SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo.

PubMed

Jimenez-Romero, Cristian; Johnson, Jeffrey

2017-01-01

The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work.

Genetic dissection of GABAergic neural circuits in mouse neocortex

PubMed Central

Taniguchi, Hiroki

2014-01-01

Diverse and flexible cortical functions rely on the ability of neural circuits to perform multiple types of neuronal computations. GABAergic inhibitory interneurons significantly contribute to this task by regulating the balance of activity, synaptic integration, spiking, synchrony, and oscillation in a neural ensemble. GABAergic interneurons display a high degree of cellular diversity in morphology, physiology, connectivity, and gene expression. A considerable number of subtypes of GABAergic interneurons diversify modes of cortical inhibition, enabling various types of information processing in the cortex. Thus, comprehensively understanding fate specification, circuit assembly, and physiological function of GABAergic interneurons is a key to elucidate the principles of cortical wiring and function. Recent advances in genetically encoded molecular tools have made a breakthrough to systematically study cortical circuitry at the molecular, cellular, circuit, and whole animal levels. However, the biggest obstacle to fully applying the power of these to analysis of GABAergic circuits was that there were no efficient and reliable methods to express them in subtypes of GABAergic interneurons. Here, I first summarize cortical interneuron diversity and current understanding of mechanisms, by which distinct classes of GABAergic interneurons are generated. I then review recent development in genetically encoded molecular tools for neural circuit research, and genetic targeting of GABAergic interneuron subtypes, particularly focusing on our recent effort to develop and characterize Cre/CreER knockin lines. Finally, I highlight recent success in genetic targeting of chandelier cells, the most unique and distinct GABAergic interneuron subtype, and discuss what kind of questions need to be addressed to understand development and function of cortical inhibitory circuits. PMID:24478631

Engineering-Aligned 3D Neural Circuit in Microfluidic Device.

PubMed

Bang, Seokyoung; Na, Sangcheol; Jang, Jae Myung; Kim, Jinhyun; Jeon, Noo Li

2016-01-07

The brain is one of the most important and complex organs in the human body. Although various neural network models have been proposed for in vitro 3D neuronal networks, it has been difficult to mimic functional and structural complexity of the in vitro neural circuit. Here, a microfluidic model of a simplified 3D neural circuit is reported. First, the microfluidic device is filled with Matrigel and continuous flow is delivered across the device during gelation. The fluidic flow aligns the extracellular matrix (ECM) components along the flow direction. Following the alignment of ECM fibers, neurites of primary rat cortical neurons are grown into the Matrigel at the average speed of 250 μm d(-1) and form axon bundles approximately 1500 μm in length at 6 days in vitro (DIV). Additionally, neural networks are developed from presynaptic to postsynaptic neurons at 14 DIV. The establishment of aligned 3D neural circuits is confirmed with the immunostaining of PSD-95 and synaptophysin and the observation of calcium signal transmission. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Development switch in neural circuitry underlying odor-malaise learning.

PubMed

Shionoya, Kiseko; Moriceau, Stephanie; Lunday, Lauren; Miner, Cathrine; Roth, Tania L; Sullivan, Regina M

2006-01-01

Fetal and infant rats can learn to avoid odors paired with illness before development of brain areas supporting this learning in adults, suggesting an alternate learning circuit. Here we begin to document the transition from the infant to adult neural circuit underlying odor-malaise avoidance learning using LiCl (0.3 M; 1% of body weight, ip) and a 30-min peppermint-odor exposure. Conditioning groups included: Paired odor-LiCl, Paired odor-LiCl-Nursing, LiCl, and odor-saline. Results showed that Paired LiCl-odor conditioning induced a learned odor aversion in postnatal day (PN) 7, 12, and 23 pups. Odor-LiCl Paired Nursing induced a learned odor preference in PN7 and PN12 pups but blocked learning in PN23 pups. 14C 2-deoxyglucose (2-DG) autoradiography indicated enhanced olfactory bulb activity in PN7 and PN12 pups with odor preference and avoidance learning. The odor aversion in weanling aged (PN23) pups resulted in enhanced amygdala activity in Paired odor-LiCl pups, but not if they were nursing. Thus, the neural circuit supporting malaise-induced aversions changes over development, indicating that similar infant and adult-learned behaviors may have distinct neural circuits.

Intergenerational neural mediators of early-life anxious temperament.

PubMed

Fox, Andrew S; Oler, Jonathan A; Shackman, Alexander J; Shelton, Steven E; Raveendran, Muthuswamy; McKay, D Reese; Converse, Alexander K; Alexander, Andrew; Davidson, Richard J; Blangero, John; Rogers, Jeffrey; Kalin, Ned H

2015-07-21

Understanding the heritability of neural systems linked to psychopathology is not sufficient to implicate them as intergenerational neural mediators. By closely examining how individual differences in neural phenotypes and psychopathology cosegregate as they fall through the family tree, we can identify the brain systems that underlie the parent-to-child transmission of psychopathology. Although research has identified genes and neural circuits that contribute to the risk of developing anxiety and depression, the specific neural systems that mediate the inborn risk for these debilitating disorders remain unknown. In a sample of 592 young rhesus monkeys that are part of an extended multigenerational pedigree, we demonstrate that metabolism within a tripartite prefrontal-limbic-midbrain circuit mediates some of the inborn risk for developing anxiety and depression. Importantly, although brain volume is highly heritable early in life, it is brain metabolism-not brain structure-that is the critical intermediary between genetics and the childhood risk to develop stress-related psychopathology.

RRAM-based hardware implementations of artificial neural networks: progress update and challenges ahead

NASA Astrophysics Data System (ADS)

Prezioso, M.; Merrikh-Bayat, F.; Chakrabarti, B.; Strukov, D.

2016-02-01

Artificial neural networks have been receiving increasing attention due to their superior performance in many information processing tasks. Typically, scaling up the size of the network results in better performance and richer functionality. However, large neural networks are challenging to implement in software and customized hardware are generally required for their practical implementations. In this work, we will discuss our group's recent efforts on the development of such custom hardware circuits, based on hybrid CMOS/memristor circuits, in particular of CMOL variety. We will start by reviewing the basics of memristive devices and of CMOL circuits. We will then discuss our recent progress towards demonstration of hybrid circuits, focusing on the experimental and theoretical results for artificial neural networks based on crossbarintegrated metal oxide memristors. We will conclude presentation with the discussion of the remaining challenges and the most pressing research needs.

Genetic dissection of neural circuits underlying sexually dimorphic social behaviours

PubMed Central

Bayless, Daniel W.; Shah, Nirao M.

2016-01-01

The unique hormonal, genetic and epigenetic environments of males and females during development and adulthood shape the neural circuitry of the brain. These differences in neural circuitry result in sex-typical displays of social behaviours such as mating and aggression. Like other neural circuits, those underlying sex-typical social behaviours weave through complex brain regions that control a variety of diverse behaviours. For this reason, the functional dissection of neural circuits underlying sex-typical social behaviours has proved to be difficult. However, molecularly discrete neuronal subpopulations can be identified in the heterogeneous brain regions that control sex-typical social behaviours. In addition, the actions of oestrogens and androgens produce sex differences in gene expression within these brain regions, thereby highlighting the neuronal subpopulations most likely to control sexually dimorphic social behaviours. These conditions permit the implementation of innovative genetic approaches that, in mammals, are most highly advanced in the laboratory mouse. Such approaches have greatly advanced our understanding of the functional significance of sexually dimorphic neural circuits in the brain. In this review, we discuss the neural circuitry of sex-typical social behaviours in mice while highlighting the genetic technical innovations that have advanced the field. PMID:26833830

Neural circuit activity in freely behaving zebrafish (Danio rerio).

PubMed

Issa, Fadi A; O'Brien, Georgeann; Kettunen, Petronella; Sagasti, Alvaro; Glanzman, David L; Papazian, Diane M

2011-03-15

Examining neuronal network activity in freely behaving animals is advantageous for probing the function of the vertebrate central nervous system. Here, we describe a simple, robust technique for monitoring the activity of neural circuits in unfettered, freely behaving zebrafish (Danio rerio). Zebrafish respond to unexpected tactile stimuli with short- or long-latency escape behaviors, which are mediated by distinct neural circuits. Using dipole electrodes immersed in the aquarium, we measured electric field potentials generated in muscle during short- and long-latency escapes. We found that activation of the underlying neural circuits produced unique field potential signatures that are easily recognized and can be repeatedly monitored. In conjunction with behavioral analysis, we used this technique to track changes in the pattern of circuit activation during the first week of development in animals whose trigeminal sensory neurons were unilaterally ablated. One day post-ablation, the frequency of short- and long-latency responses was significantly lower on the ablated side than on the intact side. Three days post-ablation, a significant fraction of escapes evoked by stimuli on the ablated side was improperly executed, with the animal turning towards rather than away from the stimulus. However, the overall response rate remained low. Seven days post-ablation, the frequency of escapes increased dramatically and the percentage of improperly executed escapes declined. Our results demonstrate that trigeminal ablation results in rapid reconfiguration of the escape circuitry, with reinnervation by new sensory neurons and adaptive changes in behavior. This technique is valuable for probing the activity, development, plasticity and regeneration of neural circuits under natural conditions.

Neural circuit activity in freely behaving zebrafish (Danio rerio)

PubMed Central

Issa, Fadi A.; O'Brien, Georgeann; Kettunen, Petronella; Sagasti, Alvaro; Glanzman, David L.; Papazian, Diane M.

2011-01-01

Examining neuronal network activity in freely behaving animals is advantageous for probing the function of the vertebrate central nervous system. Here, we describe a simple, robust technique for monitoring the activity of neural circuits in unfettered, freely behaving zebrafish (Danio rerio). Zebrafish respond to unexpected tactile stimuli with short- or long-latency escape behaviors, which are mediated by distinct neural circuits. Using dipole electrodes immersed in the aquarium, we measured electric field potentials generated in muscle during short- and long-latency escapes. We found that activation of the underlying neural circuits produced unique field potential signatures that are easily recognized and can be repeatedly monitored. In conjunction with behavioral analysis, we used this technique to track changes in the pattern of circuit activation during the first week of development in animals whose trigeminal sensory neurons were unilaterally ablated. One day post-ablation, the frequency of short- and long-latency responses was significantly lower on the ablated side than on the intact side. Three days post-ablation, a significant fraction of escapes evoked by stimuli on the ablated side was improperly executed, with the animal turning towards rather than away from the stimulus. However, the overall response rate remained low. Seven days post-ablation, the frequency of escapes increased dramatically and the percentage of improperly executed escapes declined. Our results demonstrate that trigeminal ablation results in rapid reconfiguration of the escape circuitry, with reinnervation by new sensory neurons and adaptive changes in behavior. This technique is valuable for probing the activity, development, plasticity and regeneration of neural circuits under natural conditions. PMID:21346131

Increasing Spontaneous Retinal Activity before Eye Opening Accelerates the Development of Geniculate Receptive Fields

PubMed Central

Davis, Zachary W.; Chapman, Barbara

2015-01-01

Visually evoked activity is necessary for the normal development of the visual system. However, little is known about the capacity for patterned spontaneous activity to drive the maturation of receptive fields before visual experience. Retinal waves provide instructive retinotopic information for the anatomical organization of the visual thalamus. To determine whether retinal waves also drive the maturation of functional responses, we increased the frequency of retinal waves pharmacologically in the ferret (Mustela putorius furo) during a period of retinogeniculate development before eye opening. The development of geniculate receptive fields after receiving these increased neural activities was measured using single-unit electrophysiology. We found that increased retinal waves accelerate the developmental reduction of geniculate receptive field sizes. This reduction is due to a decrease in receptive field center size rather than an increase in inhibitory surround strength. This work reveals an instructive role for patterned spontaneous activity in guiding the functional development of neural circuits. SIGNIFICANCE STATEMENT Patterned spontaneous neural activity that occurs during development is known to be necessary for the proper formation of neural circuits. However, it is unknown whether the spontaneous activity alone is sufficient to drive the maturation of the functional properties of neurons. Our work demonstrates for the first time an acceleration in the maturation of neural function as a consequence of driving patterned spontaneous activity during development. This work has implications for our understanding of how neural circuits can be modified actively to improve function prematurely or to recover from injury with guided interventions of patterned neural activity. PMID:26511250

Amigo adhesion protein regulates development of neural circuits in zebrafish brain.

PubMed

Zhao, Xiang; Kuja-Panula, Juha; Sundvik, Maria; Chen, Yu-Chia; Aho, Vilma; Peltola, Marjaana A; Porkka-Heiskanen, Tarja; Panula, Pertti; Rauvala, Heikki

2014-07-18

The Amigo protein family consists of three transmembrane proteins characterized by six leucine-rich repeat domains and one immunoglobulin-like domain in their extracellular moieties. Previous in vitro studies have suggested a role as homophilic adhesion molecules in brain neurons, but the in vivo functions remain unknown. Here we have cloned all three zebrafish amigos and show that amigo1 is the predominant family member expressed during nervous system development in zebrafish. Knockdown of amigo1 expression using morpholino oligonucleotides impairs the formation of fasciculated tracts in early fiber scaffolds of brain. A similar defect in fiber tract development is caused by mRNA-mediated expression of the Amigo1 ectodomain that inhibits adhesion mediated by the full-length protein. Analysis of differentiated neural circuits reveals defects in the catecholaminergic system. At the behavioral level, the disturbed formation of neural circuitry is reflected in enhanced locomotor activity and in the inability of the larvae to perform normal escape responses. We suggest that Amigo1 is essential for the development of neural circuits of zebrafish, where its mechanism involves homophilic interactions within the developing fiber tracts and regulation of the Kv2.1 potassium channel to form functional neural circuitry that controls locomotion. © 2014 by The American Society for Biochemistry and Molecular Biology, Inc.

Micropower circuits for bidirectional wireless telemetry in neural recording applications.

PubMed

Neihart, Nathan M; Harrison, Reid R

2005-11-01

State-of-the art neural recording systems require electronics allowing for transcutaneous, bidirectional data transfer. As these circuits will be implanted near the brain, they must be small and low power. We have developed micropower integrated circuits for recovering clock and data signals over a transcutaneous power link. The data recovery circuit produces a digital data signal from an ac power waveform that has been amplitude modulated. We have also developed an FM transmitter with the lowest power dissipation reported for biosignal telemetry. The FM transmitter consists of a low-noise biopotential amplifier and a voltage controlled oscillator used to transmit amplified neural signals at a frequency near 433 MHz. All circuits were fabricated in a standard 0.5-microm CMOS VLSI process. The resulting chip is powered through a wireless inductive link. The power consumption of the clock and data recovery circuits is measured to be 129 microW; the power consumption of the transmitter is measured to be 465 microW when using an external surface mount inductor. Using a parasitic antenna less than 2 mm long, a received power level was measured to be -59.73 dBm at a distance of one meter.

Complex computation in the retina

NASA Astrophysics Data System (ADS)

Deshmukh, Nikhil Rajiv

Elucidating the general principles of computation in neural circuits is a difficult problem requiring both a tractable model circuit as well as sophisticated measurement tools. This thesis advances our understanding of complex computation in the salamander retina and its underlying circuitry and furthers the development of advanced tools to enable detailed study of neural circuits. The retina provides an ideal model system for neural circuits in general because it is capable of producing complex representations of the visual scene, and both its inputs and outputs are accessible to the experimenter. Chapter 2 describes the biophysical mechanisms that give rise to the omitted stimulus response in retinal ganglion cells described in Schwartz et al., (2007) and Schwartz and Berry, (2008). The extra response to omitted flashes is generated at the input to bipolar cells, and is separable from the characteristic latency shift of the OSR apparent in ganglion cells, which must occur downstream in the circuit. Chapter 3 characterizes the nonlinearities at the first synapse of the ON pathway in response to high contrast flashes and develops a phenomenological model that captures the effect of synaptic activation and intracellular signaling dynamics on flash responses. This work is the first attempt to model the dynamics of the poorly characterized mGluR6 transduction cascade unique to ON bipolar cells, and explains the second lobe of the biphasic flash response. Complementary to the study of neural circuits, recent advances in wafer-scale photolithography have made possible new devices to measure the electrical and mechanical properties of neurons. Chapter 4 reports a novel piezoelectric sensor that facilitates the simultaneous measurement of electrical and mechanical signals in neural tissue. This technology could reveal the relationship between the electrical activity of neurons and their local mechanical environment, which is critical to the study of mechanoreceptors, neural development, and traumatic brain injury. Chapter 5 describes advances in the development, fabrication, and testing of a prototype silicon micropipette for patch clamp physiology. Nanoscale photolithography addresses some of the limitations of traditional glass patch electrodes, such as the rapid dialysis of the cell with internal solution, and provides a platform for integration of microfluidics and electronics into the device, which can enable novel experimental methodology.

Modeling neural circuits in Parkinson's disease.

PubMed

Psiha, Maria; Vlamos, Panayiotis

2015-01-01

Parkinson's disease (PD) is caused by abnormal neural activity of the basal ganglia which are connected to the cerebral cortex in the brain surface through complex neural circuits. For a better understanding of the pathophysiological mechanisms of PD, it is important to identify the underlying PD neural circuits, and to pinpoint the precise nature of the crucial aberrations in these circuits. In this paper, the general architecture of a hybrid Multilayer Perceptron (MLP) network for modeling the neural circuits in PD is presented. The main idea of the proposed approach is to divide the parkinsonian neural circuitry system into three discrete subsystems: the external stimuli subsystem, the life-threatening events subsystem, and the basal ganglia subsystem. The proposed model, which includes the key roles of brain neural circuit in PD, is based on both feed-back and feed-forward neural networks. Specifically, a three-layer MLP neural network with feedback in the second layer was designed. The feedback in the second layer of this model simulates the dopamine modulatory effect of compacta on striatum.

The Vite Model: A Neural Command Circuit for Generating Arm and Articulator Trajectories,

DTIC Science & Technology

1988-03-01

Principles of Learning, Perception, Development , Cognition , and Motor Control. Boston: Reidel Press, (1982). Grossberg, S . and Kuperstein, M., Neural...AD-RI92 705 THE YITE MODEL: A NEURAL COMMAND CIRCUIT FO R .# GENERATING ARM AND ARTUCULA..(U) BOSTON UNJY MA CENTER FOR ADAPTIVE SYSTEMS S GROSSUERO...and Articulator Trajectories 6 EFRIGOG EOTNME 7. AUTHOR( s ) 5. CONTRACT OR GRANT NUMBER( s ) Stephen Grossberg XM- F49620-86-C-0O37 Daniel Bullock 9. S

From biological neural networks to thinking machines: Transitioning biological organizational principles to computer technology

NASA Technical Reports Server (NTRS)

Ross, Muriel D.

1991-01-01

The three-dimensional organization of the vestibular macula is under study by computer assisted reconstruction and simulation methods as a model for more complex neural systems. One goal of this research is to transition knowledge of biological neural network architecture and functioning to computer technology, to contribute to the development of thinking computers. Maculas are organized as weighted neural networks for parallel distributed processing of information. The network is characterized by non-linearity of its terminal/receptive fields. Wiring appears to develop through constrained randomness. A further property is the presence of two main circuits, highly channeled and distributed modifying, that are connected through feedforward-feedback collaterals and biasing subcircuit. Computer simulations demonstrate that differences in geometry of the feedback (afferent) collaterals affects the timing and the magnitude of voltage changes delivered to the spike initiation zone. Feedforward (efferent) collaterals act as voltage followers and likely inhibit neurons of the distributed modifying circuit. These results illustrate the importance of feedforward-feedback loops, of timing, and of inhibition in refining neural network output. They also suggest that it is the distributed modifying network that is most involved in adaptation, memory, and learning. Tests of macular adaptation, through hyper- and microgravitational studies, support this hypothesis since synapses in the distributed modifying circuit, but not the channeled circuit, are altered. Transitioning knowledge of biological systems to computer technology, however, remains problematical.

Contemporary approaches to neural circuit manipulation and mapping: focus on reward and addiction

PubMed Central

Saunders, Benjamin T.; Richard, Jocelyn M.; Janak, Patricia H.

2015-01-01

Tying complex psychological processes to precisely defined neural circuits is a major goal of systems and behavioural neuroscience. This is critical for understanding adaptive behaviour, and also how neural systems are altered in states of psychopathology, such as addiction. Efforts to relate psychological processes relevant to addiction to activity within defined neural circuits have been complicated by neural heterogeneity. Recent advances in technology allow for manipulation and mapping of genetically and anatomically defined neurons, which when used in concert with sophisticated behavioural models, have the potential to provide great insight into neural circuit bases of behaviour. Here we discuss contemporary approaches for understanding reward and addiction, with a focus on midbrain dopamine and cortico-striato-pallidal circuits. PMID:26240425

Optogenetic interrogation of neural circuits: technology for probing mammalian brain structures

PubMed Central

Zhang, Feng; Gradinaru, Viviana; Adamantidis, Antoine R; Durand, Remy; Airan, Raag D; de Lecea, Luis; Deisseroth, Karl

2015-01-01

Elucidation of the neural substrates underlying complex animal behaviors depends on precise activity control tools, as well as compatible readout methods. Recent developments in optogenetics have addressed this need, opening up new possibilities for systems neuroscience. Interrogation of even deep neural circuits can be conducted by directly probing the necessity and sufficiency of defined circuit elements with millisecond-scale, cell type-specific optical perturbations, coupled with suitable readouts such as electrophysiology, optical circuit dynamics measures and freely moving behavior in mammals. Here we collect in detail our strategies for delivering microbial opsin genes to deep mammalian brain structures in vivo, along with protocols for integrating the resulting optical control with compatible readouts (electrophysiological, optical and behavioral). The procedures described here, from initial virus preparation to systems-level functional readout, can be completed within 4–5 weeks. Together, these methods may help in providing circuit-level insight into the dynamics underlying complex mammalian behaviors in health and disease. PMID:20203662

Differential regulation of polarized synaptic vesicle trafficking and synapse stability in neural circuit rewiring in Caenorhabditis elegans

PubMed Central

Kurup, Naina; Kono, Karina

2017-01-01

Neural circuits are dynamic, with activity-dependent changes in synapse density and connectivity peaking during different phases of animal development. In C. elegans, young larvae form mature motor circuits through a dramatic switch in GABAergic neuron connectivity, by concomitant elimination of existing synapses and formation of new synapses that are maintained throughout adulthood. We have previously shown that an increase in microtubule dynamics during motor circuit rewiring facilitates new synapse formation. Here, we further investigate cellular control of circuit rewiring through the analysis of mutants obtained in a forward genetic screen. Using live imaging, we characterize novel mutations that alter cargo binding in the dynein motor complex and enhance anterograde synaptic vesicle movement during remodeling, providing in vivo evidence for the tug-of-war between kinesin and dynein in fast axonal transport. We also find that a casein kinase homolog, TTBK-3, inhibits stabilization of nascent synapses in their new locations, a previously unexplored facet of structural plasticity of synapses. Our study delineates temporally distinct signaling pathways that are required for effective neural circuit refinement. PMID:28636662

Grand Research Plan for Neural Circuits of Emotion and Memory--current status of neural circuit studies in China.

PubMed

Zhu, Yuan-Gui; Cao, He-Qi; Dong, Er-Dan

2013-02-01

During recent years, major advances have been made in neuroscience, i.e., asynchronous release, three-dimensional structural data sets, saliency maps, magnesium in brain research, and new functional roles of long non-coding RNAs. Especially, the development of optogenetic technology provides access to important information about relevant neural circuits by allowing the activation of specific neurons in awake mammals and directly observing the resulting behavior. The Grand Research Plan for Neural Circuits of Emotion and Memory was launched by the National Natural Science Foundation of China. It takes emotion and memory as its main objects, making the best use of cutting-edge technologies from medical science, life science and information science. In this paper, we outline the current status of neural circuit studies in China and the technologies and methodologies being applied, as well as studies related to the impairments of emotion and memory. In this phase, we are making efforts to repair the current deficiencies by making adjustments, mainly involving four aspects of core scientific issues to investigate these circuits at multiple levels. Five research directions have been taken to solve important scientific problems while the Grand Research Plan is implemented. Future research into this area will be multimodal, incorporating a range of methods and sciences into each project. Addressing these issues will ensure a bright future, major discoveries, and a higher level of treatment for all affected by debilitating brain illnesses.

Optogenetics in preclinical neuroscience and psychiatry research: recent insights and potential applications.

PubMed

McDevitt, Ross A; Reed, Sean J; Britt, Jonathan P

2014-01-01

There have been significant advances in the treatment of psychiatric disease in the last half century, but it is still unclear which neural circuits are ultimately responsible for specific disease states. Fortunately, technical limitations that have constrained this research have recently been mitigated by advances in research tools that facilitate circuit-based analyses. The most prominent of these tools is optogenetics, which refers to the use of genetically encoded, light-sensitive proteins that can be used to manipulate discrete neural circuits with temporal precision. Optogenetics has recently been used to examine the neural underpinnings of both psychiatric disease and symptom relief, and this research has rapidly identified novel therapeutic targets for what could be a new generation of rational drug development. As these and related methodologies for controlling neurons ultimately make their way into the clinic, circuit-based strategies for alleviating psychiatric symptoms could become a remarkably refined approach to disease treatment.

Nuevas tecnicas basadas en redes neuronales para el diseno de filtros de microondas multicapa apantallados

NASA Astrophysics Data System (ADS)

Pascual Garcia, Juan

In this PhD thesis one method of shielded multilayer circuit neural network based analysis has been developed. One of the most successful analysis procedures of these kind of structures is the Integral Equation technique (IE) solved by the Method of Moments (MoM). In order to solve the IE, in the version which uses the media relevant potentials, it is necessary to have a formulation of the Green's functions associated to the mentioned potentials. The main computational burden in the IE resolution lies on the numerical evaluation of the Green's functions. In this work, the circuit analysis has been drastically accelerated thanks to the approximation of the Green's functions by means of neural networks. Once trained, the neural networks substitute the Green's functions in the IE. Two different types of neural networks have been used: the Radial basis function neural networks (RBFNN) and the Chebyshev neural networks. Thanks mainly to two distinct operations the correct approximation of the Green's functions has been possible. On the one hand, a very effective input space division has been developed. On the other hand, the elimination of the singularity makes feasible the approximation of slow variation functions. Two different singularity elimination strategies have been developed. The first one is based on the multiplication by the source-observation points distance (rho). The second one outperforms the first one. It consists of the extraction of two layers of spatial images from the whole summation of images. With regard to the Chebyshev neural networks, the OLS training algorithm has been applied in a novel fashion. This method allows the optimum design in this kind of neural networks. In this way, the performance of these neural networks outperforms greatly the RBFNNs one. In both networks, the time gain reached makes the neural method profitable. The time invested in the input space division and in the neural training is negligible with only few circuit analysis. To show, in a practical way, the ability of the neural based analysis method, two new design procedures have been developed. The first method uses the Genetic Algorithms to optimize an initial filter which does not fulfill the established specifications. A new fitness function, specially well suited to design filters, has been defined in order to assure the correct convergence of the optimization process. This new function measures the fulfillment of the specifications and it also prevents the appearance of the premature convergence problem. The second method is found on the approximation, by means of neural networks, of the relations between the electrical parameters, which defined the circuit response, and the physical dimensions that synthesize the aforementioned parameters. The neural networks trained with these data can be used in the design of many circuits in a given structure. Both methods had been show their ability in the design of practical filters.

Rodent Zic Genes in Neural Network Wiring.

PubMed

Herrera, Eloísa

2018-01-01

The formation of the nervous system is a multistep process that yields a mature brain. Failure in any of the steps of this process may cause brain malfunction. In the early stages of embryonic development, neural progenitors quickly proliferate and then, at a specific moment, differentiate into neurons or glia. Once they become postmitotic neurons, they migrate to their final destinations and begin to extend their axons to connect with other neurons, sometimes located in quite distant regions, to establish different neural circuits. During the last decade, it has become evident that Zic genes, in addition to playing important roles in early development (e.g., gastrulation and neural tube closure), are involved in different processes of late brain development, such as neuronal migration, axon guidance, and refinement of axon terminals. ZIC proteins are therefore essential for the proper wiring and connectivity of the brain. In this chapter, we review our current knowledge of the role of Zic genes in the late stages of neural circuit formation.

Optical Neural Interfaces

PubMed Central

Warden, Melissa R.; Cardin, Jessica A.; Deisseroth, Karl

2014-01-01

Genetically encoded optical actuators and indicators have changed the landscape of neuroscience, enabling targetable control and readout of specific components of intact neural circuits in behaving animals. Here, we review the development of optical neural interfaces, focusing on hardware designed for optical control of neural activity, integrated optical control and electrical readout, and optical readout of population and single-cell neural activity in freely moving mammals. PMID:25014785

Advances in two photon scanning and scanless microscopy technologies for functional neural circuit imaging.

PubMed

Schultz, Simon R; Copeland, Caroline S; Foust, Amanda J; Quicke, Peter; Schuck, Renaud

2017-01-01

Recent years have seen substantial developments in technology for imaging neural circuits, raising the prospect of large scale imaging studies of neural populations involved in information processing, with the potential to lead to step changes in our understanding of brain function and dysfunction. In this article we will review some key recent advances: improved fluorophores for single cell resolution functional neuroimaging using a two photon microscope; improved approaches to the problem of scanning active circuits; and the prospect of scanless microscopes which overcome some of the bandwidth limitations of current imaging techniques. These advances in technology for experimental neuroscience have in themselves led to technical challenges, such as the need for the development of novel signal processing and data analysis tools in order to make the most of the new experimental tools. We review recent work in some active topics, such as region of interest segmentation algorithms capable of demixing overlapping signals, and new highly accurate algorithms for calcium transient detection. These advances motivate the development of new data analysis tools capable of dealing with spatial or spatiotemporal patterns of neural activity, that scale well with pattern size.

Advances in two photon scanning and scanless microscopy technologies for functional neural circuit imaging

PubMed Central

Schultz, Simon R.; Copeland, Caroline S.; Foust, Amanda J.; Quicke, Peter; Schuck, Renaud

2017-01-01

Recent years have seen substantial developments in technology for imaging neural circuits, raising the prospect of large scale imaging studies of neural populations involved in information processing, with the potential to lead to step changes in our understanding of brain function and dysfunction. In this article we will review some key recent advances: improved fluorophores for single cell resolution functional neuroimaging using a two photon microscope; improved approaches to the problem of scanning active circuits; and the prospect of scanless microscopes which overcome some of the bandwidth limitations of current imaging techniques. These advances in technology for experimental neuroscience have in themselves led to technical challenges, such as the need for the development of novel signal processing and data analysis tools in order to make the most of the new experimental tools. We review recent work in some active topics, such as region of interest segmentation algorithms capable of demixing overlapping signals, and new highly accurate algorithms for calcium transient detection. These advances motivate the development of new data analysis tools capable of dealing with spatial or spatiotemporal patterns of neural activity, that scale well with pattern size. PMID:28757657

Oxytocin modulation of neural circuits for social behavior.

PubMed

Marlin, Bianca J; Froemke, Robert C

2017-02-01

Oxytocin is a hypothalamic neuropeptide that has gained attention for the effects on social behavior. Recent findings shed new light on the mechanisms of oxytocin in synaptic plasticity and adaptively modifying neural circuits for social interactions such as conspecific recognition, pair bonding, and maternal care. Here, we review several of these newer studies on oxytocin in the context of previous findings, with an emphasis on social behavior and circuit plasticity in various brain regions shown to be enriched for oxytocin receptors. We provide a framework that highlights current circuit-level mechanisms underlying the widespread action of oxytocin. © 2016 Wiley Periodicals, Inc. Develop Neurobiol 77: 169-189, 2017. © 2016 Wiley Periodicals, Inc.

Demultiplexer circuit for neural stimulation

DOEpatents

Wessendorf, Kurt O; Okandan, Murat; Pearson, Sean

2012-10-09

A demultiplexer circuit is disclosed which can be used with a conventional neural stimulator to extend the number of electrodes which can be activated. The demultiplexer circuit, which is formed on a semiconductor substrate containing a power supply that provides all the dc electrical power for operation of the circuit, includes digital latches that receive and store addressing information from the neural stimulator one bit at a time. This addressing information is used to program one or more 1:2.sup.N demultiplexers in the demultiplexer circuit which then route neural stimulation signals from the neural stimulator to an electrode array which is connected to the outputs of the 1:2.sup.N demultiplexer. The demultiplexer circuit allows the number of individual electrodes in the electrode array to be increased by a factor of 2.sup.N with N generally being in a range of 2-4.

A plausible neural circuit for decision making and its formation based on reinforcement learning.

PubMed

Wei, Hui; Dai, Dawei; Bu, Yijie

2017-06-01

A human's, or lower insects', behavior is dominated by its nervous system. Each stable behavior has its own inner steps and control rules, and is regulated by a neural circuit. Understanding how the brain influences perception, thought, and behavior is a central mandate of neuroscience. The phototactic flight of insects is a widely observed deterministic behavior. Since its movement is not stochastic, the behavior should be dominated by a neural circuit. Based on the basic firing characteristics of biological neurons and the neural circuit's constitution, we designed a plausible neural circuit for this phototactic behavior from logic perspective. The circuit's output layer, which generates a stable spike firing rate to encode flight commands, controls the insect's angular velocity when flying. The firing pattern and connection type of excitatory and inhibitory neurons are considered in this computational model. We simulated the circuit's information processing using a distributed PC array, and used the real-time average firing rate of output neuron clusters to drive a flying behavior simulation. In this paper, we also explored how a correct neural decision circuit is generated from network flow view through a bee's behavior experiment based on the reward and punishment feedback mechanism. The significance of this study: firstly, we designed a neural circuit to achieve the behavioral logic rules by strictly following the electrophysiological characteristics of biological neurons and anatomical facts. Secondly, our circuit's generality permits the design and implementation of behavioral logic rules based on the most general information processing and activity mode of biological neurons. Thirdly, through computer simulation, we achieved new understanding about the cooperative condition upon which multi-neurons achieve some behavioral control. Fourthly, this study aims in understanding the information encoding mechanism and how neural circuits achieve behavior control. Finally, this study also helps establish a transitional bridge between the microscopic activity of the nervous system and macroscopic animal behavior.

[Lower urinary tract dysfunction and neuropathological findings of the neural circuits controlling micturition in familial amyotrophic lateral sclerosis with L106V mutation in the SOD1 gene].

PubMed

Hineno, Akiyo; Oyanagi, Kiyomitsu; Nakamura, Akinori; Shimojima, Yoshio; Yoshida, Kunihiro; Ikeda, Shu-Ichi

2016-01-01

We report lower urinary tract dysfunction and neuropathological findings of the neural circuits controlling micturition in the patients with familial amyotrophic lateral sclerosis having L106V mutation in the SOD1 gene. Ten of 20 patients showed lower urinary tract dysfunction and 5 patients developed within 1 year after the onset of weakness. In 8 patients with an artificial respirator, 6 patients showed lower urinary tract dysfunction. Lower urinary tract dysfunction and respiratory failure requiring an artificial respirator occurred simultaneously in 3 patients. Neuronal loss and gliosis were observed in the neural circuits controlling micturition, such as frontal lobe, thalamus, hypothalamus, striatum, periaqueductal gray, ascending spinal tract, lateral corticospinal tract, intermediolateral nucleus and Onufrowicz' nucleus. Lower urinary tract dysfunction, especially storage symptoms, developed about 1 year after the onset of weakness, and the dysfunction occurred simultaneously with artificial respirator use in the patients.

Illuminating Neural Circuits: From Molecules to MRI.

PubMed

Lee, Jin Hyung; Kreitzer, Anatol C; Singer, Annabelle C; Schiff, Nicholas D

2017-11-08

Neurological disease drives symptoms through pathological changes to circuit functions. Therefore, understanding circuit mechanisms that drive behavioral dysfunction is of critical importance for quantitative diagnosis and systematic treatment of neurological disease. Here, we describe key technologies that enable measurement and manipulation of neural activity and neural circuits. Applying these approaches led to the discovery of circuit mechanisms underlying pathological motor behavior, arousal regulation, and protein accumulation. Finally, we discuss how optogenetic functional magnetic resonance imaging reveals global scale circuit mechanisms, and how circuit manipulations could lead to new treatments of neurological diseases. Copyright © 2017 the authors 0270-6474/17/3710817-09$15.00/0.

Conic section function neural network circuitry for offline signature recognition.

