the anterior commissure, but also present in the central amygdala. Secondly, the marker of neuronal activation c-Fos was used to explore the response of amygdalar CRF neurons in CRF-hrGFP mice under different experimental paradigms. C-Fos induction was observed in CRF neurons of CRF-hrGFP mice exposed to an acute social defeat stress event, a fasting/refeeding paradigm or LPS administration. In contrast, no c-Fos induction was detected in CRF neurons of CRF-hrGFP mice exposed to restraint stress, forced swimming test, 48 h fasting, acute high fat diet (HFD) consumption, intermittent HFD consumption, ad libitum HFD consumption, HFD withdrawal, conditioned HFD aversion, ghrelin administration or melanocortin 4 receptor agonist administration. Thus, this study fully characterizes the distribution of amygdala CRF neurons in mice and suggests that they are involved in some, but not all, stress or food intake-related behaviors recruiting the amygdala. PMID:25595987

  3. Neuroanatomical and functional characterization of CRF neurons of the amygdala using a novel transgenic mouse model.

    PubMed

    De Francesco, P N; Valdivia, S; Cabral, A; Reynaldo, M; Raingo, J; Sakata, I; Osborne-Lawrence, S; Zigman, J M; Perelló, M

    2015-03-19

    The corticotropin-releasing factor (CRF)-producing neurons of the amygdala have been implicated in behavioral and physiological responses associated with fear, anxiety, stress, food intake and reward. To overcome the difficulties in identifying CRF neurons within the amygdala, a novel transgenic mouse line, in which the humanized recombinant Renilla reniformis green fluorescent protein (hrGFP) is under the control of the CRF promoter (CRF-hrGFP mice), was developed. First, the CRF-hrGFP mouse model was validated and the localization of CRF neurons within the amygdala was systematically mapped. Amygdalar hrGFP-expressing neurons were located primarily in the interstitial nucleus of the posterior limb of the anterior commissure, but also present in the central amygdala. Secondly, the marker of neuronal activation c-Fos was used to explore the response of amygdalar CRF neurons in CRF-hrGFP mice under different experimental paradigms. C-Fos induction was observed in CRF neurons of CRF-hrGFP mice exposed to an acute social defeat stress event, a fasting/refeeding paradigm or lipopolysaccharide (LPS) administration. In contrast, no c-Fos induction was detected in CRF neurons of CRF-hrGFP mice exposed to restraint stress, forced swimming test, 48-h fasting, acute high-fat diet (HFD) consumption, intermittent HFD consumption, ad libitum HFD consumption, HFD withdrawal, conditioned HFD aversion, ghrelin administration or melanocortin 4 receptor agonist administration. Thus, this study fully characterizes the distribution of amygdala CRF neurons in mice and suggests that they are involved in some, but not all, stress or food intake-related behaviors recruiting the amygdala. PMID:25595987

  4. Minocycline reduces neuroinflammation but does not ameliorate neuron loss in a mouse model of neurodegeneration

    PubMed Central

    Cheng, Shanshan; Hou, Jinxing; Zhang, Chen; Xu, Congyu; Wang, Long; Zou, Xiaoxia; Yu, Huahong; Shi, Yun; Yin, Zhenyu; Chen, Guiquan

    2015-01-01

    Minocycline is a broad-spectrum tetracycline antibiotic. A number of preclinical studies have shown that minocycline exhibits neuroprotective effects in various animal models of neurological diseases. However, it remained unknown whether minocycline is effective to prevent neuron loss. To systematically evaluate its effects, minocycline was used to treat Dicer conditional knockout (cKO) mice which display age-related neuron loss. The drug was given to mutant mice prior to the occurrence of neuroinflammation and neurodegeneration, and the treatment had lasted 2 months. Levels of inflammation markers, including glial fibrillary acidic protein (GFAP), ionized calcium-binding adapter molecule1 (Iba1) and interleukin6 (IL6), were significantly reduced in minocycline-treated Dicer cKO mice. In contrast, levels of neuronal markers and the total number of apoptotic cells in Dicer cKO mice were not affected by the drug. In summary, inhibition of neuroinflammation by minocycline is insufficient to prevent neuron loss and apoptosis. PMID:26000566

  5. Restoring the encoding properties of a stochastic neuron model by an exogenous noise

    PubMed Central

    Paffi, Alessandra; Camera, Francesca; Apollonio, Francesca; d'Inzeo, Guglielmo; Liberti, Micaela

    2015-01-01

    Here we evaluate the possibility of improving the encoding properties of an impaired neuronal system by superimposing an exogenous noise to an external electric stimulation signal. The approach is based on the use of mathematical neuron models consisting of stochastic HH-like circuit, where the impairment of the endogenous presynaptic inputs is described as a subthreshold injected current and the exogenous stimulation signal is a sinusoidal voltage perturbation across the membrane. Our results indicate that a correlated Gaussian noise, added to the sinusoidal signal can significantly increase the encoding properties of the impaired system, through the Stochastic Resonance (SR) phenomenon. These results suggest that an exogenous noise, suitably tailored, could improve the efficacy of those stimulation techniques used in neuronal systems, where the presynaptic sensory neurons are impaired and have to be artificially bypassed. PMID:25999845

  6. Modelling the acquisition of goal-directed behaviors by populations of neurons.

    PubMed

    Guigon, E; Burnod, Y

    1995-03-01

    Recent neurophysiological studies have revealed the patterns of neuronal activity during the acquisition of goal-directed behaviors, both in single cells, and in large populations of neurons. We propose a model which helps three sets of experimental results in the monkey to be understood: (1) activity of single cells vary greatly and only population activities are causally related to behavior. The model shows how a population of stochastic neurons, whose behaviors vary widely, can learn a skilled conditioned movement with only local activity-dependent synaptic changes. (2) typical changes in neuronal activity occur when the rules governing the behavior are changed, i.e. when the relationship between cues and actions to reach a goal changes over time. There are two types of neuronal patterns during changes in reward contingency: a monotonic increasing pattern and a non-monotonic pattern which follows the change in the way the reward is obtained. Units in the model display these two types of change, which correspond to synaptic modifications related to the encoding of the behavioral significance of sensory and motor events. (3) These two patterns of neuronal activity define two populations whose anatomical distributions in the frontal lobe overlap with a gradient organized in the rostro-caudal direction. The model consists of two artificial neural networks, defined by the same set of equations, but which differ in the values of two parameters (P and Q). P defines the adaptive properties of processing units and Q describes the coding of information. The model suggests that a balance in the relative strengths of these parameters distributed along a rostro-caudal gradient can explain the distribution of neuronal types in the frontal lobe of the monkey. PMID:7622407

  7. Modelling the Effects of Electrical Coupling between Unmyelinated Axons of Brainstem Neurons Controlling Rhythmic Activity.

    PubMed

    Hull, Michael J; Soffe, Stephen R; Willshaw, David J; Roberts, Alan

    2015-05-01

    Gap junctions between fine unmyelinated axons can electrically couple groups of brain neurons to synchronise firing and contribute to rhythmic activity. To explore the distribution and significance of electrical coupling, we modelled a well analysed, small population of brainstem neurons which drive swimming in young frog tadpoles. A passive network of 30 multicompartmental neurons with unmyelinated axons was used to infer that: axon-axon gap junctions close to the soma gave the best match to experimentally measured coupling coefficients; axon diameter had a strong influence on coupling; most neurons were coupled indirectly via the axons of other neurons. When active channels were added, gap junctions could make action potential propagation along the thin axons unreliable. Increased sodium and decreased potassium channel densities in the initial axon segment improved action potential propagation. Modelling suggested that the single spike firing to step current injection observed in whole-cell recordings is not a cellular property but a dynamic consequence of shunting resulting from electrical coupling. Without electrical coupling, firing of the population during depolarising current was unsynchronised; with coupling, the population showed synchronous recruitment and rhythmic firing. When activated instead by increasing levels of modelled sensory pathway input, the population without electrical coupling was recruited incrementally to unpatterned activity. However, when coupled, the population was recruited all-or-none at threshold into a rhythmic swimming pattern: the tadpole "decided" to swim. Modelling emphasises uncertainties about fine unmyelinated axon physiology but, when informed by biological data, makes general predictions about gap junctions: locations close to the soma; relatively small numbers; many indirect connections between neurons; cause of action potential propagation failure in fine axons; misleading alteration of intrinsic firing properties

  8. Changes in Aβ non-nociceptive primary sensory neurons in a rat model of osteoarthritis pain

    PubMed Central

    2010-01-01

    Background Pain is a major debilitating factor in osteoarthritis (OA), yet few mechanism-based therapies are available. To address the need to understand underlying mechanisms the aim of the present study was to determine changes in sensory neurons in an animal model of OA pain. Results The model displayed typical osteoarthritis pathology characterized by cartilage degeneration in the knee joint and also manifested knee pathophysiology (edema and increased vasculature permeability of the joint) and altered nociception of the affected limb (hind paw tenderness and knee articulation-evoked reduction in the tail flick latency). Neurons included in this report innervated regions throughout the entire hind limb. Aβ-fiber low threshold mechanoreceptors exhibited a slowing of the dynamics of action potential (AP) genesis, including wider AP duration and slower maximum rising rate, and muscle spindle neurons were the most affected subgroup. Only minor AP configuration changes were observed in either C- or Aδ-fiber nociceptors. Conclusion Thus, at one month after induction of the OA model Aβ-fiber low threshold mechanoreceptors but not C- or Aδ-fiber nociceptors had undergone changes in electrophysiological properties. If these changes reflect a change in functional role of these neurons in primary afferent sensory processing, then Aβ-fiber non-nociceptive primary sensory neurons may be involved in the pathogenesis of OA pain. Further, it is important to point out that the patterns of the changes we observed are consistent with observations in models of peripheral neuropathy but not models of peripheral inflammation. PMID:20594346

  9. Automatic Construction of Predictive Neuron Models through Large Scale Assimilation of Electrophysiological Data.

    PubMed

    Nogaret, Alain; Meliza, C Daniel; Margoliash, Daniel; Abarbanel, Henry D I

    2016-01-01

    We report on the construction of neuron models by assimilating electrophysiological data with large-scale constrained nonlinear optimization. The method implements interior point line parameter search to determine parameters from the responses to intracellular current injections of zebra finch HVC neurons. We incorporated these parameters into a nine ionic channel conductance model to obtain completed models which we then use to predict the state of the neuron under arbitrary current stimulation. Each model was validated by successfully predicting the dynamics of the membrane potential induced by 20-50 different current protocols. The dispersion of parameters extracted from different assimilation windows was studied. Differences in constraints from current protocols, stochastic variability in neuron output, and noise behave as a residual temperature which broadens the global minimum of the objective function to an ellipsoid domain whose principal axes follow an exponentially decaying distribution. The maximum likelihood expectation of extracted parameters was found to provide an excellent approximation of the global minimum and yields highly consistent kinetics for both neurons studied. Large scale assimilation absorbs the intrinsic variability of electrophysiological data over wide assimilation windows. It builds models in an automatic manner treating all data as equal quantities and requiring minimal additional insight. PMID:27605157

  10. Mechanism of bistability: Tonic spiking and bursting in a neuron model

    NASA Astrophysics Data System (ADS)

    Shilnikov, Andrey; Calabrese, Ronald L.; Cymbalyuk, Gennady

    2005-05-01

    Neurons can demonstrate various types of activity; tonically spiking, bursting as well as silent neurons are frequently observed in electrophysiological experiments. The methods of qualitative theory of slow-fast systems applied to biophysically realistic neuron models can describe basic scenarios of how these regimes of activity can be generated and transitions between them can be made. Here we demonstrate that a bifurcation of a codimension one can explain a transition between tonic spiking behavior and bursting behavior. Namely, we argue that the Lukyanov-Shilnikov bifurcation of a saddle-node periodic orbit with noncentral homoclinics may initiate a bistability observed in a model of a leech heart interneuron under defined pharmacological conditions. This model can exhibit two coexisting types of oscillations: tonic spiking and bursting, depending on the initial state of the neuron model. Moreover, the neuron model also generates weakly chaotic bursts when a control parameter is close to the bifurcation values that correspond to homoclinic bifurcations of a saddle or a saddle-node periodic orbit.