PubMed

Erkmen, Burcu; Kahraman, Nihan; Vural, Revna A; Yildirim, Tulay

2010-04-01

In this brief, conic section function neural network (CSFNN) circuitry was designed for offline signature recognition. CSFNN is a unified framework for multilayer perceptron (MLP) and radial basis function (RBF) networks to make simultaneous use of advantages of both. The CSFNN circuitry architecture was developed using a mixed mode circuit implementation. The designed circuit system is problem independent. Hence, the general purpose neural network circuit system could be applied to various pattern recognition problems with different network sizes on condition with the maximum network size of 16-16-8. In this brief, CSFNN circuitry system has been applied to two different signature recognition problems. CSFNN circuitry was trained with chip-in-the-loop learning technique in order to compensate typical analog process variations. CSFNN hardware achieved highly comparable computational performances with CSFNN software for nonlinear signature recognition problems.

Functional neural networks underlying response inhibition in adolescents and adults.

PubMed

Stevens, Michael C; Kiehl, Kent A; Pearlson, Godfrey D; Calhoun, Vince D

2007-07-19

This study provides the first description of neural network dynamics associated with response inhibition in healthy adolescents and adults. Functional and effective connectivity analyses of whole brain hemodynamic activity elicited during performance of a Go/No-Go task were used to identify functionally integrated neural networks and characterize their causal interactions. Three response inhibition circuits formed a hierarchical, inter-dependent system wherein thalamic modulation of input to premotor cortex by fronto-striatal regions led to response suppression. Adolescents differed from adults in the degree of network engagement, regional fronto-striatal-thalamic connectivity, and network dynamics. We identify and characterize several age-related differences in the function of neural circuits that are associated with behavioral performance changes across adolescent development.

Functional neural networks underlying response inhibition in adolescents and adults

PubMed Central

Stevens, Michael C.; Kiehl, Kent A.; Pearlson, Godfrey D.; Calhoun, Vince D.

2008-01-01

This study provides the first description of neural network dynamics associated with response inhibition in healthy adolescents and adults. Functional and effective connectivity analyses of whole brain hemodynamic activity elicited during performance of a Go/No-Go task were used to identify functionally-integrated neural networks and characterize their causal interactions. Three response inhibition circuits formed a hierarchical, inter-dependent system wherein thalamic modulation of input to premotor cortex by frontostriatal regions led to response suppression. Adolescents differed from adults in the degree of network engagement, regional fronto-striatal-thalamic connectivity, and network dynamics. We identify and characterize several age-related differences in the function of neural circuits that are associated with behavioral performance changes across adolescent development. PMID:17467816

Resting-State Functional Connectivity in the Human Connectome Project: Current Status and Relevance to Understanding Psychopathology.

PubMed

Barch, Deanna M

A key tenet of modern psychiatry is that psychiatric disorders arise from abnormalities in brain circuits that support human behavior. Our ability to examine hypotheses around circuit-level abnormalities in psychiatric disorders has been made possible by advances in human neuroimaging technologies. These advances have provided the basis for recent efforts to develop a more complex understanding of the function of brain circuits in health and of their relationship to behavior-providing, in turn, a foundation for our understanding of how disruptions in such circuits contribute to the development of psychiatric disorders. This review focuses on the use of resting-state functional connectivity MRI to assess brain circuits, on the advances generated by the Human Connectome Project, and on how these advances potentially contribute to understanding neural circuit dysfunction in psychopathology. The review gives particular attention to the methods developed by the Human Connectome Project that may be especially relevant to studies of psychopathology; it outlines some of the key findings about what constitutes a brain region; and it highlights new information about the nature and stability of brain circuits. Some of the Human Connectome Project's new findings particularly relevant to psychopathology-about neural circuits and their relationships to behavior-are also presented. The review ends by discussing the extension of Human Connectome Project methods across the lifespan and into manifest illness. Potential treatment implications are also considered.

An Activity for Demonstrating the Concept of a Neural Circuit

ERIC Educational Resources Information Center

Kreiner, David S.

2012-01-01

College students in two sections of a general psychology course participated in a demonstration of a simple neural circuit. The activity was based on a neural circuit that Jeffress proposed for localizing sounds. Students in one section responded to a questionnaire prior to participating in the activity, while students in the other section…

Detection of inter-turn short-circuit at start-up of induction machine based on torque analysis

NASA Astrophysics Data System (ADS)

Pietrowski, Wojciech; Górny, Konrad

2017-12-01

Recently, interest in new diagnostics methods in a field of induction machines was observed. Research presented in the paper shows the diagnostics of induction machine based on torque pulsation, under inter-turn short-circuit, during start-up of a machine. In the paper three numerical techniques were used: finite element analysis, signal analysis and artificial neural networks (ANN). The elaborated numerical model of faulty machine consists of field, circuit and motion equations. Voltage excited supply allowed to determine the torque waveform during start-up. The inter-turn short-circuit was treated as a galvanic connection between two points of the stator winding. The waveforms were calculated for different amounts of shorted-turns from 0 to 55. Due to the non-stationary waveforms a wavelet packet decomposition was used to perform an analysis of the torque. The obtained results of analysis were used as input vector for ANN. The response of the neural network was the number of shorted-turns in the stator winding. Special attention was paid to compare response of general regression neural network (GRNN) and multi-layer perceptron neural network (MLP). Based on the results of the research, the efficiency of the developed algorithm can be inferred.

Dynamic changes in neural circuit topology following mild mechanical injury in vitro.

PubMed

Patel, Tapan P; Ventre, Scott C; Meaney, David F

2012-01-01

Despite its enormous incidence, mild traumatic brain injury is not well understood. One aspect that needs more definition is how the mechanical energy during injury affects neural circuit function. Recent developments in cellular imaging probes provide an opportunity to assess the dynamic state of neural networks with single-cell resolution. In this article, we developed imaging methods to assess the state of dissociated cortical networks exposed to mild injury. We estimated the imaging conditions needed to achieve accurate measures of network properties, and applied these methodologies to evaluate if mild mechanical injury to cortical neurons produces graded changes to either spontaneous network activity or altered network topology. We found that modest injury produced a transient increase in calcium activity that dissipated within 1 h after injury. Alternatively, moderate mechanical injury produced immediate disruption in network synchrony, loss in excitatory tone, and increased modular topology. A calcium-activated neutral protease (calpain) was a key intermediary in these changes; blocking calpain activation restored the network nearly completely to its pre-injury state. Together, these findings show a more complex change in neural circuit behavior than previously reported for mild mechanical injury, and highlight at least one important early mechanism responsible for these changes.

Neurodevelopmental effects of chronic exposure to elevated levels of pro-inflammatory cytokines in a developing visual system

PubMed Central

2010-01-01

Background Imbalances in the regulation of pro-inflammatory cytokines have been increasingly correlated with a number of severe and prevalent neurodevelopmental disorders, including autism spectrum disorder, schizophrenia and Down syndrome. Although several studies have shown that cytokines have potent effects on neural function, their role in neural development is still poorly understood. In this study, we investigated the link between abnormal cytokine levels and neural development using the Xenopus laevis tadpole visual system, a model frequently used to examine the anatomical and functional development of neural circuits. Results Using a test for a visually guided behavior that requires normal visual system development, we examined the long-term effects of prolonged developmental exposure to three pro-inflammatory cytokines with known neural functions: interleukin (IL)-1β, IL-6 and tumor necrosis factor (TNF)-α. We found that all cytokines affected the development of normal visually guided behavior. Neuroanatomical imaging of the visual projection showed that none of the cytokines caused any gross abnormalities in the anatomical organization of this projection, suggesting that they may be acting at the level of neuronal microcircuits. We further tested the effects of TNF-α on the electrophysiological properties of the retinotectal circuit and found that long-term developmental exposure to TNF-α resulted in enhanced spontaneous excitatory synaptic transmission in tectal neurons, increased AMPA/NMDA ratios of retinotectal synapses, and a decrease in the number of immature synapses containing only NMDA receptors, consistent with premature maturation and stabilization of these synapses. Local interconnectivity within the tectum also appeared to remain widespread, as shown by increased recurrent polysynaptic activity, and was similar to what is seen in more immature, less refined tectal circuits. TNF-α treatment also enhanced the overall growth of tectal cell dendrites. Finally, we found that TNF-α-reared tadpoles had increased susceptibility to pentylenetetrazol-induced seizures. Conclusions Taken together our data are consistent with a model in which TNF-α causes premature stabilization of developing synapses within the tectum, therefore preventing normal refinement and synapse elimination that occurs during development, leading to increased local connectivity and epilepsy. This experimental model also provides an integrative approach to understanding the effects of cytokines on the development of neural circuits and may provide novel insights into the etiology underlying some neurodevelopmental disorders. PMID:20067608

Neurodevelopmental effects of chronic exposure to elevated levels of pro-inflammatory cytokines in a developing visual system.

PubMed

Lee, Ryan H; Mills, Elizabeth A; Schwartz, Neil; Bell, Mark R; Deeg, Katherine E; Ruthazer, Edward S; Marsh-Armstrong, Nicholas; Aizenman, Carlos D

2010-01-12

Imbalances in the regulation of pro-inflammatory cytokines have been increasingly correlated with a number of severe and prevalent neurodevelopmental disorders, including autism spectrum disorder, schizophrenia and Down syndrome. Although several studies have shown that cytokines have potent effects on neural function, their role in neural development is still poorly understood. In this study, we investigated the link between abnormal cytokine levels and neural development using the Xenopus laevis tadpole visual system, a model frequently used to examine the anatomical and functional development of neural circuits. Using a test for a visually guided behavior that requires normal visual system development, we examined the long-term effects of prolonged developmental exposure to three pro-inflammatory cytokines with known neural functions: interleukin (IL)-1beta, IL-6 and tumor necrosis factor (TNF)-alpha. We found that all cytokines affected the development of normal visually guided behavior. Neuroanatomical imaging of the visual projection showed that none of the cytokines caused any gross abnormalities in the anatomical organization of this projection, suggesting that they may be acting at the level of neuronal microcircuits. We further tested the effects of TNF-alpha on the electrophysiological properties of the retinotectal circuit and found that long-term developmental exposure to TNF-alpha resulted in enhanced spontaneous excitatory synaptic transmission in tectal neurons, increased AMPA/NMDA ratios of retinotectal synapses, and a decrease in the number of immature synapses containing only NMDA receptors, consistent with premature maturation and stabilization of these synapses. Local interconnectivity within the tectum also appeared to remain widespread, as shown by increased recurrent polysynaptic activity, and was similar to what is seen in more immature, less refined tectal circuits. TNF-alpha treatment also enhanced the overall growth of tectal cell dendrites. Finally, we found that TNF-alpha-reared tadpoles had increased susceptibility to pentylenetetrazol-induced seizures. Taken together our data are consistent with a model in which TNF-alpha causes premature stabilization of developing synapses within the tectum, therefore preventing normal refinement and synapse elimination that occurs during development, leading to increased local connectivity and epilepsy. This experimental model also provides an integrative approach to understanding the effects of cytokines on the development of neural circuits and may provide novel insights into the etiology underlying some neurodevelopmental disorders.

Spatiotemporal Imaging of Glutamate-Induced Biophotonic Activities and Transmission in Neural Circuits

PubMed Central

Tang, Rendong; Dai, Jiapei

2014-01-01

The processing of neural information in neural circuits plays key roles in neural functions. Biophotons, also called ultra-weak photon emissions (UPE), may play potential roles in neural signal transmission, contributing to the understanding of the high functions of nervous system such as vision, learning and memory, cognition and consciousness. However, the experimental analysis of biophotonic activities (emissions) in neural circuits has been hampered due to technical limitations. Here by developing and optimizing an in vitro biophoton imaging method, we characterize the spatiotemporal biophotonic activities and transmission in mouse brain slices. We show that the long-lasting application of glutamate to coronal brain slices produces a gradual and significant increase of biophotonic activities and achieves the maximal effect within approximately 90 min, which then lasts for a relatively long time (>200 min). The initiation and/or maintenance of biophotonic activities by glutamate can be significantly blocked by oxygen and glucose deprivation, together with the application of a cytochrome c oxidase inhibitor (sodium azide), but only partly by an action potential inhibitor (TTX), an anesthetic (procaine), or the removal of intracellular and extracellular Ca2+. We also show that the detected biophotonic activities in the corpus callosum and thalamus in sagittal brain slices mostly originate from axons or axonal terminals of cortical projection neurons, and that the hyperphosphorylation of microtubule-associated protein tau leads to a significant decrease of biophotonic activities in these two areas. Furthermore, the application of glutamate in the hippocampal dentate gyrus results in increased biophotonic activities in its intrahippocampal projection areas. These results suggest that the glutamate-induced biophotonic activities reflect biophotonic transmission along the axons and in neural circuits, which may be a new mechanism for the processing of neural information. PMID:24454909

Spatiotemporal imaging of glutamate-induced biophotonic activities and transmission in neural circuits.

PubMed

Tang, Rendong; Dai, Jiapei

2014-01-01

The processing of neural information in neural circuits plays key roles in neural functions. Biophotons, also called ultra-weak photon emissions (UPE), may play potential roles in neural signal transmission, contributing to the understanding of the high functions of nervous system such as vision, learning and memory, cognition and consciousness. However, the experimental analysis of biophotonic activities (emissions) in neural circuits has been hampered due to technical limitations. Here by developing and optimizing an in vitro biophoton imaging method, we characterize the spatiotemporal biophotonic activities and transmission in mouse brain slices. We show that the long-lasting application of glutamate to coronal brain slices produces a gradual and significant increase of biophotonic activities and achieves the maximal effect within approximately 90 min, which then lasts for a relatively long time (>200 min). The initiation and/or maintenance of biophotonic activities by glutamate can be significantly blocked by oxygen and glucose deprivation, together with the application of a cytochrome c oxidase inhibitor (sodium azide), but only partly by an action potential inhibitor (TTX), an anesthetic (procaine), or the removal of intracellular and extracellular Ca(2+). We also show that the detected biophotonic activities in the corpus callosum and thalamus in sagittal brain slices mostly originate from axons or axonal terminals of cortical projection neurons, and that the hyperphosphorylation of microtubule-associated protein tau leads to a significant decrease of biophotonic activities in these two areas. Furthermore, the application of glutamate in the hippocampal dentate gyrus results in increased biophotonic activities in its intrahippocampal projection areas. These results suggest that the glutamate-induced biophotonic activities reflect biophotonic transmission along the axons and in neural circuits, which may be a new mechanism for the processing of neural information.

Evolution of central pattern generators and rhythmic behaviours

PubMed Central

Katz, Paul S.

2016-01-01

Comparisons of rhythmic movements and the central pattern generators (CPGs) that control them uncover principles about the evolution of behaviour and neural circuits. Over the course of evolutionary history, gradual evolution of behaviours and their neural circuitry within any lineage of animals has been a predominant occurrence. Small changes in gene regulation can lead to divergence of circuit organization and corresponding changes in behaviour. However, some behavioural divergence has resulted from large-scale rewiring of the neural network. Divergence of CPG circuits has also occurred without a corresponding change in behaviour. When analogous rhythmic behaviours have evolved independently, it has generally been with different neural mechanisms. Repeated evolution of particular rhythmic behaviours has occurred within some lineages due to parallel evolution or latent CPGs. Particular motor pattern generating mechanisms have also evolved independently in separate lineages. The evolution of CPGs and rhythmic behaviours shows that although most behaviours and neural circuits are highly conserved, the nature of the behaviour does not dictate the neural mechanism and that the presence of homologous neural components does not determine the behaviour. This suggests that although behaviour is generated by neural circuits, natural selection can act separately on these two levels of biological organization. PMID:26598733

Evolution of central pattern generators and rhythmic behaviours.

PubMed

Katz, Paul S

2016-01-05

Comparisons of rhythmic movements and the central pattern generators (CPGs) that control them uncover principles about the evolution of behaviour and neural circuits. Over the course of evolutionary history, gradual evolution of behaviours and their neural circuitry within any lineage of animals has been a predominant occurrence. Small changes in gene regulation can lead to divergence of circuit organization and corresponding changes in behaviour. However, some behavioural divergence has resulted from large-scale rewiring of the neural network. Divergence of CPG circuits has also occurred without a corresponding change in behaviour. When analogous rhythmic behaviours have evolved independently, it has generally been with different neural mechanisms. Repeated evolution of particular rhythmic behaviours has occurred within some lineages due to parallel evolution or latent CPGs. Particular motor pattern generating mechanisms have also evolved independently in separate lineages. The evolution of CPGs and rhythmic behaviours shows that although most behaviours and neural circuits are highly conserved, the nature of the behaviour does not dictate the neural mechanism and that the presence of homologous neural components does not determine the behaviour. This suggests that although behaviour is generated by neural circuits, natural selection can act separately on these two levels of biological organization. © 2015 The Author(s).

A Point-process Response Model for Spike Trains from Single Neurons in Neural Circuits under Optogenetic Stimulation

PubMed Central

Luo, X.; Gee, S.; Sohal, V.; Small, D.

2015-01-01

Optogenetics is a new tool to study neuronal circuits that have been genetically modified to allow stimulation by flashes of light. We study recordings from single neurons within neural circuits under optogenetic stimulation. The data from these experiments present a statistical challenge of modeling a high frequency point process (neuronal spikes) while the input is another high frequency point process (light flashes). We further develop a generalized linear model approach to model the relationships between two point processes, employing additive point-process response functions. The resulting model, Point-process Responses for Optogenetics (PRO), provides explicit nonlinear transformations to link the input point process with the output one. Such response functions may provide important and interpretable scientific insights into the properties of the biophysical process that governs neural spiking in response to optogenetic stimulation. We validate and compare the PRO model using a real dataset and simulations, and our model yields a superior area-under-the- curve value as high as 93% for predicting every future spike. For our experiment on the recurrent layer V circuit in the prefrontal cortex, the PRO model provides evidence that neurons integrate their inputs in a sophisticated manner. Another use of the model is that it enables understanding how neural circuits are altered under various disease conditions and/or experimental conditions by comparing the PRO parameters. PMID:26411923

Spontaneous network activity and synaptic development

PubMed Central

Kerschensteiner, Daniel

2014-01-01

Throughout development, the nervous system produces patterned spontaneous activity. Research over the last two decades has revealed a core group of mechanisms that mediate spontaneous activity in diverse circuits. Many circuits engage several of these mechanisms sequentially to accommodate developmental changes in connectivity. In addition to shared mechanisms, activity propagates through developing circuits and neuronal pathways (i.e. linked circuits in different brain areas) in stereotypic patterns. Increasing evidence suggests that spontaneous network activity shapes synaptic development in vivo. Variations in activity-dependent plasticity may explain how similar mechanisms and patterns of activity can be employed to establish diverse circuits. Here, I will review common mechanisms and patterns of spontaneous activity in emerging neural networks and discuss recent insights into their contribution to synaptic development. PMID:24280071

Neurotrophic actions of dopamine on the development of a serotonergic feeding circuit in Drosophila melanogaster

PubMed Central

2012-01-01

Background In the fruit fly, Drosophila melanogaster, serotonin functions both as a neurotransmitter to regulate larval feeding, and in the development of the stomatogastric feeding circuit. There is an inverse relationship between neuronal serotonin levels during late embryogenesis and the complexity of the serotonergic fibers projecting from the larval brain to the foregut, which correlate with perturbations in feeding, the functional output of the circuit. Dopamine does not modulate larval feeding, and dopaminergic fibers do not innervate the larval foregut. Since dopamine can function in central nervous system development, separate from its role as a neurotransmitter, the role of neuronal dopamine was assessed on the development, and mature function, of the 5-HT larval feeding circuit. Results Both decreased and increased neuronal dopamine levels in late embryogenesis during development of this circuit result in depressed levels of larval feeding. Perturbations in neuronal dopamine during this developmental period also result in greater branch complexity of the serotonergic fibers innervating the gut, as well as increased size and number of the serotonin-containing vesicles along the neurite length. This neurotrophic action for dopamine is modulated by the D2 dopamine receptor expressed during late embryogenesis in central 5-HT neurons. Animals carrying transgenic RNAi constructs to knock down both dopamine and serotonin synthesis in the central nervous system display normal feeding and fiber architecture. However, disparate levels of neuronal dopamine and serotonin during development of the circuit result in abnormal gut fiber architecture and feeding behavior. Conclusions These results suggest that dopamine can exert a direct trophic influence on the development of a specific neural circuit, and that dopamine and serotonin may interact with each other to generate the neural architecture necessary for normal function of the circuit. PMID:22413901

A neural circuit mechanism for regulating vocal variability during song learning in zebra finches.

PubMed

Garst-Orozco, Jonathan; Babadi, Baktash; Ölveczky, Bence P

2014-12-15

Motor skill learning is characterized by improved performance and reduced motor variability. The neural mechanisms that couple skill level and variability, however, are not known. The zebra finch, a songbird, presents a unique opportunity to address this question because production of learned song and induction of vocal variability are instantiated in distinct circuits that converge on a motor cortex analogue controlling vocal output. To probe the interplay between learning and variability, we made intracellular recordings from neurons in this area, characterizing how their inputs from the functionally distinct pathways change throughout song development. We found that inputs that drive stereotyped song-patterns are strengthened and pruned, while inputs that induce variability remain unchanged. A simple network model showed that strengthening and pruning of action-specific connections reduces the sensitivity of motor control circuits to variable input and neural 'noise'. This identifies a simple and general mechanism for learning-related regulation of motor variability.

Modulation of neural circuits: how stimulus context shapes innate behavior in Drosophila.

PubMed

Su, Chih-Ying; Wang, Jing W

2014-12-01

Remarkable advances have been made in recent years in our understanding of innate behavior and the underlying neural circuits. In particular, a wealth of neuromodulatory mechanisms have been uncovered that can alter the input-output relationship of a hereditary neural circuit. It is now clear that this inbuilt flexibility allows animals to modify their behavioral responses according to environmental cues, metabolic demands and physiological states. Here, we discuss recent insights into how modulation of neural circuits impacts innate behavior, with a special focus on how environmental cues and internal physiological states shape different aspects of feeding behavior in Drosophila. Copyright © 2014 Elsevier Ltd. All rights reserved.

Running hot and cold: behavioral strategies, neural circuits, and the molecular machinery for thermotaxis in C. elegans and Drosophila

PubMed Central

Garrity, Paul A.; Goodman, Miriam B.; Samuel, Aravinthan D.; Sengupta, Piali

2010-01-01

Like other ectotherms, the roundworm Caenorhabditis elegans and the fruit fly Drosophila melanogaster rely on behavioral strategies to stabilize their body temperature. Both animals use specialized sensory neurons to detect small changes in temperature, and the activity of these thermosensors governs the neural circuits that control migration and accumulation at preferred temperatures. Despite these similarities, the underlying molecular, neuronal, and computational mechanisms responsible for thermotaxis are distinct in these organisms. Here, we discuss the role of thermosensation in the development and survival of C. elegans and Drosophila, and review the behavioral strategies, neuronal circuits, and molecular networks responsible for thermotaxis behavior. PMID:21041406

Neural Correlates of Aggressive Behavior in Real Time: a Review of fMRI Studies of Laboratory Reactive Aggression

PubMed Central

Keedy, Sarah; Berman, Mitchell E.; Lee, Royce; Coccaro, Emil F.

2017-01-01

Purpose of review Aggressive behavior has adaptive value in many natural environments; however, it places substantial burden and costs on human society. For this reason, there has long been interest in understanding the neurobiological basis of aggression. This interest, and the flourishing of neuroimaging research in general, has spurred the development of a large and growing scientific literature on the topic. As a result, a neural circuit model of aggressive behavior has emerged that implicates interconnected brain regions that are involved in emotional reactivity, emotion regulation, and cognitive control. Recent findings Recently, behavioral paradigms that simulate provocative interactions have been adapted to neuroimaging protocols, providing an opportunity to directly probe the involvement of neural circuits in an aggressive interaction. Here we review neuroimaging studies of simulated aggressive interactions in research volunteers. We focus on studies that use a well-validated laboratory paradigm for reactive physical aggression and examine the neural correlates of provocation, retaliation, and evaluating punishment of an opponent. Summary Overall, the studies reviewed support the involvement of neural circuits that support emotional reactivity, emotion regulation, and cognitive control in aggressive behavior. Based on a synthesis of this literature, future research directions are discussed. PMID:29607288

Circuit Models and Experimental Noise Measurements of Micropipette Amplifiers for Extracellular Neural Recordings from Live Animals

PubMed Central

Chen, Chang Hao; Pun, Sio Hang; Mak, Peng Un; Vai, Mang I; Klug, Achim; Lei, Tim C.

2014-01-01

Glass micropipettes are widely used to record neural activity from single neurons or clusters of neurons extracellularly in live animals. However, to date, there has been no comprehensive study of noise in extracellular recordings with glass micropipettes. The purpose of this work was to assess various noise sources that affect extracellular recordings and to create model systems in which novel micropipette neural amplifier designs can be tested. An equivalent circuit of the glass micropipette and the noise model of this circuit, which accurately describe the various noise sources involved in extracellular recordings, have been developed. Measurement schemes using dead brain tissue as well as extracellular recordings from neurons in the inferior colliculus, an auditory brain nucleus of an anesthetized gerbil, were used to characterize noise performance and amplification efficacy of the proposed micropipette neural amplifier. According to our model, the major noise sources which influence the signal to noise ratio are the intrinsic noise of the neural amplifier and the thermal noise from distributed pipette resistance. These two types of noise were calculated and measured and were shown to be the dominating sources of background noise for in vivo experiments. PMID:25133158

Expansion microscopy: development and neuroscience applications.

PubMed

Karagiannis, Emmanouil D; Boyden, Edward S

2018-06-01

Many neuroscience questions center around understanding how the molecules and wiring in neural circuits mechanistically yield behavioral functions, or go awry in disease states. However, mapping the molecules and wiring of neurons across the large scales of neural circuits has posed a great challenge. We recently developed expansion microscopy (ExM), a process in which we physically magnify biological specimens such as brain circuits. We synthesize throughout preserved brain specimens a dense, even mesh of a swellable polymer such as sodium polyacrylate, anchoring key biomolecules such as proteins and nucleic acids to the polymer. After mechanical homogenization of the specimen-polymer composite, we add water, and the polymer swells, pulling biomolecules apart. Due to the larger separation between molecules, ordinary microscopes can then perform nanoscale resolution imaging. We here review the ExM technology as well as applications to the mapping of synapses, cells, and circuits, including deployment in species such as Drosophila, mouse, non-human primate, and human. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Circuit-Based Neural Network with Hybrid Learning of Backpropagation and Random Weight Change Algorithms

PubMed Central

Yang, Changju; Kim, Hyongsuk; Adhikari, Shyam Prasad; Chua, Leon O.

2016-01-01

A hybrid learning method of a software-based backpropagation learning and a hardware-based RWC learning is proposed for the development of circuit-based neural networks. The backpropagation is known as one of the most efficient learning algorithms. A weak point is that its hardware implementation is extremely difficult. The RWC algorithm, which is very easy to implement with respect to its hardware circuits, takes too many iterations for learning. The proposed learning algorithm is a hybrid one of these two. The main learning is performed with a software version of the BP algorithm, firstly, and then, learned weights are transplanted on a hardware version of a neural circuit. At the time of the weight transplantation, a significant amount of output error would occur due to the characteristic difference between the software and the hardware. In the proposed method, such error is reduced via a complementary learning of the RWC algorithm, which is implemented in a simple hardware. The usefulness of the proposed hybrid learning system is verified via simulations upon several classical learning problems. PMID:28025566

A TinyOS-enabled MICA2-based wireless neural interface.

PubMed

Farshchi, Shahin; Nuyujukian, Paul H; Pesterev, Aleksey; Mody, Istvan; Judy, Jack W

2006-07-01

Existing approaches used to develop compact low-power multichannel wireless neural recording systems range from creating custom-integrated circuits to assembling commercial-off-the-shelf (COTS) PC-based components. Custom-integrated-circuit designs yield extremely compact and low-power devices at the expense of high development and upgrade costs and turn-around times, while assembling COTS-PC-technology yields high performance at the expense of large system size and increased power consumption. To achieve a balance between implementing an ultra-compact custom-fabricated neural transceiver and assembling COTS-PC-technology, an overlay of a neural interface upon the TinyOS-based MICA2 platform is described. The system amplifies, digitally encodes, and transmits neural signals real-time at a rate of 9.6 kbps, while consuming less than 66 mW of power. The neural signals are received and forwarded to a client PC over a serial connection. This data rate can be divided for recording on up to 6 channels, with a resolution of 8 bits/sample. This work demonstrates the strengths and limitations of the TinyOS-based sensor technology as a foundation for chronic remote biological monitoring applications and, thus, provides an opportunity to create a system that can leverage from the frequent networking and communications advancements being made by the global TinyOS-development community.

Motor Cortical Visuomotor Feedback Activity Is Initially Isolated from Downstream Targets in Output-Null Neural State Space Dimensions.

PubMed

Stavisky, Sergey D; Kao, Jonathan C; Ryu, Stephen I; Shenoy, Krishna V

2017-07-05

Neural circuits must transform new inputs into outputs without prematurely affecting downstream circuits while still maintaining other ongoing communication with these targets. We investigated how this isolation is achieved in the motor cortex when macaques received visual feedback signaling a movement perturbation. To overcome limitations in estimating the mapping from cortex to arm movements, we also conducted brain-machine interface (BMI) experiments where we could definitively identify neural firing patterns as output-null or output-potent. This revealed that perturbation-evoked responses were initially restricted to output-null patterns that cancelled out at the neural population code readout and only later entered output-potent neural dimensions. This mechanism was facilitated by the circuit's large null space and its ability to strongly modulate output-potent dimensions when generating corrective movements. These results show that the nervous system can temporarily isolate portions of a circuit's activity from its downstream targets by restricting this activity to the circuit's output-null neural dimensions. Copyright © 2017 Elsevier Inc. All rights reserved.

Neonatal ghrelin programs development of hypothalamic feeding circuits

PubMed Central

Steculorum, Sophie M.; Collden, Gustav; Coupe, Berengere; Croizier, Sophie; Lockie, Sarah; Andrews, Zane B.; Jarosch, Florian; Klussmann, Sven; Bouret, Sebastien G.

2015-01-01

A complex neural network regulates body weight and energy balance, and dysfunction in the communication between the gut and this neural network is associated with metabolic diseases, such as obesity. The stomach-derived hormone ghrelin stimulates appetite through interactions with neurons in the arcuate nucleus of the hypothalamus (ARH). Here, we evaluated the physiological and neurobiological contribution of ghrelin during development by specifically blocking ghrelin action during early postnatal development in mice. Ghrelin blockade in neonatal mice resulted in enhanced ARH neural projections and long-term metabolic effects, including increased body weight, visceral fat, and blood glucose levels and decreased leptin sensitivity. In addition, chronic administration of ghrelin during postnatal life impaired the normal development of ARH projections and caused metabolic dysfunction. Consistent with these observations, direct exposure of postnatal ARH neuronal explants to ghrelin blunted axonal growth and blocked the neurotrophic effect of the adipocyte-derived hormone leptin. Moreover, chronic ghrelin exposure in neonatal mice also attenuated leptin-induced STAT3 signaling in ARH neurons. Collectively, these data reveal that ghrelin plays an inhibitory role in the development of hypothalamic neural circuits and suggest that proper expression of ghrelin during neonatal life is pivotal for lifelong metabolic regulation. PMID:25607843

Affective neural response to restricted interests in Autism Spectrum Disorders

PubMed Central

Cascio, Carissa J.; Foss-Feig, Jennifer H.; Heacock, Jessica; Schauder, Kimberly B.; Loring, Whitney A.; Rogers, Baxter P.; Pryweller, Jennifer R.; Newsom, Cassandra R.; Cockhren, Jurnell; Cao, Aize; Bolton, Scott

2013-01-01

Background Restricted interests are a class of repetitive behavior in autism spectrum disorders (ASD) whose intensity and narrow focus often contribute to significant interference with daily functioning. While numerous neuroimaging studies have investigated executive circuits as putative neural substrates of repetitive behavior, recent work implicates affective neural circuits in restricted interests. We sought to explore the role of affective neural circuits and determine how restricted interests are distinguished from hobbies or interests in typical development. Methods We compared a group of children with ASD to a typically developing (TD) group of children with strong interests or hobbies, employing parent report, an operant behavioral task, and functional imaging with personalized stimuli based on individual interests. Results While performance on the operant task was similar between the two groups, parent report of intensity and interference of interests was significantly higher in the ASD group. Both the ASD and TD groups showed increased BOLD response in widespread affective neural regions to pictures of their own interest. When viewing pictures of other children's interests, the TD group showed a similar pattern, whereas BOLD response in the ASD group was much more limited. Increased BOLD response in the insula and anterior cingulate cortex distinguished the ASD from the TD group, and parent report of the intensity and interference with daily life of the child's restricted interest predicted insula response. Conclusions While affective neural network response and operant behavior are comparable in typical and restricted interests, the narrowness of focus that clinically distinguishes restricted interests in ASD is reflected in more interference in daily life and aberrantly enhanced insula and anterior cingulate response to individuals’ own interests in the ASD group. These results further support the involvement of affective neural networks in repetitive behaviors in ASD. PMID:24117668

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

PubMed

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

2018-02-01

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

Femtosecond laser dissection in C. elegans neural circuits

NASA Astrophysics Data System (ADS)

Samuel, Aravinthan D. T.; Chung, Samuel H.; Clark, Damon A.; Gabel, Christopher V.; Chang, Chieh; Murthy, Venkatesh; Mazur, Eric

2006-02-01

The nematode C. elegans, a millimeter-long roundworm, is a well-established model organism for studies of neural development and behavior, however physiological methods to manipulate and monitor the activity of its neural network have lagged behind the development of powerful methods in genetics and molecular biology. The small size and transparency of C. elegans make the worm an ideal test-bed for the development of physiological methods derived from optics and microscopy. We present the development and application of a new physiological tool: femtosecond laser dissection, which allows us to selectively ablate segments of individual neural fibers within live C. elegans. Femtosecond laser dissection provides a scalpel with submicrometer resolution, and we discuss its application in studies of neural growth, regenerative growth, and the neural basis of behavior.

Demonstration of a neural circuit critical for imprinting behavior in chicks.

PubMed

Nakamori, Tomoharu; Sato, Katsushige; Atoji, Yasuro; Kanamatsu, Tomoyuki; Tanaka, Kohichi; Ohki-Hamazaki, Hiroko

2010-03-24

Imprinting behavior in birds is elicited by visual and/or auditory cues. It has been demonstrated previously that visual cues are recognized and processed in the visual Wulst (VW), and imprinting memory is stored in the intermediate medial mesopallium (IMM) of the telencephalon. Alteration of neural responses in these two regions according to imprinting has been reported, yet direct evidence of the neural circuit linking these two regions is lacking. Thus, it remains unclear how memory is formed and expressed in this circuit. Here, we present anatomical as well as physiological evidence of the neural circuit connecting the VW and IMM and show that imprinting training during the critical period strengthens and refines this circuit. A functional connection established by imprint training resulted in an imprinting behavior. After the closure of the critical period, training could not activate this circuit nor induce the imprinting behavior. Glutamatergic neurons in the ventroposterior region of the VW, the core region of the hyperpallium densocellulare (HDCo), sent their axons to the periventricular part of the HD, just dorsal and afferent to the IMM. We found that the HDCo is important in imprinting behavior. The refinement and/or enhancement of this neural circuit are attributed to increased activity of HDCo cells, and the activity depended on NR2B-containing NMDA receptors. These findings show a neural connection in the telencephalon in Aves and demonstrate that NR2B function is indispensable for the plasticity of HDCo cells, which are key mediators of imprinting.

Chaos in a neural network circuit

NASA Astrophysics Data System (ADS)

Kepler, Thomas B.; Datt, Sumeet; Meyer, Robert B.; Abott, L. F.

1990-12-01

We have constructed a neural network circuit of four clipped, high-grain, integrating operational amplifiers coupled to each other through an array of digitally programmable resistor ladders (MDACs). In addition to fixed-point and cyclic behavior, the circuit exhibits chaotic behavior with complex strange attractors which are approached through period doubling, intermittent attractor expansion and/or quasiperiodic pathways. Couplings between the nonlinear circuit elements are controlled by a computer which can automatically search through the space of couplings for interesting phenomena. We report some initial statistical results relating the behavior of the network to properties of its coupling matrix. Through these results and further research the circuit should help resolve fundamental issues concerning chaos in neural networks.

Synaptic Inputs Compete during Rapid Formation of the Calyx of Held: A New Model System for Neural Development

PubMed Central

Holcomb, Paul S.; Hoffpauir, Brian K.; Hoyson, Mitchell C.; Jackson, Dakota R.; Deerinck, Thomas J.; Marrs, Glenn S.; Dehoff, Marlin; Wu, Jonathan; Ellisman, Mark H.

2013-01-01

Hallmark features of neural circuit development include early exuberant innervation followed by competition and pruning to mature innervation topography. Several neural systems, including the neuromuscular junction and climbing fiber innervation of Purkinje cells, are models to study neural development in part because they establish a recognizable endpoint of monoinnervation of their targets and because the presynaptic terminals are large and easily monitored. We demonstrate here that calyx of Held (CH) innervation of its target, which forms a key element of auditory brainstem binaural circuitry, exhibits all of these characteristics. To investigate CH development, we made the first application of serial block-face scanning electron microscopy to neural development with fine temporal resolution and thereby accomplished the first time series for 3D ultrastructural analysis of neural circuit formation. This approach revealed a growth spurt of added apposed surface area (ASA) >200 μm2/d centered on a single age at postnatal day 3 in mice and an initial rapid phase of growth and competition that resolved to monoinnervation in two-thirds of cells within 3 d. This rapid growth occurred in parallel with an increase in action potential threshold, which may mediate selection of the strongest input as the winning competitor. ASAs of competing inputs were segregated on the cell body surface. These data suggest mechanisms to select “winning” inputs by regional reinforcement of postsynaptic membrane to mediate size and strength of competing synaptic inputs. PMID:23926251

A Translational Neuroscience Approach to Understanding the Development of Social Anxiety Disorder and its Pathophysiology

PubMed Central

Fox, Andrew S.; Kalin, Ned H.

2014-01-01

This review brings together recent research from molecular, neural circuit, animal model, and human studies to understand the neurodevelopmental mechanisms underlying Social Anxiety Disorder (SAD). SAD is common, debilitating, and often leads to further psychopathology. Numerous studies demonstrate that extremely behaviorally inhibited and temperamentally anxious young children are at marked risk to develop SAD. Recent work in human and nonhuman primates has identified a distributed brain network that underlies early-life anxiety including: central nucleus of the amygdala, anterior hippocampus and orbitofrontal cortex. Moreover, studies in nonhuman primates demonstrate that alterations in this circuit are trait-like in that they are stable over time and across contexts. Importantly, the components of this circuit are differentially influenced by heritable and environmental factors and specific lesion studies demonstrate a causal role for multiple components of the circuit. Molecular studies in rodents and primates are pointing to disrupted neurodevelopmental and neuroplastic processes within critical components of the early-life dispositional anxiety neural circuit. The possibility of identifying an early-life at-risk phenotype, along with an understanding of its neurobiology, provides an unusual opportunity to conceptualize novel preventive intervention strategies aimed at reducing the suffering of anxious children and preventing them from developing further psychopathology. PMID:25157566

A translational neuroscience approach to understanding the development of social anxiety disorder and its pathophysiology.

PubMed

Fox, Andrew S; Kalin, Ned H

2014-11-01

This review brings together recent research from molecular, neural circuit, animal model, and human studies to help understand the neurodevelopmental mechanisms underlying social anxiety disorder. Social anxiety disorder is common and debilitating, and it often leads to further psychopathology. Numerous studies have demonstrated that extremely behaviorally inhibited and temperamentally anxious young children are at marked risk of developing social anxiety disorder. Recent work in human and nonhuman primates has identified a distributed brain network that underlies early-life anxiety including the central nucleus of the amygdala, the anterior hippocampus, and the orbitofrontal cortex. Studies in nonhuman primates have demonstrated that alterations in this circuit are trait-like in that they are stable over time and across contexts. Notably, the components of this circuit are differentially influenced by heritable and environmental factors, and specific lesion studies have demonstrated a causal role for multiple components of the circuit. Molecular studies in rodents and primates point to disrupted neurodevelopmental and neuroplastic processes within critical components of the early-life dispositional anxiety neural circuit. The possibility of identifying an early-life at-risk phenotype, along with an understanding of its neurobiology, provides an unusual opportunity to conceptualize novel preventive intervention strategies aimed at reducing the suffering of anxious children and preventing them from developing further psychopathology.

Bidirectional global spontaneous network activity precedes the canonical unidirectional circuit organization in the developing hippocampus.

PubMed

Shi, Yulin; Ikrar, Taruna; Olivas, Nicholas D; Xu, Xiangmin

2014-06-15

Spontaneous network activity is believed to sculpt developing neural circuits. Spontaneous giant depolarizing potentials (GDPs) were first identified with single-cell recordings from rat CA3 pyramidal neurons, but here we identify and characterize a large-scale spontaneous network activity we term global network activation (GNA) in the developing mouse hippocampal slices, which is measured macroscopically by fast voltage-sensitive dye imaging. The initiation and propagation of GNA in the mouse is largely GABA-independent and dominated by glutamatergic transmission via AMPA receptors. Despite the fact that signal propagation in the adult hippocampus is strongly unidirectional through the canonical trisynaptic circuit (dentate gyrus [DG] to CA3 to CA1), spontaneous GNA in the developing hippocampus originates in distal CA3 and propagates both forward to CA1 and backward to DG. Photostimulation-evoked GNA also shows prominent backward propagation in the developing hippocampus from CA3 to DG. Mouse GNA is strongly correlated to electrophysiological recordings of highly localized single-cell and local field potential events. Photostimulation mapping of neural circuitry demonstrates that the enhancement of local circuit connections to excitatory pyramidal neurons occurs over the same time course as GNA and reveals the underlying pathways accounting for GNA backward propagation from CA3 to DG. The disappearance of GNA coincides with a transition to the adult-like unidirectional circuit organization at about 2 weeks of age. Taken together, our findings strongly suggest a critical link between GNA activity and maturation of functional circuit connections in the developing hippocampus. Copyright © 2013 Wiley Periodicals, Inc.

Dissociable attentional and affective circuits in medication-naïve children with attention-deficit/hyperactivity disorder.

PubMed

Posner, Jonathan; Rauh, Virginia; Gruber, Allison; Gat, Inbal; Wang, Zhishun; Peterson, Bradley S

2013-07-30

Current neurocognitive models of attention-deficit/hyperactivity disorder (ADHD) suggest that neural circuits involving both attentional and affective processing make independent contributions to the phenomenology of the disorder. However, a clear dissociation of attentional and affective circuits and their behavioral correlates has yet to be shown in medication-naïve children with ADHD. Using resting-state functional connectivity MRI (rs-fcMRI) in a cohort of medication naïve children with (N=22) and without (N=20) ADHD, we demonstrate that children with ADHD have reduced connectivity in two neural circuits: one underlying executive attention (EA) and the other emotional regulation (ER). We also demonstrate a double dissociation between these two neural circuits and their behavioral correlates such that reduced connectivity in the EA circuit correlates with executive attention deficits but not with emotional lability, while on the other hand, reduced connectivity in the ER circuit correlates with emotional lability but not with executive attention deficits. These findings suggest potential avenues for future research such as examining treatment effects on these two neural circuits as well as the potential prognostic and developmental significance of disturbances in one circuit vs the other. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Maintenance of Mouse Gustatory Terminal Field Organization Is Disrupted following Selective Removal of Peripheral Sodium Salt Taste Activity at Adulthood

PubMed Central

Sun, Chengsan

2017-01-01

Neural activity plays a critical role in the development of central circuits in sensory systems. However, the maintenance of these circuits at adulthood is usually not dependent on sensory-elicited neural activity. Recent work in the mouse gustatory system showed that selectively deleting the primary transduction channel for sodium taste, the epithelial sodium channel (ENaC), throughout development dramatically impacted the organization of the central terminal fields of three nerves that carry taste information to the nucleus of the solitary tract. More specifically, deleting ENaCs during development prevented the normal maturation of the fields. The present study was designed to extend these findings by testing the hypothesis that the loss of sodium taste activity impacts the maintenance of the normal adult terminal field organization in male and female mice. To do this, we used an inducible Cre-dependent genetic recombination strategy to delete ENaC function after terminal field maturation occurred. We found that removal of sodium taste neural activity at adulthood resulted in significant reorganization of mature gustatory afferent terminal fields in the nucleus of the solitary tract. Specifically, the chorda tympani and greater superficial petrosal nerve terminal fields were 1.4× and 1.6× larger than age-matched controls, respectively. By contrast, the glossopharyngeal nerve, which is not highly sensitive to sodium taste stimulation, did not undergo terminal field reorganization. These surprising results suggest that gustatory nerve terminal fields remain plastic well into adulthood, which likely impacts central coding of taste information and taste-related behaviors with altered taste experience. SIGNIFICANCE STATEMENT Neural activity plays a major role in the development of sensory circuits in the mammalian brain. However, the importance of sensory-driven activity in maintaining these circuits at adulthood, especially in subcortical structures, appears to be much less. Here, we tested whether the loss of sodium taste activity in adult mice impacts the maintenance of how taste nerves project to the first central relay. We found that specific loss of sodium-elicited taste activity at adulthood produced dramatic and selective reorganization of terminal fields in the brainstem. This demonstrates, for the first time, that taste-elicited activity is necessary for the normal maintenance of central gustatory circuits at adulthood and highlights a level of plasticity not seen in other sensory system subcortical circuits. PMID:28676575

Deep learning with coherent nanophotonic circuits

NASA Astrophysics Data System (ADS)

Shen, Yichen; Harris, Nicholas C.; Skirlo, Scott; Prabhu, Mihika; Baehr-Jones, Tom; Hochberg, Michael; Sun, Xin; Zhao, Shijie; Larochelle, Hugo; Englund, Dirk; Soljačić, Marin

2017-07-01

Artificial neural networks are computational network models inspired by signal processing in the brain. These models have dramatically improved performance for many machine-learning tasks, including speech and image recognition. However, today's computing hardware is inefficient at implementing neural networks, in large part because much of it was designed for von Neumann computing schemes. Significant effort has been made towards developing electronic architectures tuned to implement artificial neural networks that exhibit improved computational speed and accuracy. Here, we propose a new architecture for a fully optical neural network that, in principle, could offer an enhancement in computational speed and power efficiency over state-of-the-art electronics for conventional inference tasks. We experimentally demonstrate the essential part of the concept using a programmable nanophotonic processor featuring a cascaded array of 56 programmable Mach-Zehnder interferometers in a silicon photonic integrated circuit and show its utility for vowel recognition.

Netrin-G1 regulates fear-like and anxiety-like behaviors in dissociable neural circuits.

PubMed

Zhang, Qi; Sano, Chie; Masuda, Akira; Ando, Reiko; Tanaka, Mika; Itohara, Shigeyoshi

2016-06-27

In vertebrate mammals, distributed neural circuits in the brain are involved in emotion-related behavior. Netrin-G1 is a glycosyl-phosphatidylinositol-anchored synaptic adhesion molecule whose deficiency results in impaired fear-like and anxiety-like behaviors under specific circumstances. To understand the cell type and circuit specificity of these responses, we generated netrin-G1 conditional knockout mice with loss of expression in cortical excitatory neurons, inhibitory neurons, or thalamic neurons. Genetic deletion of netrin-G1 in cortical excitatory neurons resulted in altered anxiety-like behavior, but intact fear-like behavior, whereas loss of netrin-G1 in inhibitory neurons resulted in attenuated fear-like behavior, but intact anxiety-like behavior. These data indicate a remarkable double dissociation of fear-like and anxiety-like behaviors involving netrin-G1 in excitatory and inhibitory neurons, respectively. Our findings support a crucial role for netrin-G1 in dissociable neural circuits for the modulation of emotion-related behaviors, and provide genetic models for investigating the mechanisms underlying the dissociation. The results also suggest the involvement of glycosyl-phosphatidylinositol-anchored synaptic adhesion molecules in the development and pathogenesis of emotion-related behavior.

Experience-dependent emergence of beta and gamma band oscillations in the primary visual cortex during the critical period

PubMed Central

Chen, Guang; Rasch, Malte J.; Wang, Ran; Zhang, Xiao-hui

2015-01-01

Neural oscillatory activities have been shown to play important roles in neural information processing and the shaping of circuit connections during development. However, it remains unknown whether and how specific neural oscillations emerge during a postnatal critical period (CP), in which neuronal connections are most substantially modified by neural activity and experience. By recording local field potentials (LFPs) and single unit activity in developing primary visual cortex (V1) of head-fixed awake mice, we here demonstrate an emergence of characteristic oscillatory activities during the CP. From the pre-CP to CP, the peak frequency of spontaneous fast oscillatory activities shifts from the beta band (15–35 Hz) to the gamma band (40–70 Hz), accompanied by a decrease of cross-frequency coupling (CFC) and broadband spike-field coherence (SFC). Moreover, visual stimulation induced a large increase of beta-band activity but a reduction of gamma-band activity specifically from the CP onwards. Dark rearing of animals from the birth delayed this emergence of oscillatory activities during the CP, suggesting its dependence on early visual experience. These findings suggest that the characteristic neuronal oscillatory activities emerged specifically during the CP may represent as neural activity trait markers for the experience-dependent maturation of developing visual cortical circuits. PMID:26648548

Visual complexity: a review.

PubMed

Donderi, Don C

2006-01-01

The idea of visual complexity, the history of its measurement, and its implications for behavior are reviewed, starting with structuralism and Gestalt psychology at the beginning of the 20th century and ending with visual complexity theory, perceptual learning theory, and neural circuit theory at the beginning of the 21st. Evidence is drawn from research on single forms, form and texture arrays and visual displays. Form complexity and form probability are shown to be linked through their reciprocal relationship in complexity theory, which is in turn shown to be consistent with recent developments in perceptual learning and neural circuit theory. Directions for further research are suggested.

High Aspect-Ratio Neural Probes using Conventional Blade Dicing

NASA Astrophysics Data System (ADS)

Goncalves, S. B.; Ribeiro, J. F.; Silva, A. F.; Correia, J. H.

2016-10-01

Exploring deep neural circuits has triggered the development of long penetrating neural probes. Moreover, driven by brain displacement, the long neural probes require also a high aspect-ratio shafts design. In this paper, a simple and reproducible method of manufacturing long-shafts neural probes using blade dicing technology is presented. Results shows shafts up to 8 mm long and 200 µm wide, features competitive to the current state-of-art, being its outline simply accomplished by a single blade dicing program. Therefore, conventional blade dicing presents itself as a viable option to manufacture long neural probes.

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Multispectral image fusion using neural networks

NASA Technical Reports Server (NTRS)

Kagel, J. H.; Platt, C. A.; Donaven, T. W.; Samstad, E. A.

1990-01-01

A prototype system is being developed to demonstrate the use of neural network hardware to fuse multispectral imagery. This system consists of a neural network IC on a motherboard, a circuit card assembly, and a set of software routines hosted by a PC-class computer. Research in support of this consists of neural network simulations fusing 4 to 7 bands of Landsat imagery and fusing (separately) multiple bands of synthetic imagery. The simulations, results, and a description of the prototype system are presented.

Obstacle Avoidance and Target Acquisition for Robot Navigation Using a Mixed Signal Analog/Digital Neuromorphic Processing System

PubMed Central

Milde, Moritz B.; Blum, Hermann; Dietmüller, Alexander; Sumislawska, Dora; Conradt, Jörg; Indiveri, Giacomo; Sandamirskaya, Yulia

2017-01-01

Neuromorphic hardware emulates dynamics of biological neural networks in electronic circuits offering an alternative to the von Neumann computing architecture that is low-power, inherently parallel, and event-driven. This hardware allows to implement neural-network based robotic controllers in an energy-efficient way with low latency, but requires solving the problem of device variability, characteristic for analog electronic circuits. In this work, we interfaced a mixed-signal analog-digital neuromorphic processor ROLLS to a neuromorphic dynamic vision sensor (DVS) mounted on a robotic vehicle and developed an autonomous neuromorphic agent that is able to perform neurally inspired obstacle-avoidance and target acquisition. We developed a neural network architecture that can cope with device variability and verified its robustness in different environmental situations, e.g., moving obstacles, moving target, clutter, and poor light conditions. We demonstrate how this network, combined with the properties of the DVS, allows the robot to avoid obstacles using a simple biologically-inspired dynamics. We also show how a Dynamic Neural Field for target acquisition can be implemented in spiking neuromorphic hardware. This work demonstrates an implementation of working obstacle avoidance and target acquisition using mixed signal analog/digital neuromorphic hardware. PMID:28747883

Obstacle Avoidance and Target Acquisition for Robot Navigation Using a Mixed Signal Analog/Digital Neuromorphic Processing System.

PubMed

Milde, Moritz B; Blum, Hermann; Dietmüller, Alexander; Sumislawska, Dora; Conradt, Jörg; Indiveri, Giacomo; Sandamirskaya, Yulia

2017-01-01

Neuromorphic hardware emulates dynamics of biological neural networks in electronic circuits offering an alternative to the von Neumann computing architecture that is low-power, inherently parallel, and event-driven. This hardware allows to implement neural-network based robotic controllers in an energy-efficient way with low latency, but requires solving the problem of device variability, characteristic for analog electronic circuits. In this work, we interfaced a mixed-signal analog-digital neuromorphic processor ROLLS to a neuromorphic dynamic vision sensor (DVS) mounted on a robotic vehicle and developed an autonomous neuromorphic agent that is able to perform neurally inspired obstacle-avoidance and target acquisition. We developed a neural network architecture that can cope with device variability and verified its robustness in different environmental situations, e.g., moving obstacles, moving target, clutter, and poor light conditions. We demonstrate how this network, combined with the properties of the DVS, allows the robot to avoid obstacles using a simple biologically-inspired dynamics. We also show how a Dynamic Neural Field for target acquisition can be implemented in spiking neuromorphic hardware. This work demonstrates an implementation of working obstacle avoidance and target acquisition using mixed signal analog/digital neuromorphic hardware.

Updating Procedures Can Reorganize the Neural Circuit Supporting a Fear Memory.

PubMed

Kwapis, Janine L; Jarome, Timothy J; Ferrara, Nicole C; Helmstetter, Fred J

2017-07-01

Established memories undergo a period of vulnerability following retrieval, a process termed 'reconsolidation.' Recent work has shown that the hypothetical process of reconsolidation is only triggered when new information is presented during retrieval, suggesting that this process may allow existing memories to be modified. Reconsolidation has received increasing attention as a possible therapeutic target for treating disorders that stem from traumatic memories, yet little is known about how this process changes the original memory. In particular, it is unknown whether reconsolidation can reorganize the neural circuit supporting an existing memory after that memory is modified with new information. Here, we show that trace fear memory undergoes a protein synthesis-dependent reconsolidation process following exposure to a single updating trial of delay conditioning. Further, this reconsolidation-dependent updating process appears to reorganize the neural circuit supporting the trace-trained memory, so that it better reflects the circuit supporting delay fear. Specifically, after a trace-to-delay update session, the amygdala is now required for extinction of the updated memory but the retrosplenial cortex is no longer required for retrieval. These results suggest that updating procedures could be used to force a complex, poorly defined memory circuit to rely on a better-defined neural circuit that may be more amenable to behavioral or pharmacological manipulation. This is the first evidence that exposure to new information can fundamentally reorganize the neural circuit supporting an existing memory.

Understanding the dynamical control of animal movement

NASA Astrophysics Data System (ADS)

Edwards, Donald

2008-03-01

Over the last 50 years, neurophysiologists have described many neural circuits that transform sensory input into motor commands, while biomechanicians and behavioral biologists have described many patterns of animal movement that occur in response to sensory input. Attempts to link these two have been frustrated by our technical inability to record from the necessary neurons in a freely behaving animal. As a result, we don't know how these neural circuits function in the closed loop context of free behavior, where the sensory and motor context changes on a millisecond time-scale. To address this problem, we have developed a software package, AnimatLab (www.AnimatLab.com), that enables users to reconstruct an animal's body and its relevant neural circuits, to link them at the sensory and motor ends, and through simulation, to test their ability to reproduce appropriate patterns of the animal's movements in a simulated Newtonian world. A Windows-based program, AnimatLab consists of a neural editor, a body editor, a world editor, stimulus and recording facilities, neural and physics engines, and an interactive 3-D graphical display. We have used AnimatLab to study three patterns of behavior: the grasshopper jump, crayfish escape, and crayfish leg movements used in postural control, walking, reaching and grasping. In each instance, the simulation helped identify constraints on both nervous function and biomechanical performance that have provided the basis for new experiments. Colleagues elsewhere have begun to use AnimatLab to study control of paw movements in cats and postural control in humans. We have also used AnimatLab simulations to guide the development of an autonomous hexapod robot in which the neural control circuitry is downloaded to the robot from the test computer.

Anomalous neural circuit function in schizophrenia during a virtual Morris water task.

PubMed

Folley, Bradley S; Astur, Robert; Jagannathan, Kanchana; Calhoun, Vince D; Pearlson, Godfrey D

2010-02-15

Previous studies have reported learning and navigation impairments in schizophrenia patients during virtual reality allocentric learning tasks. The neural bases of these deficits have not been explored using functional MRI despite well-explored anatomic characterization of these paradigms in non-human animals. Our objective was to characterize the differential distributed neural circuits involved in virtual Morris water task performance using independent component analysis (ICA) in schizophrenia patients and controls. Additionally, we present behavioral data in order to derive relationships between brain function and performance, and we have included a general linear model-based analysis in order to exemplify the incremental and differential results afforded by ICA. Thirty-four individuals with schizophrenia and twenty-eight healthy controls underwent fMRI scanning during a block design virtual Morris water task using hidden and visible platform conditions. Independent components analysis was used to deconstruct neural contributions to hidden and visible platform conditions for patients and controls. We also examined performance variables, voxel-based morphometry and hippocampal subparcellation, and regional BOLD signal variation. Independent component analysis identified five neural circuits. Mesial temporal lobe regions, including the hippocampus, were consistently task-related across conditions and groups. Frontal, striatal, and parietal circuits were recruited preferentially during the visible condition for patients, while frontal and temporal lobe regions were more saliently recruited by controls during the hidden platform condition. Gray matter concentrations and BOLD signal in hippocampal subregions were associated with task performance in controls but not patients. Patients exhibited impaired performance on the hidden and visible conditions of the task, related to negative symptom severity. While controls showed coupling between neural circuits, regional neuroanatomy, and behavior, patients activated different task-related neural circuits, not associated with appropriate regional neuroanatomy. GLM analysis elucidated several comparable regions, with the exception of the hippocampus. Inefficient allocentric learning and memory in patients may be related to an inability to recruit appropriate task-dependent neural circuits. Copyright 2009 Elsevier Inc. All rights reserved.

Implementing a Bayes Filter in a Neural Circuit: The Case of Unknown Stimulus Dynamics.

PubMed

Sokoloski, Sacha

2017-09-01

In order to interact intelligently with objects in the world, animals must first transform neural population responses into estimates of the dynamic, unknown stimuli that caused them. The Bayesian solution to this problem is known as a Bayes filter, which applies Bayes' rule to combine population responses with the predictions of an internal model. The internal model of the Bayes filter is based on the true stimulus dynamics, and in this note, we present a method for training a theoretical neural circuit to approximately implement a Bayes filter when the stimulus dynamics are unknown. To do this we use the inferential properties of linear probabilistic population codes to compute Bayes' rule and train a neural network to compute approximate predictions by the method of maximum likelihood. In particular, we perform stochastic gradient descent on the negative log-likelihood of the neural network parameters with a novel approximation of the gradient. We demonstrate our methods on a finite-state, a linear, and a nonlinear filtering problem and show how the hidden layer of the neural network develops tuning curves consistent with findings in experimental neuroscience.

Integrated circuits and molecular components for stress and feeding: implications for eating disorders.

PubMed

Hardaway, J A; Crowley, N A; Bulik, C M; Kash, T L

2015-01-01

Eating disorders are complex brain disorders that afflict millions of individuals worldwide. The etiology of these diseases is not fully understood, but a growing body of literature suggests that stress and anxiety may play a critical role in their development. As our understanding of the genetic and environmental factors that contribute to disease in clinical populations like anorexia nervosa, bulimia nervosa and binge eating disorder continue to grow, neuroscientists are using animal models to understand the neurobiology of stress and feeding. We hypothesize that eating disorder clinical phenotypes may result from stress-induced maladaptive alterations in neural circuits that regulate feeding, and that these circuits can be neurochemically isolated using animal model of eating disorders. © 2014 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

Gene Expression Profiling with Cre-Conditional Pseudorabies Virus Reveals a Subset of Midbrain Neurons That Participate in Reward Circuitry

PubMed Central

Pomeranz, Lisa E.; Ekstrand, Mats I.; Latcha, Kaamashri N.; Smith, Gregory A.; Enquist, Lynn W.

2017-01-01

The mesolimbic dopamine pathway receives inputs from numerous regions of the brain as part of a neural system that detects rewarding stimuli and coordinates a behavioral response. The capacity to simultaneously map and molecularly define the components of this complex multisynaptic circuit would thus advance our understanding of the determinants of motivated behavior. To accomplish this, we have constructed pseudorabies virus (PRV) strains in which viral propagation and fluorophore expression are activated only after exposure to Cre recombinase. Once activated in Cre-expressing neurons, the virus serially labels chains of presynaptic neurons. Dual injection of GFP and mCherry tracing viruses simultaneously illuminates nigrostriatal and mesolimbic circuitry and shows no overlap, demonstrating that PRV transmission is confined to synaptically connected neurons. To molecularly profile mesolimbic dopamine neurons and their presynaptic inputs, we injected Cre-conditional GFP virus into the NAc of (anti-GFP) nanobody-L10 transgenic mice and immunoprecipitated translating ribosomes from neurons infected after retrograde tracing. Analysis of purified RNA revealed an enrichment of transcripts expressed in neurons of the dorsal raphe nuclei and lateral hypothalamus that project to the mesolimbic dopamine circuit. These studies identify important inputs to the mesolimbic dopamine pathway and further show that PRV circuit-directed translating ribosome affinity purification can be broadly applied to identify molecularly defined neurons comprising complex, multisynaptic circuits. SIGNIFICANCE STATEMENT The mesolimbic dopamine circuit integrates signals from key brain regions to detect and respond to rewarding stimuli. To further define this complex multisynaptic circuit, we constructed a panel of Cre recombinase-activated pseudorabies viruses (PRVs) that enabled retrograde tracing of neural inputs that terminate on Cre-expressing neurons. Using these viruses and Retro-TRAP (translating ribosome affinity purification), a previously reported molecular profiling method, we developed a novel technique that provides anatomic as well as molecular information about the neural components of polysynaptic circuits. We refer to this new method as PRV-Circuit-TRAP (PRV circuit-directed TRAP). Using it, we have identified major projections to the mesolimbic dopamine circuit from the lateral hypothalamus and dorsal raphe nucleus and defined a discrete subset of transcripts expressed in these projecting neurons, which will allow further characterization of this important pathway. Moreover, the method we report is general and can be applied to the study of other neural circuits. PMID:28283558

Distinct Neural Circuits Subserve Interpersonal and Non-interpersonal Emotions

PubMed Central

Landa, Alla; Wang, Zhishun; Russell, James A.; Posner, Jonathan; Duan, Yunsuo; Kangarlu, Alayar; Huo, Yuankai; Fallon, Brian A.; Peterson, Bradley S.

2013-01-01

Emotions elicited by interpersonal versus non-interpersonal experiences have different effects on neurobiological functioning in both animals and humans. However, the extent to which the brain circuits underlying interpersonal and non-interpersonal emotions are distinct still remains unclear. The goal of our study was to assess whether different neural circuits are implicated in the processing of arousal and valence of interpersonal versus non-interpersonal emotions. During functional magnetic resonance imaging, participants imagined themselves in emotion-eliciting interpersonal or non-interpersonal situations and then rated the arousal and valence of emotions they experienced. We identified (a) separate neural circuits that are implicated in the arousal and valence dimensions of interpersonal versus non-interpersonal emotions, (b) circuits that are implicated in arousal and valence for both types of emotion, and (c) circuits that are responsive to the type of emotion, regardless of the valence or arousal level of the emotion. We found extensive recruitment of limbic (for arousal) and temporal-parietal (for valence) systems associated with processing of specifically interpersonal emotions compared to non-interpersonal ones. The neural bases of interpersonal and non-interpersonal emotions may, therefore, be largely distinct. PMID:24028312

PCSIM: A Parallel Simulation Environment for Neural Circuits Fully Integrated with Python

PubMed Central

Pecevski, Dejan; Natschläger, Thomas; Schuch, Klaus

2008-01-01

The Parallel Circuit SIMulator (PCSIM) is a software package for simulation of neural circuits. It is primarily designed for distributed simulation of large scale networks of spiking point neurons. Although its computational core is written in C++, PCSIM's primary interface is implemented in the Python programming language, which is a powerful programming environment and allows the user to easily integrate the neural circuit simulator with data analysis and visualization tools to manage the full neural modeling life cycle. The main focus of this paper is to describe PCSIM's full integration into Python and the benefits thereof. In particular we will investigate how the automatically generated bidirectional interface and PCSIM's object-oriented modular framework enable the user to adopt a hybrid modeling approach: using and extending PCSIM's functionality either employing pure Python or C++ and thus combining the advantages of both worlds. Furthermore, we describe several supplementary PCSIM packages written in pure Python and tailored towards setting up and analyzing neural simulations. PMID:19543450

Image Segmentation for Connectomics Using Machine Learning

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

Tasdizen, Tolga; Seyedhosseini, Mojtaba; Liu, TIng

Reconstruction of neural circuits at the microscopic scale of individual neurons and synapses, also known as connectomics, is an important challenge for neuroscience. While an important motivation of connectomics is providing anatomical ground truth for neural circuit models, the ability to decipher neural wiring maps at the individual cell level is also important in studies of many neurodegenerative diseases. Reconstruction of a neural circuit at the individual neuron level requires the use of electron microscopy images due to their extremely high resolution. Computational challenges include pixel-by-pixel annotation of these images into classes such as cell membrane, mitochondria and synaptic vesiclesmore » and the segmentation of individual neurons. State-of-the-art image analysis solutions are still far from the accuracy and robustness of human vision and biologists are still limited to studying small neural circuits using mostly manual analysis. In this chapter, we describe our image analysis pipeline that makes use of novel supervised machine learning techniques to tackle this problem.« less

Complexity and Competition in Appetitive and Aversive Neural Circuits

PubMed Central

Barberini, Crista L.; Morrison, Sara E.; Saez, Alex; Lau, Brian; Salzman, C. Daniel

2012-01-01

Decision-making often involves using sensory cues to predict possible rewarding or punishing reinforcement outcomes before selecting a course of action. Recent work has revealed complexity in how the brain learns to predict rewards and punishments. Analysis of neural signaling during and after learning in the amygdala and orbitofrontal cortex, two brain areas that process appetitive and aversive stimuli, reveals a dynamic relationship between appetitive and aversive circuits. Specifically, the relationship between signaling in appetitive and aversive circuits in these areas shifts as a function of learning. Furthermore, although appetitive and aversive circuits may often drive opposite behaviors – approaching or avoiding reinforcement depending upon its valence – these circuits can also drive similar behaviors, such as enhanced arousal or attention; these processes also may influence choice behavior. These data highlight the formidable challenges ahead in dissecting how appetitive and aversive neural circuits interact to produce a complex and nuanced range of behaviors. PMID:23189037

Neural Circuits via Which Single Prolonged Stress Exposure Leads to Fear Extinction Retention Deficits

ERIC Educational Resources Information Center

Knox, Dayan; Stanfield, Briana R.; Staib, Jennifer M.; David, Nina P.; Keller, Samantha M.; DePietro, Thomas

2016-01-01

Single prolonged stress (SPS) has been used to examine mechanisms via which stress exposure leads to post-traumatic stress disorder symptoms. SPS induces fear extinction retention deficits, but neural circuits critical for mediating these deficits are unknown. To address this gap, we examined the effect of SPS on neural activity in brain regions…

Targeting circuits

PubMed Central

Rajasethupathy, Priyamvada; Ferenczi, Emily; Deisseroth, Karl

2017-01-01

Current optogenetic methodology enables precise inhibition or excitation of neural circuits, spanning timescales as needed from the acute (milliseconds) to the chronic (many days or more), for experimental modulation of network activity and animal behavior. Such broad temporal versatility, unique to optogenetic control, is particularly powerful when combined with brain activity measurements that span both acute and chronic timescales as well. This enables, for instance, the study of adaptive circuit dynamics across the intact brain, and tuning interventions to match activity patterns naturally observed during behavior in the same individual. Although the impact of this approach has been greater on basic research than on clinical translation, it is natural to ask if specific neural circuit activity patterns discovered to be involved in controlling adaptive or maladaptive behaviors could become targets for treatment of neuropsychiatric diseases. Here we consider the landscape of such ideas related to therapeutic targeting of circuit dynamics, taking note of developments not only in optical but also in ultrasonic, magnetic, and thermal methods. We note the recent emergence of first-in-kind optogenetically-guided clinical outcomes, as well as opportunities related to the integration of interventions and readouts spanning diverse circuit-physiology, molecular, and behavioral modalities. PMID:27104976

Implantable neurotechnologies: bidirectional neural interfaces--applications and VLSI circuit implementations.

PubMed

Greenwald, Elliot; Masters, Matthew R; Thakor, Nitish V

2016-01-01

A bidirectional neural interface is a device that transfers information into and out of the nervous system. This class of devices has potential to improve treatment and therapy in several patient populations. Progress in very large-scale integration has advanced the design of complex integrated circuits. System-on-chip devices are capable of recording neural electrical activity and altering natural activity with electrical stimulation. Often, these devices include wireless powering and telemetry functions. This review presents the state of the art of bidirectional circuits as applied to neuroprosthetic, neurorepair, and neurotherapeutic systems.

Affective neural response to restricted interests in autism spectrum disorders.

PubMed

Cascio, Carissa J; Foss-Feig, Jennifer H; Heacock, Jessica; Schauder, Kimberly B; Loring, Whitney A; Rogers, Baxter P; Pryweller, Jennifer R; Newsom, Cassandra R; Cockhren, Jurnell; Cao, Aize; Bolton, Scott

2014-01-01

Restricted interests are a class of repetitive behavior in autism spectrum disorders (ASD) whose intensity and narrow focus often contribute to significant interference with daily functioning. While numerous neuroimaging studies have investigated executive circuits as putative neural substrates of repetitive behavior, recent work implicates affective neural circuits in restricted interests. We sought to explore the role of affective neural circuits and determine how restricted interests are distinguished from hobbies or interests in typical development. We compared a group of children with ASD to a typically developing (TD) group of children with strong interests or hobbies, employing parent report, an operant behavioral task, and functional imaging with personalized stimuli based on individual interests. While performance on the operant task was similar between the two groups, parent report of intensity and interference of interests was significantly higher in the ASD group. Both the ASD and TD groups showed increased BOLD response in widespread affective neural regions to the pictures of their own interest. When viewing pictures of other children's interests, the TD group showed a similar pattern, whereas BOLD response in the ASD group was much more limited. Increased BOLD response in the insula and anterior cingulate cortex distinguished the ASD from the TD group, and parent report of the intensity and interference with daily life of the child's restricted interest predicted insula response. While affective neural network response and operant behavior are comparable in typical and restricted interests, the narrowness of focus that clinically distinguishes restricted interests in ASD is reflected in more interference in daily life and aberrantly enhanced insula and anterior cingulate response to individuals' own interests in the ASD group. These results further support the involvement of affective neural networks in repetitive behaviors in ASD. © 2013 The Authors. Journal of Child Psychology and Psychiatry © 2013 Association for Child and Adolescent Mental Health.

Converging on a core cognitive deficit: the impact of various neurodevelopmental insults on cognitive control

PubMed Central

O'Reilly, Kally C.; Kao, Hsin-Yi; Lee, Heekyung; Fenton, André A.