  11. Automatic Construction of Predictive Neuron Models through Large Scale Assimilation of Electrophysiological Data

    PubMed Central

    Nogaret, Alain; Meliza, C. Daniel; Margoliash, Daniel; Abarbanel, Henry D. I.

    2016-01-01

    We report on the construction of neuron models by assimilating electrophysiological data with large-scale constrained nonlinear optimization. The method implements interior point line parameter search to determine parameters from the responses to intracellular current injections of zebra finch HVC neurons. We incorporated these parameters into a nine ionic channel conductance model to obtain completed models which we then use to predict the state of the neuron under arbitrary current stimulation. Each model was validated by successfully predicting the dynamics of the membrane potential induced by 20–50 different current protocols. The dispersion of parameters extracted from different assimilation windows was studied. Differences in constraints from current protocols, stochastic variability in neuron output, and noise behave as a residual temperature which broadens the global minimum of the objective function to an ellipsoid domain whose principal axes follow an exponentially decaying distribution. The maximum likelihood expectation of extracted parameters was found to provide an excellent approximation of the global minimum and yields highly consistent kinetics for both neurons studied. Large scale assimilation absorbs the intrinsic variability of electrophysiological data over wide assimilation windows. It builds models in an automatic manner treating all data as equal quantities and requiring minimal additional insight. PMID:27605157

  12. Cofilin Inhibition Restores Neuronal Cell Death in Oxygen-Glucose Deprivation Model of Ischemia.

    PubMed

    Madineni, Anusha; Alhadidi, Qasim; Shah, Zahoor A

    2016-03-01

    Ischemia is a condition associated with decreased blood supply to the brain, eventually leading to death of neurons. It is associated with a diverse cascade of responses involving both degenerative and regenerative mechanisms. At the cellular level, the changes are initiated prominently in the neuronal cytoskeleton. Cofilin, a cytoskeletal actin severing protein, is known to be involved in the early stages of apoptotic cell death. Evidence supports its intervention in the progression of disease states like Alzheimer's and ischemic kidney disease. In the present study, we have hypothesized the possible involvement of cofilin in ischemia. Using PC12 cells and mouse primary cultures of cortical neurons, we investigated the potential role of cofilin in ischemia in two different in vitro ischemic models: chemical induced oxidative stress and oxygen-glucose deprivation/reperfusion (OGD/R). The expression profile studies demonstrated a decrease in phosphocofilin levels in all models of ischemia, implying stress-induced cofilin activation. Furthermore, calcineurin and slingshot 1L (SSH) phosphatases were found to be the signaling mediators of the cofilin activation. In primary cultures of cortical neurons, cofilin was found to be significantly activated after 1 h of OGD. To delineate the role of activated cofilin in ischemia, we knocked down cofilin by small interfering RNA (siRNA) technique and tested the impact of cofilin silencing on neuronal viability. Cofilin siRNA-treated neurons showed a significant reduction of cofilin levels in all treatment groups (control, OGD, and OGD/R). Additionally, cofilin siRNA-reduced cofilin mitochondrial translocation and caspase 3 cleavage, with a concomitant increase in neuronal viability. These results strongly support the active role of cofilin in ischemia-induced neuronal degeneration and apoptosis. We believe that targeting this protein mediator has a potential for therapeutic intervention in ischemic brain injury and stroke

  13. Carbon monoxide improves neuronal differentiation and yield by increasing the functioning and number of mitochondria.

    PubMed

    Almeida, Ana S; Sonnewald, Ursula; Alves, Paula M; Vieira, Helena L A

    2016-08-01

    The process of cell differentiation goes hand-in-hand with metabolic adaptations, which are needed to provide energy and new metabolites. Carbon monoxide (CO) is an endogenous cytoprotective molecule able to inhibit cell death and improve mitochondrial metabolism. Neuronal differentiation processes were studied using the NT2 cell line, which is derived from human testicular embryonic teratocarcinoma and differentiates into post-mitotic neurons upon retinoic acid treatment. CO-releasing molecule A1 (CORM-A1) was used do deliver CO into cell culture. CO treatment improved NT2 neuronal differentiation and yield, since there were more neurons and the total cell number increased following the differentiation process. CO supplementation enhanced the mitochondrial population in post-mitotic neurons derived from NT2 cells, as indicated by an increase in mitochondrial DNA. CO treatment during neuronal differentiation increased the extent of the classical metabolic change that occurs during neuronal differentiation, from glycolytic to more oxidative metabolism, by decreasing the ratio of lactate production and glucose consumption. The expression of pyruvate and lactate dehydrogenases was higher, indicating an augmented oxidative metabolism. Moreover, these findings were corroborated by an increased percentage of (13) C incorporation from [U-(13) C]glucose into the tricarboxylic acid cycle metabolites malate and citrate, and also glutamate and aspartate in CO-treated cells. Finally, under low levels of oxygen (5%), which enhances glycolytic metabolism, some of the enhancing effects of CO on mitochondria were not observed. In conclusion, our data show that CO improves neuronal and mitochondrial yield by stimulation of tricarboxylic acid cycle activity, and thus oxidative metabolism of NT2 cells during the process of neuronal differentiation. The process of cell differentiation is coupled with metabolic adaptations. Carbon monoxide (CO) is an endogenous cytoprotective

  14. Nonlinear Dynamic Modeling of Synaptically Driven Single Hippocampal Neuron Intracellular Activity

    PubMed Central

    Song, Dong; Berger, Theodore W.

    2011-01-01

    A high-order nonlinear dynamic model of the input–output properties of single hippocampal CA1 pyramidal neurons was developed based on synaptically driven intracellular activity. The purpose of this study is to construct a model that: 1) can capture the nonlinear dynamics of both subthreshold activities [postsynaptic potentials (PSPs)] and suprathreshold activities (action potentials) in a single formalism; 2) is sufficiently general to be applied to any spike-input and spike-output neurons (point process input and point process output neural systems); and 3) is computationally efficient. The model consisted of three major components: 1) feedforward kernels (up to third order) that transform presynaptic action potentials into PSPs; 2) a constant threshold, above which action potentials are generated; and 3) a feedback kernel (first order) that describes spike-triggered after-potentials. The model was applied to CA1 pyramidal cells, as they were electrically stimulated with broadband Poisson random impulse trains through the Schaffer collaterals. The random impulse trains used here have physiological properties similar to spiking patterns observed in CA3 hippocampal neurons. PSPs and action potentials were recorded from the soma of CA1 pyramidal neurons using whole-cell patch-clamp recording. We evaluated the model performance separately with respect to PSP waveforms and the occurrence of spikes. The average normalized mean square error of PSP prediction is 14.4%. The average spike prediction error rate is 18.8%. In summary, although prediction errors still could be reduced, the model successfully captures the majority of high-order nonlinear dynamics of the single-neuron intracellular activity. The model captures the general biophysical processes with a small set of open parameters that are directly constrained by the intracellular recording, and thus, can be easily applied to any spike-input and spike-output neuron. PMID:21233041

  15. Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models

    PubMed Central

    Hagens, Olivier; Naud, Richard; Koch, Christof; Gerstner, Wulfram

    2015-01-01

    Single-neuron models are useful not only for studying the emergent properties of neural circuits in large-scale simulations, but also for extracting and summarizing in a principled way the information contained in electrophysiological recordings. Here we demonstrate that, using a convex optimization procedure we previously introduced, a Generalized Integrate-and-Fire model can be accurately fitted with a limited amount of data. The model is capable of predicting both the spiking activity and the subthreshold dynamics of different cell types, and can be used for online characterization of neuronal properties. A protocol is proposed that, combined with emergent technologies for automatic patch-clamp recordings, permits automated, in vitro high-throughput characterization of single neurons. PMID:26083597

  16. Mechanism of stochastic resonance enhancement in neuronal models driven by 1/f noise

    NASA Astrophysics Data System (ADS)

    Nozaki, Daichi; Collins, James J.; Yamamoto, Yoshiharu

    1999-10-01

    Noise can assist neurons in the detection of weak signals via a mechanism known as stochastic resonance (SR). In a previous study [Phys. Lett. A 243, 281 (1998)], we showed that when colored noise with 1/fβ spectrum is added to the FitzHugh-Nagumo (FHN) neuronal model, the optimal noise variance for SR could be minimized with β~1. In this study, we investigate analytically how the noise color (β) affects the SR profile in a linearized version of the FHN model. We demonstrate that the aforementioned effect of 1/f noise is related to the dynamical characteristics of the model neuron, i.e., the refractory period, the low-pass filtering effect of the membrane capacitance, and the high-pass filtering effect of the recovery variable.

  17. Statistics of a leaky integrate-and-fire model of neurons driven by dichotomous noise

    NASA Astrophysics Data System (ADS)

    Mankin, Romi; Lumi, Neeme

    2016-05-01

    The behavior of a stochastic leaky integrate-and-fire model of neurons is considered. The effect of temporally correlated random neuronal input is modeled as a colored two-level (dichotomous) Markovian noise. Relying on the Riemann method, exact expressions for the output interspike interval density and for the serial correlation coefficient are derived, and their dependence on noise parameters (such as correlation time and amplitude) is analyzed. Particularly, noise-induced sign reversal and a resonancelike amplification of the kurtosis of the interspike interval distribution are established. The features of spike statistics, analytically revealed in our study, are compared with recently obtained results for a perfect integrate-and-fire neuron model.

  18. Emergent Central Pattern Generator Behavior in Gap-Junction-Coupled Hodgkin-Huxley Style Neuron Model

    PubMed Central

    Memelli, Heraldo; Solomon, Irene C.

    2012-01-01

    Most models of central pattern generators (CPGs) involve two distinct nuclei mutually inhibiting one another via synapses. Here, we present a single-nucleus model of biologically realistic Hodgkin-Huxley neurons with random gap junction coupling. Despite no explicit division of neurons into two groups, we observe a spontaneous division of neurons into two distinct firing groups. In addition, we also demonstrate this phenomenon in a simplified version of the model, highlighting the importance of afterhyperpolarization currents (IAHP) to CPGs utilizing gap junction coupling. The properties of these CPGs also appear sensitive to gap junction conductance, probability of gap junction coupling between cells, topology of gap junction coupling, and, to a lesser extent, input current into our simulated nucleus. PMID:23365558

  19. Use of model organisms for the study of neuronal ceroid lipofuscinosis.

    PubMed

    Bond, Michael; Holthaus, Sophia-Martha Kleine; Tammen, Imke; Tear, Guy; Russell, Claire

    2013-11-01

    Neuronal ceroid lipofuscinoses are a group of fatal progressive neurodegenerative diseases predominantly affecting children. Identification of mutations that cause neuronal ceroid lipofuscinosis, and subsequent functional and pathological studies of the affected genes, underpins efforts to investigate disease mechanisms and identify and test potential therapeutic strategies. These functional studies and pre-clinical trials necessitate the use of model organisms in addition to cell and tissue culture models as they enable the study of protein function within a complex organ such as the brain and the testing of therapies on a whole organism. To this end, a large number of disease models and genetic tools have been identified or created in a variety of model organisms. In this review, we will discuss the ethical issues associated with experiments using model organisms, the factors underlying the choice of model organism, the disease models and genetic tools available, and the contributions of those disease models and tools to neuronal ceroid lipofuscinosis research. This article is part of a Special Issue entitled: The Neuronal Ceroid Lipofuscinoses or Batten Disease. PMID:23338040

  20. Biophysical model for integrating neuronal activity, EEG, fMRI and metabolism.

    PubMed

    Sotero, Roberto C; Trujillo-Barreto, Nelson J

    2008-01-01

    Our goal is to model the coupling between neuronal activity, cerebral metabolic rates of glucose and oxygen consumption, cerebral blood flow (CBF), electroencephalography (EEG) and blood oxygenation level-dependent (BOLD) responses. In order to accomplish this, two previous models are coupled: a metabolic/hemodynamic model (MHM) for a voxel, linking BOLD signals and neuronal activity, and a neural mass model describing the neuronal dynamics within a voxel and its interactions with voxels of the same area (short-range interactions) and other areas (long-range interactions). For coupling both models, we take as the input to the BOLD model, the number of active synapses within the voxel, that is, the average number of synapses that will receive an action potential within the time unit. This is obtained by considering the action potentials transmitted between neuronal populations within the voxel, as well as those arriving from other voxels. Simulations are carried out for testing the integrated model. Results show that realistic evoked potentials (EP) at electrodes on the scalp surface and the corresponding BOLD signals for each voxel are produced by the model. In another simulation, the alpha rhythm was reproduced and reasonable similarities with experimental data were obtained when calculating correlations between BOLD signals and the alpha power curve. The origin of negative BOLD responses and the characteristics of EEG, PET and BOLD signals in Alzheimer's disease were also studied. PMID:17919931

  1. Neuron-specific antioxidant OXR1 extends survival of a mouse model of amyotrophic lateral sclerosis

    PubMed Central

    Liu, Kevin X.; Edwards, Benjamin; Lee, Sheena; Finelli, Mattéa J.; Davies, Ben

    2015-01-01

    Amyotrophic lateral sclerosis is a devastating neurodegenerative disorder characterized by the progressive loss of spinal motor neurons. While the aetiological mechanisms underlying the disease remain poorly understood, oxidative stress is a central component of amyotrophic lateral sclerosis and contributes to motor neuron injury. Recently, oxidation resistance 1 (OXR1) has emerged as a critical regulator of neuronal survival in response to oxidative stress, and is upregulated in the spinal cord of patients with amyotrophic lateral sclerosis. Here, we tested the hypothesis that OXR1 is a key neuroprotective factor during amyotrophic lateral sclerosis pathogenesis by crossing a new transgenic mouse line that overexpresses OXR1 in neurons with the SOD1G93A mouse model of amyotrophic lateral sclerosis. Interestingly, we report that overexpression of OXR1 significantly extends survival, improves motor deficits, and delays pathology in the spinal cord and in muscles of SOD1G93A mice. Furthermore, we find that overexpression of OXR1 in neurons significantly delays non-cell-autonomous neuroinflammatory response, classic complement system activation, and STAT3 activation through transcriptomic analysis of spinal cords of SOD1G93A mice. Taken together, these data identify OXR1 as the first neuron-specific antioxidant modulator of pathogenesis and disease progression in SOD1-mediated amyotrophic lateral sclerosis, and suggest that OXR1 may serve as a novel target for future therapeutic strategies. PMID:25753484

  2. Theoretical Analysis of Transcranial Magneto-Acoustical Stimulation with Hodgkin-Huxley Neuron Model.

    PubMed

    Yuan, Yi; Chen, Yudong; Li, Xiaoli

    2016-01-01

    Transcranial magneto-acoustical stimulation (TMAS) is a novel stimulation technology in which an ultrasonic wave within a magnetostatic field generates an electric current in an area of interest in the brain to modulate neuronal activities. As a key part of the neural network, neurons transmit information in the nervous system. However, the effect of TMAS on the neuronal firing pattern remains unknown. To address this problem, we investigated the stimulatory mechanism of TMAS on neurons, by using a Hodgkin-Huxley neuron model. The simulation results indicated that the magnetostatic field intensity and ultrasonic power affect the amplitude and interspike interval of neuronal action potential under a continuous wave ultrasound. The simulation results also showed that the ultrasonic power, duty cycle and repetition frequency can alter the firing pattern of neural action potential under pulsed wave ultrasound. This study may help to reveal and explain the biological mechanism of TMAS and to provide a theoretical basis for TMAS in the treatment or rehabilitation of neuropsychiatric disorders. PMID:27148032

  3. Loss of Miro1-directed mitochondrial movement results in a novel murine model for neuron disease

    PubMed Central

    Nguyen, Tammy T.; Oh, Sang S.; Weaver, David; Lewandowska, Agnieszka; Maxfield, Dane; Schuler, Max-Hinderk; Smith, Nathan K.; Macfarlane, Jane; Saunders, Gerald; Palmer, Cheryl A.; Debattisti, Valentina; Koshiba, Takumi; Pulst, Stefan; Feldman, Eva L.; Hajnóczky, György; Shaw, Janet M.

    2014-01-01

    Defective mitochondrial distribution in neurons is proposed to cause ATP depletion and calcium-buffering deficiencies that compromise cell function. However, it is unclear whether aberrant mitochondrial motility and distribution alone are sufficient to cause neurological disease. Calcium-binding mitochondrial Rho (Miro) GTPases attach mitochondria to motor proteins for anterograde and retrograde transport in neurons. Using two new KO mouse models, we demonstrate that Miro1 is essential for development of cranial motor nuclei required for respiratory control and maintenance of upper motor neurons required for ambulation. Neuron-specific loss of Miro1 causes depletion of mitochondria from corticospinal tract axons and progressive neurological deficits mirroring human upper motor neuron disease. Although Miro1-deficient neurons exhibit defects in retrograde axonal mitochondrial transport, mitochondrial respiratory function continues. Moreover, Miro1 is not essential for calcium-mediated inhibition of mitochondrial movement or mitochondrial calcium buffering. Our findings indicate that defects in mitochondrial motility and distribution are sufficient to cause neurological disease. PMID:25136135

  4. Loss of Miro1-directed mitochondrial movement results in a novel murine model for neuron disease.

    PubMed

    Nguyen, Tammy T; Oh, Sang S; Weaver, David; Lewandowska, Agnieszka; Maxfield, Dane; Schuler, Max-Hinderk; Smith, Nathan K; Macfarlane, Jane; Saunders, Gerald; Palmer, Cheryl A; Debattisti, Valentina; Koshiba, Takumi; Pulst, Stefan; Feldman, Eva L; Hajnóczky, György; Shaw, Janet M

    2014-09-01

    Defective mitochondrial distribution in neurons is proposed to cause ATP depletion and calcium-buffering deficiencies that compromise cell function. However, it is unclear whether aberrant mitochondrial motility and distribution alone are sufficient to cause neurological disease. Calcium-binding mitochondrial Rho (Miro) GTPases attach mitochondria to motor proteins for anterograde and retrograde transport in neurons. Using two new KO mouse models, we demonstrate that Miro1 is essential for development of cranial motor nuclei required for respiratory control and maintenance of upper motor neurons required for ambulation. Neuron-specific loss of Miro1 causes depletion of mitochondria from corticospinal tract axons and progressive neurological deficits mirroring human upper motor neuron disease. Although Miro1-deficient neurons exhibit defects in retrograde axonal mitochondrial transport, mitochondrial respiratory function continues. Moreover, Miro1 is not essential for calcium-mediated inhibition of mitochondrial movement or mitochondrial calcium buffering. Our findings indicate that defects in mitochondrial motility and distribution are sufficient to cause neurological disease. PMID:25136135

  5. Theoretical Analysis of Transcranial Magneto-Acoustical Stimulation with Hodgkin-Huxley Neuron Model

    PubMed Central

    Yuan, Yi; Chen, Yudong; Li, Xiaoli

    2016-01-01

    Transcranial magneto-acoustical stimulation (TMAS) is a novel stimulation technology in which an ultrasonic wave within a magnetostatic field generates an electric current in an area of interest in the brain to modulate neuronal activities. As a key part of the neural network, neurons transmit information in the nervous system. However, the effect of TMAS on the neuronal firing pattern remains unknown. To address this problem, we investigated the stimulatory mechanism of TMAS on neurons, by using a Hodgkin-Huxley neuron model. The simulation results indicated that the magnetostatic field intensity and ultrasonic power affect the amplitude and interspike interval of neuronal action potential under a continuous wave ultrasound. The simulation results also showed that the ultrasonic power, duty cycle and repetition frequency can alter the firing pattern of neural action potential under pulsed wave ultrasound. This study may help to reveal and explain the biological mechanism of TMAS and to provide a theoretical basis for TMAS in the treatment or rehabilitation of neuropsychiatric disorders. PMID:27148032

  6. Models and simulation of 3D neuronal dendritic trees using Bayesian networks.

    PubMed

    López-Cruz, Pedro L; Bielza, Concha; Larrañaga, Pedro; Benavides-Piccione, Ruth; DeFelipe, Javier

    2011-12-01

    Neuron morphology is crucial for neuronal connectivity and brain information processing. Computational models are important tools for studying dendritic morphology and its role in brain function. We applied a class of probabilistic graphical models called Bayesian networks to generate virtual dendrites from layer III pyramidal neurons from three different regions of the neocortex of the mouse. A set of 41 morphological variables were measured from the 3D reconstructions of real dendrites and their probability distributions used in a machine learning algorithm to induce the model from the data. A simulation algorithm is also proposed to obtain new dendrites by sampling values from Bayesian networks. The main advantage of this approach is that it takes into account and automatically locates the relationships between variables in the data instead of using predefined dependencies. Therefore, the methodology can be applied to any neuronal class while at the same time exploiting class-specific properties. Also, a Bayesian network was defined for each part of the dendrite, allowing the relationships to change in the different sections and to model heterogeneous developmental factors or spatial influences. Several univariate statistical tests and a novel multivariate test based on Kullback-Leibler divergence estimation confirmed that virtual dendrites were similar to real ones. The analyses of the models showed relationships that conform to current neuroanatomical knowledge and support model correctness. At the same time, studying the relationships in the models can help to identify new interactions between variables related to dendritic morphology. PMID:21305364

  7. Computational modeling of direct neuronal recruitment during intracortical microstimulation in somatosensory cortex

    NASA Astrophysics Data System (ADS)

    Overstreet, C. K.; Klein, J. D.; Helms Tillery, S. I.