2014-01-01

Despite substantial effort and immense need, the treatment options for major neuropsychiatric illnesses like schizophrenia are limited and largely ineffective at improving the most debilitating cognitive symptoms that are central to mental illness. These symptoms include cognitive control deficits, the inability to selectively use information that is currently relevant and ignore what is currently irrelevant. Contemporary attempts to accelerate progress are in part founded on an effort to reconceptualize neuropsychiatric illness as a disorder of neural development. This neuro-developmental framework emphasizes abnormal neural circuits on the one hand, and on the other, it suggests there are therapeutic opportunities to exploit the developmental processes of excitatory neuron pruning, inhibitory neuron proliferation, elaboration of myelination, and other circuit refinements that extend through adolescence and into early adulthood. We have crafted a preclinical research program aimed at cognition failures that may be relevant to mental illness. By working with a variety of neurodevelopmental rodent models, we strive to identify a common pathophysiology that underlies cognitive control failure as well as a common strategy for improving cognition in the face of neural circuit abnormalities. Here we review our work to characterize cognitive control deficits in rats with a neonatal ventral hippocampus lesion and rats that were exposed to Methylazoxymethanol acetate (MAM) in utero. We review our findings as they pertain to early developmental processes, including neurogenesis, as well as the power of cognitive experience to refine neural circuit function within the mature and maturing brain's cognitive circuitry. PMID:24966811

Large-scale neural circuit mapping data analysis accelerated with the graphical processing unit (GPU).

PubMed

Shi, Yulin; Veidenbaum, Alexander V; Nicolau, Alex; Xu, Xiangmin

2015-01-15

Modern neuroscience research demands computing power. Neural circuit mapping studies such as those using laser scanning photostimulation (LSPS) produce large amounts of data and require intensive computation for post hoc processing and analysis. Here we report on the design and implementation of a cost-effective desktop computer system for accelerated experimental data processing with recent GPU computing technology. A new version of Matlab software with GPU enabled functions is used to develop programs that run on Nvidia GPUs to harness their parallel computing power. We evaluated both the central processing unit (CPU) and GPU-enabled computational performance of our system in benchmark testing and practical applications. The experimental results show that the GPU-CPU co-processing of simulated data and actual LSPS experimental data clearly outperformed the multi-core CPU with up to a 22× speedup, depending on computational tasks. Further, we present a comparison of numerical accuracy between GPU and CPU computation to verify the precision of GPU computation. In addition, we show how GPUs can be effectively adapted to improve the performance of commercial image processing software such as Adobe Photoshop. To our best knowledge, this is the first demonstration of GPU application in neural circuit mapping and electrophysiology-based data processing. Together, GPU enabled computation enhances our ability to process large-scale data sets derived from neural circuit mapping studies, allowing for increased processing speeds while retaining data precision. Copyright © 2014 Elsevier B.V. All rights reserved.

Predicting better performance on a college preparedness test from narrative comprehension at the age of 6 years: An fMRI study.

PubMed

Horowitz-Kraus, Tzipi; Eaton, Kenneth; Farah, Rola; Hajinazarian, Ardag; Vannest, Jennifer; Holland, Scott K

2015-12-10

To investigate whether high performance on college preparedness tests at 18 years of age can be predicted from brain activation patterns during narrative comprehension at 5-7 years of age. In this longitudinal study, functional MRI data during an auditory narrative-comprehension task were acquired from 15 children (5-7 years of age) who also provided their American College Testing (ACT) scores at the age of 18 years. Active voxels during the narrative-comprehension task were correlated with both composite ACT scores and the reading-comprehension component of the exam. Higher composite ACT scores and behavioral scores for reading comprehension were positively correlated with greater activation in frontal and anterior brain regions during the narrative-comprehension task. Our results suggest that neural circuits supporting higher ACT performance are predictable from a narrative-comprehension task at the age of 5-7 years. This supports a critical role for the anterior cingulate cortex, which is a part of the cingulo-opercular cognitive-control network early in development, as a facilitator for better ACT scores. This study highlights that shared neural circuits that support overall ACT performance and neural circuits that support reading comprehension both rely on neural circuits related to narrative comprehension in childhood, suggesting that interventions involving narrative comprehension should be considered for individuals with reading and other academic difficulties. Copyright © 2015 Elsevier B.V. All rights reserved.

Large scale neural circuit mapping data analysis accelerated with the graphical processing unit (GPU)

PubMed Central

Shi, Yulin; Veidenbaum, Alexander V.; Nicolau, Alex; Xu, Xiangmin

2014-01-01

Background Modern neuroscience research demands computing power. Neural circuit mapping studies such as those using laser scanning photostimulation (LSPS) produce large amounts of data and require intensive computation for post-hoc processing and analysis. New Method Here we report on the design and implementation of a cost-effective desktop computer system for accelerated experimental data processing with recent GPU computing technology. A new version of Matlab software with GPU enabled functions is used to develop programs that run on Nvidia GPUs to harness their parallel computing power. Results We evaluated both the central processing unit (CPU) and GPU-enabled computational performance of our system in benchmark testing and practical applications. The experimental results show that the GPU-CPU co-processing of simulated data and actual LSPS experimental data clearly outperformed the multi-core CPU with up to a 22x speedup, depending on computational tasks. Further, we present a comparison of numerical accuracy between GPU and CPU computation to verify the precision of GPU computation. In addition, we show how GPUs can be effectively adapted to improve the performance of commercial image processing software such as Adobe Photoshop. Comparison with Existing Method(s) To our best knowledge, this is the first demonstration of GPU application in neural circuit mapping and electrophysiology-based data processing. Conclusions Together, GPU enabled computation enhances our ability to process large-scale data sets derived from neural circuit mapping studies, allowing for increased processing speeds while retaining data precision. PMID:25277633

An fMRI Examination of Developmental Differences in the Neural Correlates of Uncertainty and Decision-Making

ERIC Educational Resources Information Center

Krain, Amy L.; Hefton, Sara; Pine, Daniel S.; Ernst, Monique; Castellanos, F. Xavier; Klein, Rachel G.; Milham, Michael P.

2006-01-01

Background: Maturation of prefrontal circuits during adolescence contributes to the development of cognitive processes such as decision-making. Recent theories suggest that these neural changes also play a role in the shift from generalized anxiety disorder (GAD) to depression that often occurs during this developmental period. Cognitive models of…

Control of excessive neural circuit excitability and prevention of epileptic seizures by endocannabinoid signaling.

PubMed

Sugaya, Yuki; Kano, Masanobu

2018-05-08

Progress in research on endocannabinoid signaling has greatly advanced our understanding of how it controls neural circuit excitability in health and disease. In general, endocannabinoid signaling at excitatory synapses suppresses seizures by inhibiting glutamate release. In contrast, endocannabinoid signaling promotes seizures by inhibiting GABA release at inhibitory synapses. The physiological distribution of endocannabinoid signaling molecules becomes disrupted with the development of epileptic focus in patients with mesial temporal lobe epilepsy and in animal models of experimentally induced epilepsy. Augmentation of endocannabinoid signaling can promote the development of epileptic focus at initial stages. However, at later stages, increased endocannabinoid signaling delays it and suppresses spontaneous seizures. Thus, the regulation of endocannabinoid signaling at specific synapses that cause hyperexcitability during particular stages of disease development may be effective for treating epilepsy and epileptogenesis.

Precise Spatiotemporal Control of Optogenetic Activation Using an Acousto-Optic Device

PubMed Central

Guo, Yanmeng; Song, Peipei; Zhang, Xiaohui; Zeng, Shaoqun; Wang, Zuoren

2011-01-01

Light activation and inactivation of neurons by optogenetic techniques has emerged as an important tool for studying neural circuit function. To achieve a high resolution, new methods are being developed to selectively manipulate the activity of individual neurons. Here, we report that the combination of an acousto-optic device (AOD) and single-photon laser was used to achieve rapid and precise spatiotemporal control of light stimulation at multiple points in a neural circuit with millisecond time resolution. The performance of this system in activating ChIEF expressed on HEK 293 cells as well as cultured neurons was first evaluated, and the laser stimulation patterns were optimized. Next, the spatiotemporally selective manipulation of multiple neurons was achieved in a precise manner. Finally, we demonstrated the versatility of this high-resolution method in dissecting neural circuits both in the mouse cortical slice and the Drosophila brain in vivo. Taken together, our results show that the combination of AOD-assisted laser stimulation and optogenetic tools provides a flexible solution for manipulating neuronal activity at high efficiency and with high temporal precision. PMID:22174813

Infant phantom head circuit board for EEG head phantom and pediatric brain simulation

NASA Astrophysics Data System (ADS)

Almohsen, Safa

The infant's skull differs from an adult skull because of the characteristic features of the human skull during early development. The fontanels and the conductivity of the infant skull influence surface currents, generated by neurons, which underlie electroencephalography (EEG) signals. An electric circuit was built to power a set of simulated neural sources for an infant brain activity simulator. Also, in the simulator, three phantom tissues were created using saline solution plus Agarose gel to mimic the conductivity of each layer in the head [scalp, skull brain]. The conductivity measurement was accomplished by two different techniques: using the four points' measurement technique, and a conductivity meter. Test results showed that the optimized phantom tissues had appropriate conductivities to simulate each tissue layer to fabricate a physical head phantom. In this case, the best results should be achieved by testing the electrical neural circuit with the sample physical model to generate simulated EEG data and use that to solve both the forward and the inverse problems for the purpose of localizing the neural sources in the head phantom.

Reward and Aversion.

PubMed

Hu, Hailan

2016-07-08

To benefit from opportunities and cope with challenges in the environment, animals must adapt their behavior to acquire rewards and to avoid punishments. Maladaptive changes in the neuromodulatory systems and neural circuits for reward and aversion can lead to manifestation of several prominent psychiatric disorders including addiction and depression. Recent progress is pushing the boundaries of knowledge on two major fronts in research on reward and aversion: First, new layers of complexity have been reported on the functions of dopamine (DA) and serotonin (5-HT) neuromodulatory systems in reward and aversion. Second, specific circuit components in the neural pathways that encode reward and aversion have begun to be identified. This review aims to outline historic perspectives and new insights into the functions of DA and 5-HT systems in coding the distinct components of rewards. It also highlights recent advances in neural circuit studies enabled by new technologies, such as cell-type-specific electrophysiology and tracing, and optogenetics-based behavioral manipulation. This knowledge may provide guidance for developing novel treatment strategies for neuropsychiatric diseases related to the malfunction of the reward system.

A Role of Phase-Resetting in Coordinating Large Scale Neural Networks During Attention and Goal-Directed Behavior

PubMed Central

Voloh, Benjamin; Womelsdorf, Thilo

2016-01-01

Short periods of oscillatory activation are ubiquitous signatures of neural circuits. A broad range of studies documents not only their circuit origins, but also a fundamental role for oscillatory activity in coordinating information transfer during goal directed behavior. Recent studies suggest that resetting the phase of ongoing oscillatory activity to endogenous or exogenous cues facilitates coordinated information transfer within circuits and between distributed brain areas. Here, we review evidence that pinpoints phase resetting as a critical marker of dynamic state changes of functional networks. Phase resets: (1) set a “neural context” in terms of narrow band frequencies that uniquely characterizes the activated circuits; (2) impose coherent low frequency phases to which high frequency activations can synchronize, identifiable as cross-frequency correlations across large anatomical distances; (3) are critical for neural coding models that depend on phase, increasing the informational content of neural representations; and (4) likely originate from the dynamics of canonical E-I circuits that are anatomically ubiquitous. These multiple signatures of phase resets are directly linked to enhanced information transfer and behavioral success. We survey how phase resets re-organize oscillations in diverse task contexts, including sensory perception, attentional stimulus selection, cross-modal integration, Pavlovian conditioning, and spatial navigation. The evidence we consider suggests that phase-resets can drive changes in neural excitability, ensemble organization, functional networks, and ultimately, overt behavior. PMID:27013986

Implantable neurotechnologies: a review of integrated circuit neural amplifiers.

PubMed

Ng, Kian Ann; Greenwald, Elliot; Xu, Yong Ping; Thakor, Nitish V

2016-01-01

Neural signal recording is critical in modern day neuroscience research and emerging neural prosthesis programs. Neural recording requires the use of precise, low-noise amplifier systems to acquire and condition the weak neural signals that are transduced through electrode interfaces. Neural amplifiers and amplifier-based systems are available commercially or can be designed in-house and fabricated using integrated circuit (IC) technologies, resulting in very large-scale integration or application-specific integrated circuit solutions. IC-based neural amplifiers are now used to acquire untethered/portable neural recordings, as they meet the requirements of a miniaturized form factor, light weight and low power consumption. Furthermore, such miniaturized and low-power IC neural amplifiers are now being used in emerging implantable neural prosthesis technologies. This review focuses on neural amplifier-based devices and is presented in two interrelated parts. First, neural signal recording is reviewed, and practical challenges are highlighted. Current amplifier designs with increased functionality and performance and without penalties in chip size and power are featured. Second, applications of IC-based neural amplifiers in basic science experiments (e.g., cortical studies using animal models), neural prostheses (e.g., brain/nerve machine interfaces) and treatment of neuronal diseases (e.g., DBS for treatment of epilepsy) are highlighted. The review concludes with future outlooks of this technology and important challenges with regard to neural signal amplification.

Implantable neurotechnologies: a review of integrated circuit neural amplifiers

PubMed Central

Greenwald, Elliot; Xu, Yong Ping; Thakor, Nitish V.

2016-01-01

Neural signal recording is critical in modern day neuroscience research and emerging neural prosthesis programs. Neural recording requires the use of precise, low-noise amplifier systems to acquire and condition the weak neural signals that are transduced through electrode interfaces. Neural amplifiers and amplifier-based systems are available commercially or can be designed in-house and fabricated using integrated circuit (IC) technologies, resulting in very large-scale integration or application-specific integrated circuit solutions. IC-based neural amplifiers are now used to acquire untethered/portable neural recordings, as they meet the requirements of a miniaturized form factor, light weight and low power consumption. Furthermore, such miniaturized and low-power IC neural amplifiers are now being used in emerging implantable neural prosthesis technologies. This review focuses on neural amplifier-based devices and is presented in two interrelated parts. First, neural signal recording is reviewed, and practical challenges are highlighted. Current amplifier designs with increased functionality and performance and without penalties in chip size and power are featured. Second, applications of IC-based neural amplifiers in basic science experiments (e.g., cortical studies using animal models), neural prostheses (e.g., brain/nerve machine interfaces) and treatment of neuronal diseases (e.g., DBS for treatment of epilepsy) are highlighted. The review concludes with future outlooks of this technology and important challenges with regard to neural signal amplification. PMID:26798055

Computer model of a reverberant and parallel circuit coupling

NASA Astrophysics Data System (ADS)

Kalil, Camila de Andrade; de Castro, Maria Clícia Stelling; Cortez, Célia Martins

2017-11-01

The objective of the present study was to deepen the knowledge about the functioning of the neural circuits by implementing a signal transmission model using the Graph Theory in a small network of neurons composed of an interconnected reverberant and parallel circuit, in order to investigate the processing of the signals in each of them and the effects on the output of the network. For this, a program was developed in C language and simulations were done using neurophysiological data obtained in the literature.

Neural circuits via which single prolonged stress exposure leads to fear extinction retention deficits

PubMed Central

Stanfield, Briana R.; Staib, Jennifer M.; David, Nina P.; Keller, Samantha M.; DePietro, Thomas

2016-01-01

Single prolonged stress (SPS) has been used to examine mechanisms via which stress exposure leads to post-traumatic stress disorder symptoms. SPS induces fear extinction retention deficits, but neural circuits critical for mediating these deficits are unknown. To address this gap, we examined the effect of SPS on neural activity in brain regions critical for extinction retention (i.e., fear extinction circuit). These were the ventral hippocampus (vHipp), dorsal hippocampus (dHipp), basolateral amygdala (BLA), prelimbic cortex (PL), and infralimbic cortex (IL). SPS or control rats were fear conditioned then subjected to extinction training and testing. Subsets of rats were euthanized after extinction training, extinction testing, or immediate removal from the housing colony (baseline condition) to assay c-Fos levels (measure of neural activity) in respective brain region. SPS induced extinction retention deficits. During extinction training SPS disrupted enhanced IL neural activity and inhibited BLA neural activity. SPS also disrupted inhibited BLA and vHipp neural activity during extinction testing. Statistical analyses suggested that SPS disrupted functional connectivity within the dHipp during extinction training and increased functional connectivity between the BLA and vHipp during extinction testing. Our findings suggest that SPS induces extinction retention deficits by disrupting both excitatory and inhibitory changes in neural activity within the fear extinction circuit and inducing changes in functional connectivity within the Hipp and BLA. PMID:27918273

Maintenance of Mouse Gustatory Terminal Field Organization Is Disrupted following Selective Removal of Peripheral Sodium Salt Taste Activity at Adulthood.

PubMed

Skyberg, Rolf; Sun, Chengsan; Hill, David L

2017-08-09

Neural activity plays a critical role in the development of central circuits in sensory systems. However, the maintenance of these circuits at adulthood is usually not dependent on sensory-elicited neural activity. Recent work in the mouse gustatory system showed that selectively deleting the primary transduction channel for sodium taste, the epithelial sodium channel (ENaC), throughout development dramatically impacted the organization of the central terminal fields of three nerves that carry taste information to the nucleus of the solitary tract. More specifically, deleting ENaCs during development prevented the normal maturation of the fields. The present study was designed to extend these findings by testing the hypothesis that the loss of sodium taste activity impacts the maintenance of the normal adult terminal field organization in male and female mice. To do this, we used an inducible Cre-dependent genetic recombination strategy to delete ENaC function after terminal field maturation occurred. We found that removal of sodium taste neural activity at adulthood resulted in significant reorganization of mature gustatory afferent terminal fields in the nucleus of the solitary tract. Specifically, the chorda tympani and greater superficial petrosal nerve terminal fields were 1.4× and 1.6× larger than age-matched controls, respectively. By contrast, the glossopharyngeal nerve, which is not highly sensitive to sodium taste stimulation, did not undergo terminal field reorganization. These surprising results suggest that gustatory nerve terminal fields remain plastic well into adulthood, which likely impacts central coding of taste information and taste-related behaviors with altered taste experience. SIGNIFICANCE STATEMENT Neural activity plays a major role in the development of sensory circuits in the mammalian brain. However, the importance of sensory-driven activity in maintaining these circuits at adulthood, especially in subcortical structures, appears to be much less. Here, we tested whether the loss of sodium taste activity in adult mice impacts the maintenance of how taste nerves project to the first central relay. We found that specific loss of sodium-elicited taste activity at adulthood produced dramatic and selective reorganization of terminal fields in the brainstem. This demonstrates, for the first time, that taste-elicited activity is necessary for the normal maintenance of central gustatory circuits at adulthood and highlights a level of plasticity not seen in other sensory system subcortical circuits. Copyright © 2017 the authors 0270-6474/17/377619-12$15.00/0.

Maternal Neural Responses to Infant Cries and Faces: Relationships with Substance Use

PubMed Central

Landi, Nicole; Montoya, Jessica; Kober, Hedy; Rutherford, Helena J. V.; Mencl, W. Einar; Worhunsky, Patrick D.; Potenza, Marc N.; Mayes, Linda C.

2011-01-01

Substance abuse in pregnant and recently post-partum women is a major public health concern because of effects on the infant and on the ability of the adult to care for the infant. In addition to the negative health effects of teratogenic substances on fetal development, substance use can contribute to difficulties associated with the social and behavioral aspects of parenting. Neural circuits associated with parenting behavior overlap with circuits involved in addiction (e.g., frontal, striatal, and limbic systems) and thus may be co-opted for the craving/reward cycle associated with substance use and abuse and be less available for parenting. The current study investigates the degree to which neural circuits associated with parenting are disrupted in mothers who are substance-using. Specifically, we used functional magnetic resonance imaging to examine the neural response to emotional infant cues (faces and cries) in substance-using compared to non-using mothers. In response to both faces (of varying emotional valence) and cries (of varying distress levels), substance-using mothers evidenced reduced neural activation in regions that have been previously implicated in reward and motivation as well as regions involved in cognitive control. Specifically, in response to faces, substance users showed reduced activation in prefrontal regions, including the dorsolateral and ventromedial prefrontal cortices, as well as visual processing (occipital lobes) and limbic regions (parahippocampus and amygdala). Similarly, in response to infant cries, substance-using mothers showed reduced activation relative to non-using mothers in prefrontal regions, auditory sensory processing regions, insula and limbic regions (parahippocampus and amygdala). These findings suggest that infant stimuli may be less salient for substance-using mothers, and such reduced saliency may impair developing infant-caregiver attachment and the ability of mothers to respond appropriately to their infants. PMID:21720537

The Neural Circuits that Generate Tics in Gilles de la Tourette Syndrome

PubMed Central

Wang, Zhishun; Maia, Tiago V.; Marsh, Rachel; Colibazzi, Tiziano; Gerber, Andrew; Peterson, Bradley S.

2014-01-01

Objective To study neural activity and connectivity within cortico-striato-thalamo-cortical circuits and to reveal circuit-based neural mechanisms that govern tic generation in Tourette syndrome. Method We acquired fMRI data from 13 participants with Tourette syndrome and 21 controls during spontaneous or simulated tics. We used independent component analysis with hierarchical partner matching to isolate neural activity within functionally distinct regions of cortico-striato-thalamo-cortical circuits. We used Granger causality to investigate causal interactions among these regions. Results We found that the Tourette group exhibited stronger neural activity and interregional causality than controls throughout all portions of the motor pathway including sensorimotor cortex, putamen, pallidum, and substania nigra. Activity in these areas correlated positively with the severity of tic symptoms. Activity within the Tourette group was stronger during spontaneous tics than during voluntary tics in somatosensory and posterior parietal cortices, putamen, and amygdala/hippocampus complex, suggesting that activity in these regions may represent features of the premonitory urges that generate spontaneous tic behaviors. In contrast, activity was weaker in the Tourette group than in controls within portions of cortico-striato-thalamo-cortical circuits that exert top-down control over motor pathways (caudate and anterior cingulate cortex), and progressively less activity in these regions accompanied more severe tic symptoms, suggesting that faulty activity in these circuits may fail to control tic behaviors or the premonitory urges that generate them. Conclusions Our findings taken together suggest that tics are caused by the combined effects of excessive activity in motor pathways and reduced activation in control portions of cortico-striato-thalamo-cortical circuits. PMID:21955933

Emerging roles of gut microbiota and the immune system in the development of the enteric nervous system

PubMed Central

Kabouridis, Panagiotis S.; Pachnis, Vassilis

2015-01-01

The enteric nervous system (ENS) consists of neurons and glial cells that differentiate from neural crest progenitors. During embryogenesis, development of the ENS is controlled by the interplay of neural crest cell–intrinsic factors and instructive cues from the surrounding gut mesenchyme. However, postnatal ENS development occurs in a different context, which is characterized by the presence of microbiota and an extensive immune system, suggesting an important role of these factors on enteric neural circuit formation and function. Initial reports confirm this idea while further studies in this area promise new insights into ENS physiology and pathophysiology. PMID:25729852

Reconfigurable visible nanophotonic switch for optogenetic applications (Conference Presentation)

NASA Astrophysics Data System (ADS)

Mohanty, Aseema; Li, Qian; Tadayon, Mohammad Amin; Bhatt, Gaurang R.; Cardenas, Jaime; Miller, Steven A.; Kepecs, Adam; Lipson, Michal

2017-02-01

High spatiotemporal resolution deep-brain optical excitation for optogenetics would enable activation of specific neural populations and in-depth study of neural circuits. Conventionally, a single fiber is used to flood light into a large area of the brain with limited resolution. The scalability of silicon photonics could enable neural excitation over large areas with single-cell resolution similar to electrical probes. However, active control of these optical circuits has yet to be demonstrated for optogenetics. Here we demonstrate the first active integrated optical switch for neural excitation at 473 nm, enabling control of multiple beams for deep-brain neural stimulation. Using a silicon nitride waveguide platform, we develop a cascaded Mach-Zehnder interferometer (MZI) network located outside the brain to direct light to 8 different grating emitters located at the tip of the neural probe. We use integrated platinum microheaters to induce a local thermo-optic phase shift in the MZI to control the switch output. We measure an ON/OFF extinction ratio of >8dB for a single switch and a switching speed of 20 microseconds. We characterize the optical output of the switch by imaging its excitation of fluorescent dye. Finally, we demonstrate in vivo single-neuron optical activation from different grating emitters using a fully packaged device inserted into a mouse brain. Directly activated neurons showed robust spike firing activities with low first-spike latency and small jitter. Active switching on a nanophotonic platform is necessary for eventually controlling highly-multiplexed reconfigurable optical circuits, enabling high-resolution optical stimulation in deep-brain regions.

The Zebrafish Brain in Research and Teaching: A Simple in Vivo and in Vitro Model for the Study of Spontaneous Neural Activity

ERIC Educational Resources Information Center

Vargas, R.; Johannesdottir, I. P.; Sigurgeirsson, B.; Porsteinsson, H.; Karlsson, K. AE.

2011-01-01

Recently, the zebrafish ("Danio rerio") has been established as a key animal model in neuroscience. Behavioral, genetic, and immunohistochemical techniques have been used to describe the connectivity of diverse neural circuits. However, few studies have used zebrafish to understand the function of cerebral structures or to study neural circuits.…

Social Status-Dependent Shift in Neural Circuit Activation Affects Decision Making.

PubMed

Miller, Thomas H; Clements, Katie; Ahn, Sungwoo; Park, Choongseok; Hye Ji, Eoon; Issa, Fadi A

2017-02-22

In a social group, animals make behavioral decisions that fit their social ranks. These behavioral choices are dependent on the various social cues experienced during social interactions. In vertebrates, little is known of how social status affects the underlying neural mechanisms regulating decision-making circuits that drive competing behaviors. Here, we demonstrate that social status in zebrafish ( Danio rerio ) influences behavioral decisions by shifting the balance in neural circuit activation between two competing networks (escape and swim). We show that socially dominant animals enhance activation of the swim circuit. Conversely, social subordinates display a decreased activation of the swim circuit, but an enhanced activation of the escape circuit. In an effort to understand how social status mediates these effects, we constructed a neurocomputational model of the escape and swim circuits. The model replicates our findings and suggests that social status-related shift in circuit dynamics could be mediated by changes in the relative excitability of the escape and swim networks. Together, our results reveal that changes in the excitabilities of the Mauthner command neuron for escape and the inhibitory interneurons that regulate swimming provide a cellular mechanism for the nervous system to adapt to changes in social conditions by permitting the animal to select a socially appropriate behavioral response. SIGNIFICANCE STATEMENT Understanding how social factors influence nervous system function is of great importance. Using zebrafish as a model system, we demonstrate how social experience affects decision making to enable animals to produce socially appropriate behavior. Based on experimental evidence and computational modeling, we show that behavioral decisions reflect the interplay between competing neural circuits whose activation thresholds shift in accordance with social status. We demonstrate this through analysis of the behavior and neural circuit responses that drive escape and swim behaviors in fish. We show that socially subordinate animals favor escape over swimming, while socially dominants favor swimming over escape. We propose that these differences are mediated by shifts in relative circuit excitability. Copyright © 2017 the authors 0270-6474/17/372137-12$15.00/0.

Frank Beach Award Winner: Steroids as Neuromodulators of Brain Circuits and Behavior

PubMed Central

Remage-Healey, Luke

2014-01-01

Neurons communicate primarily via action potentials that transmit information on the timescale of milliseconds. Neurons also integrate information via alterations in gene transcription and protein translation that are sustained for hours to days after initiation. Positioned between these two signaling timescales are the minute-by-minute actions of neuromodulators. Over the course of minutes, the classical neuromodulators (such as serotonin, dopamine, octopamine, and norepinephrine) can alter and/or stabilize neural circuit patterning as well as behavioral states. Neuromodulators allow many flexible outputs from neural circuits and can encode information content into the firing state of neural networks. The idea that steroid molecules can operate as genuine behavioral neuromodulators - synthesized by and acting within brain circuits on a minute-by-minute timescale - has gained traction in recent years. Evidence for brain steroid synthesis at synaptic terminals has converged with evidence for the rapid actions of brain-derived steroids on neural circuits and behavior. The general principle emerging from this work is that the production of steroid hormones within brain circuits can alter their functional connectivity and shift sensory representations by enhancing their information coding. Steroids produced in the brain can therefore change the information content of neuronal networks to rapidly modulate sensory experience and sensorimotor functions. PMID:25110187

Neural circuits via which single prolonged stress exposure leads to fear extinction retention deficits.

PubMed

Knox, Dayan; Stanfield, Briana R; Staib, Jennifer M; David, Nina P; Keller, Samantha M; DePietro, Thomas

2016-12-01

Single prolonged stress (SPS) has been used to examine mechanisms via which stress exposure leads to post-traumatic stress disorder symptoms. SPS induces fear extinction retention deficits, but neural circuits critical for mediating these deficits are unknown. To address this gap, we examined the effect of SPS on neural activity in brain regions critical for extinction retention (i.e., fear extinction circuit). These were the ventral hippocampus (vHipp), dorsal hippocampus (dHipp), basolateral amygdala (BLA), prelimbic cortex (PL), and infralimbic cortex (IL). SPS or control rats were fear conditioned then subjected to extinction training and testing. Subsets of rats were euthanized after extinction training, extinction testing, or immediate removal from the housing colony (baseline condition) to assay c-Fos levels (measure of neural activity) in respective brain region. SPS induced extinction retention deficits. During extinction training SPS disrupted enhanced IL neural activity and inhibited BLA neural activity. SPS also disrupted inhibited BLA and vHipp neural activity during extinction testing. Statistical analyses suggested that SPS disrupted functional connectivity within the dHipp during extinction training and increased functional connectivity between the BLA and vHipp during extinction testing. Our findings suggest that SPS induces extinction retention deficits by disrupting both excitatory and inhibitory changes in neural activity within the fear extinction circuit and inducing changes in functional connectivity within the Hipp and BLA. © 2016 Knox et al.; Published by Cold Spring Harbor Laboratory Press.

Bias-dependent hybrid PKI empirical-neural model of microwave FETs

NASA Astrophysics Data System (ADS)

Marinković, Zlatica; Pronić-Rančić, Olivera; Marković, Vera

2011-10-01

Empirical models of microwave transistors based on an equivalent circuit are valid for only one bias point. Bias-dependent analysis requires repeated extractions of the model parameters for each bias point. In order to make model bias-dependent, a new hybrid empirical-neural model of microwave field-effect transistors is proposed in this article. The model is a combination of an equivalent circuit model including noise developed for one bias point and two prior knowledge input artificial neural networks (PKI ANNs) aimed at introducing bias dependency of scattering (S) and noise parameters, respectively. The prior knowledge of the proposed ANNs involves the values of the S- and noise parameters obtained by the empirical model. The proposed hybrid model is valid in the whole range of bias conditions. Moreover, the proposed model provides better accuracy than the empirical model, which is illustrated by an appropriate modelling example of a pseudomorphic high-electron mobility transistor device.

Natural neural projection dynamics underlying social behavior

PubMed Central

Gunaydin, Lisa A.; Grosenick, Logan; Finkelstein, Joel C.; Kauvar, Isaac V.; Fenno, Lief E.; Adhikari, Avishek; Lammel, Stephan; Mirzabekov, Julie J.; Airan, Raag D.; Zalocusky, Kelly A.; Tye, Kay M.; Anikeeva, Polina; Malenka, Robert C.; Deisseroth, Karl

2014-01-01

Social interaction is a complex behavior essential for many species, and is impaired in major neuropsychiatric disorders. Pharmacological studies have implicated certain neurotransmitter systems in social behavior, but circuit-level understanding of endogenous neural activity during social interaction is lacking. We therefore developed and applied a new methodology, termed fiber photometry, to optically record natural neural activity in genetically- and connectivity-defined projections to elucidate the real-time role of specified pathways in mammalian behavior. Fiber photometry revealed that activity dynamics of a ventral tegmental area (VTA)-to-nucleus accumbens (NAc) projection could encode and predict key features of social but not novel-object interaction. Consistent with this observation, optogenetic control of cells specifically contributing to this projection was sufficient to modulate social behavior, which was mediated by type-1 dopamine receptor signaling downstream in the NAc. Direct observation of projection-specific activity in this way captures a fundamental and previously inaccessible dimension of circuit dynamics. PMID:24949967

Impaired activity-dependent neural circuit assembly and refinement in autism spectrum disorder genetic models

PubMed Central

Doll, Caleb A.; Broadie, Kendal

2014-01-01

Early-use activity during circuit-specific critical periods refines brain circuitry by the coupled processes of eliminating inappropriate synapses and strengthening maintained synapses. We theorize these activity-dependent (A-D) developmental processes are specifically impaired in autism spectrum disorders (ASDs). ASD genetic models in both mouse and Drosophila have pioneered our insights into normal A-D neural circuit assembly and consolidation, and how these developmental mechanisms go awry in specific genetic conditions. The monogenic fragile X syndrome (FXS), a common cause of heritable ASD and intellectual disability, has been particularly well linked to defects in A-D critical period processes. The fragile X mental retardation protein (FMRP) is positively activity-regulated in expression and function, in turn regulates excitability and activity in a negative feedback loop, and appears to be required for the A-D remodeling of synaptic connectivity during early-use critical periods. The Drosophila FXS model has been shown to functionally conserve the roles of human FMRP in synaptogenesis, and has been centrally important in generating our current mechanistic understanding of the FXS disease state. Recent advances in Drosophila optogenetics, transgenic calcium reporters, highly-targeted transgenic drivers for individually-identified neurons, and a vastly improved connectome of the brain are now being combined to provide unparalleled opportunities to both manipulate and monitor A-D processes during critical period brain development in defined neural circuits. The field is now poised to exploit this new Drosophila transgenic toolbox for the systematic dissection of A-D mechanisms in normal versus ASD brain development, particularly utilizing the well-established Drosophila FXS disease model. PMID:24570656

Refinement and Pattern Formation in Neural Circuits by the Interaction of Traveling Waves with Spike-Timing Dependent Plasticity

PubMed Central

Bennett, James E. M.; Bair, Wyeth

2015-01-01

Traveling waves in the developing brain are a prominent source of highly correlated spiking activity that may instruct the refinement of neural circuits. A candidate mechanism for mediating such refinement is spike-timing dependent plasticity (STDP), which translates correlated activity patterns into changes in synaptic strength. To assess the potential of these phenomena to build useful structure in developing neural circuits, we examined the interaction of wave activity with STDP rules in simple, biologically plausible models of spiking neurons. We derive an expression for the synaptic strength dynamics showing that, by mapping the time dependence of STDP into spatial interactions, traveling waves can build periodic synaptic connectivity patterns into feedforward circuits with a broad class of experimentally observed STDP rules. The spatial scale of the connectivity patterns increases with wave speed and STDP time constants. We verify these results with simulations and demonstrate their robustness to likely sources of noise. We show how this pattern formation ability, which is analogous to solutions of reaction-diffusion systems that have been widely applied to biological pattern formation, can be harnessed to instruct the refinement of postsynaptic receptive fields. Our results hold for rich, complex wave patterns in two dimensions and over several orders of magnitude in wave speeds and STDP time constants, and they provide predictions that can be tested under existing experimental paradigms. Our model generalizes across brain areas and STDP rules, allowing broad application to the ubiquitous occurrence of traveling waves and to wave-like activity patterns induced by moving stimuli. PMID:26308406

Refinement and Pattern Formation in Neural Circuits by the Interaction of Traveling Waves with Spike-Timing Dependent Plasticity.

PubMed

Bennett, James E M; Bair, Wyeth

2015-08-01

Traveling waves in the developing brain are a prominent source of highly correlated spiking activity that may instruct the refinement of neural circuits. A candidate mechanism for mediating such refinement is spike-timing dependent plasticity (STDP), which translates correlated activity patterns into changes in synaptic strength. To assess the potential of these phenomena to build useful structure in developing neural circuits, we examined the interaction of wave activity with STDP rules in simple, biologically plausible models of spiking neurons. We derive an expression for the synaptic strength dynamics showing that, by mapping the time dependence of STDP into spatial interactions, traveling waves can build periodic synaptic connectivity patterns into feedforward circuits with a broad class of experimentally observed STDP rules. The spatial scale of the connectivity patterns increases with wave speed and STDP time constants. We verify these results with simulations and demonstrate their robustness to likely sources of noise. We show how this pattern formation ability, which is analogous to solutions of reaction-diffusion systems that have been widely applied to biological pattern formation, can be harnessed to instruct the refinement of postsynaptic receptive fields. Our results hold for rich, complex wave patterns in two dimensions and over several orders of magnitude in wave speeds and STDP time constants, and they provide predictions that can be tested under existing experimental paradigms. Our model generalizes across brain areas and STDP rules, allowing broad application to the ubiquitous occurrence of traveling waves and to wave-like activity patterns induced by moving stimuli.