    2013-12-01

    Objective. Electrical stimulation of cortical tissue could be used to deliver sensory information as part of a neuroprosthetic device, but current control of the location, resolution, quality, and intensity of sensations elicited by intracortical microstimulation (ICMS) remains inadequate for this purpose. One major obstacle to resolving this problem is the poor understanding of the neural activity induced by ICMS. Even with new imaging methods, quantifying the activity of many individual neurons within cortex is difficult. Approach. We used computational modeling to examine the response of somatosensory cortex to ICMS. We modeled the axonal arbors of eight distinct morphologies of interneurons and seven types of pyramidal neurons found in somatosensory cortex and identified their responses to extracellular stimulation. We then combined these axonal elements to form a multi-layered slab of simulated cortex and investigated the patterns of neural activity directly induced by ICMS. Specifically we estimated the number, location, and variety of neurons directly recruited by stimulation on a single penetrating microelectrode. Main results. The population of neurons activated by ICMS was dependent on both stimulation strength and the depth of the electrode within cortex. Strikingly, stimulation recruited interneurons and pyramidal neurons in very different patterns. Interneurons are primarily recruited within a dense, continuous region around the electrode, while pyramidal neurons were recruited in a sparse fashion both near the electrode and up to several millimeters away. Thus ICMS can lead to an unexpectedly complex spatial distribution of firing neurons. Significance. These results lend new insights to the complexity and range of neural activity that can be induced by ICMS. This work also suggests mechanisms potentially responsible for the inconsistency and unnatural quality of sensations initiated by ICMS. Understanding these mechanisms will aid in the design of

  8. Emulating the Visual Receptive Field Properties of MST Neurons with a Template Model of Heading Estimation

    NASA Technical Reports Server (NTRS)

    Perrone, John A.; Stone, Leland S.

    1997-01-01

    We have previously proposed a computational neural-network model by which the complex patterns of retinal image motion generated during locomotion (optic flow) can be processed by specialized detectors acting as templates for specific instances of self-motion. The detectors in this template model respond to global optic flow by sampling image motion over a large portion of the visual field through networks of local motion sensors with properties similar to neurons found in the middle temporal (MT) area of primate extrastriate visual cortex. The model detectors were designed to extract self-translation (heading), self-rotation, as well as the scene layout (relative distances) ahead of a moving observer, and are arranged in cortical-like heading maps to perform this function. Heading estimation from optic flow has been postulated by some to be implemented within the medial superior temporal (MST) area. Others have questioned whether MST neurons can fulfill this role because some of their receptive-field properties appear inconsistent with a role in heading estimation. To resolve this issue, we systematically compared MST single-unit responses with the outputs of model detectors under matched stimulus conditions. We found that the basic physiological properties of MST neurons can be explained by the template model. We conclude that MST neurons are well suited to support heading estimation and that the template model provides an explicit set of testable hypotheses which can guide future exploration of MST and adjacent areas within the primate superior temporal sulcus.

  9. Transgenic Expression of Glud1 (Glutamate Dehydrogenase 1) in Neurons: In Vivo Model of Enhanced Glutamate Release, Altered Synaptic Plasticity, and Selective Neuronal Vulnerability

    PubMed Central

    Bao, Xiaodong; Pal, Ranu; Hascup, Kevin N.; Wang, Yongfu; Wang, Wen-Tung; Xu, Wenhao; Hui, Dongwei; Agbas, Abdulbaki; Wang, Xinkun; Michaelis, Mary L.; Choi, In-Young; Belousov, Andrei B.; Gerhardt, Greg A.; Michaelis, Elias K.

    2010-01-01

    The effects of lifelong, moderate excess release of glutamate (Glu) in the CNS have not been previously characterized. We created a transgenic (Tg) mouse model of lifelong excess synaptic Glu release in the CNS by introducing the gene for glutamate dehydrogenase 1 (Glud1) under the control of the neuron-specific enolase promoter. Glud1 is, potentially, an important enzyme in the pathway of Glu synthesis in nerve terminals. Increased levels of GLUD protein and activity in CNS neurons of hemizygous Tg mice were associated with increases in the in vivo release of Glu after neuronal depolarization in striatum and in the frequency and amplitude of miniature EPSCs in the CA1 region of the hippocampus. Despite overexpression of Glud1 in all neurons of the CNS, the Tg mice suffered neuronal losses in select brain regions (e.g., the CA1 but not the CA3 region). In vulnerable regions, Tg mice had decreases in MAP2A labeling of dendrites and in synaptophysin labeling of presynaptic terminals; the decreases in neuronal numbers and dendrite and presynaptic terminal labeling increased with advancing age. In addition, the Tg mice exhibited decreases in long-term potentiation of synaptic activity and in spine density in dendrites of CA1 neurons. Behaviorally, the Tg mice were significantly more resistant than wild-type mice to induction and duration of anesthesia produced by anesthetics that suppress Glu neurotransmission. The Glud1 mouse might be a useful model for the effects of lifelong excess synaptic Glu release on CNS neurons and for age-associated neurodegenerative processes. PMID:19890003

  10. Computational Modeling of Single Neuron Extracellular Electric Potentials and Network Local Field Potentials using LFPsim.

    PubMed

    Parasuram, Harilal; Nair, Bipin; D'Angelo, Egidio; Hines, Michael; Naldi, Giovanni; Diwakar, Shyam

    2016-01-01

    Local Field Potentials (LFPs) are population signals generated by complex spatiotemporal interaction of current sources and dipoles. Mathematical computations of LFPs allow the study of circuit functions and dysfunctions via simulations. This paper introduces LFPsim, a NEURON-based tool for computing population LFP activity and single neuron extracellular potentials. LFPsim was developed to be used on existing cable compartmental neuron and network models. Point source, line source, and RC based filter approximations can be used to compute extracellular activity. As a demonstration of efficient implementation, we showcase LFPs from mathematical models of electrotonically compact cerebellum granule neurons and morphologically complex neurons of the neocortical column. LFPsim reproduced neocortical LFP at 8, 32, and 56 Hz via current injection, in vitro post-synaptic N2a, N2b waves and in vivo T-C waves in cerebellum granular layer. LFPsim also includes a simulation of multi-electrode array of LFPs in network populations to aid computational inference between biophysical activity in neural networks and corresponding multi-unit activity resulting in extracellular and evoked LFP signals. PMID:27445781

  11. Meox2 haploinsufficiency increases neuronal cell loss in a mouse model of Alzheimer's disease.

    PubMed

    Soto, Ileana; Grabowska, Weronika A; Onos, Kristen D; Graham, Leah C; Jackson, Harriet M; Simeone, Stephen N; Howell, Gareth R

    2016-06-01

    Evidence suggests that multiple genetic and environmental factors conspire together to increase susceptibility to Alzheimer's disease (AD). The amyloid cascade hypothesis states that deposition of the amyloid-β (Aβ) peptide is central to AD; however, evidence in humans and animals suggests that Aβ buildup alone is not sufficient to cause neuronal cell loss and cognitive decline. Mouse models that express high levels of mutant forms of amyloid precursor protein and/or cleaving enzymes deposit amyloid but do not show neuron loss. Therefore, a double-hit hypothesis for AD has been proposed whereby vascular dysfunction precedes and promotes Aβ toxicity. In support of this, copy number variations in mesenchyme homeobox 2 (MEOX2), a gene involved in vascular development, are associated with severe forms of AD. However, the role of MEOX2 in AD has not been studied. Here, we tested Meox2 haploinsufficiency in B6.APP/PS1 (B6.APB(Tg)) mice, a mouse model of AD. Despite no overt differences in plaque deposition or glial activation, B6.APB(Tg) mice that carry only one copy of Meox2 (B6.APB(Tg).Mx(-/+)) show increased neuronal cell loss, particularly in regions containing plaques, compared with B6.APB(Tg) mice. Neuronal cell loss corresponds with a significant decrease in plaque-associated microvessels, further supporting a synergistic effect of vascular compromise and amyloid deposition on neuronal cell dysfunction in AD. PMID:27143421

  12. The CNP signal is able to silence a supra threshold neuronal model

    PubMed Central

    Camera, Francesca; Paffi, Alessandra; Thomas, Alex W.; Apollonio, Francesca; D'Inzeo, Guglielmo; Prato, Frank S.; Liberti, Micaela

    2015-01-01

    Several experimental results published in the literature showed that weak pulsed magnetic fields affected the response of the central nervous system. However, the specific biological mechanisms that regulate the observed behaviors are still unclear and further scientific investigation is required. In this work we performed simulations on a neuronal network model exposed to a specific pulsed magnetic field signal that seems to be very effective in modulating the brain activity: the Complex Neuroelectromagnetic Pulse (CNP). Results show that CNP can silence the neurons of a feed-forward network for signal intensities that depend on the strength of the bias current, the endogenous noise level and the specific waveforms of the pulses. Therefore, it is conceivable that a neuronal network model responds to the CNP signal with an inhibition of its activity. Further studies on more realistic neuronal networks are needed to clarify if such an inhibitory effect on neuronal tissue may be the basis of the induced analgesia seen in humans and the antinociceptive effects seen in animals when exposed to the CNP. PMID:25972807

  13. Synaptic potentiation onto habenula neurons in the learned helplessness model of depression.

    PubMed

    Li, Bo; Piriz, Joaquin; Mirrione, Martine; Chung, ChiHye; Proulx, Christophe D; Schulz, Daniela; Henn, Fritz; Malinow, Roberto

    2011-02-24

    The cellular basis of depressive disorders is poorly understood. Recent studies in monkeys indicate that neurons in the lateral habenula (LHb), a nucleus that mediates communication between forebrain and midbrain structures, can increase their activity when an animal fails to receive an expected positive reward or receives a stimulus that predicts aversive conditions (that is, disappointment or anticipation of a negative outcome). LHb neurons project to, and modulate, dopamine-rich regions, such as the ventral tegmental area (VTA), that control reward-seeking behaviour and participate in depressive disorders. Here we show that in two learned helplessness models of depression, excitatory synapses onto LHb neurons projecting to the VTA are potentiated. Synaptic potentiation correlates with an animal's helplessness behaviour and is due to an enhanced presynaptic release probability. Depleting transmitter release by repeated electrical stimulation of LHb afferents, using a protocol that can be effective for patients who are depressed, markedly suppresses synaptic drive onto VTA-projecting LHb neurons in brain slices and can significantly reduce learned helplessness behaviour in rats. Our results indicate that increased presynaptic action onto LHb neurons contributes to the rodent learned helplessness model of depression. PMID:21350486

  14. Synaptic potentiation onto habenula neurons in the learned helplessness model of depression

    SciTech Connect

    Li, B.; Schulz, D.; Li, B; Piriz, J.; Mirrione, M.; Chung, C.H.; Proulx, C.D.; Schulz, D.; Henn, F.; Malinow, R.

    2011-02-24

    The cellular basis of depressive disorders is poorly understood. Recent studies in monkeys indicate that neurons in the lateral habenula (LHb), a nucleus that mediates communication between forebrain and midbrain structures, can increase their activity when an animal fails to receive an expected positive reward or receives a stimulus that predicts aversive conditions (that is, disappointment or anticipation of a negative outcome). LHb neurons project to, and modulate, dopamine-rich regions, such as the ventral tegmental area (VTA), that control reward-seeking behaviour and participate in depressive disorders. Here we show that in two learned helplessness models of depression, excitatory synapses onto LHb neurons projecting to the VTA are potentiated. Synaptic potentiation correlates with an animal's helplessness behaviour and is due to an enhanced presynaptic release probability. Depleting transmitter release by repeated electrical stimulation of LHb afferents, using a protocol that can be effective for patients who are depressed, markedly suppresses synaptic drive onto VTA-projecting LHb neurons in brain slices and can significantly reduce learned helplessness behaviour in rats. Our results indicate that increased presynaptic action onto LHb neurons contributes to the rodent learned helplessness model of depression.

  15. Computational Modeling of Single Neuron Extracellular Electric Potentials and Network Local Field Potentials using LFPsim

    PubMed Central

    Parasuram, Harilal; Nair, Bipin; D'Angelo, Egidio; Hines, Michael; Naldi, Giovanni; Diwakar, Shyam

    2016-01-01

    Local Field Potentials (LFPs) are population signals generated by complex spatiotemporal interaction of current sources and dipoles. Mathematical computations of LFPs allow the study of circuit functions and dysfunctions via simulations. This paper introduces LFPsim, a NEURON-based tool for computing population LFP activity and single neuron extracellular potentials. LFPsim was developed to be used on existing cable compartmental neuron and network models. Point source, line source, and RC based filter approximations can be used to compute extracellular activity. As a demonstration of efficient implementation, we showcase LFPs from mathematical models of electrotonically compact cerebellum granule neurons and morphologically complex neurons of the neocortical column. LFPsim reproduced neocortical LFP at 8, 32, and 56 Hz via current injection, in vitro post-synaptic N2a, N2b waves and in vivo T-C waves in cerebellum granular layer. LFPsim also includes a simulation of multi-electrode array of LFPs in network populations to aid computational inference between biophysical activity in neural networks and corresponding multi-unit activity resulting in extracellular and evoked LFP signals. PMID:27445781

  16. A low-dimensional, time-resolved and adapting model neuron.

    PubMed

    Cartling, B

    1996-07-01

    A low-dimensional, time-resolved and adapting model neuron is formulated and evaluated. The model is an extension of the integrate-and-fire type of model with respect to adaptation and of a recent adapting firing-rate model with respect to time-resolution. It is obtained from detailed conductance-based models by a separation of fast and slow ionic processes of action potential generation. The model explicitly includes firing-rate regulation via the slow afterhyperpolarization phase of action potentials, which is controlled by calcium-sensitive potassium channels. It is demonstrated that the model closely reproduces the firing pattern and excitability behaviour of a detailed multicompartment conductance-based model of a neocortical pyramidal cell. The inclusion of adaptation in a model neuron is important for its capability to generate complex dynamics of networks of interconnected neurons. The time-resolution is required for studies of systems in which the temporal aspects of neural coding are important. The simplicity of the model facilitates analytical studies, insight into neurocomputational mechanisms and simulations of large-scale systems. The capability to generate complex network computations may also make the model useful in practical applications of artificial neural networks. PMID:8891839

  17. Modeling non-syndromic autism and the impact of TRPC6 disruption in human neurons

    PubMed Central

    Griesi-Oliveira, Karina; Acab, Allan; Gupta, Abha R.; Sunaga, Daniele Yumi; Chailangkarn, Thanathom; Nicol, Xavier; Nunez, Yanelli; Walker, Michael F.; Murdoch, John D.; Sanders, Stephan J.; Fernandez, Thomas V.; Ji, Weizhen; Lifton, Richard P.; Vadasz, Estevão; Dietrich, Alexander; Pradhan, Dennis; Song, Hongjun; Ming, Guo-li; Guoe, Xiang; Haddad, Gabriel; Marchetto, Maria C. N.; Spitzer, Nicholas; Passos-Bueno, Maria Rita; State, Matthew W.; Muotri, Alysson R.

    2014-01-01

    An increasing number of genetic variants have been implicated in autism spectrum disorders (ASD), and the functional study of such variants will be critical for the elucidation of autism pathophysiology. Here, we report a de novo balanced translocation disruption of TRPC6, a cation channel, in a non-syndromic autistic individual. Using multiple models, such as dental pulp cells, iPSC-derived neuronal cells and mouse models, we demonstrate that TRPC6 reduction or haploinsufficiency leads to altered neuronal development, morphology, and function. The observed neuronal phenotypes could then be rescued by TRPC6 complementation and by treatment with IGF1 or hyperforin, a TRPC6-specific agonist, suggesting that ASD individuals with alterations in this pathway might benefit from these drugs. We also demonstrate that MeCP2 levels affect TRPC6 expression. Mutations in MeCP2 cause Rett syndrome, revealing common pathways among ASDs. Genetic sequencing of TRPC6 in 1041 ASD individuals and 2872 controls revealed significantly more nonsynonymous mutations in the ASD population, and identified loss-of-function mutations with incomplete penetrance in two patients. Taken together, these findings suggest that TRPC6 is a novel predisposing gene for ASD that may act in a multiple-hit model. This is the first study to use iPSC-derived human neurons to model non-syndromic ASD and illustrate the potential of modeling genetically complex sporadic diseases using such cells. PMID:25385366

  18. Synergy of AMPA and NMDA Receptor Currents in Dopaminergic Neurons: A Modeling Study.

    PubMed

    Zakharov, Denis; Lapish, Christopher; Gutkin, Boris; Kuznetsov, Alexey

    2016-01-01

    Dopaminergic (DA) neurons display two modes of firing: low-frequency tonic and high-frequency bursts. The high frequency firing within the bursts is attributed to NMDA, but not AMPA receptor activation. In our models of the DA neuron, both biophysical and abstract, the NMDA receptor current can significantly increase their firing frequency, whereas the AMPA receptor current is not able to evoke high-frequency activity and usually suppresses firing. However, both currents are produced by glutamate receptors and, consequently, are often co-activated. Here we consider combined influence of AMPA and NMDA synaptic input in the models of the DA neuron. Different types of neuronal activity (resting state, low frequency, or high frequency firing) are observed depending on the conductance of the AMPAR and NMDAR currents. In two models, biophysical and reduced, we show that the firing frequency increases more effectively if both receptors are co-activated for certain parameter values. In particular, in the more quantitative biophysical model, the maximal frequency is 40% greater than that with NMDAR alone. The dynamical mechanism of such frequency growth is explained in the framework of phase space evolution using the reduced model. In short, both the AMPAR and NMDAR currents flatten the voltage nullcline, providing the frequency increase, whereas only NMDA prevents complete unfolding of the nullcline, providing robust firing. Thus, we confirm a major role of the NMDAR in generating high-frequency firing and conclude that AMPAR activation further significantly increases the frequency. PMID:27252643

  19. Synergy of AMPA and NMDA Receptor Currents in Dopaminergic Neurons: A Modeling Study

    PubMed Central

    Zakharov, Denis; Lapish, Christopher; Gutkin, Boris; Kuznetsov, Alexey

    2016-01-01

    Dopaminergic (DA) neurons display two modes of firing: low-frequency tonic and high-frequency bursts. The high frequency firing within the bursts is attributed to NMDA, but not AMPA receptor activation. In our models of the DA neuron, both biophysical and abstract, the NMDA receptor current can significantly increase their firing frequency, whereas the AMPA receptor current is not able to evoke high-frequency activity and usually suppresses firing. However, both currents are produced by glutamate receptors and, consequently, are often co-activated. Here we consider combined influence of AMPA and NMDA synaptic input in the models of the DA neuron. Different types of neuronal activity (resting state, low frequency, or high frequency firing) are observed depending on the conductance of the AMPAR and NMDAR currents. In two models, biophysical and reduced, we show that the firing frequency increases more effectively if both receptors are co-activated for certain parameter values. In particular, in the more quantitative biophysical model, the maximal frequency is 40% greater than that with NMDAR alone. The dynamical mechanism of such frequency growth is explained in the framework of phase space evolution using the reduced model. In short, both the AMPAR and NMDAR currents flatten the voltage nullcline, providing the frequency increase, whereas only NMDA prevents complete unfolding of the nullcline, providing robust firing. Thus, we confirm a major role of the NMDAR in generating high-frequency firing and conclude that AMPAR activation further significantly increases the frequency. PMID:27252643

  20. Computational and Electronic Analog Implementation of the Hodgkin-Huxley Model of Action Potentials in Neurons

    NASA Astrophysics Data System (ADS)

    Smith, Peter; Link, Justin

    2012-02-01

    Alan Loyd Hodgkin and Andrew Huxley's mathematical model of action potential initiation and propagation in neurons is one of the greatest hallmarks of biophysics. Two techniques for implementing the Hodgkin-Huxley model were explored: computational and electronic analog. Computational modeling was done using NEURON 7.1. NEURON is a free, robust, and relatively user friendly simulation environment that enables quantitatively accurate computational modeling of neurons and neural networks. An analog electronic circuit was built using field-effect transistors (FETs) to simulate the non-linear, voltage-dependent (sodium and potassium) conductances that are responsible for membrane excitability. While the electronic analog qualitatively reproduces many of the key features of the action potential including overall shape, inactivation period, and propagation, it was difficult to quantitatively reproduce the Hodgkin-Huxley model. In addition, while the relative cost to build circuits equivalent to small membrane patches is minimal (˜50), implementation of larger cells or networks would prove uneconomical. Still, both techniques are viable avenues toward introducing interdisciplinary research into either a computational or electronics lab setting at the undergraduate level.

  1. Evaluation of animal models of obsessive-compulsive disorder: correlation with phasic dopamine neuron activity.

    PubMed

    Sesia, Thibaut; Bizup, Brandon; Grace, Anthony A

    2013-07-01

    Obsessive compulsive disorder (OCD) is a psychiatric condition defined by intrusive thoughts (obsessions) associated with compensatory and repetitive behaviour (compulsions). However, advancement in our understanding of this disorder has been hampered by the absence of effective animal models and correspondingly analysis of the physiological changes that may be present in these models. To address this, we have evaluated two current rodent models of OCD; repeated injection of dopamine D2 agonist quinpirole and repeated adolescent injection of the tricyclic agent clomipramine in combination with a behavioural paradigm designed to produce compulsive lever pressing. These results were then compared with their relative impact on the state of activity of the mesolimbic dopaminergic system using extracellular recoding of spontaneously active dopamine neurons in the ventral tegmental area (VTA). The clomipramine model failed to exacerbate compulsive lever pressing and VTA dopamine neurons in clomipramine-treated rats had mildly diminished bursting activity. In contrast, quinpirole-treated animals showed significant increases in compulsive lever pressing, which was concurrent with a substantial diminution of bursting activity of VTA dopamine neurons. Therefore, VTA dopamine activity correlated with the behavioural response in these models. Taken together, these data support the view that compulsive behaviours likely reflect, at least in part, a disruption of the dopaminergic system, more specifically by a decrease in baseline phasic dopamine signalling mediated by burst firing of dopamine neurons. PMID:23360787

  2. microRNA-22 attenuates neuronal cell apoptosis in a cell model of traumatic brain injury

    PubMed Central

    Ma, Ji; Shui, Shaofeng; Han, Xinwei; Guo, Dong; Li, Tengfei; Yan, Lei

    2016-01-01

    Traumatic brain injury (TBI) is a major cause of injury-related deaths, and the mechanism of TBI has become a research focus, but little is known about the mechanism of microRNAs in TBI. The aim of this study is the role of microRNA-22 (miR-22) in TBI-induced neuronal cell apoptosis. Rat cortical neurons were cultured and the TBI model was induced by scratch injury in vitro, before which miR-22 level was altered by transfection of agomir or antagomir. Lactate dehydrogenase (LDH) release and TUNEL assays were performed to examine neuronal cell injury and apoptosis. The activity of caspase 3 (CASP3) and level changes of several apoptosis factors including B-cell lymphoma 2 (BCL2), BCL2-associated X protein (BAX), phosphatase and tensin homolog (PTEN) and v-AKT murine thymoma viral oncogene homolog 1 (AKT1) were detected. Results showed that TBI model cells possessed a downregulated miR-22 level (P < 0.001) and more LDH release and apoptotic cells indicating the aggravated neuronal cell injury and apoptosis induced by TBI. miR-22 agomir attenuated neuronal cell injury and apoptosis of the TBI model. It also caused the corresponding changes in CASP3 activity and other apoptosis factors, with cleaved CASP3, BAX and PTEN inhibited and BCL2 and phosphorylated AKT1 promoted, while miR-22 antagomir had the opposite effects. So miR-22 has neuroprotective roles of attenuating neuronal cell injury and apoptosis induced by TBI, which may be associated with its regulation on apoptosis factors. This study reveals miR-22 as a potential approach to TBI treatment and detailed mechanism remains to be uncovered. PMID:27186313