The Impact of Ecological Niche on Adaptive Flexibility of Sensory Circuitry

PubMed Central

Pallas, Sarah L.

2017-01-01

Evolution and development are interdependent, particularly with regard to the construction of the nervous system and its position as the machine that produces behavior. On the one hand, the processes directing development and plasticity of the brain provide avenues through which natural selection can sculpt neural cell fate and connectivity, and on the other hand, they are themselves subject to selection pressure. For example, mutations that produce heritable perturbations in neuronal birth and death rates, transcription factor expression, or availability of axon guidance factors within sensory pathways can markedly affect the development of form and thus the function of stimulus decoding circuitry. This evolvability of flexible circuits makes them more adaptable to environmental variation. Although there is general agreement on this point, whether the sensitivity of circuits to environmental influence and the mechanisms underlying development and plasticity of sensory pathways are similar across species from different ecological niches has received almost no attention. Neural circuits are generally more sensitive to environmental influences during an early critical period, but not all niches afford the same access to stimuli in early life. Furthermore, depending on predictability of the habitat and ecological niche, sensory coding circuits might be more susceptible to sensory experience in some species than in others. Despite decades of work on understanding the mechanisms underlying critical period plasticity, the importance of ecological niche in visual pathway development has received little attention. Here, I will explore the relationship between critical period plasticity and ecological niche in mammalian sensory pathways. PMID:28701910

The Impact of Ecological Niche on Adaptive Flexibility of Sensory Circuitry.

PubMed

Pallas, Sarah L

2017-01-01

Evolution and development are interdependent, particularly with regard to the construction of the nervous system and its position as the machine that produces behavior. On the one hand, the processes directing development and plasticity of the brain provide avenues through which natural selection can sculpt neural cell fate and connectivity, and on the other hand, they are themselves subject to selection pressure. For example, mutations that produce heritable perturbations in neuronal birth and death rates, transcription factor expression, or availability of axon guidance factors within sensory pathways can markedly affect the development of form and thus the function of stimulus decoding circuitry. This evolvability of flexible circuits makes them more adaptable to environmental variation. Although there is general agreement on this point, whether the sensitivity of circuits to environmental influence and the mechanisms underlying development and plasticity of sensory pathways are similar across species from different ecological niches has received almost no attention. Neural circuits are generally more sensitive to environmental influences during an early critical period, but not all niches afford the same access to stimuli in early life. Furthermore, depending on predictability of the habitat and ecological niche, sensory coding circuits might be more susceptible to sensory experience in some species than in others. Despite decades of work on understanding the mechanisms underlying critical period plasticity, the importance of ecological niche in visual pathway development has received little attention. Here, I will explore the relationship between critical period plasticity and ecological niche in mammalian sensory pathways.

The neural circuits that generate tics in Tourette's syndrome.

PubMed

Wang, Zhishun; Maia, Tiago V; Marsh, Rachel; Colibazzi, Tiziano; Gerber, Andrew; Peterson, Bradley S

2011-12-01

The purpose of this study was to examine neural activity and connectivity within cortico-striato-thalamo-cortical circuits and to reveal circuit-based neural mechanisms that govern tic generation in Tourette's syndrome. Functional magnetic resonance imaging data were acquired from 13 individuals with Tourette's syndrome and 21 healthy comparison subjects during spontaneous or simulated tics. Independent component analysis with hierarchical partner matching was used to isolate neural activity within functionally distinct regions of cortico-striato-thalamo-cortical circuits. Granger causality was used to investigate causal interactions among these regions. The Tourette's syndrome group exhibited stronger neural activity and interregional causality than healthy comparison subjects throughout all portions of the motor pathway, including the sensorimotor cortex, putamen, pallidum, and substantia nigra. Activity in these areas correlated positively with the severity of tic symptoms. Activity within the Tourette's syndrome group was stronger during spontaneous tics than during voluntary tics in the somatosensory and posterior parietal cortices, putamen, and amygdala/hippocampus complex, suggesting that activity in these regions may represent features of the premonitory urges that generate spontaneous tic behaviors. In contrast, activity was weaker in the Tourette's syndrome group than in the healthy comparison group within portions of cortico-striato-thalamo-cortical circuits that exert top-down control over motor pathways (the caudate and anterior cingulate cortex), and progressively less activity in these regions accompanied more severe tic symptoms, suggesting that faulty activity in these circuits may result in their failure to control tic behaviors or the premonitory urges that generate them. Our findings, taken together, suggest that tics are caused by the combined effects of excessive activity in motor pathways and reduced activation in control portions of cortico-striato-thalamo-cortical circuits.

Novel four-sided neural probe fabricated by a thermal lamination process of polymer films.

PubMed

Shin, Soowon; Kim, Jae-Hyun; Jeong, Joonsoo; Gwon, Tae Mok; Lee, Seung-Hee; Kim, Sung June

2017-02-15

Ideally, neural probes should have channels with a three-dimensional (3-D) configuration to record the activities of 3-D neural circuits. Many types of 3-D neural probes have been developed; however, most of them were designed as an array of multiple shanks with electrodes located along one side of the shanks. We developed a novel liquid crystal polymer (LCP)-based neural probe with four-sided electrodes. This probe has electrodes on four sides of the shank, i.e., the front, back and two sidewalls. To generate the proposed configuration of the electrodes, we used a thermal lamination process involving LCP films and laser micromachining. The proposed novel four-sided neural probe, was used to successfully perform in vivo multichannel neural recording in the mouse primary somatosensory cortex. The multichannel neural recording showed that the proposed four-sided neural probe can record spiking activities from a more diverse neuronal population than single-sided probes. This was confirmed by a pairwise Pearson correlation coefficient (Pearson's r) analysis and a cross-correlation analysis. The developed four-sided neural probe can be used to record various signals from a complex neural network. Copyright © 2016 Elsevier B.V. All rights reserved.

Optogenetic dissection of neural circuits underlying emotional valence and motivated behaviors

PubMed Central

Nieh, Edward H.; Kim, Sung-Yon; Namburi, Praneeth; Tye, Kay M.

2014-01-01

The neural circuits underlying emotional valence and motivated behaviors are several synapses away from both defined sensory inputs and quantifiable motor outputs. Electrophysiology has provided us with a suitable means for observing neural activity during behavior, but methods for controlling activity for the purpose of studying motivated behaviors have been inadequate: electrical stimulation lacks cellular specificity and pharmacological manipulation lacks temporal resolution. The recent emergence of optogenetic tools provides a new means for establishing causal relationships between neural activity and behavior. Optogenetics, the use of genetically-encodable light-activated proteins, permits the modulation of specific neural circuit elements with millisecond precision. The ability to control individual cell types, and even projections between distal regions, allows us to investigate functional connectivity in a causal manner. The greatest consequence of controlling neural activity with finer precision has been the characterization of individual neural circuits within anatomical brain regions as defined functional units. Within the mesolimbic dopamine system, optogenetics has helped separate subsets of dopamine neurons with distinct functions for reward, aversion and salience processing, elucidated GABA neuronal effects on behavior, and characterized connectivity with forebrain and cortical structures. Within the striatum, optogenetics has confirmed the opposing relationship between direct and indirect pathway medium spiny neurons (MSNs), in addition to characterizing the inhibition of MSNs by cholinergic interneurons. Within the hypothalamus, optogenetics has helped overcome the heterogeneity in neuronal cell-type and revealed distinct circuits mediating aggression and feeding. Within the amygdala, optogenetics has allowed the study of intra-amygdala microcircuitry as well as interconnections with distal regions involved in fear and anxiety. In this review, we will present the body of optogenetic studies that has significantly enhanced our understanding of emotional valence and motivated behaviors. PMID:23142759

Neural integrators for decision making: a favorable tradeoff between robustness and sensitivity

PubMed Central

Cain, Nicholas; Barreiro, Andrea K.; Shadlen, Michael

2013-01-01

A key step in many perceptual decision tasks is the integration of sensory inputs over time, but a fundamental questions remain about how this is accomplished in neural circuits. One possibility is to balance decay modes of membranes and synapses with recurrent excitation. To allow integration over long timescales, however, this balance must be exceedingly precise. The need for fine tuning can be overcome via a “robust integrator” mechanism in which momentary inputs must be above a preset limit to be registered by the circuit. The degree of this limiting embodies a tradeoff between sensitivity to the input stream and robustness against parameter mistuning. Here, we analyze the consequences of this tradeoff for decision-making performance. For concreteness, we focus on the well-studied random dot motion discrimination task and constrain stimulus parameters by experimental data. We show that mistuning feedback in an integrator circuit decreases decision performance but that the robust integrator mechanism can limit this loss. Intriguingly, even for perfectly tuned circuits with no immediate need for a robustness mechanism, including one often does not impose a substantial penalty for decision-making performance. The implication is that robust integrators may be well suited to subserve the basic function of evidence integration in many cognitive tasks. We develop these ideas using simulations of coupled neural units and the mathematics of sequential analysis. PMID:23446688

Corticotropin-Releasing Factor Critical for Zebrafish Camouflage Behavior Is Regulated by Light and Sensitive to Ethanol

PubMed Central

Wagle, Mahendra; Mathur, Priya; Guo, Su

2011-01-01

The zebrafish camouflage response is an innate “hard-wired” behavior that offers an excellent opportunity to explore neural circuit assembly and function. Moreover, the camouflage response is sensitive to ethanol, making it a tractable system for understanding how ethanol influences neural circuit development and function. Here we report the identification of corticotropin releasing factor (CRF) as a critical component of the camouflage response pathway. We further show that ethanol, having no direct effect on the visual sensory system or the melanocytes, acts downstream of retinal ganglion cells and requires the CRF-proopiomelanocortin (POMC) pathway to exert its effect on camouflage. Treatment with ethanol, as well as alteration of light exposure that changes sensory input into the camouflage circuit, robustly modifies CRF expression in subsets of neurons. Activity of both Adenylyl Cyclase 5 and Extracellular signal Regulated Kinase (ERK) is required for such ethanol- or light- induced plasticity of crf expression. These results reveal an essential role of a peptidergic pathway in camouflage that is regulated by light and influenced by ethanol at concentrations relevant to abuse and anxiolysis, in a cAMP- and ERK- dependent manner. We conclude that this ethanol-modulated camouflage response represents a novel and relevant system for molecular genetic dissection of a neural circuit that is regulated by light and sensitive to ethanol. PMID:21209207

Corticotropin-releasing factor critical for zebrafish camouflage behavior is regulated by light and sensitive to ethanol.

PubMed

Wagle, Mahendra; Mathur, Priya; Guo, Su

2011-01-05

The zebrafish camouflage response is an innate "hard-wired" behavior that offers an excellent opportunity to explore neural circuit assembly and function. Moreover, the camouflage response is sensitive to ethanol, making it a tractable system for understanding how ethanol influences neural circuit development and function. Here we report the identification of corticotropin-releasing factor (CRF) as a critical component of the camouflage response pathway. We further show that ethanol, having no direct effect on the visual sensory system or the melanocytes, acts downstream of retinal ganglion cells and requires the CRF-proopiomelanocortin pathway to exert its effect on camouflage. Treatment with ethanol, as well as alteration of light exposure that changes sensory input into the camouflage circuit, robustly modifies CRF expression in subsets of neurons. Activity of both adenylyl cyclase 5 and extracellular signal-regulated kinase (ERK) is required for such ethanol-induced or light-induced plasticity of crf expression. These results reveal an essential role of a peptidergic pathway in camouflage that is regulated by light and influenced by ethanol at concentrations relevant to abuse and anxiolysis, in a cAMP-dependent and ERK-dependent manner. We conclude that this ethanol-modulated camouflage response represents a novel and relevant system for molecular genetic dissection of a neural circuit that is regulated by light and sensitive to ethanol.

Developmental metaplasticity in neural circuit codes of firing and structure.

PubMed

Baram, Yoram

2017-01-01

Firing-rate dynamics have been hypothesized to mediate inter-neural information transfer in the brain. While the Hebbian paradigm, relating learning and memory to firing activity, has put synaptic efficacy variation at the center of cortical plasticity, we suggest that the external expression of plasticity by changes in the firing-rate dynamics represents a more general notion of plasticity. Hypothesizing that time constants of plasticity and firing dynamics increase with age, and employing the filtering property of the neuron, we obtain the elementary code of global attractors associated with the firing-rate dynamics in each developmental stage. We define a neural circuit connectivity code as an indivisible set of circuit structures generated by membrane and synapse activation and silencing. Synchronous firing patterns under parameter uniformity, and asynchronous circuit firing are shown to be driven, respectively, by membrane and synapse silencing and reactivation, and maintained by the neuronal filtering property. Analytic, graphical and simulation representation of the discrete iteration maps and of the global attractor codes of neural firing rate are found to be consistent with previous empirical neurobiological findings, which have lacked, however, a specific correspondence between firing modes, time constants, circuit connectivity and cortical developmental stages. Copyright © 2016 Elsevier Ltd. All rights reserved.

Neural circuits and motivational processes for hunger

PubMed Central

Sternson, Scott M; Betley, J Nicholas; Huang Cao, Zhen Fang

2014-01-01

How does an organism’s internal state direct its actions? At one moment an animal forages for food with acrobatic feats such as tree climbing and jumping between branches. At another time, it travels along the ground to find water or a mate, exposing itself to predators along the way. These behaviors are costly in terms of energy or physical risk, and the likelihood of performing one set of actions relative to another is strongly modulated by internal state. For example, an animal in energy deficit searches for food and a dehydrated animal looks for water. The crosstalk between physiological state and motivational processes influences behavioral intensity and intent, but the underlying neural circuits are poorly understood. Molecular genetics along with optogenetic and pharmacogenetic tools for perturbing neuron function have enabled cell type-selective dissection of circuits that mediate behavioral responses to physiological state changes. Here, we review recent progress into neural circuit analysis of hunger in the mouse by focusing on a starvation-sensitive neuron population in the hypothalamus that is sufficient to promote voracious eating. We also consider research into the motivational processes that are thought to underlie hunger in order to outline considerations for bridging the gap between homeostatic and motivational neural circuits. PMID:23648085

Analog Delta-Back-Propagation Neural-Network Circuitry

NASA Technical Reports Server (NTRS)

Eberhart, Silvio

1990-01-01

Changes in synapse weights due to circuit drifts suppressed. Proposed fully parallel analog version of electronic neural-network processor based on delta-back-propagation algorithm. Processor able to "learn" when provided with suitable combinations of inputs and enforced outputs. Includes programmable resistive memory elements (corresponding to synapses), conductances (synapse weights) adjusted during learning. Buffer amplifiers, summing circuits, and sample-and-hold circuits arranged in layers of electronic neurons in accordance with delta-back-propagation algorithm.

Habenula Circuit Development: Past, Present, and Future

PubMed Central

Beretta, Carlo A.; Dross, Nicolas; Guiterrez-Triana, Jose A.; Ryu, Soojin; Carl, Matthias

2012-01-01

The habenular neural circuit is attracting increasing attention from researchers in fields as diverse as neuroscience, medicine, behavior, development, and evolution. Recent studies have revealed that this part of the limbic system in the dorsal diencephalon is involved in reward, addiction, and other behaviors and its impairment is associated with various neurological conditions and diseases. Since the initial description of the dorsal diencephalic conduction system (DDC) with the habenulae in its center at the end of the nineteenth century, increasingly sophisticated techniques have resolved much of its anatomy and have shown that these pathways relay information from different parts of the forebrain to the tegmentum, midbrain, and hindbrain. The first part of this review gives a brief historical overview on how the improving experimental approaches have allowed the stepwise uncovering much of the architecture of the habenula circuit as we know it today. Our brain distributes tasks differentially between left and right and it has become a paradigm that this functional lateralization is a universal feature of vertebrates. Moreover, task dependent differential brain activities have been linked to anatomical differences across the left–right axis in humans. A good way to further explore this fundamental issue will be to study the functional consequences of subtle changes in neural network formation, which requires that we fully understand DDC system development. As the habenular circuit is evolutionarily highly conserved, researchers have the option to perform such difficult experiments in more experimentally amenable vertebrate systems. Indeed, research in the last decade has shown that the zebrafish is well suited for the study of DDC system development and the phenomenon of functional lateralization. We will critically discuss the advantages of the zebrafish model, available techniques, and others that are needed to fully understand habenular circuit development. PMID:22536170

Habenula circuit development: past, present, and future.

PubMed

Beretta, Carlo A; Dross, Nicolas; Guiterrez-Triana, Jose A; Ryu, Soojin; Carl, Matthias

2012-01-01

The habenular neural circuit is attracting increasing attention from researchers in fields as diverse as neuroscience, medicine, behavior, development, and evolution. Recent studies have revealed that this part of the limbic system in the dorsal diencephalon is involved in reward, addiction, and other behaviors and its impairment is associated with various neurological conditions and diseases. Since the initial description of the dorsal diencephalic conduction system (DDC) with the habenulae in its center at the end of the nineteenth century, increasingly sophisticated techniques have resolved much of its anatomy and have shown that these pathways relay information from different parts of the forebrain to the tegmentum, midbrain, and hindbrain. The first part of this review gives a brief historical overview on how the improving experimental approaches have allowed the stepwise uncovering much of the architecture of the habenula circuit as we know it today. Our brain distributes tasks differentially between left and right and it has become a paradigm that this functional lateralization is a universal feature of vertebrates. Moreover, task dependent differential brain activities have been linked to anatomical differences across the left-right axis in humans. A good way to further explore this fundamental issue will be to study the functional consequences of subtle changes in neural network formation, which requires that we fully understand DDC system development. As the habenular circuit is evolutionarily highly conserved, researchers have the option to perform such difficult experiments in more experimentally amenable vertebrate systems. Indeed, research in the last decade has shown that the zebrafish is well suited for the study of DDC system development and the phenomenon of functional lateralization. We will critically discuss the advantages of the zebrafish model, available techniques, and others that are needed to fully understand habenular circuit development.

Circuit Motifs for Contrast-Adaptive Differentiation in Early Sensory Systems: The Role of Presynaptic Inhibition and Short-Term Plasticity

PubMed Central

Zhang, Danke; Wu, Si; Rasch, Malte J.

2015-01-01

In natural signals, such as the luminance value across of a visual scene, abrupt changes in intensity value are often more relevant to an organism than intensity values at other positions and times. Thus to reduce redundancy, sensory systems are specialized to detect the times and amplitudes of informative abrupt changes in the input stream rather than coding the intensity values at all times. In theory, a system that responds transiently to fast changes is called a differentiator. In principle, several different neural circuit mechanisms exist that are capable of responding transiently to abrupt input changes. However, it is unclear which circuit would be best suited for early sensory systems, where the dynamic range of the natural input signals can be very wide. We here compare the properties of different simple neural circuit motifs for implementing signal differentiation. We found that a circuit motif based on presynaptic inhibition (PI) is unique in a sense that the vesicle resources in the presynaptic site can be stably maintained over a wide range of stimulus intensities, making PI a biophysically plausible mechanism to implement a differentiator with a very wide dynamical range. Moreover, by additionally considering short-term plasticity (STP), differentiation becomes contrast adaptive in the PI-circuit but not in other potential neural circuit motifs. Numerical simulations show that the behavior of the adaptive PI-circuit is consistent with experimental observations suggesting that adaptive presynaptic inhibition might be a good candidate neural mechanism to achieve differentiation in early sensory systems. PMID:25723493

Circuit motifs for contrast-adaptive differentiation in early sensory systems: the role of presynaptic inhibition and short-term plasticity.

PubMed

Zhang, Danke; Wu, Si; Rasch, Malte J

2015-01-01

In natural signals, such as the luminance value across of a visual scene, abrupt changes in intensity value are often more relevant to an organism than intensity values at other positions and times. Thus to reduce redundancy, sensory systems are specialized to detect the times and amplitudes of informative abrupt changes in the input stream rather than coding the intensity values at all times. In theory, a system that responds transiently to fast changes is called a differentiator. In principle, several different neural circuit mechanisms exist that are capable of responding transiently to abrupt input changes. However, it is unclear which circuit would be best suited for early sensory systems, where the dynamic range of the natural input signals can be very wide. We here compare the properties of different simple neural circuit motifs for implementing signal differentiation. We found that a circuit motif based on presynaptic inhibition (PI) is unique in a sense that the vesicle resources in the presynaptic site can be stably maintained over a wide range of stimulus intensities, making PI a biophysically plausible mechanism to implement a differentiator with a very wide dynamical range. Moreover, by additionally considering short-term plasticity (STP), differentiation becomes contrast adaptive in the PI-circuit but not in other potential neural circuit motifs. Numerical simulations show that the behavior of the adaptive PI-circuit is consistent with experimental observations suggesting that adaptive presynaptic inhibition might be a good candidate neural mechanism to achieve differentiation in early sensory systems.

A tale of two species: neural integration in zebrafish and monkeys

PubMed Central

Joshua, Mati; Lisberger, Stephen G.

2014-01-01

Selection of a model organism creates a tension between competing constraints. The recent explosion of modern molecular techniques has revolutionized the analysis of neural systems in organisms that are amenable to genetic techniques. Yet, the non-human primate remains the gold-standard for the analysis of the neural basis of behavior, and as a bridge to the operation of the human brain. The challenge is to generalize across species in a way that exposes the operation of circuits as well as the relationship of circuits to behavior. Eye movements provide an opportunity to cross the bridge from mechanism to behavior through research on diverse species. Here, we review experiments and computational studies on a circuit function called “neural integration” that occurs in the brainstems of larval zebrafish, non-human primates, and species “in between”. We show that analysis of circuit structure using modern molecular and imaging approaches in zebrafish has remarkable explanatory power for the details of the responses of integrator neurons in the monkey. The combination of research from the two species has led to a much stronger hypothesis for the implementation of the neural integrator than could have been achieved using either species alone. PMID:24797331

A tale of two species: Neural integration in zebrafish and monkeys.

PubMed

Joshua, M; Lisberger, S G

2015-06-18

Selection of a model organism creates tension between competing constraints. The recent explosion of modern molecular techniques has revolutionized the analysis of neural systems in organisms that are amenable to genetic techniques. Yet, the non-human primate remains the gold-standard for the analysis of the neural basis of behavior, and as a bridge to the operation of the human brain. The challenge is to generalize across species in a way that exposes the operation of circuits as well as the relationship of circuits to behavior. Eye movements provide an opportunity to cross the bridge from mechanism to behavior through research on diverse species. Here, we review experiments and computational studies on a circuit function called "neural integration" that occurs in the brainstems of larval zebrafish, primates, and species "in between". We show that analysis of circuit structure using modern molecular and imaging approaches in zebrafish has remarkable explanatory power for details of the responses of integrator neurons in the monkey. The combination of research from the two species has led to a much stronger hypothesis for the implementation of the neural integrator than could have been achieved using either species alone. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

A Brain for Speech. Evolutionary Continuity in Primate and Human Auditory-Vocal Processing

PubMed Central

Aboitiz, Francisco

2018-01-01

In this review article, I propose a continuous evolution from the auditory-vocal apparatus and its mechanisms of neural control in non-human primates, to the peripheral organs and the neural control of human speech. Although there is an overall conservatism both in peripheral systems and in central neural circuits, a few changes were critical for the expansion of vocal plasticity and the elaboration of proto-speech in early humans. Two of the most relevant changes were the acquisition of direct cortical control of the vocal fold musculature and the consolidation of an auditory-vocal articulatory circuit, encompassing auditory areas in the temporoparietal junction and prefrontal and motor areas in the frontal cortex. This articulatory loop, also referred to as the phonological loop, enhanced vocal working memory capacity, enabling early humans to learn increasingly complex utterances. The auditory-vocal circuit became progressively coupled to multimodal systems conveying information about objects and events, which gradually led to the acquisition of modern speech. Gestural communication accompanies the development of vocal communication since very early in human evolution, and although both systems co-evolved tightly in the beginning, at some point speech became the main channel of communication. PMID:29636657

Commentary: Elucidating the Neural Correlates of Early Childhood Memory

ERIC Educational Resources Information Center

Mullally, Sinead L.

2015-01-01

Both episodic memory and the key neural structure believed to support it, namely the hippocampus, are believed to undergo protracted periods of postnatal developmental. Critically however, the hippocampus is comprised of distinct subfields and circuits, and these circuits appear to mature at different rates (Lavenex and Banta Lavenex, 2013).…

Brain-derived neurotrophic factor and addiction: Pathological versus therapeutic effects on drug seeking

PubMed Central

Barker, Jacqueline M.; Taylor, Jane R.; De Vries, Taco J.; Peters, Jamie

2015-01-01

Many abused drugs lead to changes in endogenous brain-derived neurotrophic factor (BDNF) expression in neural circuits responsible for addictive behaviors. BDNF is a known molecular mediator of memory consolidation processes, evident at both behavioral and neurophysiological levels. Specific neural circuits are responsible for storing and executing drug-procuring motor programs, whereas other neural circuits are responsible for the active suppression of these “seeking” systems. These seeking-circuits are established as associations are formed between drug-associated cues and the conditioned responses they elicit. Such conditioned responses (e.g. drug seeking) can be diminished either through a passive weakening of seeking-circuits or an active suppression of those circuits through extinction. Extinction learning occurs when the association between cues and drug are violated, for example, by cue exposure without the drug present. Cue exposure therapy has been proposed as a therapeutic avenue for the treatment of addictions. Here we explore the role of BDNF in extinction circuits, compared to seeking-circuits that “incubate” over prolonged withdrawal periods. We begin by discussing the role of BDNF in extinction memory for fear and cocaine-seeking behaviors, where extinction circuits overlap in infralimbic prefrontal cortex (PFC). We highlight the ability of estrogen to promote BDNF-like effects in hippocampal–prefrontal circuits and consider the role of sex differences in extinction and incubation of drug-seeking behaviors. Finally, we examine how opiates and alcohol “break the mold” in terms of BDNF function in extinction circuits. PMID:25451116

The neurobiological basis of orientation in insects: insights from the silkmoth mating dance.

PubMed

Namiki, Shigehiro; Kanzaki, Ryohei

2016-06-01

Counterturning is a common movement pattern during orientation behavior in insects. Once male moths sense sex pheromones and then lose the input, they demonstrate zigzag movements, alternating between left and right turns, to increase the probability to contact with the pheromone plume. We summarize the anatomy and function of the neural circuit involved in pheromone orientation in the silkmoth. A neural circuit, the lateral accessory lobe (LAL), serves a role as the circuit module for zigzag movements and controls this operation using a flip-flop neural switch. Circuit design of the LAL is well conserved across species. We hypothesize that this zigzag module is utilized in a wide range of insect behavior. We introduce two examples of the potential use: orientation flight and the waggle dance in bees. Copyright © 2016 Elsevier Inc. All rights reserved.

Massively parallel neural circuits for stereoscopic color vision: encoding, decoding and identification.

PubMed

Lazar, Aurel A; Slutskiy, Yevgeniy B; Zhou, Yiyin

2015-03-01

Past work demonstrated how monochromatic visual stimuli could be faithfully encoded and decoded under Nyquist-type rate conditions. Color visual stimuli were then traditionally encoded and decoded in multiple separate monochromatic channels. The brain, however, appears to mix information about color channels at the earliest stages of the visual system, including the retina itself. If information about color is mixed and encoded by a common pool of neurons, how can colors be demixed and perceived? We present Color Video Time Encoding Machines (Color Video TEMs) for encoding color visual stimuli that take into account a variety of color representations within a single neural circuit. We then derive a Color Video Time Decoding Machine (Color Video TDM) algorithm for color demixing and reconstruction of color visual scenes from spikes produced by a population of visual neurons. In addition, we formulate Color Video Channel Identification Machines (Color Video CIMs) for functionally identifying color visual processing performed by a spiking neural circuit. Furthermore, we derive a duality between TDMs and CIMs that unifies the two and leads to a general theory of neural information representation for stereoscopic color vision. We provide examples demonstrating that a massively parallel color visual neural circuit can be first identified with arbitrary precision and its spike trains can be subsequently used to reconstruct the encoded stimuli. We argue that evaluation of the functional identification methodology can be effectively and intuitively performed in the stimulus space. In this space, a signal reconstructed from spike trains generated by the identified neural circuit can be compared to the original stimulus. Copyright © 2014 Elsevier Ltd. All rights reserved.

A ‘tool box’ for deciphering neuronal circuits in the developing chick spinal cord

PubMed Central

Hadas, Yoav; Etlin, Alex; Falk, Haya; Avraham, Oshri; Kobiler, Oren; Panet, Amos; Lev-Tov, Aharon; Klar, Avihu

2014-01-01

The genetic dissection of spinal circuits is an essential new means for understanding the neural basis of mammalian behavior. Molecular targeting of specific neuronal populations, a key instrument in the genetic dissection of neuronal circuits in the mouse model, is a complex and time-demanding process. Here we present a circuit-deciphering ‘tool box’ for fast, reliable and cheap genetic targeting of neuronal circuits in the developing spinal cord of the chick. We demonstrate targeting of motoneurons and spinal interneurons, mapping of axonal trajectories and synaptic targeting in both single and populations of spinal interneurons, and viral vector-mediated labeling of pre-motoneurons. We also demonstrate fluorescent imaging of the activity pattern of defined spinal neurons during rhythmic motor behavior, and assess the role of channel rhodopsin-targeted population of interneurons in rhythmic behavior using specific photoactivation. PMID:25147209

Neural learning circuits utilizing nano-crystalline silicon transistors and memristors.

PubMed

Cantley, Kurtis D; Subramaniam, Anand; Stiegler, Harvey J; Chapman, Richard A; Vogel, Eric M

2012-04-01

Properties of neural circuits are demonstrated via SPICE simulations and their applications are discussed. The neuron and synapse subcircuits include ambipolar nano-crystalline silicon transistor and memristor device models based on measured data. Neuron circuit characteristics and the Hebbian synaptic learning rule are shown to be similar to biology. Changes in the average firing rate learning rule depending on various circuit parameters are also presented. The subcircuits are then connected into larger neural networks that demonstrate fundamental properties including associative learning and pulse coincidence detection. Learned extraction of a fundamental frequency component from noisy inputs is demonstrated. It is then shown that if the fundamental sinusoid of one neuron input is out of phase with the rest, its synaptic connection changes differently than the others. Such behavior indicates that the system can learn to detect which signals are important in the general population, and that there is a spike-timing-dependent component of the learning mechanism. Finally, future circuit design and considerations are discussed, including requirements for the memristive device.

Neural, not gonadal, origin of brain sex differences in a gynandromorphic finch.

PubMed

Agate, Robert J; Grisham, William; Wade, Juli; Mann, Suzanne; Wingfield, John; Schanen, Carolyn; Palotie, Aarno; Arnold, Arthur P

2003-04-15

In mammals and birds, sex differences in brain function and disease are thought to derive exclusively from sex differences in gonadal hormone secretions. For example, testosterone in male mammals acts during fetal and neonatal life to cause masculine neural development. However, male and female brain cells also differ in genetic sex; thus, sex chromosome genes acting within cells could contribute to sex differences in cell function. We analyzed the sexual phenotype of the brain of a rare gynandromorphic finch in which the right half of the brain was genetically male and the left half genetically female. The neural song circuit on the right had a more masculine phenotype than that on the left. Because both halves of the brain were exposed to a common gonadal hormone environment, the lateral differences indicate that the genetic sex of brain cells contributes to the process of sexual differentiation. Because both sides of the song circuit were more masculine than that of females, diffusible factors such as hormones of gonadal or neural origin also likely played a role in sexual differentiation.

Cellular and Synaptic Properties of Local Inhibitory Circuits.

PubMed

Hull, Court

2017-05-01

Inhibitory interneurons play a key role in sculpting the information processed by neural circuits. Despite the wide range of physiologically and morphologically distinct types of interneurons that have been identified, common principles have emerged that have shed light on how synaptic inhibition operates, both mechanistically and functionally, across cell types and circuits. This introduction summarizes how electrophysiological approaches have been used to illuminate these key principles, including basic interneuron circuit motifs, the functional properties of inhibitory synapses, and the main roles for synaptic inhibition in regulating neural circuit function. It also highlights how some key electrophysiological methods and experiments have advanced our understanding of inhibitory synapse function. © 2017 Cold Spring Harbor Laboratory Press.

Sequencing the Connectome

PubMed Central

Zador, Anthony M.; Dubnau, Joshua; Oyibo, Hassana K.; Zhan, Huiqing; Cao, Gang; Peikon, Ian D.

2012-01-01

Connectivity determines the function of neural circuits. Historically, circuit mapping has usually been viewed as a problem of microscopy, but no current method can achieve high-throughput mapping of entire circuits with single neuron precision. Here we describe a novel approach to determining connectivity. We propose BOINC (“barcoding of individual neuronal connections”), a method for converting the problem of connectivity into a form that can be read out by high-throughput DNA sequencing. The appeal of using sequencing is that its scale—sequencing billions of nucleotides per day is now routine—is a natural match to the complexity of neural circuits. An inexpensive high-throughput technique for establishing circuit connectivity at single neuron resolution could transform neuroscience research. PMID:23109909

mTOR regulates brain morphogenesis by mediating GSK3 signaling

PubMed Central

Ka, Minhan; Condorelli, Gianluigi; Woodgett, James R.; Kim, Woo-Yang

2014-01-01

Balanced control of neural progenitor maintenance and neuron production is crucial in establishing functional neural circuits during brain development, and abnormalities in this process are implicated in many neurological diseases. However, the regulatory mechanisms of neural progenitor homeostasis remain poorly understood. Here, we show that mammalian target of rapamycin (mTOR) is required for maintaining neural progenitor pools and plays a key role in mediating glycogen synthase kinase 3 (GSK3) signaling during brain development. First, we generated and characterized conditional mutant mice exhibiting deletion of mTOR in neural progenitors and neurons in the developing brain using Nestin-cre and Nex-cre lines, respectively. The elimination of mTOR resulted in abnormal cell cycle progression of neural progenitors in the developing brain and thereby disruption of progenitor self-renewal. Accordingly, production of intermediate progenitors and postmitotic neurons were markedly suppressed. Next, we discovered that GSK3, a master regulator of neural progenitors, interacts with mTOR and controls its activity in cortical progenitors. Finally, we found that inactivation of mTOR activity suppresses the abnormal proliferation of neural progenitors induced by GSK3 deletion. Our findings reveal that the interaction between mTOR and GSK3 signaling plays an essential role in dynamic homeostasis of neural progenitors during brain development. PMID:25273085

Three Pillars for the Neural Control of Appetite.

PubMed

Sternson, Scott M; Eiselt, Anne-Kathrin

2017-02-10

The neural control of appetite is important for understanding motivated behavior as well as the present rising prevalence of obesity. Over the past several years, new tools for cell type-specific neuron activity monitoring and perturbation have enabled increasingly detailed analyses of the mechanisms underlying appetite-control systems. Three major neural circuits strongly and acutely influence appetite but with notably different characteristics. Although these circuits interact, they have distinct properties and thus appear to contribute to separate but interlinked processes influencing appetite, thereby forming three pillars of appetite control. Here, we summarize some of the key characteristics of appetite circuits that are emerging from recent work and synthesize the findings into a provisional framework that can guide future studies.

Possible Explanation for Cancer in Rats due to Cell Phone Radio Frequency Radiation

NASA Astrophysics Data System (ADS)

Feldman, Bernard J.

Very recently, the National Toxicology Program reported a correlation between exposure to whole body 900 MHz radio frequency radiation and cancer in the brains and hearts of Sprague Dawley male rats. Assuming that the National Toxicology Program is statistically significant, I propose the following explanation for these results. The neurons around the brain and heart form closed electrical circuits and, following Faraday's Law, 900 MHz radio frequency radiation induces 900 MHz electrical currents in these neural circuits. In turn, these 900 MHz currents in the neural circuits generate sufficient localized heat in the neural cells to shift the equilibrium concentration of carcinogenic radicals to higher levels and thus, to higher incidences of cancer.

A power-efficient analog integrated circuit for amplification and detection of neural signals.

PubMed

Borghi, T; Bonfanti, A; Gusmeroli, R; Zambra, G; Spinelli, A S

2008-01-01

We present a neural amplifier that optimizes the trade-off between power consumption and noise performance down to the best so far reported. In the perspective of realizing a fully autonomous implantable system we also address the problem of spike detection by using a new simple algorithm and we discuss the implementation with analog integrated circuits. Implemented in 0.35-microm CMOS technology and with total current consumption of about 20 microA, the whole circuit occupies an area of 0.18 mm(2). Reduced power consumption and small area make it suited to be used in chronic multichannel recording systems for neural prosthetics and neuroscience experiments.