  3. On modeling the neuronal activity in movement disorder patients by using the Ornstein Uhlenbeck process.

    PubMed

    Shukla, Pitamber; Basu, Ishita; Tuninetti, Daniela; Graupe, Daniel; Slavin, Konstantin V

    2014-01-01

    Mathematical models of the neuronal activity in the affected brain regions of Essential Tremor (ET) and Parkinson's Disease (PD) patients could shed light into the underlying pathophysiology of these diseases, which in turn could help develop personalized treatments including adaptive Deep Brain Stimulation (DBS). In this paper, we use an Ornstein Uhlenbeck Process (OUP) to model the neuronal spiking activity recorded from the brain of ET and PD patients during DBS stereotactic surgery. The parameters of the OUP are estimated based on Inter Spike Interval (ISI) measurements, i.e., the time interval between two consecutive neuronal firings, by means of the Fortet Integral Equation (FIE). The OUP model parameters identified with the FIE method (OUP-FIE) are then used to simulate the ISI distribution resulting from the OUP. Other widely used neuronal activity models, such as the Poisson Process (PP), the Brownian Motion (BM), and the OUP whose parameters are extracted by matching the first two moments of the ISI (OUP-MOM), are also considered. To quantify how close the simulated ISI distribution is to the measured ISI distribution, the Integral Square Error (ISE) criterion is adopted. Amongst all considered stochastic processes, the ISI distribution generated by the OUP-FIE method is shown to produce the least ISE. Finally, a directional Wilcoxon signed rank test is used to show statistically significant reduction in the ISE value obtained from the OUP-FIE compared to the other stochastic processes. PMID:25570525

  4. A stochastic neuronal model predicts random search behaviors at multiple spatial scales in C. elegans

    PubMed Central

    Roberts, William M; Augustine, Steven B; Lawton, Kristy J; Lindsay, Theodore H; Thiele, Tod R; Izquierdo, Eduardo J; Faumont, Serge; Lindsay, Rebecca A; Britton, Matthew Cale; Pokala, Navin; Bargmann, Cornelia I; Lockery, Shawn R

    2016-01-01

    Random search is a behavioral strategy used by organisms from bacteria to humans to locate food that is randomly distributed and undetectable at a distance. We investigated this behavior in the nematode Caenorhabditis elegans, an organism with a small, well-described nervous system. Here we formulate a mathematical model of random search abstracted from the C. elegans connectome and fit to a large-scale kinematic analysis of C. elegans behavior at submicron resolution. The model predicts behavioral effects of neuronal ablations and genetic perturbations, as well as unexpected aspects of wild type behavior. The predictive success of the model indicates that random search in C. elegans can be understood in terms of a neuronal flip-flop circuit involving reciprocal inhibition between two populations of stochastic neurons. Our findings establish a unified theoretical framework for understanding C. elegans locomotion and a testable neuronal model of random search that can be applied to other organisms. DOI: http://dx.doi.org/10.7554/eLife.12572.001 PMID:26824391

  5. ACCUMULATION OF METHYLMERCURY OR POLYCHLORINATED BIPHENYLS IN IN VITRO MODELS OF RAT NEURONAL TISSUE.

    EPA Science Inventory

    This manuscript reports data examining the accumulation of PCBs or methylmercury into the tissue of three commonly used in vitro neuronal models.

    ? The results demonstrate that these lipophilic compounds can accumulate to levels 5 to 100 fold higher than the surrounding s...

  6. Stochastic Wilson-Cowan models of neuronal network dynamics with memory and delay

    NASA Astrophysics Data System (ADS)

    Goychuk, Igor; Goychuk, Andriy

    2015-04-01

    We consider a simple Markovian class of the stochastic Wilson-Cowan type models of neuronal network dynamics, which incorporates stochastic delay caused by the existence of a refractory period of neurons. From the point of view of the dynamics of the individual elements, we are dealing with a network of non-Markovian stochastic two-state oscillators with memory, which are coupled globally in a mean-field fashion. This interrelation of a higher-dimensional Markovian and lower-dimensional non-Markovian dynamics is discussed in its relevance to the general problem of the network dynamics of complex elements possessing memory. The simplest model of this class is provided by a three-state Markovian neuron with one refractory state, which causes firing delay with an exponentially decaying memory within the two-state reduced model. This basic model is used to study critical avalanche dynamics (the noise sustained criticality) in a balanced feedforward network consisting of the excitatory and inhibitory neurons. Such avalanches emerge due to the network size dependent noise (mesoscopic noise). Numerical simulations reveal an intermediate power law in the distribution of avalanche sizes with the critical exponent around -1.16. We show that this power law is robust upon a variation of the refractory time over several orders of magnitude. However, the avalanche time distribution is biexponential. It does not reflect any genuine power law dependence.

  7. A mathematical model of adult GnRH neurons in mouse brain and its bifurcation analysis.

    PubMed

    Duan, Wen; Lee, Kiho; Herbison, Allan E; Sneyd, James

    2011-05-01

    GnRH neurons are hypothalamic neurons that secrete gonadotropin-releasing hormone (GnRH) which stimulates the release of gonadotropins, one of the crucial hormones for sexual development, fertility and maturation. A mathematical model was built to help elucidate the mechanisms underlying electrical bursting and synchronous [Ca²(+)] transients in GnRH neurons (Lee et al., 2010). The model predicted that bursting in GnRH neurons (at least of the short-bursting type) requires the existence of a [Ca²(+)]-dependent slow after-hyperpolarisation current (sI(AHP-UCL)), and this predicted current was found experimentally. GnRH behaviour under a wide range of conditions (inhibition of Na(+) channels, IP₃ receptors, [Ca²(+)]-dependent K(+) channels, or Ca²(+) pumps, or in the presence of zero extracellular [Ca²(+)]) is successfully reproduced by the model. In this paper, a simplified version of the previous model, with the same qualitative behaviour, is constructed and studied using timescale separation techniques and bifurcation analysis. PMID:21300070

  8. Modeling the Formation Process of Grouping Stimuli Sets through Cortical Columns and Microcircuits to Feature Neurons

    PubMed Central

    Williamson, Adam

    2013-01-01

    A computational model of a self-structuring neuronal net is presented in which repetitively applied pattern sets induce the formation of cortical columns and microcircuits which decode distinct patterns after a learning phase. In a case study, it is demonstrated how specific neurons in a feature classifier layer become orientation selective if they receive bar patterns of different slopes from an input layer. The input layer is mapped and intertwined by self-evolving neuronal microcircuits to the feature classifier layer. In this topical overview, several models are discussed which indicate that the net formation converges in its functionality to a mathematical transform which maps the input pattern space to a feature representing output space. The self-learning of the mathematical transform is discussed and its implications are interpreted. Model assumptions are deduced which serve as a guide to apply model derived repetitive stimuli pattern sets to in vitro cultures of neuron ensembles to condition them to learn and execute a mathematical transform. PMID:24369455

  9. On the dynamical mechanisms of influence of synaptic currents on the neuron model with response differentiation

    NASA Astrophysics Data System (ADS)

    Zakharov, D. G.; Kuznetsov, A. S.

    2015-08-01

    The combined effect of synaptic NMDA, AMPA, and GABA currents on the neuron model with response differentiation has been considered. It has been shown that the GABA and NMDA currents can compensate the effects of each other, whereas the AMPA current not only leads to the suppression of oscillations but also significantly amplifies the high-frequency activity of the neuron induced by the NMDA current. Two bifurcation scenarios underlying these effects have been revealed. It has been predicted which scenario takes place under the combined influence of all three currents.

  10. Degeneration in Arousal Neurons in Chronic Sleep Disruption Modeling Sleep Apnea

    PubMed Central

    Zhu, Yan; Fenik, Polina; Zhan, Guanxia; Xin, Ryan; Veasey, Sigrid C.

    2015-01-01

    Chronic sleep disruption (CSD) is a cardinal feature of sleep apnea that predicts impaired wakefulness. Despite effective treatment of apneas and sleep disruption, patients with sleep apnea may have persistent somnolence. Lasting wake disturbances in treated sleep apnea raise the possibility that CSD may induce sufficient degeneration in wake-activated neurons (WAN) to cause irreversible wake impairments. Implementing a stereological approach in a murine model of CSD, we found reduced neuronal counts in representative WAN groups, locus coeruleus (LC) and orexinergic neurons, reduced by 50 and 25%, respectively. Mice exposed to CSD showed shortened sleep latencies lasting at least 4 weeks into recovery from CSD. As CSD results in frequent activation of WAN, we hypothesized that CSD promotes mitochondrial metabolic stress in WAN. In support, CSD increased lipofuscin within select WAN. Further, examining the LC as a representative WAN nucleus, we observed increased mitochondrial protein acetylation and down-regulation of anti-oxidant enzyme and brain-derived neurotrophic factor mRNA. Remarkably, CSD markedly increased tumor necrosis factor-alpha within WAN, and not in adjacent neurons or glia. Thus, CSD, as observed in sleep apnea, results in a composite of lasting wake impairments, loss of select neurons, a pro-inflammatory, pro-oxidative mitochondrial stress response in WAN, consistent with a degenerative process with behavioral consequences. PMID:26074865

  11. Oral Resveratrol Reduces Neuronal Damage in a Model of Multiple Sclerosis

    PubMed Central

    Shindler, Kenneth S.; Ventura, Elvira; Dutt, Mahasweta; Elliott, Peter; Fitzgerald, Denise C.; Rostami, Abdolmohamad

    2012-01-01

    Background Neuronal loss in multiple sclerosis (MS) and its animal model, experimental autoimmune encephalomyelitis (EAE), correlates with permanent neurological dysfunction. Current MS therapies have limited ability to prevent neuronal damage. Methods We examined whether oral therapy with SRT501, a pharmaceutical-grade formulation of resveratrol, reduces neuronal loss during relapsing/remitting EAE. Resveratrol activates SIRT1, an NAD+-dependent deacetylase that promotes mitochondrial function. Results Oral SRT501 prevented neuronal loss during optic neuritis, an inflammatory optic nerve lesion in MS and EAE. SRT501 also suppressed neurological dysfunction during EAE remission, and spinal cords from SRT501-treated mice had significantly higher axonal density than vehicle-treated mice. Similar neuroprotection was mediated by SRT1720, another SIRT1-activating compound; and sirtinol, a SIRT1 inhibitor, attenuated SRT501 neuroprotective effects. SIRT1 activators did not prevent inflammation. Conclusions These studies demonstrate SRT501 attenuates neuronal damage and neurological dysfunction in EAE by a mechanism involving SIRT1 activation. SIRT1 activators are a potential oral therapy in MS. PMID:21107122

  12. Graph-based unsupervised segmentation algorithm for cultured neuronal networks' structure characterization and modeling.