Simplified Design Equations for Class-E Neural Prosthesis Transmitters

PubMed Central

Troyk, Philip; Hu, Zhe

2013-01-01

Extreme miniaturization of implantable electronic devices is recognized as essential for the next generation of neural prostheses, owing to the need for minimizing the damage and disruption of the surrounding neural tissue. Transcutaneous power and data transmission via a magnetic link remains the most effective means of powering and controlling implanted neural prostheses. Reduction in the size of the coil, within the neural prosthesis, demands the generation of a high-intensity radio frequency magnetic field from the extracoporeal transmitter. The Class-E power amplifier circuit topology has been recognized as a highly effective means of producing large radio frequency currents within the transmitter coil. Unfortunately, design of a Class-E circuit is most often fraught by the need to solve a complex set of equations so as to implement both the zero-voltage-switching and zero-voltage-derivative-switching conditions that are required for efficient operation. This paper presents simple explicit design equations for designing the Class-E circuit topology. Numerical design examples are presented to illustrate the design procedure. PMID:23292784

Stretchable Transparent Electrode Arrays for Simultaneous Electrical and Optical Interrogation of Neural Circuits in Vivo.

PubMed

Zhang, Jing; Liu, Xiaojun; Xu, Wenjing; Luo, Wenhan; Li, Ming; Chu, Fangbing; Xu, Lu; Cao, Anyuan; Guan, Jisong; Tang, Shiming; Duan, Xiaojie

2018-05-09

Recent developments of transparent electrode arrays provide a unique capability for simultaneous optical and electrical interrogation of neural circuits in the brain. However, none of these electrode arrays possess the stretchability highly desired for interfacing with mechanically active neural systems, such as the brain under injury, the spinal cord, and the peripheral nervous system (PNS). Here, we report a stretchable transparent electrode array from carbon nanotube (CNT) web-like thin films that retains excellent electrochemical performance and broad-band optical transparency under stretching and is highly durable under cyclic stretching deformation. We show that the CNT electrodes record well-defined neuronal response signals with negligible light-induced artifacts from cortical surfaces under optogenetic stimulation. Simultaneous two-photon calcium imaging through the transparent CNT electrodes from cortical surfaces of GCaMP-expressing mice with epilepsy shows individual activated neurons in brain regions from which the concurrent electrical recording is taken, thus providing complementary cellular information in addition to the high-temporal-resolution electrical recording. Notably, the studies on rats show that the CNT electrodes remain operational during and after brain contusion that involves the rapid deformation of both the electrode array and brain tissue. This enables real-time, continuous electrophysiological monitoring of cortical activity under traumatic brain injury. These results highlight the potential application of the stretchable transparent CNT electrode arrays in combining electrical and optical modalities to study neural circuits, especially under mechanically active conditions, which could potentially provide important new insights into the local circuit dynamics of the spinal cord and PNS as well as the mechanism underlying traumatic injuries of the nervous system.

Neural responses to macronutrients: hedonic and homeostatic mechanisms.

PubMed

Tulloch, Alastair J; Murray, Susan; Vaicekonyte, Regina; Avena, Nicole M

2015-05-01

The brain responds to macronutrients via intricate mechanisms. We review how the brain's neural systems implicated in homeostatic control of feeding and hedonic responses are influenced by the ingestion of specific types of food. We discuss how these neural systems are dysregulated in preclinical models of obesity. Findings from these studies can increase our understanding of overeating and, perhaps in some cases, the development of obesity. In addition, a greater understanding of the neural circuits affected by the consumption of specific macronutrients, and by obesity, might lead to new treatments and strategies for preventing unhealthy weight gain. Copyright © 2015 AGA Institute. Published by Elsevier Inc. All rights reserved.

Distinct neural circuits for control of movement vs. holding still

PubMed Central

2017-01-01

In generating a point-to-point movement, the brain does more than produce the transient commands needed to move the body part; it also produces the sustained commands that are needed to hold the body part at its destination. In the oculomotor system, these functions are mapped onto two distinct circuits: a premotor circuit that specializes in generating the transient activity that displaces the eyes and a “neural integrator” that transforms that transient input into sustained activity that holds the eyes. Different parts of the cerebellum adaptively control the motor commands during these two phases: the oculomotor vermis participates in fine tuning the transient neural signals that move the eyes, monitoring the activity of the premotor circuit via efference copy, whereas the flocculus participates in controlling the sustained neural signals that hold the eyes, monitoring the activity of the neural integrator. Here, I review the oculomotor literature and then ask whether this separation of control between moving and holding is a design principle that may be shared with other modalities of movement. To answer this question, I consider neurophysiological and psychophysical data in various species during control of head movements, arm movements, and locomotion, focusing on the brain stem, motor cortex, and hippocampus, respectively. The review of the data raises the possibility that across modalities of motor control, circuits that are responsible for producing commands that change the sensory state of a body part are distinct from those that produce commands that maintain that sensory state. PMID:28053244

History of winning remodels thalamo-PFC circuit to reinforce social dominance.

PubMed

Zhou, Tingting; Zhu, Hong; Fan, Zhengxiao; Wang, Fei; Chen, Yang; Liang, Hexing; Yang, Zhongfei; Zhang, Lu; Lin, Longnian; Zhan, Yang; Wang, Zheng; Hu, Hailan

2017-07-14

Mental strength and history of winning play an important role in the determination of social dominance. However, the neural circuits mediating these intrinsic and extrinsic factors have remained unclear. Working in mice, we identified a dorsomedial prefrontal cortex (dmPFC) neural population showing "effort"-related firing during moment-to-moment competition in the dominance tube test. Activation or inhibition of the dmPFC induces instant winning or losing, respectively. In vivo optogenetic-based long-term potentiation and depression experiments establish that the mediodorsal thalamic input to the dmPFC mediates long-lasting changes in the social dominance status that are affected by history of winning. The same neural circuit also underlies transfer of dominance between different social contests. These results provide a framework for understanding the circuit basis of adaptive and pathological social behaviors. Copyright © 2017, American Association for the Advancement of Science.

Hard to Swallow: Developmental Biological Insights into Pediatric Dysphagia

PubMed Central

LaMantia, Anthony-Samuel; Moody, Sally A.; Maynard, Thomas M.; Karpinski, Beverly A.; Zohn, Irene E.; Mendelowitz, David; Lee, Norman H.; Popratiloff, Anastas

2015-01-01

Pediatric dysphagia—feeding and swallowing difficulties that begin at birth, last throughout childhood, and continue into maturity—is one of the most common, least understood complications in children with developmental disorders. We argue that a major cause of pediatric dysphagia is altered hindbrain patterning during pre-natal development. Such changes can compromise craniofacial structures including oropharyngeal muscles and skeletal elements as well as motor and sensory circuits necessary for normal feeding and swallowing. Animal models of developmental disorders that include pediatric dysphagia in their phenotypic spectrum can provide mechanistic insight into pathogenesis of feeding and swallowing difficulties. A fairly common human genetic developmental disorder, DiGeorge/22q11.2 Deletion Syndrome (22q11DS) includes a substantial incidence of pediatric dysphagia in its phenotypic spectrum. Infant mice carrying a parallel deletion to 22q11DS patients have feeding and swallowing difficulties. Altered hindbrain patterning, neural crest migration, craniofacial malformations, and changes in cranial nerve growth prefigure these difficulties. Thus, in addition to craniofacial and pharyngeal anomalies that arise independently of altered neural development, pediatric dysphagia may reflect disrupted hindbrain patterning and its impact on neural circuit development critical for feeding and swallowing. The mechanisms that disrupt hindbrain patterning and circuitry may provide a foundation to develop novel therapeutic approaches for improved clinical management of pediatric dysphagia. PMID:26554723

A neural circuit transforming temporal periodicity information into a rate-based representation in the mammalian auditory system.

PubMed

Dicke, Ulrike; Ewert, Stephan D; Dau, Torsten; Kollmeier, Birger

2007-01-01

Periodic amplitude modulations (AMs) of an acoustic stimulus are presumed to be encoded in temporal activity patterns of neurons in the cochlear nucleus. Physiological recordings indicate that this temporal AM code is transformed into a rate-based periodicity code along the ascending auditory pathway. The present study suggests a neural circuit for the transformation from the temporal to the rate-based code. Due to the neural connectivity of the circuit, bandpass shaped rate modulation transfer functions are obtained that correspond to recorded functions of inferior colliculus (IC) neurons. In contrast to previous modeling studies, the present circuit does not employ a continuously changing temporal parameter to obtain different best modulation frequencies (BMFs) of the IC bandpass units. Instead, different BMFs are yielded from varying the number of input units projecting onto different bandpass units. In order to investigate the compatibility of the neural circuit with a linear modulation filterbank analysis as proposed in psychophysical studies, complex stimuli such as tones modulated by the sum of two sinusoids, narrowband noise, and iterated rippled noise were processed by the model. The model accounts for the encoding of AM depth over a large dynamic range and for modulation frequency selective processing of complex sounds.

Mechanisms of Hierarchical Reinforcement Learning in Corticostriatal Circuits 1: Computational Analysis

PubMed Central

Badre, David

2012-01-01

Growing evidence suggests that the prefrontal cortex (PFC) is organized hierarchically, with more anterior regions having increasingly abstract representations. How does this organization support hierarchical cognitive control and the rapid discovery of abstract action rules? We present computational models at different levels of description. A neural circuit model simulates interacting corticostriatal circuits organized hierarchically. In each circuit, the basal ganglia gate frontal actions, with some striatal units gating the inputs to PFC and others gating the outputs to influence response selection. Learning at all of these levels is accomplished via dopaminergic reward prediction error signals in each corticostriatal circuit. This functionality allows the system to exhibit conditional if–then hypothesis testing and to learn rapidly in environments with hierarchical structure. We also develop a hybrid Bayesian-reinforcement learning mixture of experts (MoE) model, which can estimate the most likely hypothesis state of individual participants based on their observed sequence of choices and rewards. This model yields accurate probabilistic estimates about which hypotheses are attended by manipulating attentional states in the generative neural model and recovering them with the MoE model. This 2-pronged modeling approach leads to multiple quantitative predictions that are tested with functional magnetic resonance imaging in the companion paper. PMID:21693490

[Not Available].

PubMed

Pecevski, Dejan; Natschläger, Thomas; Schuch, Klaus

2009-01-01

The Parallel Circuit SIMulator (PCSIM) is a software package for simulation of neural circuits. It is primarily designed for distributed simulation of large scale networks of spiking point neurons. Although its computational core is written in C++, PCSIM's primary interface is implemented in the Python programming language, which is a powerful programming environment and allows the user to easily integrate the neural circuit simulator with data analysis and visualization tools to manage the full neural modeling life cycle. The main focus of this paper is to describe PCSIM's full integration into Python and the benefits thereof. In particular we will investigate how the automatically generated bidirectional interface and PCSIM's object-oriented modular framework enable the user to adopt a hybrid modeling approach: using and extending PCSIM's functionality either employing pure Python or C++ and thus combining the advantages of both worlds. Furthermore, we describe several supplementary PCSIM packages written in pure Python and tailored towards setting up and analyzing neural simulations.

A tweaking principle for executive control: neuronal circuit mechanism for rule-based task switching and conflict resolution.

PubMed

Ardid, Salva; Wang, Xiao-Jing

2013-12-11

A hallmark of executive control is the brain's agility to shift between different tasks depending on the behavioral rule currently in play. In this work, we propose a "tweaking hypothesis" for task switching: a weak rule signal provides a small bias that is dramatically amplified by reverberating attractor dynamics in neural circuits for stimulus categorization and action selection, leading to an all-or-none reconfiguration of sensory-motor mapping. Based on this principle, we developed a biologically realistic model with multiple modules for task switching. We found that the model quantitatively accounts for complex task switching behavior: switch cost, congruency effect, and task-response interaction; as well as monkey's single-neuron activity associated with task switching. The model yields several testable predictions, in particular, that category-selective neurons play a key role in resolving sensory-motor conflict. This work represents a neural circuit model for task switching and sheds insights in the brain mechanism of a fundamental cognitive capability.

Artificial Neural Network with Hardware Training and Hardware Refresh

NASA Technical Reports Server (NTRS)

Duong, Tuan A. (Inventor)

2003-01-01

A neural network circuit is provided having a plurality of circuits capable of charge storage. Also provided is a plurality of circuits each coupled to at least one of the plurality of charge storage circuits and constructed to generate an output in accordance with a neuron transfer function. Each of a plurality of circuits is coupled to one of the plurality of neuron transfer function circuits and constructed to generate a derivative of the output. A weight update circuit updates the charge storage circuits based upon output from the plurality of transfer function circuits and output from the plurality of derivative circuits. In preferred embodiments, separate training and validation networks share the same set of charge storage circuits and may operate concurrently. The validation network has a separate transfer function circuits each being coupled to the charge storage circuits so as to replicate the training network s coupling of the plurality of charge storage to the plurality of transfer function circuits. The plurality of transfer function circuits may be constructed each having a transconductance amplifier providing differential currents combined to provide an output in accordance with a transfer function. The derivative circuits may have a circuit constructed to generate a biased differential currents combined so as to provide the derivative of the transfer function.

High-Gain AlxGa1-xAs/GaAs Transistors For Neural Networks

NASA Technical Reports Server (NTRS)

Kim, Jae-Hoon; Lin, Steven H.

1991-01-01

High-gain AlxGa1-xAs/GaAs npn double heterojunction bipolar transistors developed for use as phototransistors in optoelectronic integrated circuits, especially in artificial neural networks. Transistors perform both photodetection and saturating-amplification functions of neurons. Good candidates for such application because structurally compatible with laser diodes and light-emitting diodes, detect light, and provide high current gain needed to compensate for losses in holographic optical elements.

The lighter side of BDNF

PubMed Central

Noble, Emily E.; Billington, Charles J.; Kotz, Catherine M.

2011-01-01

Brain-derived neurotrophic factor (BDNF) mediates energy metabolism and feeding behavior. As a neurotrophin, BDNF promotes neuronal differentiation, survival during early development, adult neurogenesis, and neural plasticity; thus, there is the potential that BDNF could modify circuits important to eating behavior and energy expenditure. The possibility that “faulty” circuits could be remodeled by BDNF is an exciting concept for new therapies for obesity and eating disorders. In the hypothalamus, BDNF and its receptor, tropomyosin-related kinase B (TrkB), are extensively expressed in areas associated with feeding and metabolism. Hypothalamic BDNF and TrkB appear to inhibit food intake and increase energy expenditure, leading to negative energy balance. In the hippocampus, the involvement of BDNF in neural plasticity and neurogenesis is important to learning and memory, but less is known about how BDNF participates in energy homeostasis. We review current research about BDNF in specific brain locations related to energy balance, environmental, and behavioral influences on BDNF expression and the possibility that BDNF may influence energy homeostasis via its role in neurogenesis and neural plasticity. PMID:21346243

An Integrated Circuit for Simultaneous Extracellular Electrophysiology Recording and Optogenetic Neural Manipulation

PubMed Central

Chen, Chang Hao; McCullagh, Elizabeth A.; Pun, Sio Hang; Mak, Peng Un; Vai, Mang I; Mak, Pui In; Klug, Achim; Lei, Tim C.

2017-01-01

The ability to record and to control action potential firing in neuronal circuits of the brain is critical to understand how the brain functions on the cellular and network levels. Recent development of optogenetic proteins allows direct stimulation or inhibition of action potential firing of neurons upon optical illumination. In this paper, we combined a low-noise and high input impedance (or low input capacitance) neural recording amplifier, and a high current laser/LED driver in a monolithic integrated circuit (IC) for simultaneous neural recording and optogenetic neural control. The low input capacitance of the amplifier (9.7 pF) was achieved through adding a dedicated unity gain input stage optimized for high impedance metal electrodes. The input referred noise of the amplifier was measured to be 4.57 µVrms, which is lower than the estimated thermal noise of the metal electrode. Thus, action potentials originating from a single neuron can be recorded with a signal-to-noise ratio of ~6.6. The LED/laser current driver delivers a maximum current of 330 mA to generate adequate light for optogenetic control. We experimentally tested the functionality of the IC with an anesthetized Mongolian gerbil and recorded auditory stimulated action potentials from the inferior colliculus. Furthermore, we showed that spontaneous firing of 5th (trigeminal) nerve fibers was inhibited using the optogenetic protein Halorhodopsin. A noise model was also derived including the equivalent electronic components of the metal electrode and the high current driver to guide the design. PMID:28221990

Light Activated Escape Circuits: A Behavior and Neurophysiology Lab Module using Drosophila Optogenetics

PubMed Central

Titlow, Josh S.; Johnson, Bruce R.; Pulver, Stefan R.

2015-01-01

The neural networks that control escape from predators often show very clear relationships between defined sensory inputs and stereotyped motor outputs. This feature provides unique opportunities for researchers, but it also provides novel opportunities for neuroscience educators. Here we introduce new teaching modules using adult Drosophila that have been engineered to express csChrimson, a red-light sensitive channelrhodopsin, in specific sets of neurons and muscles mediating visually guided escape behaviors. This lab module consists of both behavior and electrophysiology experiments that explore the neural basis of flight escape. Three preparations are described that demonstrate photo-activation of the giant fiber circuit and how to quantify these behaviors. One of the preparations is then used to acquire intracellular electrophysiology recordings from different flight muscles. The diversity of action potential waveforms and firing frequencies observed in the flight muscles make this a rich preparation to study the ionic basic of cellular excitability. By activating different cells within the giant fiber pathway we also demonstrate principles of synaptic transmission and neural circuits. Beyond conveying core neurobiological concepts it is also expected that using these cutting edge techniques will enhance student motivation and attitudes towards biological research. Data collected from students and educators who have been involved in development of the module are presented to support this notion. PMID:26240526

The Cerebellum and Neurodevelopmental Disorders.

PubMed

Stoodley, Catherine J

2016-02-01

Cerebellar dysfunction is evident in several developmental disorders, including autism, attention deficit-hyperactivity disorder (ADHD), and developmental dyslexia, and damage to the cerebellum early in development can have long-term effects on movement, cognition, and affective regulation. Early cerebellar damage is often associated with poorer outcomes than cerebellar damage in adulthood, suggesting that the cerebellum is particularly important during development. Differences in cerebellar development and/or early cerebellar damage could impact a wide range of behaviors via the closed-loop circuits connecting the cerebellum with multiple cerebral cortical regions. Based on these anatomical circuits, behavioral outcomes should depend on which cerebro-cerebellar circuits are affected. Here, we briefly review cerebellar structural and functional differences in autism, ADHD, and developmental dyslexia, and discuss clinical outcomes following pediatric cerebellar damage. These data confirm the prediction that abnormalities in different cerebellar subregions produce behavioral symptoms related to the functional disruption of specific cerebro-cerebellar circuits. These circuits might also be crucial to structural brain development, as peri-natal cerebellar lesions have been associated with impaired growth of the contralateral cerebral cortex. The specific contribution of the cerebellum to typical development may therefore involve the optimization of both the structure and function of cerebro-cerebellar circuits underlying skill acquisition in multiple domains; when this process is disrupted, particularly in early development, there could be long-term alterations of these neural circuits, with significant impacts on behavior.

The cerebellum and neurodevelopmental disorders

PubMed Central

Stoodley, Catherine J.

2015-01-01

Cerebellar dysfunction is evident in several developmental disorders, including autism, attention deficit hyperactivity disorder (ADHD), and developmental dyslexia, and damage to the cerebellum early in development can have long-term effects on movement, cognition, and affective regulation. Early cerebellar damage is often associated with poorer outcomes than cerebellar damage in adulthood, suggesting that the cerebellum is particularly important during development. Differences in cerebellar development and/or early cerebellar damage could impact a wide range of behaviors via the closed-loop circuits connecting the cerebellum with multiple cerebral cortical regions. Based on these anatomical circuits, behavioral outcomes should depend on which cerebro-cerebellar circuits are affected. Here, we briefly review cerebellar structural and functional differences in autism, ADHD, and developmental dyslexia, and discuss clinical outcomes following pediatric cerebellar damage. These data confirm the prediction that abnormalities in different cerebellar subregions produce behavioral symptoms related to the functional disruption of specific cerebro-cerebellar circuits. These circuits might also be crucial to structural brain development, as peri-natal cerebellar lesions have been associated with impaired growth of the contralateral cerebral cortex. The specific contribution of the cerebellum to typical development may therefore involve the optimization of both the structure and function of cerebro-cerebellar circuits underlying skill acquisition in multiple domains; when this process is disrupted, particularly in early development, there could be long-term alterations of these neural circuits, with significant impacts on behavior. PMID:26298473

On the origin of reproducible sequential activity in neural circuits

NASA Astrophysics Data System (ADS)

Afraimovich, V. S.; Zhigulin, V. P.; Rabinovich, M. I.

2004-12-01

Robustness and reproducibility of sequential spatio-temporal responses is an essential feature of many neural circuits in sensory and motor systems of animals. The most common mathematical images of dynamical regimes in neural systems are fixed points, limit cycles, chaotic attractors, and continuous attractors (attractive manifolds of neutrally stable fixed points). These are not suitable for the description of reproducible transient sequential neural dynamics. In this paper we present the concept of a stable heteroclinic sequence (SHS), which is not an attractor. SHS opens the way for understanding and modeling of transient sequential activity in neural circuits. We show that this new mathematical object can be used to describe robust and reproducible sequential neural dynamics. Using the framework of a generalized high-dimensional Lotka-Volterra model, that describes the dynamics of firing rates in an inhibitory network, we present analytical results on the existence of the SHS in the phase space of the network. With the help of numerical simulations we confirm its robustness in presence of noise in spite of the transient nature of the corresponding trajectories. Finally, by referring to several recent neurobiological experiments, we discuss possible applications of this new concept to several problems in neuroscience.

On the origin of reproducible sequential activity in neural circuits.

PubMed

Afraimovich, V S; Zhigulin, V P; Rabinovich, M I

2004-12-01

Robustness and reproducibility of sequential spatio-temporal responses is an essential feature of many neural circuits in sensory and motor systems of animals. The most common mathematical images of dynamical regimes in neural systems are fixed points, limit cycles, chaotic attractors, and continuous attractors (attractive manifolds of neutrally stable fixed points). These are not suitable for the description of reproducible transient sequential neural dynamics. In this paper we present the concept of a stable heteroclinic sequence (SHS), which is not an attractor. SHS opens the way for understanding and modeling of transient sequential activity in neural circuits. We show that this new mathematical object can be used to describe robust and reproducible sequential neural dynamics. Using the framework of a generalized high-dimensional Lotka-Volterra model, that describes the dynamics of firing rates in an inhibitory network, we present analytical results on the existence of the SHS in the phase space of the network. With the help of numerical simulations we confirm its robustness in presence of noise in spite of the transient nature of the corresponding trajectories. Finally, by referring to several recent neurobiological experiments, we discuss possible applications of this new concept to several problems in neuroscience.

Signal transfer within a cultured asymmetric cortical neuron circuit

NASA Astrophysics Data System (ADS)

Isomura, Takuya; Shimba, Kenta; Takayama, Yuzo; Takeuchi, Akimasa; Kotani, Kiyoshi; Jimbo, Yasuhiko

2015-12-01

Objective. Simplified neuronal circuits are required for investigating information representation in nervous systems and for validating theoretical neural network models. Here, we developed patterned neuronal circuits using micro fabricated devices, comprising a micro-well array bonded to a microelectrode-array substrate. Approach. The micro-well array consisted of micrometre-scale wells connected by tunnels, all contained within a silicone slab called a micro-chamber. The design of the micro-chamber confined somata to the wells and allowed axons to grow through the tunnels bidirectionally but with a designed, unidirectional bias. We guided axons into the point of the arrow structure where one of the two tunnel entrances is located, making that the preferred direction. Main results. When rat cortical neurons were cultured in the wells, their axons grew through the tunnels and connected to neurons in adjoining wells. Unidirectional burst transfers and other asymmetric signal-propagation phenomena were observed via the substrate-embedded electrodes. Seventy-nine percent of burst transfers were in the forward direction. We also observed rapid propagation of activity from sites of local electrical stimulation, and significant effects of inhibitory synapse blockade on bursting activity. Significance. These results suggest that this simple, substrate-controlled neuronal circuit can be applied to develop in vitro models of the function of cortical microcircuits or deep neural networks, better to elucidate the laws governing the dynamics of neuronal networks.

Signal transfer within a cultured asymmetric cortical neuron circuit.

PubMed

Isomura, Takuya; Shimba, Kenta; Takayama, Yuzo; Takeuchi, Akimasa; Kotani, Kiyoshi; Jimbo, Yasuhiko

2015-12-01

Simplified neuronal circuits are required for investigating information representation in nervous systems and for validating theoretical neural network models. Here, we developed patterned neuronal circuits using micro fabricated devices, comprising a micro-well array bonded to a microelectrode-array substrate. The micro-well array consisted of micrometre-scale wells connected by tunnels, all contained within a silicone slab called a micro-chamber. The design of the micro-chamber confined somata to the wells and allowed axons to grow through the tunnels bidirectionally but with a designed, unidirectional bias. We guided axons into the point of the arrow structure where one of the two tunnel entrances is located, making that the preferred direction. When rat cortical neurons were cultured in the wells, their axons grew through the tunnels and connected to neurons in adjoining wells. Unidirectional burst transfers and other asymmetric signal-propagation phenomena were observed via the substrate-embedded electrodes. Seventy-nine percent of burst transfers were in the forward direction. We also observed rapid propagation of activity from sites of local electrical stimulation, and significant effects of inhibitory synapse blockade on bursting activity. These results suggest that this simple, substrate-controlled neuronal circuit can be applied to develop in vitro models of the function of cortical microcircuits or deep neural networks, better to elucidate the laws governing the dynamics of neuronal networks.

Homology and homoplasy of swimming behaviors and neural circuits in the Nudipleura (Mollusca, Gastropoda, Opisthobranchia)

PubMed Central

Newcomb, James M.; Sakurai, Akira; Lillvis, Joshua L.; Gunaratne, Charuni A.; Katz, Paul S.

2012-01-01

How neural circuit evolution relates to behavioral evolution is not well understood. Here the relationship between neural circuits and behavior is explored with respect to the swimming behaviors of the Nudipleura (Mollusca, Gastropoda, Opithobranchia). Nudipleura is a diverse monophyletic clade of sea slugs among which only a small percentage of species can swim. Swimming falls into a limited number of categories, the most prevalent of which are rhythmic left–right body flexions (LR) and rhythmic dorsal–ventral body flexions (DV). The phylogenetic distribution of these behaviors suggests a high degree of homoplasy. The central pattern generator (CPG) underlying DV swimming has been well characterized in Tritonia diomedea and in Pleurobranchaea californica. The CPG for LR swimming has been elucidated in Melibe leonina and Dendronotus iris, which are more closely related. The CPGs for the categorically distinct DV and LR swimming behaviors consist of nonoverlapping sets of homologous identified neurons, whereas the categorically similar behaviors share some homologous identified neurons, although the exact composition of neurons and synapses in the neural circuits differ. The roles played by homologous identified neurons in categorically distinct behaviors differ. However, homologous identified neurons also play different roles even in the swim CPGs of the two LR swimming species. Individual neurons can be multifunctional within a species. Some of those functions are shared across species, whereas others are not. The pattern of use and reuse of homologous neurons in various forms of swimming and other behaviors further demonstrates that the composition of neural circuits influences the evolution of behaviors. PMID:22723353

Neural mechanism of optimal limb coordination in crustacean swimming

PubMed Central

Zhang, Calvin; Guy, Robert D.; Mulloney, Brian; Zhang, Qinghai; Lewis, Timothy J.

2014-01-01

A fundamental challenge in neuroscience is to understand how biologically salient motor behaviors emerge from properties of the underlying neural circuits. Crayfish, krill, prawns, lobsters, and other long-tailed crustaceans swim by rhythmically moving limbs called swimmerets. Over the entire biological range of animal size and paddling frequency, movements of adjacent swimmerets maintain an approximate quarter-period phase difference with the more posterior limbs leading the cycle. We use a computational fluid dynamics model to show that this frequency-invariant stroke pattern is the most effective and mechanically efficient paddling rhythm across the full range of biologically relevant Reynolds numbers in crustacean swimming. We then show that the organization of the neural circuit underlying swimmeret coordination provides a robust mechanism for generating this stroke pattern. Specifically, the wave-like limb coordination emerges robustly from a combination of the half-center structure of the local central pattern generating circuits (CPGs) that drive the movements of each limb, the asymmetric network topology of the connections between local CPGs, and the phase response properties of the local CPGs, which we measure experimentally. Thus, the crustacean swimmeret system serves as a concrete example in which the architecture of a neural circuit leads to optimal behavior in a robust manner. Furthermore, we consider all possible connection topologies between local CPGs and show that the natural connectivity pattern generates the biomechanically optimal stroke pattern most robustly. Given the high metabolic cost of crustacean swimming, our results suggest that natural selection has pushed the swimmeret neural circuit toward a connection topology that produces optimal behavior. PMID:25201976

Neural Networks For Demodulation Of Phase-Modulated Signals

NASA Technical Reports Server (NTRS)

Altes, Richard A.

1995-01-01

Hopfield neural networks proposed for demodulating quadrature phase-shift-keyed (QPSK) signals carrying digital information. Networks solve nonlinear integral equations prior demodulation circuits cannot solve. Consists of set of N operational amplifiers connected in parallel, with weighted feedback from output terminal of each amplifier to input terminals of other amplifiers. Used to solve signal processing problems. Implemented as analog very-large-scale integrated circuit that achieves rapid convergence. Alternatively, implemented as digital simulation of such circuit. Also used to improve phase estimation performance over that of phase-locked loop.

A physiological and behavioral system for hearing restoration with cochlear implants

PubMed Central

King, Julia; Shehu, Ina; Roland, J. Thomas; Svirsky, Mario A.

2016-01-01

Cochlear implants are neuroprosthetic devices that provide hearing to deaf patients, although outcomes are highly variable even with prolonged training and use. The central auditory system must process cochlear implant signals, but it is unclear how neural circuits adapt—or fail to adapt—to such inputs. The knowledge of these mechanisms is required for development of next-generation neuroprosthetics that interface with existing neural circuits and enable synaptic plasticity to improve perceptual outcomes. Here, we describe a new system for cochlear implant insertion, stimulation, and behavioral training in rats. Animals were first ensured to have significant hearing loss via physiological and behavioral criteria. We developed a surgical approach for multichannel (2- or 8-channel) array insertion, comparable with implantation procedures and depth in humans. Peripheral and cortical responses to stimulation were used to program the implant objectively. Animals fitted with implants learned to use them for an auditory-dependent task that assesses frequency detection and recognition in a background of environmentally and self-generated noise and ceased responding appropriately to sounds when the implant was temporarily inactivated. This physiologically calibrated and behaviorally validated system provides a powerful opportunity to study the neural basis of neuroprosthetic device use and plasticity. PMID:27281743

Moving towards causality in attention-deficit hyperactivity disorder: overview of neural and genetic mechanisms

PubMed Central

Gallo, Eduardo F; Posner, Jonathan

2016-01-01

Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterised by developmentally inappropriate levels of inattention and hyperactivity or impulsivity. The heterogeneity of its clinical manifestations and the differential responses to treatment and varied prognoses have long suggested myriad underlying causes. Over the past decade, clinical and basic research efforts have uncovered many behavioural and neurobiological alterations associated with ADHD, from genes to higher order neural networks. Here, we review the neurobiology of ADHD by focusing on neural circuits implicated in the disorder and discuss how abnormalities in circuitry relate to symptom presentation and treatment. We summarise the literature on genetic variants that are potentially related to the development of ADHD, and how these, in turn, might affect circuit function and relevant behaviours. Whether these underlying neurobiological factors are causally related to symptom presentation remains unresolved. Therefore, we assess efforts aimed at disentangling issues of causality, and showcase the shifting research landscape towards endophenotype refinement in clinical and preclinical settings. Furthermore, we review approaches being developed to understand the neurobiological underpinnings of this complex disorder including the use of animal models, neuromodulation, and pharmaco-imaging studies. PMID:27183902

Neural Networks Based Approach to Enhance Space Hardware Reliability

NASA Technical Reports Server (NTRS)

Zebulum, Ricardo S.; Thakoor, Anilkumar; Lu, Thomas; Franco, Lauro; Lin, Tsung Han; McClure, S. S.

2011-01-01

This paper demonstrates the use of Neural Networks as a device modeling tool to increase the reliability analysis accuracy of circuits targeted for space applications. The paper tackles a number of case studies of relevance to the design of Flight hardware. The results show that the proposed technique generates more accurate models than the ones regularly used to model circuits.

Biologically based neural circuit modelling for the study of fear learning and extinction

NASA Astrophysics Data System (ADS)

Nair, Satish S.; Paré, Denis; Vicentic, Aleksandra

2016-11-01

The neuronal systems that promote protective defensive behaviours have been studied extensively using Pavlovian conditioning. In this paradigm, an initially neutral-conditioned stimulus is paired with an aversive unconditioned stimulus leading the subjects to display behavioural signs of fear. Decades of research into the neural bases of this simple behavioural paradigm uncovered that the amygdala, a complex structure comprised of several interconnected nuclei, is an essential part of the neural circuits required for the acquisition, consolidation and expression of fear memory. However, emerging evidence from the confluence of electrophysiological, tract tracing, imaging, molecular, optogenetic and chemogenetic methodologies, reveals that fear learning is mediated by multiple connections between several amygdala nuclei and their distributed targets, dynamical changes in plasticity in local circuit elements as well as neuromodulatory mechanisms that promote synaptic plasticity. To uncover these complex relations and analyse multi-modal data sets acquired from these studies, we argue that biologically realistic computational modelling, in conjunction with experiments, offers an opportunity to advance our understanding of the neural circuit mechanisms of fear learning and to address how their dysfunction may lead to maladaptive fear responses in mental disorders.

Video data compression using artificial neural network differential vector quantization

NASA Technical Reports Server (NTRS)

Krishnamurthy, Ashok K.; Bibyk, Steven B.; Ahalt, Stanley C.

1991-01-01

An artificial neural network vector quantizer is developed for use in data compression applications such as Digital Video. Differential Vector Quantization is used to preserve edge features, and a new adaptive algorithm, known as Frequency-Sensitive Competitive Learning, is used to develop the vector quantizer codebook. To develop real time performance, a custom Very Large Scale Integration Application Specific Integrated Circuit (VLSI ASIC) is being developed to realize the associative memory functions needed in the vector quantization algorithm. By using vector quantization, the need for Huffman coding can be eliminated, resulting in superior performance against channel bit errors than methods that use variable length codes.

Bidirectional Neural Interfaces

PubMed Central

Masters, Matthew R.; Thakor, Nitish V.

2016-01-01

A bidirectional neural interface is a device that transfers information into and out of the nervous system. This class of devices has potential to improve treatment and therapy in several patient populations. Progress in very-large-scale integration (VLSI) has advanced the design of complex integrated circuits. System-on-chip (SoC) devices are capable of recording neural electrical activity and altering natural activity with electrical stimulation. Often, these devices include wireless powering and telemetry functions. This review presents the state of the art of bidirectional circuits as applied to neuroprosthetic, neurorepair, and neurotherapeutic systems. PMID:26753776

Temporal Loss of Tsc1: Neural Development and Brain Disease in Tuberous Sclerosis

DTIC Science & Technology

2013-06-01

2001). The thalamus has also been linked to the autism component in human TS (Asano et al., 2001) and is poised to play an important role in brain...and Autism -related Disorders. June 10-15, 2012, Stonehill College, MA. Normand E, Browning C, Machan JT, Voelcker B, Zervas M (2012) The deletion of...Foundation Autism Research Initiative Annual RFA (2013) Linking genetic mosaicism, neural circuit abnormalities and behavior. Simons Foundation Autism

Integration and long distance axonal regeneration in the central nervous system from transplanted primitive neural stem cells.