    PubMed

    de Santos-Sierra, Daniel; Sendiña-Nadal, Irene; Leyva, Inmaculada; Almendral, Juan A; Ayali, Amir; Anava, Sarit; Sánchez-Ávila, Carmen; Boccaletti, Stefano

    2015-06-01

    Large scale phase-contrast images taken at high resolution through the life of a cultured neuronal network are analyzed by a graph-based unsupervised segmentation algorithm with a very low computational cost, scaling linearly with the image size. The processing automatically retrieves the whole network structure, an object whose mathematical representation is a matrix in which nodes are identified neurons or neurons' clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocytochemistry techniques, our non invasive measures entitle us to perform a longitudinal analysis during the maturation of a single culture. Such an analysis furnishes the way of individuating the main physical processes underlying the self-organization of the neurons' ensemble into a complex network, and drives the formulation of a phenomenological model yet able to describe qualitatively the overall scenario observed during the culture growth. PMID:25393432

  13. Persistent neuronal Ube3a expression in the suprachiasmatic nucleus of Angelman syndrome model mice.

    PubMed

    Jones, Kelly A; Han, Ji Eun; DeBruyne, Jason P; Philpot, Benjamin D

    2016-01-01

    Mutations or deletions of the maternal allele of the UBE3A gene cause Angelman syndrome (AS), a severe neurodevelopmental disorder. The paternal UBE3A/Ube3a allele becomes epigenetically silenced in most neurons during postnatal development in humans and mice; hence, loss of the maternal allele largely eliminates neuronal expression of UBE3A protein. However, recent studies suggest that paternal Ube3a may escape silencing in certain neuron populations, allowing for persistent expression of paternal UBE3A protein. Here we extend evidence in AS model mice (Ube3a(m-/p+)) of paternal UBE3A expression within the suprachiasmatic nucleus (SCN), the master circadian pacemaker. Paternal UBE3A-positive cells in the SCN show partial colocalization with the neuropeptide arginine vasopressin (AVP) and clock proteins (PER2 and BMAL1), supporting that paternal UBE3A expression in the SCN is often of neuronal origin. Paternal UBE3A also partially colocalizes with a marker of neural progenitors, SOX2, implying that relaxed or incomplete imprinting of paternal Ube3a reflects an overall immature molecular phenotype. Our findings highlight the complexity of Ube3a imprinting in the brain and illuminate a subpopulation of SCN neurons as a focal point for future studies aimed at understanding the mechanisms of Ube3a imprinting. PMID:27306933

  14. Neuronal and astrocytic metabolism in a transgenic rat model of Alzheimer's disease

    PubMed Central

    Nilsen, Linn Hege; Witter, Menno P; Sonnewald, Ursula

    2014-01-01

    Regional hypometabolism of glucose in the brain is a hallmark of Alzheimer's disease (AD). However, little is known about the specific alterations of neuronal and astrocytic metabolism involved in homeostasis of glutamate and GABA in AD. Here, we investigated the effects of amyloid β (Aβ) pathology on neuronal and astrocytic metabolism and glial-neuronal interactions in amino acid neurotransmitter homeostasis in the transgenic McGill-R-Thy1-APP rat model of AD compared with healthy controls at age 15 months. Rats were injected with [1-13C]glucose and [1,2-13C]acetate, and extracts of the hippocampal formation as well as several cortical regions were analyzed using 1H- and 13C nuclear magnetic resonance spectroscopy and high-performance liquid chromatography. Reduced tricarboxylic acid cycle turnover was evident for glutamatergic and GABAergic neurons in hippocampal formation and frontal cortex, and for astrocytes in frontal cortex. Pyruvate carboxylation, which is necessary for de novo synthesis of amino acids, was decreased and affected the level of glutamine in hippocampal formation and those of glutamate, glutamine, GABA, and aspartate in the retrosplenial/cingulate cortex. Metabolic alterations were also detected in the entorhinal cortex. Overall, perturbations in energy- and neurotransmitter homeostasis, mitochondrial astrocytic and neuronal metabolism, and aspects of the glutamate–glutamine cycle were found in McGill-R-Thy1-APP rats. PMID:24594625

  15. Persistent neuronal Ube3a expression in the suprachiasmatic nucleus of Angelman syndrome model mice

    PubMed Central

    Jones, Kelly A.; Han, Ji Eun; DeBruyne, Jason P.; Philpot, Benjamin D.

    2016-01-01

    Mutations or deletions of the maternal allele of the UBE3A gene cause Angelman syndrome (AS), a severe neurodevelopmental disorder. The paternal UBE3A/Ube3a allele becomes epigenetically silenced in most neurons during postnatal development in humans and mice; hence, loss of the maternal allele largely eliminates neuronal expression of UBE3A protein. However, recent studies suggest that paternal Ube3a may escape silencing in certain neuron populations, allowing for persistent expression of paternal UBE3A protein. Here we extend evidence in AS model mice (Ube3am–/p+) of paternal UBE3A expression within the suprachiasmatic nucleus (SCN), the master circadian pacemaker. Paternal UBE3A-positive cells in the SCN show partial colocalization with the neuropeptide arginine vasopressin (AVP) and clock proteins (PER2 and BMAL1), supporting that paternal UBE3A expression in the SCN is often of neuronal origin. Paternal UBE3A also partially colocalizes with a marker of neural progenitors, SOX2, implying that relaxed or incomplete imprinting of paternal Ube3a reflects an overall immature molecular phenotype. Our findings highlight the complexity of Ube3a imprinting in the brain and illuminate a subpopulation of SCN neurons as a focal point for future studies aimed at understanding the mechanisms of Ube3a imprinting. PMID:27306933

  16. Emergent spindle oscillations and intermittent burst firing in a thalamic model: specific neuronal mechanisms.

    PubMed Central

    Wang, X J; Golomb, D; Rinzel, J

    1995-01-01

    The rhythmogenesis of 10-Hz sleep spindles is studied in a large-scale thalamic network model with two cell populations: the excitatory thalamocortical (TC) relay neurons and the inhibitory nucleus reticularis thalami (RE) neurons. Spindle-like bursting oscillations emerge naturally from reciprocal interactions between TC and RE neurons. We find that the network oscillations can be synchronized coherently, even though the RE-TC connections are random and sparse, and even though individual neurons fire rebound bursts intermittently in time. When the fast gamma-aminobutyrate type A synaptic inhibition is blocked, synchronous slow oscillations resembling absence seizures are observed. Near-maximal network synchrony is established with even modest convergence in the RE-to-TC projection (as few as 5-10 RE inputs per TC cell suffice). The hyperpolarization-activated cation current (Ih) is found to provide a cellular basis for the intermittency of rebound bursting that is commonly observed in TC neurons during spindles. Such synchronous oscillations with intermittency can be maintained only with a significant degree of convergence for the TC-to-RE projection. PMID:7777551

  17. Progression of Neuronal Damage in an In Vitro Model of the Ischemic Penumbra

    PubMed Central

    le Feber, Joost; Tzafi Pavlidou, Stelina; Erkamp, Niels; van Putten, Michel J. A. M.; Hofmeijer, Jeannette

    2016-01-01

    Improvement of neuronal recovery in the ischemic penumbra around a brain infarct has a large potential to advance clinical recovery of patients with acute ischemic stroke. However, pathophysiological mechanisms leading to either recovery or secondary damage in the penumbra are not completely understood. We studied neuronal dynamics in a model system of the penumbra consisting of networks of cultured cortical neurons exposed to controlled levels and durations of hypoxia. Short periods of hypoxia (pO2≈20mmHg) reduced spontaneous activity, due to impeded synaptic function. After ≈6 hours, activity and connectivity partially recovered, even during continuing hypoxia. If the oxygen supply was restored within 12 hours, changes in network connectivity were completely reversible. For longer periods of hypoxia (12–30 h), activity levels initially increased, but eventually decreased and connectivity changes became partially irreversible. After ≈30 hours, all functional connections disappeared and no activity remained. Since this complete silence seemed unrelated to hypoxic depths, but always followed an extended period of low activity, we speculate that irreversible damage (at least partly) results from insufficient neuronal activation. This opens avenues for therapies to improve recovery by neuronal activation. PMID:26871437

  18. Genetic manipulation of adult-born hippocampal neurons rescues memory in a mouse model of Alzheimer's disease.

    PubMed

    Richetin, Kevin; Leclerc, Clémence; Toni, Nicolas; Gallopin, Thierry; Pech, Stéphane; Roybon, Laurent; Rampon, Claire

    2015-02-01

    In adult mammals, neural progenitors located in the dentate gyrus retain their ability to generate neurons and glia throughout lifetime. In rodents, increased production of new granule neurons is associated with improved memory capacities, while decreased hippocampal neurogenesis results in impaired memory performance in several memory tasks. In mouse models of Alzheimer's disease, neurogenesis is impaired and the granule neurons that are generated fail to integrate existing networks. Thus, enhancing neurogenesis should improve functional plasticity in the hippocampus and restore cognitive deficits in these mice. Here, we performed a screen of transcription factors that could potentially enhance adult hippocampal neurogenesis. We identified Neurod1 as a robust neuronal determinant with the capability to direct hippocampal progenitors towards an exclusive granule neuron fate. Importantly, Neurod1 also accelerated neuronal maturation and functional integration of new neurons during the period of their maturation when they contribute to memory processes. When tested in an APPxPS1 mouse model of Alzheimer's disease, directed expression of Neurod1 in cycling hippocampal progenitors conspicuously reduced dendritic spine density deficits on new hippocampal neurons, to the same level as that observed in healthy age-matched control animals. Remarkably, this population of highly connected new neurons was sufficient to restore spatial memory in these diseased mice. Collectively our findings demonstrate that endogenous neural stem cells of the diseased brain can be manipulated to become new neurons that could allow cognitive improvement. PMID:25518958

  19. Predicting every spike: a model for the responses of visual neurons.

    PubMed

    Keat, J; Reinagel, P; Reid, R C; Meister, M

    2001-06-01

    In the early visual system, neuronal responses can be extremely precise. Under a wide range of stimuli, cells in the retina and thalamus fire spikes very reproducibly, often with millisecond precision on subsequent stimulus repeats. Here we develop a mathematical description of the firing process that, given the recent visual input, accurately predicts the timing of individual spikes. The formalism is successful in matching the spike trains from retinal ganglion cells in salamander, rabbit, and cat, as well as from lateral geniculate nucleus neurons in cat. It adapts to many different response types, from very precise to highly variable. The accuracy of the model allows a compact description of how these neurons encode the visual stimulus. PMID:11430813

  20. Effects of neuronal loss in the dynamic model of neural networks

    NASA Astrophysics Data System (ADS)

    Yoon, B.-G.; Choi, J.; Choi, M. Y.

    2008-09-01

    We study the phase transitions and dynamic behavior of the dynamic model of neural networks, with an emphasis on the effects of neuronal loss due to external stress. In the absence of loss the overall results obtained numerically are found to agree excellently with the theoretical ones. When the external stress is turned on, some neurons may deteriorate and die; such loss of neurons, in general, weakens the memory in the system. As the loss increases beyond a critical value, the order parameter measuring the strength of memory decreases to zero either continuously or discontinuously, namely, the system loses its memory via a second- or a first-order transition, depending on the ratio of the refractory period to the duration of action potential.