PubMed

Zhao, Jiagang; Sun, Woong; Cho, Hyo Min; Ouyang, Hong; Li, Wenlin; Lin, Ying; Do, Jiun; Zhang, Liangfang; Ding, Sheng; Liu, Yizhi; Lu, Paul; Zhang, Kang

2013-01-04

Spinal cord injury (SCI) results in devastating motor and sensory deficits secondary to disrupted neuronal circuits and poor regenerative potential. Efforts to promote regeneration through cell extrinsic and intrinsic manipulations have met with limited success. Stem cells represent an as yet unrealized therapy in SCI. Recently, we identified novel culture methods to induce and maintain primitive neural stem cells (pNSCs) from human embryonic stem cells. We tested whether transplanted human pNSCs can integrate into the CNS of the developing chick neural tube and injured adult rat spinal cord. Following injection of pNSCs into the developing chick CNS, pNSCs integrated into the dorsal aspects of the neural tube, forming cell clusters that spontaneously differentiated into neurons. Furthermore, following transplantation of pNSCs into the lesioned rat spinal cord, grafted pNSCs survived, differentiated into neurons, and extended long distance axons through the scar tissue at the graft-host interface and into the host spinal cord to form terminal-like structures near host spinal neurons. Together, these findings suggest that pNSCs derived from human embryonic stem cells differentiate into neuronal cell types with the potential to extend axons that associate with circuits of the CNS and, more importantly, provide new insights into CNS integration and axonal regeneration, offering hope for repair in SCI.

A network model of behavioural performance in a rule learning task.

PubMed

Hasselmo, Michael E; Stern, Chantal E

2018-04-19

Humans demonstrate differences in performance on cognitive rule learning tasks which could involve differences in properties of neural circuits. An example model is presented to show how gating of the spread of neural activity could underlie rule learning and the generalization of rules to previously unseen stimuli. This model uses the activity of gating units to regulate the pattern of connectivity between neurons responding to sensory input and subsequent gating units or output units. This model allows analysis of network parameters that could contribute to differences in cognitive rule learning. These network parameters include differences in the parameters of synaptic modification and presynaptic inhibition of synaptic transmission that could be regulated by neuromodulatory influences on neural circuits. Neuromodulatory receptors play an important role in cognitive function, as demonstrated by the fact that drugs that block cholinergic muscarinic receptors can cause cognitive impairments. In discussions of the links between neuromodulatory systems and biologically based traits, the issue of mechanisms through which these linkages are realized is often missing. This model demonstrates potential roles of neural circuit parameters regulated by acetylcholine in learning context-dependent rules, and demonstrates the potential contribution of variation in neural circuit properties and neuromodulatory function to individual differences in cognitive function.This article is part of the theme issue 'Diverse perspectives on diversity: multi-disciplinary approaches to taxonomies of individual differences'. © 2018 The Author(s).

Conserved neural circuit structure across Drosophila larval development revealed by comparative connectomics.

PubMed

Gerhard, Stephan; Andrade, Ingrid; Fetter, Richard D; Cardona, Albert; Schneider-Mizell, Casey M

2017-10-23

During postembryonic development, the nervous system must adapt to a growing body. How changes in neuronal structure and connectivity contribute to the maintenance of appropriate circuit function remains unclear. Previously , we measured the cellular neuroanatomy underlying synaptic connectivity in Drosophila (Schneider-Mizell et al., 2016). Here, we examined how neuronal morphology and connectivity change between first instar and third instar larval stages using serial section electron microscopy. We reconstructed nociceptive circuits in a larva of each stage and found consistent topographically arranged connectivity between identified neurons. Five-fold increases in each size, number of terminal dendritic branches, and total number of synaptic inputs were accompanied by cell type-specific connectivity changes that preserved the fraction of total synaptic input associated with each pre-synaptic partner. We propose that precise patterns of structural growth act to conserve the computational function of a circuit, for example determining the location of a dangerous stimulus.

Maturation of Cortico-Subcortical Structural Networks-Segregation and Overlap of Medial Temporal and Fronto-Striatal Systems in Development.

PubMed

Walhovd, Kristine B; Tamnes, Christian K; Bjørnerud, Atle; Due-Tønnessen, Paulina; Holland, Dominic; Dale, Anders M; Fjell, Anders M

2015-07-01

The brain consists of partly segregated neural circuits within which structural convergence and functional integration occurs during development. The relationship of structural cortical and subcortical maturation is largely unknown. We aimed to study volumetric development of the hippocampus and basal ganglia (caudate, putamen, pallidum, accumbens) in relation to volume changes throughout the cortex. Longitudinal MRI data were obtained across a mean interval of 2.6 years in 85 participants with an age range of 8-19 years at study start. Left and right subcortical changes were related to cortical change vertex-wise in the ipsilateral hemisphere with general linear models with age, sex, interval between scans, and mean cortical volume change as covariates. Hippocampal-cortical change relationships centered on parts of the Papez circuit, including entorhinal, parahippocampal, and isthmus cingulate areas, and lateral temporal, insular, and orbitofrontal cortices in the left hemisphere. Basal ganglia-cortical change relationships were observed in mostly nonoverlapping and more anterior cortical areas, all including the anterior cingulate. Other patterns were unique to specific basal ganglia structures, including pre-, post-, and paracentral patterns relating to putamen change. In conclusion, patterns of cortico-subcortical development as assessed by morphometric analyses in part map out segregated neural circuits at the macrostructural level. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

IP3R-mediated Ca2+ release regulates protein metabolism in Drosophila neuroendocrine cells: implications for development under nutrient stress.

PubMed

Megha; Hasan, Gaiti

2017-04-15

Successful completion of animal development is fundamentally reliant on nutritional cues. Surviving periods of nutritional insufficiency requires adaptations that are coordinated, in part, by neural circuits. As neuropeptides secreted by neuroendocrine (NE) cells modulate neural circuits, we investigated NE cell function during development under nutrient stress. Starved Drosophila larvae exhibited reduced pupariation if either insulin signaling or IP 3 /Ca 2+ signaling were downregulated in NE cells. Moreover, an IP 3 R (inositol 1,4,5-trisphosphate receptor) loss-of-function mutant displayed reduced protein synthesis, which was rescued by overexpression of either InR (insulin receptor) or IP 3 R in NE cells of the mutant, suggesting that the two signaling pathways might be functionally compensatory. Furthermore, cultured IP 3 R mutant NE cells, but not neurons, exhibited reduced protein translation. Thus cell-specific regulation of protein synthesis by IP 3 R in NE cells influences protein metabolism. We propose that this regulation helps developing animals survive in poor nutritional conditions. © 2017. Published by The Company of Biologists Ltd.

Characterization of the central neural projections to brown, white, and beige adipose tissue.

PubMed

Wiedmann, Nicole M; Stefanidis, Aneta; Oldfield, Brian J

2017-11-01

The functional recruitment of classic brown adipose tissue (BAT) and inducible brown-like or beige fat is, to a large extent, dependent on intact sympathetic neural input. Whereas the central neural circuits directed specifically to BAT or white adipose tissue (WAT) are well established, there is only a developing insight into the nature of neural inputs common to both fat types. Moreover, there is no clear view of the specific central and peripheral innervation of the browned component of WAT: beige fat. The objective of the present study is to examine the neural input to both BAT and WAT in the same animal and, by exposing different cohorts of rats to either thermoneutral or cold conditions, define changes in central neural organization that will ensure that beige fat is appropriately recruited and modulated after browning of inguinal WAT (iWAT). At thermoneutrality, injection of the neurotropic (pseudorabies) viruses into BAT and WAT demonstrates that there are dedicated axonal projections, as well as collateral axonal branches of command neurons projecting to both types of fat. After cold exposure, central neural circuits directed to iWAT showed evidence of reorganization with a greater representation of command neurons projecting to both brown and beiged WAT in hypothalamic (paraventricular nucleus and lateral hypothalamus) and brainstem (raphe pallidus and locus coeruleus) sites. This shift was driven by a greater number of supraspinal neurons projecting to iWAT under cold conditions. These data provide evidence for a reorganization of the nervous system at the level of neural connectivity following browning of WAT.-Wiedmann, N. M., Stefanidis, A., Oldfield, B. J. Characterization of the central neural projections to brown, white, and beige adipose tissue. © FASEB.

Functional identification of spike-processing neural circuits.

PubMed

Lazar, Aurel A; Slutskiy, Yevgeniy B

2014-02-01

We introduce a novel approach for a complete functional identification of biophysical spike-processing neural circuits. The circuits considered accept multidimensional spike trains as their input and comprise a multitude of temporal receptive fields and conductance-based models of action potential generation. Each temporal receptive field describes the spatiotemporal contribution of all synapses between any two neurons and incorporates the (passive) processing carried out by the dendritic tree. The aggregate dendritic current produced by a multitude of temporal receptive fields is encoded into a sequence of action potentials by a spike generator modeled as a nonlinear dynamical system. Our approach builds on the observation that during any experiment, an entire neural circuit, including its receptive fields and biophysical spike generators, is projected onto the space of stimuli used to identify the circuit. Employing the reproducing kernel Hilbert space (RKHS) of trigonometric polynomials to describe input stimuli, we quantitatively describe the relationship between underlying circuit parameters and their projections. We also derive experimental conditions under which these projections converge to the true parameters. In doing so, we achieve the mathematical tractability needed to characterize the biophysical spike generator and identify the multitude of receptive fields. The algorithms obviate the need to repeat experiments in order to compute the neurons' rate of response, rendering our methodology of interest to both experimental and theoretical neuroscientists.

Neural electrical activity and neural network growth.

PubMed

Gafarov, F M

2018-05-01

The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization. Copyright © 2018 Elsevier Ltd. All rights reserved.

Neural circuits. Labeling of active neural circuits in vivo with designed calcium integrators.

PubMed

Fosque, Benjamin F; Sun, Yi; Dana, Hod; Yang, Chao-Tsung; Ohyama, Tomoko; Tadross, Michael R; Patel, Ronak; Zlatic, Marta; Kim, Douglas S; Ahrens, Misha B; Jayaraman, Vivek; Looger, Loren L; Schreiter, Eric R

2015-02-13

The identification of active neurons and circuits in vivo is a fundamental challenge in understanding the neural basis of behavior. Genetically encoded calcium (Ca(2+)) indicators (GECIs) enable quantitative monitoring of cellular-resolution activity during behavior. However, such indicators require online monitoring within a limited field of view. Alternatively, post hoc staining of immediate early genes (IEGs) indicates highly active cells within the entire brain, albeit with poor temporal resolution. We designed a fluorescent sensor, CaMPARI, that combines the genetic targetability and quantitative link to neural activity of GECIs with the permanent, large-scale labeling of IEGs, allowing a temporally precise "activity snapshot" of a large tissue volume. CaMPARI undergoes efficient and irreversible green-to-red conversion only when elevated intracellular Ca(2+) and experimenter-controlled illumination coincide. We demonstrate the utility of CaMPARI in freely moving larvae of zebrafish and flies, and in head-fixed mice and adult flies. Copyright © 2015, American Association for the Advancement of Science.

Lag Synchronization of Switched Neural Networks via Neural Activation Function and Applications in Image Encryption.

PubMed

Wen, Shiping; Zeng, Zhigang; Huang, Tingwen; Meng, Qinggang; Yao, Wei

2015-07-01

This paper investigates the problem of global exponential lag synchronization of a class of switched neural networks with time-varying delays via neural activation function and applications in image encryption. The controller is dependent on the output of the system in the case of packed circuits, since it is hard to measure the inner state of the circuits. Thus, it is critical to design the controller based on the neuron activation function. Comparing the results, in this paper, with the existing ones shows that we improve and generalize the results derived in the previous literature. Several examples are also given to illustrate the effectiveness and potential applications in image encryption.

Illuminating the multifaceted roles of neurotransmission in shaping neuronal circuitry.

PubMed

Okawa, Haruhisa; Hoon, Mrinalini; Yoshimatsu, Takeshi; Della Santina, Luca; Wong, Rachel O L

2014-09-17

Across the nervous system, neurons form highly stereotypic patterns of synaptic connections that are designed to serve specific functions. Mature wiring patterns are often attained upon the refinement of early, less precise connectivity. Much work has led to the prevailing view that many developing circuits are sculpted by activity-dependent competition among converging afferents, which results in the elimination of unwanted synapses and the maintenance and strengthening of desired connections. Studies of the vertebrate retina, however, have recently revealed that activity can play a role in shaping developing circuits without engaging competition among converging inputs that differ in their activity levels. Such neurotransmission-mediated processes can produce stereotypic wiring patterns by promoting selective synapse formation rather than elimination. We discuss how the influence of transmission may also be limited by circuit design and further highlight the importance of transmission beyond development in maintaining wiring specificity and synaptic organization of neural circuits. Copyright © 2014 Elsevier Inc. All rights reserved.

Changes in the Spinal Neural Circuits are Dependent on the Movement Speed of the Visuomotor Task

PubMed Central

Kubota, Shinji; Hirano, Masato; Koizume, Yoshiki; Tanabe, Shigeo; Funase, Kozo

2015-01-01

Previous studies have shown that spinal neural circuits are modulated by motor skill training. However, the effects of task movement speed on changes in spinal neural circuits have not been clarified. The aim of this research was to investigate whether spinal neural circuits were affected by task movement speed. Thirty-eight healthy subjects participated in this study. In experiment 1, the effects of task movement speed on the spinal neural circuits were examined. Eighteen subjects performed a visuomotor task involving ankle muscle slow (nine subjects) or fast (nine subjects) movement speed. Another nine subjects performed a non-visuomotor task (controls) in fast movement speed. The motor task training lasted for 20 min. The amounts of D1 inhibition and reciprocal Ia inhibition were measured using H-relfex condition-test paradigm and recorded before, and at 5, 15, and 30 min after the training session. In experiment 2, using transcranial magnetic stimulation (TMS), the effects of corticospinal descending inputs on the presynaptic inhibitory pathway were examined before and after performing either a visuomotor (eight subjects) or a control task (eight subjects). All measurements were taken under resting conditions. The amount of D1 inhibition increased after the visuomotor task irrespective of movement speed (P < 0.01). The amount of reciprocal Ia inhibition increased with fast movement speed conditioning (P < 0.01), but was unchanged by slow movement speed conditioning. These changes lasted up to 15 min in D1 inhibition and 5 min in reciprocal Ia inhibition after the training session. The control task did not induce changes in D1 inhibition and reciprocal Ia inhibition. The TMS conditioned inhibitory effects of presynaptic inhibitory pathways decreased following visuomotor tasks (P < 0.01). The size of test H-reflex was almost the same size throughout experiments. The results suggest that supraspinal descending inputs for controlling joint movement are responsible for changes in the spinal neural circuits, and that task movement speed is one of the critical factors for inducing plastic changes in reciprocal Ia inhibition. PMID:26696873

Role of motoneuron-derived neurotrophin 3 in survival and axonal projection of sensory neurons during neural circuit formation.

PubMed

Usui, Noriyoshi; Watanabe, Keisuke; Ono, Katsuhiko; Tomita, Koichi; Tamamaki, Nobuaki; Ikenaka, Kazuhiro; Takebayashi, Hirohide

2012-03-01

Sensory neurons possess the central and peripheral branches and they form unique spinal neural circuits with motoneurons during development. Peripheral branches of sensory axons fasciculate with the motor axons that extend toward the peripheral muscles from the central nervous system (CNS), whereas the central branches of proprioceptive sensory neurons directly innervate motoneurons. Although anatomically well documented, the molecular mechanism underlying sensory-motor interaction during neural circuit formation is not fully understood. To investigate the role of motoneuron on sensory neuron development, we analyzed sensory neuron phenotypes in the dorsal root ganglia (DRG) of Olig2 knockout (KO) mouse embryos, which lack motoneurons. We found an increased number of apoptotic cells in the DRG of Olig2 KO embryos at embryonic day (E) 10.5. Furthermore, abnormal axonal projections of sensory neurons were observed in both the peripheral branches at E10.5 and central branches at E15.5. To understand the motoneuron-derived factor that regulates sensory neuron development, we focused on neurotrophin 3 (Ntf3; NT-3), because Ntf3 and its receptors (Trk) are strongly expressed in motoneurons and sensory neurons, respectively. The significance of motoneuron-derived Ntf3 was analyzed using Ntf3 conditional knockout (cKO) embryos, in which we observed increased apoptosis and abnormal projection of the central branch innervating motoneuron, the phenotypes being apparently comparable with that of Olig2 KO embryos. Taken together, we show that the motoneuron is a functional source of Ntf3 and motoneuron-derived Ntf3 is an essential pre-target neurotrophin for survival and axonal projection of sensory neurons.

Cortical Feedback Regulates Feedforward Retinogeniculate Refinement

PubMed Central

Thompson, Andrew D; Picard, Nathalie; Min, Lia; Fagiolini, Michela; Chen, Chinfei

2016-01-01

SUMMARY According to the prevailing view of neural development, sensory pathways develop sequentially in a feedforward manner, whereby each local microcircuit refines and stabilizes before directing the wiring of its downstream target. In the visual system, retinal circuits are thought to mature first and direct refinement in the thalamus, after which cortical circuits refine with experience-dependent plasticity. In contrast, we now show that feedback from cortex to thalamus critically regulates refinement of the retinogeniculate projection during a discrete window in development, beginning at postnatal day 20 in mice. Disrupting cortical activity during this window, pharmacologically or chemogenetically, increases the number of retinal ganglion cells innervating each thalamic relay neuron. These results suggest that primary sensory structures develop through the concurrent and interdependent remodeling of subcortical and cortical circuits in response to sensory experience, rather than through a simple feedforward process. Our findings also highlight an unexpected function for the corticothalamic projection. PMID:27545712

Technologies for imaging neural activity in large volumes

PubMed Central

Ji, Na; Freeman, Jeremy; Smith, Spencer L.

2017-01-01

Neural circuitry has evolved to form distributed networks that act dynamically across large volumes. Collecting data from individual planes, conventional microscopy cannot sample circuitry across large volumes at the temporal resolution relevant to neural circuit function and behaviors. Here, we review emerging technologies for rapid volume imaging of neural circuitry. We focus on two critical challenges: the inertia of optical systems, which limits image speed, and aberrations, which restrict the image volume. Optical sampling time must be long enough to ensure high-fidelity measurements, but optimized sampling strategies and point spread function engineering can facilitate rapid volume imaging of neural activity within this constraint. We also discuss new computational strategies for the processing and analysis of volume imaging data of increasing size and complexity. Together, optical and computational advances are providing a broader view of neural circuit dynamics, and help elucidate how brain regions work in concert to support behavior. PMID:27571194

Neural mechanisms of movement planning: motor cortex and beyond.

PubMed

Svoboda, Karel; Li, Nuo

2018-04-01

Neurons in motor cortex and connected brain regions fire in anticipation of specific movements, long before movement occurs. This neural activity reflects internal processes by which the brain plans and executes volitional movements. The study of motor planning offers an opportunity to understand how the structure and dynamics of neural circuits support persistent internal states and how these states influence behavior. Recent advances in large-scale neural recordings are beginning to decipher the relationship of the dynamics of populations of neurons during motor planning and movements. New behavioral tasks in rodents, together with quantified perturbations, link dynamics in specific nodes of neural circuits to behavior. These studies reveal a neural network distributed across multiple brain regions that collectively supports motor planning. We review recent advances and highlight areas where further work is needed to achieve a deeper understanding of the mechanisms underlying motor planning and related cognitive processes. Copyright © 2017. Published by Elsevier Ltd.

The relationship between stress and Alzheimer's disease.

PubMed

Justice, Nicholas J

2018-02-01

Stress is critically involved in the development and progression of disease. From the stress of undergoing treatments to facing your own mortality, the physiological processes that stress drives have a serious detrimental effect on the ability to heal, cope and maintain a positive quality of life. This is becoming increasingly clear in the case of neurodegenerative diseases. Neurodegenerative diseases involve the devastating loss of cognitive and motor function which is stressful in itself, but can also disrupt neural circuits that mediate stress responses. Disrupting these circuits produces aberrant emotional and aggressive behavior that causes long-term care to be especially difficult. In addition, added stress drives progression of the disease and can exacerbate symptoms. In this review, I describe how neural and endocrine pathways activated by stress interact with ongoing neurodegenerative disease from both a clinical and experimental perspective.

Computational Models and Emergent Properties of Respiratory Neural Networks

PubMed Central

Lindsey, Bruce G.; Rybak, Ilya A.; Smith, Jeffrey C.

2012-01-01

Computational models of the neural control system for breathing in mammals provide a theoretical and computational framework bringing together experimental data obtained from different animal preparations under various experimental conditions. Many of these models were developed in parallel and iteratively with experimental studies and provided predictions guiding new experiments. This data-driven modeling approach has advanced our understanding of respiratory network architecture and neural mechanisms underlying generation of the respiratory rhythm and pattern, including their functional reorganization under different physiological conditions. Models reviewed here vary in neurobiological details and computational complexity and span multiple spatiotemporal scales of respiratory control mechanisms. Recent models describe interacting populations of respiratory neurons spatially distributed within the Bötzinger and pre-Bötzinger complexes and rostral ventrolateral medulla that contain core circuits of the respiratory central pattern generator (CPG). Network interactions within these circuits along with intrinsic rhythmogenic properties of neurons form a hierarchy of multiple rhythm generation mechanisms. The functional expression of these mechanisms is controlled by input drives from other brainstem components, including the retrotrapezoid nucleus and pons, which regulate the dynamic behavior of the core circuitry. The emerging view is that the brainstem respiratory network has rhythmogenic capabilities at multiple levels of circuit organization. This allows flexible, state-dependent expression of different neural pattern-generation mechanisms under various physiological conditions, enabling a wide repertoire of respiratory behaviors. Some models consider control of the respiratory CPG by pulmonary feedback and network reconfiguration during defensive behaviors such as cough. Future directions in modeling of the respiratory CPG are considered. PMID:23687564

A closed-loop compressive-sensing-based neural recording system.

PubMed

Zhang, Jie; Mitra, Srinjoy; Suo, Yuanming; Cheng, Andrew; Xiong, Tao; Michon, Frederic; Welkenhuysen, Marleen; Kloosterman, Fabian; Chin, Peter S; Hsiao, Steven; Tran, Trac D; Yazicioglu, Firat; Etienne-Cummings, Ralph

2015-06-01

This paper describes a low power closed-loop compressive sensing (CS) based neural recording system. This system provides an efficient method to reduce data transmission bandwidth for implantable neural recording devices. By doing so, this technique reduces a majority of system power consumption which is dissipated at data readout interface. The design of the system is scalable and is a viable option for large scale integration of electrodes or recording sites onto a single device. The entire system consists of an application-specific integrated circuit (ASIC) with 4 recording readout channels with CS circuits, a real time off-chip CS recovery block and a recovery quality evaluation block that provides a closed feedback to adaptively adjust compression rate. Since CS performance is strongly signal dependent, the ASIC has been tested in vivo and with standard public neural databases. Implemented using efficient digital circuit, this system is able to achieve >10 times data compression on the entire neural spike band (500-6KHz) while consuming only 0.83uW (0.53 V voltage supply) additional digital power per electrode. When only the spikes are desired, the system is able to further compress the detected spikes by around 16 times. Unlike other similar systems, the characteristic spikes and inter-spike data can both be recovered which guarantes a >95% spike classification success rate. The compression circuit occupied 0.11mm(2)/electrode in a 180nm CMOS process. The complete signal processing circuit consumes <16uW/electrode. Power and area efficiency demonstrated by the system make it an ideal candidate for integration into large recording arrays containing thousands of electrode. Closed-loop recording and reconstruction performance evaluation further improves the robustness of the compression method, thus making the system more practical for long term recording.

From Molecular Circuit Dysfunction to Disease: Case Studies in Epilepsy, Traumatic Brain Injury, and Alzheimer’s Disease

PubMed Central

Dulla, Chris G.; Coulter, Douglas A.; Ziburkus, Jokubas

2015-01-01

Complex circuitry with feed-forward and feed-back systems regulate neuronal activity throughout the brain. Cell biological, electrical, and neurotransmitter systems enable neural networks to process and drive the entire spectrum of cognitive, behavioral, and motor functions. Simultaneous orchestration of distinct cells and interconnected neural circuits relies on hundreds, if not thousands, of unique molecular interactions. Even single molecule dysfunctions can be disrupting to neural circuit activity, leading to neurological pathology. Here, we sample our current understanding of how molecular aberrations lead to disruptions in networks using three neurological pathologies as exemplars: epilepsy, traumatic brain injury (TBI), and Alzheimer’s disease (AD). Epilepsy provides a window into how total destabilization of network balance can occur. TBI is an abrupt physical disruption that manifests in both acute and chronic neurological deficits. Last, in AD progressive cell loss leads to devastating cognitive consequences. Interestingly, all three of these neurological diseases are interrelated. The goal of this review, therefore, is to identify molecular changes that may lead to network dysfunction, elaborate on how altered network activity and circuit structure can contribute to neurological disease, and suggest common threads that may lie at the heart of molecular circuit dysfunction. PMID:25948650

From Molecular Circuit Dysfunction to Disease: Case Studies in Epilepsy, Traumatic Brain Injury, and Alzheimer's Disease.

PubMed

Dulla, Chris G; Coulter, Douglas A; Ziburkus, Jokubas

2016-06-01

Complex circuitry with feed-forward and feed-back systems regulate neuronal activity throughout the brain. Cell biological, electrical, and neurotransmitter systems enable neural networks to process and drive the entire spectrum of cognitive, behavioral, and motor functions. Simultaneous orchestration of distinct cells and interconnected neural circuits relies on hundreds, if not thousands, of unique molecular interactions. Even single molecule dysfunctions can be disrupting to neural circuit activity, leading to neurological pathology. Here, we sample our current understanding of how molecular aberrations lead to disruptions in networks using three neurological pathologies as exemplars: epilepsy, traumatic brain injury (TBI), and Alzheimer's disease (AD). Epilepsy provides a window into how total destabilization of network balance can occur. TBI is an abrupt physical disruption that manifests in both acute and chronic neurological deficits. Last, in AD progressive cell loss leads to devastating cognitive consequences. Interestingly, all three of these neurological diseases are interrelated. The goal of this review, therefore, is to identify molecular changes that may lead to network dysfunction, elaborate on how altered network activity and circuit structure can contribute to neurological disease, and suggest common threads that may lie at the heart of molecular circuit dysfunction. © The Author(s) 2015.

Beyond Molecular Codes: Simple Rules to Wire Complex Brains

PubMed Central

Hassan, Bassem A.; Hiesinger, P. Robin

2015-01-01

Summary Molecular codes, like postal zip codes, are generally considered a robust way to ensure the specificity of neuronal target selection. However, a code capable of unambiguously generating complex neural circuits is difficult to conceive. Here, we re-examine the notion of molecular codes in the light of developmental algorithms. We explore how molecules and mechanisms that have been considered part of a code may alternatively implement simple pattern formation rules sufficient to ensure wiring specificity in neural circuits. This analysis delineates a pattern-based framework for circuit construction that may contribute to our understanding of brain wiring. PMID:26451480

Basal forebrain projections to the lateral habenula modulate aggression reward.

PubMed

Golden, Sam A; Heshmati, Mitra; Flanigan, Meghan; Christoffel, Daniel J; Guise, Kevin; Pfau, Madeline L; Aleyasin, Hossein; Menard, Caroline; Zhang, Hongxing; Hodes, Georgia E; Bregman, Dana; Khibnik, Lena; Tai, Jonathan; Rebusi, Nicole; Krawitz, Brian; Chaudhury, Dipesh; Walsh, Jessica J; Han, Ming-Hu; Shapiro, Matt L; Russo, Scott J

2016-06-30

Maladaptive aggressive behaviour is associated with a number of neuropsychiatric disorders and is thought to result partly from the inappropriate activation of brain reward systems in response to aggressive or violent social stimuli. Nuclei within the ventromedial hypothalamus, extended amygdala and limbic circuits are known to encode initiation of aggression; however, little is known about the neural mechanisms that directly modulate the motivational component of aggressive behaviour. Here we established a mouse model to measure the valence of aggressive inter-male social interaction with a smaller subordinate intruder as reinforcement for the development of conditioned place preference (CPP). Aggressors develop a CPP, whereas non-aggressors develop a conditioned place aversion to the intruder-paired context. Furthermore, we identify a functional GABAergic projection from the basal forebrain (BF) to the lateral habenula (lHb) that bi-directionally controls the valence of aggressive interactions. Circuit-specific silencing of GABAergic BF-lHb terminals of aggressors with halorhodopsin (NpHR3.0) increases lHb neuronal firing and abolishes CPP to the intruder-paired context. Activation of GABAergic BF-lHb terminals of non-aggressors with channelrhodopsin (ChR2) decreases lHb neuronal firing and promotes CPP to the intruder-paired context. Finally, we show that altering inhibitory transmission at BF-lHb terminals does not control the initiation of aggressive behaviour. These results demonstrate that the BF-lHb circuit has a critical role in regulating the valence of inter-male aggressive behaviour and provide novel mechanistic insight into the neural circuits modulating aggression reward processing.

Using high-throughput barcode sequencing to efficiently map connectomes

PubMed Central

Peikon, Ian D.; Kebschull, Justus M.; Vagin, Vasily V.; Ravens, Diana I.; Sun, Yu-Chi; Brouzes, Eric; Corrêa, Ivan R.; Bressan, Dario

2017-01-01

Abstract The function of a neural circuit is determined by the details of its synaptic connections. At present, the only available method for determining a neural wiring diagram with single synapse precision—a ‘connectome’—is based on imaging methods that are slow, labor-intensive and expensive. Here, we present SYNseq, a method for converting the connectome into a form that can exploit the speed and low cost of modern high-throughput DNA sequencing. In SYNseq, each neuron is labeled with a unique random nucleotide sequence—an RNA ‘barcode’—which is targeted to the synapse using engineered proteins. Barcodes in pre- and postsynaptic neurons are then associated through protein-protein crosslinking across the synapse, extracted from the tissue, and joined into a form suitable for sequencing. Although our failure to develop an efficient barcode joining scheme precludes the widespread application of this approach, we expect that with further development SYNseq will enable tracing of complex circuits at high speed and low cost. PMID:28449067

The Complexity of Dynamics in Small Neural Circuits

PubMed Central

Panzeri, Stefano

2016-01-01

Mean-field approximations are a powerful tool for studying large neural networks. However, they do not describe well the behavior of networks composed of a small number of neurons. In this case, major differences between the mean-field approximation and the real behavior of the network can arise. Yet, many interesting problems in neuroscience involve the study of mesoscopic networks composed of a few tens of neurons. Nonetheless, mathematical methods that correctly describe networks of small size are still rare, and this prevents us to make progress in understanding neural dynamics at these intermediate scales. Here we develop a novel systematic analysis of the dynamics of arbitrarily small networks composed of homogeneous populations of excitatory and inhibitory firing-rate neurons. We study the local bifurcations of their neural activity with an approach that is largely analytically tractable, and we numerically determine the global bifurcations. We find that for strong inhibition these networks give rise to very complex dynamics, caused by the formation of multiple branching solutions of the neural dynamics equations that emerge through spontaneous symmetry-breaking. This qualitative change of the neural dynamics is a finite-size effect of the network, that reveals qualitative and previously unexplored differences between mesoscopic cortical circuits and their mean-field approximation. The most important consequence of spontaneous symmetry-breaking is the ability of mesoscopic networks to regulate their degree of functional heterogeneity, which is thought to help reducing the detrimental effect of noise correlations on cortical information processing. PMID:27494737

Variation in social relationships relates to song preferences and EGR1 expression in a female songbird.

PubMed

Schubloom, Hannah E; Woolley, Sarah C

2016-09-01

Social experiences can profoundly shape social behavior and the underlying neural circuits. Across species, the formation of enduring social relationships is associated with both neural and behavioral changes. However, it remains unclear how longer-term relationships between individuals influence brain and behavior. Here, we investigated how variation in social relationships relates to variation in female preferences for and neural responses to song in a pair-bonding songbird. We assessed variation in the interactions between individuals in male-female zebra finch pairs and found that female preferences for their mate's song were correlated with the degree of affiliation and amount of socially modulated singing, but not with the frequency of aggressive interactions. Moreover, variation in measures of pair quality and preference correlated with variation in the song-induced expression of EGR1, an immediate early gene related to neural activity and plasticity, in brain regions important for auditory processing and social behavior. For example, females with weaker preferences for their mate's song had greater EGR1 expression in the nucleus Taeniae, the avian homologue of the mammalian medial amygdala, in response to playback of their mate's courtship song. Our data indicate that the quality of social interactions within pairs relates to variation in song preferences and neural responses to ethologically relevant stimuli and lend insight into neural circuits sensitive to social information. © 2016 Wiley Periodicals, Inc. Develop Neurobiol 76: 1029-1040, 2016. © 2016 Wiley Periodicals, Inc.

Temporal pattern processing in songbirds.

PubMed

Comins, Jordan A; Gentner, Timothy Q

2014-10-01

Understanding how the brain perceives, organizes and uses patterned information is directly related to the neurobiology of language. Given the present limitations, such knowledge at the scale of neurons, neural circuits and neural populations can only come from non-human models, focusing on shared capacities that are relevant to language processing. Here we review recent advances in the behavioral and neural basis of temporal pattern processing of natural auditory communication signals in songbirds, focusing on European starlings. We suggest a general inhibitory circuit for contextual modulation that can act to control sensory representations based on patterning rules. Copyright © 2014. Published by Elsevier Ltd.

The Topographical Mapping in Drosophila Central Complex Network and Its Signal Routing

PubMed Central

Chang, Po-Yen; Su, Ta-Shun; Shih, Chi-Tin; Lo, Chung-Chuan

2017-01-01

Neural networks regulate brain functions by routing signals. Therefore, investigating the detailed organization of a neural circuit at the cellular levels is a crucial step toward understanding the neural mechanisms of brain functions. To study how a complicated neural circuit is organized, we analyzed recently published data on the neural circuit of the Drosophila central complex, a brain structure associated with a variety of functions including sensory integration and coordination of locomotion. We discovered that, except for a small number of “atypical” neuron types, the network structure formed by the identified 194 neuron types can be described by only a few simple mathematical rules. Specifically, the topological mapping formed by these neurons can be reconstructed by applying a generation matrix on a small set of initial neurons. By analyzing how information flows propagate with or without the atypical neurons, we found that while the general pattern of signal propagation in the central complex follows the simple topological mapping formed by the “typical” neurons, some atypical neurons can substantially re-route the signal pathways, implying specific roles of these neurons in sensory signal integration. The present study provides insights into the organization principle and signal integration in the central complex. PMID:28443014

Alternative forms of axial startle behaviors in fishes.

PubMed

Liu, Yen-Chyi; Hale, Melina E

2014-02-01

For most aquatic vertebrates, axial movements play key roles in the performance of startle responses. In fishes, these axis-based startle behaviors fall into three distinct categories - the C-start, withdrawal, and S-start - defined by patterns of body bending and underlying motor control. Startle behaviors have been widely studied due to their importance for predator evasion. In addition, the neural circuits that control startles are relatively accessible, compared to other vertebrate circuits, and have provided opportunities to understand basic nervous system function. The C-start neural circuit has long been a model in systems neuroscience and considerable work on neural control of withdrawal response has been conducted in the larval lamprey. The S-start response has only recently been explored from a physiological perspective and we focus here on reviewing S-start motor control and movement in the context of the other two responses. Axial elongation has previously been associated with startle behavior in comparisons of C-starts and withdrawal, with extremely elongate animals performing withdrawals. We suggest that the S-start tends to occur with moderate body elongation, complementing the C-start in animals with this body form. As many larval fishes are moderately elongate, we suggest that the S-start may be common in larvae but may be secondarily lost with body shape change through development. Copyright © 2013 Elsevier GmbH. All rights reserved.

Deconstruction and Control of Neural Circuits in Posttraumatic Epilepsy

DTIC Science & Technology

2017-10-01

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Optimization Methods for Spiking Neurons and Networks

PubMed Central

Russell, Alexander; Orchard, Garrick; Dong, Yi; Mihalaş, Ştefan; Niebur, Ernst; Tapson, Jonathan; Etienne-Cummings, Ralph

2011-01-01

Spiking neurons and spiking neural circuits are finding uses in a multitude of tasks such as robotic locomotion control, neuroprosthetics, visual sensory processing, and audition. The desired neural output is achieved through the use of complex neuron models, or by combining multiple simple neurons into a network. In either case, a means for configuring the neuron or neural circuit is required. Manual manipulation of parameters is both time consuming and non-intuitive due to the nonlinear relationship between parameters and the neuron’s output. The complexity rises even further as the neurons are networked and the systems often become mathematically intractable. In large circuits, the desired behavior and timing of action potential trains may be known but the timing of the individual action potentials is unknown and unimportant, whereas in single neuron systems the timing of individual action potentials is critical. In this paper, we automate the process of finding parameters. To configure a single neuron we derive a maximum likelihood method for configuring a neuron model, specifically the Mihalas–Niebur Neuron. Similarly, to configure neural circuits, we show how we use genetic algorithms (GAs) to configure parameters for a network of simple integrate and fire with adaptation neurons. The GA approach is demonstrated both in software simulation and hardware implementation on a reconfigurable custom very large scale integration chip. PMID:20959265

Postnatal Developmental Trajectories of Neural Circuits in the Primate Prefrontal Cortex: Identifying Sensitive Periods for Vulnerability to Schizophrenia

PubMed Central

Hoftman, Gil D.; Lewis, David A.