  1. Development of modified cable models to simulate accurate neuronal active behaviors

    PubMed Central

    2014-01-01

    In large network and single three-dimensional (3-D) neuron simulations, high computing speed dictates using reduced cable models to simulate neuronal firing behaviors. However, these models are unwarranted under active conditions and lack accurate representation of dendritic active conductances that greatly shape neuronal firing. Here, realistic 3-D (R3D) models (which contain full anatomical details of dendrites) of spinal motoneurons were systematically compared with their reduced single unbranched cable (SUC, which reduces the dendrites to a single electrically equivalent cable) counterpart under passive and active conditions. The SUC models matched the R3D model's passive properties but failed to match key active properties, especially active behaviors originating from dendrites. For instance, persistent inward currents (PIC) hysteresis, frequency-current (FI) relationship secondary range slope, firing hysteresis, plateau potential partial deactivation, staircase currents, synaptic current transfer ratio, and regional FI relationships were not accurately reproduced by the SUC models. The dendritic morphology oversimplification and lack of dendritic active conductances spatial segregation in the SUC models caused significant underestimation of those behaviors. Next, SUC models were modified by adding key branching features in an attempt to restore their active behaviors. The addition of primary dendritic branching only partially restored some active behaviors, whereas the addition of secondary dendritic branching restored most behaviors. Importantly, the proposed modified models successfully replicated the active properties without sacrificing model simplicity, making them attractive candidates for running R3D single neuron and network simulations with accurate firing behaviors. The present results indicate that using reduced models to examine PIC behaviors in spinal motoneurons is unwarranted. PMID:25277743

  2. A review of the methods for signal estimation in stochastic diffusion leaky integrate-and-fire neuronal models.

    PubMed

    Lansky, Petr; Ditlevsen, Susanne

    2008-11-01

    Parameters in diffusion neuronal models are divided into two groups; intrinsic and input parameters. Intrinsic parameters are related to the properties of the neuronal membrane and are assumed to be known throughout the paper. Input parameters characterize processes generated outside the neuron and methods for their estimation are reviewed here. Two examples of the diffusion neuronal model, which are based on the integrate-and-fire concept, are investigated--the Ornstein--Uhlenbeck model as the most common one and the Feller model as an illustration of state-dependent behavior in modeling the neuronal input. Two types of experimental data are assumed-intracellular describing the membrane trajectories and extracellular resulting in knowledge of the interspike intervals. The literature on estimation from the trajectories of the diffusion process is extensive and thus the stress in this review is set on the inference made from the interspike intervals. PMID:18496710

  3. A model for the polarization of neurons by extrinsically applied electric fields.

    PubMed Central

    Tranchina, D; Nicholson, C

    1986-01-01

    A model is presented for the subthreshold polarization of a neuron by an applied electric field. It gives insight into how morphological features of a neuron affect its polarizability. The neuronal model consists of one or more extensively branched dendritic trees, a lumped somatic impedance, and a myelinated axon with nodes of Ranvier. The dendritic trees branch according to the 3/2-power rule of Rall, so that each tree has an equivalent cylinder representation. Equations for the membrane potential at the soma and at the nodes of Ranvier, given an arbitrary specified external potential, are derived. The solutions determine the contributions made by the dendritic tree and the axon to the net polarization at the soma. In the case of a spatially constant electric field, both the magnitude and sign of the polarization depend on simple combinations of parameters describing the neuron. One important combination is given by the ratio of internal resistances for longitudinal current spread along the dendritic tree trunk and along the axon. A second is given by the ratio between the DC space constant for the dendritic tree trunk and the distance between nodes of Ranvier in the axon. A third is given by the product of the electric field and the space constant for the trunk of the dendritic tree. When a neuron with a straight axon is subjected to a constant field, the membrane potential decays exponentially with distance from the soma. Thus, the soma seems to be a likely site for action potential initiation when the field is strong enough to elicit suprathreshold polarization. In a simple example, the way in which orientation of the various parts of the neuron affects its polarization is examined. When an axon with a bend is subjected to a spatially constant field, polarization is focused at the bend, and this is another likely site for action potential initiation. PMID:3801574

  4. 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

  5. An Augmented Two-Layer Model Captures Nonlinear Analog Spatial Integration Effects in Pyramidal Neuron Dendrites

    PubMed Central

    JADI, MONIKA P.; BEHABADI, BARDIA F.; POLEG-POLSKY, ALON; SCHILLER, JACKIE; MEL, BARTLETT W.

    2014-01-01

    In pursuit of the goal to understand and eventually reproduce the diverse functions of the brain, a key challenge lies in reverse engineering the peculiar biology-based “technology” that underlies the brain’s remarkable ability to process and store information. The basic building block of the nervous system is the nerve cell, or “neuron,” yet after more than 100 years of neurophysiological study and 60 years of modeling, the information processing functions of individual neurons, and the parameters that allow them to engage in so many different types of computation (sensory, motor, mnemonic, executive, etc.) remain poorly understood. In this paper, we review both historical and recent findings that have led to our current understanding of the analog spatial processing capabilities of dendrites, the major input structures of neurons, with a focus on the principal cell type of the neocortex and hippocampus, the pyramidal neuron (PN). We encapsulate our current understanding of PN dendritic integration in an abstract layered model whose spatially sensitive branch-subunits compute multidimensional sigmoidal functions. Unlike the 1-D sigmoids found in conventional neural network models, multidimensional sigmoids allow the cell to implement a rich spectrum of nonlinear modulation effects directly within their dendritic trees. PMID:25554708

  6. Silibinin prevents dopaminergic neuronal loss in a mouse model of Parkinson's disease via mitochondrial stabilization.

    PubMed

    Lee, Yujeong; Park, Hee Ra; Chun, Hye Jeong; Lee, Jaewon

    2015-05-01

    Parkinson's disease (PD) is a progressive neurodegenerative disease characterized by the selective loss of dopaminergic neurons in the nigrostriatal pathway. The lipophile 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) can cross the blood-brain barrier and is subsequently metabolized into toxic1-methyl-4-phenylpyridine (MPP(+) ), which causes mitochondrial dysfunction and the selective cell death of dopaminergic neurons. The present article reports the neuroprotective effects of silibinin in a murine MPTP model of PD. The flavonoid silibinin is the major active constituent of silymarin, an extract of milk thistle seeds, and is known to have hepatoprotective, anticancer, antioxidative, and neuroprotective effects. In the present study, silibinin effectively attenuated motor deficit and dopaminergic neuronal loss caused by MPTP. Furthermore, in vitro study confirmed that silibinin protects primary cultured neurons against MPP(+) -induced cell death and mitochondrial membrane disruption. The findings of the present study indicate that silibinin has neuroprotective effects in MPTP-induced models of PD rather than antioxidative or anti-inflammatory effects and that the neuroprotection afforded might be mediated by the stabilization of mitochondrial membrane potential. Furthermore, these findings suggest that silibinin protects mitochondria in MPTP-induced PD models and that it offers a starting point for the development of treatments that ameliorate the symptoms of PD. PMID:25677261

  7. Brain lactate kinetics: Modeling evidence for neuronal lactate uptake upon activation.

    PubMed

    Aubert, Agnès; Costalat, Robert; Magistretti, Pierre J; Pellerin, Luc

    2005-11-01

    A critical issue in brain energy metabolism is whether lactate produced within the brain by astrocytes is taken up and metabolized by neurons upon activation. Although there is ample evidence that neurons can efficiently use lactate as an energy substrate, at least in vitro, few experimental data exist to indicate that it is indeed the case in vivo. To address this question, we used a modeling approach to determine which mechanisms are necessary to explain typical brain lactate kinetics observed upon activation. On the basis of a previously validated model that takes into account the compartmentalization of energy metabolism, we developed a mathematical model of brain lactate kinetics, which was applied to published data describing the changes in extracellular lactate levels upon activation. Results show that the initial dip in the extracellular lactate concentration observed at the onset of stimulation can only be satisfactorily explained by a rapid uptake within an intraparenchymal cellular compartment. In contrast, neither blood flow increase, nor extracellular pH variation can be major causes of the lactate initial dip, whereas tissue lactate diffusion only tends to reduce its amplitude. The kinetic properties of monocarboxylate transporter isoforms strongly suggest that neurons represent the most likely compartment for activation-induced lactate uptake and that neuronal lactate utilization occurring early after activation onset is responsible for the initial dip in brain lactate levels observed in both animals and humans. PMID:16260743

  8. Building a realistic neuronal model that simulates multi-joint arm and hand movements in 3D space.

    PubMed

    Alstermark, Bror; Lan, Ning; Pettersson, Lars-Gunnar

    2007-11-01

    The question as to how the brain controls voluntary movements of the arm and hand still remains largely unsolved despite much research focused on behavioral studies, neurophysiological investigations, and neuronal modeling in computer science. This is because behavioral studies are usually performed without detailed knowledge of the underlying neuronal networks, neurophysiological studies often lack an understanding of the function, and neuronal models are frequently focused on a particular control problem with restricted knowledge of the underlying neuronal networks involved. Therefore, it seems appropriate to start by trying to integrate knowledge of neuronal networks with known function and computer based neuronal models to seek more realistic models that can better control robots or artificial limbs and hands. We propose to combine knowledge of a behavioral model for reaching with the hand toward an object, which is based on detailed knowledge of the underlying neuronal network, and a neuronal model that includes several functional levels, from the planning level via intermediate levels to the final level of control of motoneurons and muscles. PMID:19404420

  9. Application of existing design software to problems in neuronal modeling.

    PubMed

    Vranić-Sowers, S; Fleshman, J W

    1994-03-01

    In this communication, we describe the application of the Valid/Analog Design Tools circuit simulation package called PC Workbench to the problem of modeling the electrical behavior of neural tissue. A nerve cell representation as an equivalent electrical circuit using compartmental models is presented. Several types of nonexcitable and excitable membranes are designed, and simulation results for different types of electrical stimuli are compared to the corresponding analytical data. It is shown that the hardware/software platform and the models developed constitute an accurate, flexible, and powerful way to study neural tissue. PMID:8045583

  10. Neuronal Injury, Gliosis, and Glial Proliferation in Two Models of Temporal Lobe Epilepsy.

    PubMed

    Loewen, Jaycie L; Barker-Haliski, Melissa L; Dahle, E Jill; White, H Steve; Wilcox, Karen S

    2016-04-01

    It is estimated that 30%-40% of epilepsy patients are refractory to therapy and animal models are useful for the identification of more efficacious therapeutic agents. Various well-characterized syndrome-specific models are needed to assess their relevance to human seizure disorders and their validity for testing potential therapies. The corneal kindled mouse model of temporal lobe epilepsy (TLE) allows for the rapid screening of investigational compounds, but there is a lack of information as to the specific inflammatory pathology in this model. Similarly, the Theiler murine encephalomyelitis virus (TMEV) model of TLE may prove to be useful for screening, but quantitative assessment of hippocampal pathology is also lacking. We used immunohistochemistry to characterize and quantitate acute neuronal injury and inflammatory features in dorsal CA1 and dentate gyrus regions and in the directly overlying posterior parietal cortex at 2 time points in each of these TLE models. Corneal kindled mice were observed to have astrogliosis, but not microgliosis or neuron cell death. In contrast, TMEV-injected mice had astrogliosis, microgliosis, neuron death, and astrocyte and microglial proliferation. Our results suggest that these 2 animal models might be appropriate for evaluation of distinct therapies for TLE. PMID:26945036