2011-01-01

Schizophrenia is a disorder of cognitive neurodevelopment with characteristic abnormalities in working memory attributed, at least in part, to alterations in the circuitry of the dorsolateral prefrontal cortex. Various environmental exposures from conception through adolescence increase risk for the illness, possibly by altering the developmental trajectories of prefrontal cortical circuits. Macaque monkeys provide an excellent model system for studying the maturation of prefrontal cortical circuits. Here, we review the development of glutamatergic and γ-aminobutyric acid (GABA)-ergic circuits in macaque monkey prefrontal cortex and discuss how these trajectories may help to identify sensitive periods during which environmental exposures, such as those associated with increased risk for schizophrenia, might lead to the types of abnormalities in prefrontal cortical function present in schizophrenia. PMID:21505116

Temporal relation between neural activity and neurite pruning on a numerical model and a microchannel device with micro electrode array.

PubMed

Kondo, Yohei; Yada, Yuichiro; Haga, Tatsuya; Takayama, Yuzo; Isomura, Takuya; Jimbo, Yasuhiko; Fukayama, Osamu; Hoshino, Takayuki; Mabuchi, Kunihiko

2017-04-29

Synapse elimination and neurite pruning are essential processes for the formation of neuronal circuits. These regressive events depend on neural activity and occur in the early postnatal days known as the critical period, but what makes this temporal specificity is not well understood. One possibility is that the neural activities during the developmentally regulated shift of action of GABA inhibitory transmission lead to the critical period. Moreover, it has been reported that the shifting action of the inhibitory transmission on immature neurons overlaps with synapse elimination and neurite pruning and that increased inhibitory transmission by drug treatment could induce temporal shift of the critical period. However, the relationship among these phenomena remains unclear because it is difficult to experimentally show how the developmental shift of inhibitory transmission influences neural activities and whether the activities promote synapse elimination and neurite pruning. In this study, we modeled synapse elimination in neuronal circuits using the modified Izhikevich's model with functional shifting of GABAergic transmission. The simulation results show that synaptic pruning within a specified period like the critical period is spontaneously generated as a function of the developmentally shifting inhibitory transmission and that the specific firing rate and increasing synchronization of neural circuits are seen at the initial stage of the critical period. This temporal relationship was experimentally supported by an in vitro primary culture of rat cortical neurons in a microchannel on a multi-electrode array (MEA). The firing rate decreased remarkably between the 18-25 days in vitro (DIV), and following these changes in the firing rate, the neurite density was slightly reduced. Our simulation and experimental results suggest that decreasing neural activity due to developing inhibitory synaptic transmission could induce synapse elimination and neurite pruning at particular time such as the critical period. Additionally, these findings indicate that we can estimate the maturity level of inhibitory transmission and the critical period by measuring the firing rate and the degree of synchronization in engineered neural networks. Copyright © 2017 Elsevier Inc. All rights reserved.

Social Influences on Neurobiology and Behavior: Epigenetic Effects During Development

PubMed Central

Curley, JP; Jensen, CL; Mashoodh, R; Champagne, FA

2010-01-01

The quality of the social environment can have profound influences on the development and activity of neural systems with implications for numerous behavioral and physiological responses, including the expression of emotionality. Though social experiences occurring early in development may be particularly influential on the developing brain, there is continued plasticity within these neural circuits amongst juveniles and into early adulthood. In this review, we explore the evidence derived from studies in rodents which illustrates the social modulation during development of neural systems, with a particular emphasis on those systems in which a long-term effect is observed. One possible explanation for the persistence of dynamic changes in these systems in response to the environment is the involvement of epigenetic mechanisms, and here we discuss recent studies which support the role of these mechanisms in mediating the link between social experiences, gene expression, neurobiological changes, and behavioral variation. This literature raises critical questions about the interaction between neural systems, the concordance between neural and behavioral changes, sexual dimorphism in effects, the importance of considering individual differences in response to the social environment, and the potential of an epigenetic perspective in advancing our understanding of the pathways leading to variations in mental health. PMID:20650569

SMN is required for sensory-motor circuit function in Drosophila

PubMed Central

Imlach, Wendy L.; Beck, Erin S.; Choi, Ben Jiwon; Lotti, Francesco; Pellizzoni, Livio; McCabe, Brian D.

2012-01-01

Summary Spinal muscular atrophy (SMA) is a lethal human disease characterized by motor neuron dysfunction and muscle deterioration due to depletion of the ubiquitous Survival Motor Neuron (SMN) protein. Drosophila SMN mutants have reduced muscle size and defective locomotion, motor rhythm and motor neuron neurotransmission. Unexpectedly, restoration of SMN in either muscles or motor neurons did not alter these phenotypes. Instead, SMN must be expressed in proprioceptive neurons and interneurons in the motor circuit to non-autonomously correct defects in motor neurons and muscles. SMN depletion disrupts the motor system subsequent to circuit development and can be mimicked by the inhibition of motor network function. Furthermore, increasing motor circuit excitability by genetic or pharmacological inhibition of K+ channels can correct SMN-dependent phenotypes. These results establish sensory-motor circuit dysfunction as the origin of motor system deficits in this SMA model and suggest that enhancement of motor neural network activity could ameliorate the disease. PMID:23063130

High on food: the interaction between the neural circuits for feeding and for reward.

PubMed

Liu, Jing-Jing; Mukherjee, Diptendu; Haritan, Doron; Ignatowska-Jankowska, Bogna; Liu, Ji; Citri, Ami; Pang, Zhiping P

2015-04-01

Hunger, mostly initiated by a deficiency in energy, induces food seeking and intake. However, the drive toward food is not only regulated by physiological needs, but is motivated by the pleasure derived from ingestion of food, in particular palatable foods. Therefore, feeding is viewed as an adaptive motivated behavior that involves integrated communication between homeostatic feeding circuits and reward circuits. The initiation and termination of a feeding episode are instructed by a variety of neuronal signals, and maladaptive plasticity in almost any component of the network may lead to the development of pathological eating disorders. In this review we will summarize the latest understanding of how the feeding circuits and reward circuits in the brain interact. We will emphasize communication between the hypothalamus and the mesolimbic dopamine system and highlight complexities, discrepancies, open questions and future directions for the field.

A modeling framework for deriving the structural and functional architecture of a short-term memory microcircuit.

PubMed

Fisher, Dimitry; Olasagasti, Itsaso; Tank, David W; Aksay, Emre R F; Goldman, Mark S

2013-09-04

Although many studies have identified neural correlates of memory, relatively little is known about the circuit properties connecting single-neuron physiology to behavior. Here we developed a modeling framework to bridge this gap and identify circuit interactions capable of maintaining short-term memory. Unlike typical studies that construct a phenomenological model and test whether it reproduces select aspects of neuronal data, we directly fit the synaptic connectivity of an oculomotor memory circuit to a broad range of anatomical, electrophysiological, and behavioral data. Simultaneous fits to all data, combined with sensitivity analyses, revealed complementary roles of synaptic and neuronal recruitment thresholds in providing the nonlinear interactions required to generate the observed circuit behavior. This work provides a methodology for identifying the cellular and synaptic mechanisms underlying short-term memory and demonstrates how the anatomical structure of a circuit may belie its functional organization. Copyright © 2013 Elsevier Inc. All rights reserved.

Efficient Digital Implementation of The Sigmoidal Function For Artificial Neural Network

NASA Astrophysics Data System (ADS)

Pratap, Rana; Subadra, M.

2011-10-01

An efficient piecewise linear approximation of a nonlinear function (PLAN) is proposed. This uses simulink environment design to perform a direct transformation from X to Y, where X is the input and Y is the approximated sigmoidal output. This PLAN is then used within the outputs of an artificial neural network to perform the nonlinear approximation. In This paper, is proposed a method to implement in FPGA (Field Programmable Gate Array) circuits different approximation of the sigmoid function.. The major benefit of the proposed method resides in the possibility to design neural networks by means of predefined block systems created in System Generator environment and the possibility to create a higher level design tools used to implement neural networks in logical circuits.

The Physiology of Moral Maturity.

ERIC Educational Resources Information Center

Hemming, James

1991-01-01

Discusses an evolutionary approach to human morality. Emphasizes the rapid development of brain weight, neural circuits, and synaptic systems during early childhood. Concludes that the human brain has resources for generating responsible, caring behavior but must be nurtured and educated. Urges that moral training in a proper social climate be…

Information Flow through a Model of the C. elegans Klinotaxis Circuit.

PubMed

Izquierdo, Eduardo J; Williams, Paul L; Beer, Randall D

2015-01-01

Understanding how information about external stimuli is transformed into behavior is one of the central goals of neuroscience. Here we characterize the information flow through a complete sensorimotor circuit: from stimulus, to sensory neurons, to interneurons, to motor neurons, to muscles, to motion. Specifically, we apply a recently developed framework for quantifying information flow to a previously published ensemble of models of salt klinotaxis in the nematode worm Caenorhabditis elegans. Despite large variations in the neural parameters of individual circuits, we found that the overall information flow architecture circuit is remarkably consistent across the ensemble. This suggests structural connectivity is not necessarily predictive of effective connectivity. It also suggests information flow analysis captures general principles of operation for the klinotaxis circuit. In addition, information flow analysis reveals several key principles underlying how the models operate: (1) Interneuron class AIY is responsible for integrating information about positive and negative changes in concentration, and exhibits a strong left/right information asymmetry. (2) Gap junctions play a crucial role in the transfer of information responsible for the information symmetry observed in interneuron class AIZ. (3) Neck motor neuron class SMB implements an information gating mechanism that underlies the circuit's state-dependent response. (4) The neck carries more information about small changes in concentration than about large ones, and more information about positive changes in concentration than about negative ones. Thus, not all directions of movement are equally informative for the worm. Each of these findings corresponds to hypotheses that could potentially be tested in the worm. Knowing the results of these experiments would greatly refine our understanding of the neural circuit underlying klinotaxis.

Brain Magnetic Resonance Spectroscopy in Tourette's Disorder

ERIC Educational Resources Information Center

DeVito, Timothy J.; Drost, Dick J.; Pavlosky, William; Neufeld, Richard W.J.; Rajakumar, Nagalingam; McKinlay, B. Duncan; Williamson, Peter C.; Nicolson, Rob

2005-01-01

Objective: Although abnormalities of neural circuits involving the cortex, striatum, and thalamus are hypothesized to underlie Tourette's disorder, the neuronal abnormalities within components of these circuits are unknown. The purpose of this study was to examine the cellular neurochemistry within these circuits in Tourette's disorder using…

Neural correlates underlying micrographia in Parkinson’s disease

PubMed Central

Zhang, Jiarong; Hallett, Mark; Feng, Tao; Hou, Yanan; Chan, Piu

2016-01-01

Micrographia is a common symptom in Parkinson’s disease, which manifests as either a consistent or progressive reduction in the size of handwriting or both. Neural correlates underlying micrographia remain unclear. We used functional magnetic resonance imaging to investigate micrographia-related neural activity and connectivity modulations. In addition, the effect of attention and dopaminergic administration on micrographia was examined. We found that consistent micrographia was associated with decreased activity and connectivity in the basal ganglia motor circuit; while progressive micrographia was related to the dysfunction of basal ganglia motor circuit together with disconnections between the rostral supplementary motor area, rostral cingulate motor area and cerebellum. Attention significantly improved both consistent and progressive micrographia, accompanied by recruitment of anterior putamen and dorsolateral prefrontal cortex. Levodopa improved consistent micrographia accompanied by increased activity and connectivity in the basal ganglia motor circuit, but had no effect on progressive micrographia. Our findings suggest that consistent micrographia is related to dysfunction of the basal ganglia motor circuit; while dysfunction of the basal ganglia motor circuit and disconnection between the rostral supplementary motor area, rostral cingulate motor area and cerebellum likely contributes to progressive micrographia. Attention improves both types of micrographia by recruiting additional brain networks. Levodopa improves consistent micrographia by restoring the function of the basal ganglia motor circuit, but does not improve progressive micrographia, probably because of failure to repair the disconnected networks. PMID:26525918

Development of a computational model on the neural activity patterns of a visual working memory in a hierarchical feedforward Network

NASA Astrophysics Data System (ADS)

An, Soyoung; Choi, Woochul; Paik, Se-Bum

2015-11-01

Understanding the mechanism of information processing in the human brain remains a unique challenge because the nonlinear interactions between the neurons in the network are extremely complex and because controlling every relevant parameter during an experiment is difficult. Therefore, a simulation using simplified computational models may be an effective approach. In the present study, we developed a general model of neural networks that can simulate nonlinear activity patterns in the hierarchical structure of a neural network system. To test our model, we first examined whether our simulation could match the previously-observed nonlinear features of neural activity patterns. Next, we performed a psychophysics experiment for a simple visual working memory task to evaluate whether the model could predict the performance of human subjects. Our studies show that the model is capable of reproducing the relationship between memory load and performance and may contribute, in part, to our understanding of how the structure of neural circuits can determine the nonlinear neural activity patterns in the human brain.

Striatal Circuits as a Common Node for Autism Pathophysiology

PubMed Central

Fuccillo, Marc V.

2016-01-01

Autism spectrum disorders (ASD) are characterized by two seemingly unrelated symptom domains—deficits in social interactions and restrictive, repetitive patterns of behavioral output. Whether the diverse nature of ASD symptomatology represents distributed dysfunction of brain networks or abnormalities within specific neural circuits is unclear. Striatal dysfunction is postulated to underlie the repetitive motor behaviors seen in ASD, and neurological and brain-imaging studies have supported this assumption. However, as our appreciation of striatal function expands to include regulation of behavioral flexibility, motivational state, goal-directed learning, and attention, we consider whether alterations in striatal physiology are a central node mediating a range of autism-associated behaviors, including social and cognitive deficits that are hallmarks of the disease. This review investigates multiple genetic mouse models of ASD to explore whether abnormalities in striatal circuits constitute a common pathophysiological mechanism in the development of autism-related behaviors. Despite the heterogeneity of genetic insult investigated, numerous genetic ASD models display alterations in the structure and function of striatal circuits, as well as abnormal behaviors including repetitive grooming, stereotypic motor routines, deficits in social interaction and decision-making. Comparative analysis in rodents provides a unique opportunity to leverage growing genetic association data to reveal canonical neural circuits whose dysfunction directly contributes to discrete aspects of ASD symptomatology. The description of such circuits could provide both organizing principles for understanding the complex genetic etiology of ASD as well as novel treatment routes. Furthermore, this focus on striatal mechanisms of behavioral regulation may also prove useful for exploring the pathogenesis of other neuropsychiatric diseases, which display overlapping behavioral deficits with ASD. PMID:26903795

Computer simulations of stimulus dependent state switching in basic circuits of bursting neurons

NASA Astrophysics Data System (ADS)

Rabinovich, Mikhail; Huerta, Ramón; Bazhenov, Maxim; Kozlov, Alexander K.; Abarbanel, Henry D. I.

1998-11-01

We investigate the ability of oscillating neural circuits to switch between different states of oscillation in two basic neural circuits. We model two quite distinct small neural circuits. The first circuit is based on invertebrate central pattern generator (CPG) studies [A. I. Selverston and M. Moulins, The Crustacean Stomatogastric System (Springer-Verlag, Berlin, 1987)] and is composed of two neurons coupled via both gap junction and inhibitory synapses. The second consists of coupled pairs of interconnected thalamocortical relay and thalamic reticular neurons with both inhibitory and excitatory synaptic coupling. The latter is an elementary unit of the thalamic networks passing sensory information to the cerebral cortex [M. Steriade, D. A. McCormick, and T. J. Sejnowski, Science 262, 679 (1993)]. Both circuits have contradictory coupling between symmetric parts. The thalamocortical model has excitatory and inhibitory connections and the CPG has reciprocal inhibitory and electrical coupling. We describe the dynamics of the individual neurons in these circuits by conductance based ordinary differential equations of Hodgkin-Huxley type [J. Physiol. (London) 117, 500 (1952)]. Both model circuits exhibit bistability and hysteresis in a wide region of coupling strengths. The two main modes of behavior are in-phase and out-of-phase oscillations of the symmetric parts of the network. We investigate the response of these circuits, while they are operating in bistable regimes, to externally imposed excitatory spike trains with varying interspike timing and small amplitude pulses. These are meant to represent spike trains received by the basic circuits from sensory neurons. Circuits operating in a bistable region are sensitive to the frequency of these excitatory inputs. Frequency variations lead to changes from in-phase to out-of-phase coordination or vice versa. The signaling information contained in a spike train driving the network can place the circuit into one or another state depending on the interspike interval and this happens within a few spikes. These states are maintained by the basic circuit after the input signal is ended. When a new signal of the correct frequency enters the circuit, it can be switched to another state with the same ease.

Parallel Computations in Insect and Mammalian Visual Motion Processing

PubMed Central

Clark, Damon A.; Demb, Jonathan B.

2016-01-01

Sensory systems use receptors to extract information from the environment and neural circuits to perform subsequent computations. These computations may be described as algorithms composed of sequential mathematical operations. Comparing these operations across taxa reveals how different neural circuits have evolved to solve the same problem, even when using different mechanisms to implement the underlying math. In this review, we compare how insect and mammalian neural circuits have solved the problem of motion estimation, focusing on the fruit fly Drosophila and the mouse retina. Although the two systems implement computations with grossly different anatomy and molecular mechanisms, the underlying circuits transform light into motion signals with strikingly similar processing steps. These similarities run from photoreceptor gain control and spatiotemporal tuning to ON and OFF pathway structures, motion detection, and computed motion signals. The parallels between the two systems suggest that a limited set of algorithms for estimating motion satisfies both the needs of sighted creatures and the constraints imposed on them by metabolism, anatomy, and the structure and regularities of the visual world. PMID:27780048

Parallel Computations in Insect and Mammalian Visual Motion Processing.

PubMed

Clark, Damon A; Demb, Jonathan B

2016-10-24

Sensory systems use receptors to extract information from the environment and neural circuits to perform subsequent computations. These computations may be described as algorithms composed of sequential mathematical operations. Comparing these operations across taxa reveals how different neural circuits have evolved to solve the same problem, even when using different mechanisms to implement the underlying math. In this review, we compare how insect and mammalian neural circuits have solved the problem of motion estimation, focusing on the fruit fly Drosophila and the mouse retina. Although the two systems implement computations with grossly different anatomy and molecular mechanisms, the underlying circuits transform light into motion signals with strikingly similar processing steps. These similarities run from photoreceptor gain control and spatiotemporal tuning to ON and OFF pathway structures, motion detection, and computed motion signals. The parallels between the two systems suggest that a limited set of algorithms for estimating motion satisfies both the needs of sighted creatures and the constraints imposed on them by metabolism, anatomy, and the structure and regularities of the visual world. Copyright © 2016 Elsevier Ltd. All rights reserved.

Reactivating Neural Circuits with Clinically Accessible Stimulation to Restore Hand Function in Persons with Tetraplegia

DTIC Science & Technology

2017-09-01

AWARD NUMBER: W81XWH-16-1-0395 TITLE: Reactivating Neural Circuits with Clinically Accessible Stimulation to Restore Hand Function in...estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data...Clinically Accessible Stimulation to Restore Hand Function in Persons with Tetraplegia 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S

Serotonin-related pathways and developmental plasticity: relevance for psychiatric disorders

PubMed Central

Dayer, Alexandre

2014-01-01

Risk for adult psychiatric disorders is partially determined by early-life alterations occurring during neural circuit formation and maturation. In this perspective, recent data show that the serotonin system regulates key cellular processes involved in the construction of cortical circuits. Translational data for rodents indicate that early-life serotonin dysregulation leads to a wide range of behavioral alterations, ranging from stress-related phenotypes to social deficits. Studies in humans have revealed that serotonin-related genetic variants interact with early-life stress to regulate stress-induced cortisol responsiveness and activate the neural circuits involved in mood and anxiety disorders. Emerging data demonstrate that early-life adversity induces epigenetic modifications in serotonin-related genes. Finally, recent findings reveal that selective serotonin reuptake inhibitors can reinstate juvenile-like forms of neural plasticity, thus allowing the erasure of long-lasting fear memories. These approaches are providing new insights on the biological mechanisms and clinical application of antidepressants. PMID:24733969

Cortical activity in the null space: permitting preparation without movement

PubMed Central

Kaufman, Matthew T.; Churchland, Mark M.; Ryu, Stephen I.; Shenoy, Krishna V.

2014-01-01

Neural circuits must perform computations and then selectively output the results to other circuits. Yet synapses do not change radically at millisecond timescales. A key question then is: how is communication between neural circuits controlled? In motor control, brain areas directly involved in driving movement are active well before movement begins. Muscle activity is some readout of neural activity, yet remains largely unchanged during preparation. Here we find that during preparation, while the monkey holds still, changes in motor cortical activity cancel out at the level of these population readouts. Motor cortex can thereby prepare the movement without prematurely causing it. Further, we found evidence that this mechanism also operates in dorsal premotor cortex (PMd), largely accounting for how preparatory activity is attenuated in primary motor cortex (M1). Selective use of “output-null” vs. “output-potent” patterns of activity may thus help control communication to the muscles and between these brain areas. PMID:24487233

A decision-making model based on a spiking neural circuit and synaptic plasticity.

PubMed

Wei, Hui; Bu, Yijie; Dai, Dawei

2017-10-01

To adapt to the environment and survive, most animals can control their behaviors by making decisions. The process of decision-making and responding according to cues in the environment is stable, sustainable, and learnable. Understanding how behaviors are regulated by neural circuits and the encoding and decoding mechanisms from stimuli to responses are important goals in neuroscience. From results observed in Drosophila experiments, the underlying decision-making process is discussed, and a neural circuit that implements a two-choice decision-making model is proposed to explain and reproduce the observations. Compared with previous two-choice decision making models, our model uses synaptic plasticity to explain changes in decision output given the same environment. Moreover, biological meanings of parameters of our decision-making model are discussed. In this paper, we explain at the micro-level (i.e., neurons and synapses) how observable decision-making behavior at the macro-level is acquired and achieved.

Dysfunctional Autism Risk Genes Cause Circuit-Specific Connectivity Deficits With Distinct Developmental Trajectories.

PubMed

Zerbi, Valerio; Ielacqua, Giovanna D; Markicevic, Marija; Haberl, Matthias Georg; Ellisman, Mark H; A-Bhaskaran, Arjun; Frick, Andreas; Rudin, Markus; Wenderoth, Nicole

2018-07-01

Autism spectrum disorders (ASD) are a set of complex neurodevelopmental disorders for which there is currently no targeted therapeutic approach. It is thought that alterations of genes regulating migration and synapse formation during development affect neural circuit formation and result in aberrant connectivity within distinct circuits that underlie abnormal behaviors. However, it is unknown whether deviant developmental trajectories are circuit-specific for a given autism risk-gene. We used MRI to probe changes in functional and structural connectivity from childhood to adulthood in Fragile-X (Fmr1-/y) and contactin-associated (CNTNAP2-/-) knockout mice. Young Fmr1-/y mice (30 days postnatal) presented with a robust hypoconnectivity phenotype in corticocortico and corticostriatal circuits in areas associated with sensory information processing, which was maintained until adulthood. Conversely, only small differences in hippocampal and striatal areas were present during early postnatal development in CNTNAP2-/- mice, while major connectivity deficits in prefrontal and limbic pathways developed between adolescence and adulthood. These findings are supported by viral tracing and electron micrograph approaches and define 2 clearly distinct connectivity endophenotypes within the autism spectrum. We conclude that the genetic background of ASD strongly influences which circuits are most affected, the nature of the phenotype, and the developmental time course of the associated changes.

Two multichannel integrated circuits for neural recording and signal processing.

PubMed

Obeid, Iyad; Morizio, James C; Moxon, Karen A; Nicolelis, Miguel A L; Wolf, Patrick D

2003-02-01

We have developed, manufactured, and tested two analog CMOS integrated circuit "neurochips" for recording from arrays of densely packed neural electrodes. Device A is a 16-channel buffer consisting of parallel noninverting amplifiers with a gain of 2 V/V. Device B is a 16-channel two-stage analog signal processor with differential amplification and high-pass filtering. It features selectable gains of 250 and 500 V/V as well as reference channel selection. The resulting amplifiers on Device A had a mean gain of 1.99 V/V with an equivalent input noise of 10 microV(rms). Those on Device B had mean gains of 53.4 and 47.4 dB with a high-pass filter pole at 211 Hz and an equivalent input noise of 4.4 microV(rms). Both devices were tested in vivo with electrode arrays implanted in the somatosensory cortex.

Mechanisms of input and output synaptic specificity: finding partners, building synapses, and fine-tuning communication.

PubMed

Rawson, Randi L; Martin, E Anne; Williams, Megan E

2017-08-01

For most neurons to function properly, they need to develop synaptic specificity. This requires finding specific partner neurons, building the correct types of synapses, and fine-tuning these synapses in response to neural activity. Synaptic specificity is common at both a neuron's input and output synapses, whereby unique synapses are built depending on the partnering neuron. Neuroscientists have long appreciated the remarkable specificity of neural circuits but identifying molecular mechanisms mediating synaptic specificity has only recently accelerated. Here, we focus on recent progress in understanding input and output synaptic specificity in the mammalian brain. We review newly identified circuit examples for both and the latest research identifying molecular mediators including Kirrel3, FGFs, and DGLα. Lastly, we expect the pace of research on input and output specificity to continue to accelerate with the advent of new technologies in genomics, microscopy, and proteomics. Copyright © 2017 Elsevier Ltd. All rights reserved.

Dynamic labelling of neural connections in multiple colours by trans-synaptic fluorescence complementation

PubMed Central

Macpherson, Lindsey J.; Zaharieva, Emanuela E.; Kearney, Patrick J.; Alpert, Michael H.; Lin, Tzu-Yang; Turan, Zeynep; Lee, Chi-Hon; Gallio, Marco

2015-01-01

Determining the pattern of activity of individual connections within a neural circuit could provide insights into the computational processes that underlie brain function. Here, we develop new strategies to label active synapses by trans-synaptic fluorescence complementation in Drosophila. First, we demonstrate that a synaptobrevin-GRASP chimera functions as a powerful activity-dependent marker for synapses in vivo. Next, we create cyan and yellow variants, achieving activity-dependent, multi-colour fluorescence reconstitution across synapses (X-RASP). Our system allows for the first time retrospective labelling of synapses (rather than whole neurons) based on their activity, in multiple colours, in the same animal. As individual synapses often act as computational units in the brain, our method will promote the design of experiments that are not possible using existing techniques. Moreover, our strategies are easily adaptable to circuit mapping in any genetic system. PMID:26635273

Machine Vision Within The Framework Of Collective Neural Assemblies

NASA Astrophysics Data System (ADS)

Gupta, Madan M.; Knopf, George K.

1990-03-01

The proposed mechanism for designing a robust machine vision system is based on the dynamic activity generated by the various neural populations embedded in nervous tissue. It is postulated that a hierarchy of anatomically distinct tissue regions are involved in visual sensory information processing. Each region may be represented as a planar sheet of densely interconnected neural circuits. Spatially localized aggregates of these circuits represent collective neural assemblies. Four dynamically coupled neural populations are assumed to exist within each assembly. In this paper we present a state-variable model for a tissue sheet derived from empirical studies of population dynamics. Each population is modelled as a nonlinear second-order system. It is possible to emulate certain observed physiological and psychophysiological phenomena of biological vision by properly programming the interconnective gains . Important early visual phenomena such as temporal and spatial noise insensitivity, contrast sensitivity and edge enhancement will be discussed for a one-dimensional tissue model.

Neural circuit basis of visuo-spatial working memory precision: a computational and behavioral study.

PubMed

Almeida, Rita; Barbosa, João; Compte, Albert

2015-09-01

The amount of information that can be retained in working memory (WM) is limited. Limitations of WM capacity have been the subject of intense research, especially in trying to specify algorithmic models for WM. Comparatively, neural circuit perspectives have barely been used to test WM limitations in behavioral experiments. Here we used a neuronal microcircuit model for visuo-spatial WM (vsWM) to investigate memory of several items. The model assumes that there is a topographic organization of the circuit responsible for spatial memory retention. This assumption leads to specific predictions, which we tested in behavioral experiments. According to the model, nearby locations should be recalled with a bias, as if the two memory traces showed attraction or repulsion during the delay period depending on distance. Another prediction is that the previously reported loss of memory precision for an increasing number of memory items (memory load) should vanish when the distances between items are controlled for. Both predictions were confirmed experimentally. Taken together, our findings provide support for a topographic neural circuit organization of vsWM, they suggest that interference between similar memories underlies some WM limitations, and they put forward a circuit-based explanation that reconciles previous conflicting results on the dependence of WM precision with load. Copyright © 2015 the American Physiological Society.

The role of the medial prefrontal cortex in trace fear extinction

PubMed Central

Kwapis, Janine L.; Jarome, Timothy J.

2015-01-01

The extinction of delay fear conditioning relies on a neural circuit that has received much attention and is relatively well defined. Whether this established circuit also supports the extinction of more complex associations, however, is unclear. Trace fear conditioning is a better model of complex relational learning, yet the circuit that supports extinction of this memory has received very little attention. Recent research has indicated that trace fear extinction requires a different neural circuit than delay extinction; trace extinction requires the participation of the retrosplenial cortex, but not the amygdala, as noted in a previous study. Here, we tested the roles of the prelimbic and infralimbic regions of the medial prefrontal cortex in trace and delay fear extinction by blocking NMDA receptors during extinction learning. We found that the prelimbic cortex is necessary for trace, but not for delay fear extinction, whereas the infralimbic cortex is involved in both types of extinction. These results are consistent with the idea that trace fear associations require plasticity in multiple cortical areas for successful extinction. Further, the infralimbic cortex appears to play a role in extinction regardless of whether the animal was initially trained in trace or delay conditioning. Together, our results provide new information about how the neural circuits supporting trace and delay fear extinction differ. PMID:25512576

Neuroimaging the interaction of mind and metabolism in humans

PubMed Central

D’Agostino, Alexandra E.; Small, Dana M.

2012-01-01

Hormonal and metabolic signals interact with neural circuits orchestrating behavior to guide food intake. Neuroimaging techniques such as functional magnetic resonance imaging (fMRI) enable the identification of where in the brain particular mental processes like desire, satiety and pleasure occur. Once these neural circuits are described it then becomes possible to determine how metabolic and hormonal signals can alter brain response to influence psychological states and decision-making processes to guide intake. Here, we provide an overview of the contributions of functional neuroimaging to the understanding of how subjective and neural responses to food and food cues interact with metabolic/hormonal factors. PMID:24024114

A Mechanism for Graded, Dynamically Routable Current Propagation in Pulse-Gated Synfire Chains and Implications for Information Coding

PubMed Central

Sornborger, Andrew T.; Wang, Zhuo; Tao, Louis

2015-01-01

Neural oscillations can enhance feature recognition [1], modulate interactions between neurons [2], and improve learning and memory [3]. Numerical studies have shown that coherent spiking can give rise to windows in time during which information transfer can be enhanced in neuronal networks [4–6]. Unanswered questions are: 1) What is the transfer mechanism? And 2) how well can a transfer be executed? Here, we present a pulse-based mechanism by which a graded current amplitude may be exactly propagated from one neuronal population to another. The mechanism relies on the downstream gating of mean synaptic current amplitude from one population of neurons to another via a pulse. Because transfer is pulse-based, information may be dynamically routed through a neural circuit with fixed connectivity. We demonstrate the transfer mechanism in a realistic network of spiking neurons and show that it is robust to noise in the form of pulse timing inaccuracies, random synaptic strengths and finite size effects. We also show that the mechanism is structurally robust in that it may be implemented using biologically realistic pulses. The transfer mechanism may be used as a building block for fast, complex information processing in neural circuits. We show that the mechanism naturally leads to a framework wherein neural information coding and processing can be considered as a product of linear maps under the active control of a pulse generator. Distinct control and processing components combine to form the basis for the binding, propagation, and processing of dynamically routed information within neural pathways. Using our framework, we construct example neural circuits to 1) maintain a short-term memory, 2) compute time-windowed Fourier transforms, and 3) perform spatial rotations. We postulate that such circuits, with automatic and stereotyped control and processing of information, are the neural correlates of Crick and Koch’s zombie modes. PMID:26227067

Somatoform Pain: A developmental theory and translational research review

PubMed Central

Landa, Alla; Peterson, Bradley S.; Fallon, Brian A.

2013-01-01

Somatoform pain is a highly prevalent, debilitating condition and a tremendous public health problem. Effective treatments for somatoform pain are urgently needed. The etiology of this condition is, however, still unknown. On the basis of a review of recent basic and clinical research, we propose one potential mechanisms of symptom formation in somatoform pain and a developmental theory of its pathogenesis. The emerging evidence from animal and human studies in developmental neurobiology, cognitive-affective neuroscience, psychoneuroimmunology, genetics, epigenetics, and clinical and treatment studies of somatoform pain all point to the existence of a shared physical and social pain neural system. Research findings also show that non-optimal early experiences interact with genetic predispositions to influence the development of this shared system and ability to regulate it in an effective way. Interpersonal affect regulation between infant and caregiver is crucial for the optimal development of these brain circuits. The aberrant development of this shared neural system during infancy, childhood and adolescence, therefore, may ultimately lead to an increased sensitivity to physical and social pain and to problems with their regulation in adulthood. The authors critically review translational research findings that support this theory and discuss its clinical and research implications. Specifically, the proposed theory and reviewed research suggest that psychotherapeutic and/or pharmacologic interventions that foster the development of affect regulation capacities in an interpersonal context will also serve to more effectively modulate aberrantly activated neural pain circuits and thus be of particular benefit in the treatment of somatoform pain. PMID:22929064

Commentary: The Development of Hippocampal-Dependent Memory Functions: Theoretical Comments on Jabès and Nelson Review (2015)

ERIC Educational Resources Information Center

Bachevalier, Jocelyne

2015-01-01

Studies investigating the development of memory processes and their neural substrates have flourished over the past two decades. The review by Jabès and Nelson (2015) adds an important piece to our understanding of the maturation of different elements and circuits within the hippocampal system and their association with the progressive development…

Development of brain-wide connectivity architecture in awake rats.

PubMed

Ma, Zilu; Ma, Yuncong; Zhang, Nanyin

2018-08-01

Childhood and adolescence are both critical developmental periods, evidenced by complex neurophysiological changes the brain undergoes and high occurrence rates of neuropsychiatric disorders during these periods. Despite substantial progress in elucidating the developmental trajectories of individual neural circuits, our knowledge of developmental changes of whole-brain connectivity architecture in animals is sparse. To fill this gap, here we longitudinally acquired rsfMRI data in awake rats during five developmental stages from juvenile to adulthood. We found that the maturation timelines of brain circuits were heterogeneous and system specific. Functional connectivity (FC) tended to decrease in subcortical circuits, but increase in cortical circuits during development. In addition, the developing brain exhibited hemispheric functional specialization, evidenced by reduced inter-hemispheric FC between homotopic regions, and lower similarity of region-to-region FC patterns between the two hemispheres. Finally, we showed that whole-brain network development was characterized by reduced clustering (i.e. local communication) but increased integration (distant communication). Taken together, the present study has systematically characterized the development of brain-wide connectivity architecture from juvenile to adulthood in awake rats. It also serves as a critical reference point for understanding circuit- and network-level changes in animal models of brain development-related disorders. Furthermore, FC data during brain development in awake rodents contain high translational value and can shed light onto comparative neuroanatomy. Copyright © 2018 Elsevier Inc. All rights reserved.

Evolution of behavior and neural control of the fast-start escape response.

PubMed

Hale, Melina E; Long, John H; McHenry, Matthew J; Westneat, Mark W

2002-05-01

The fast-start startle behavior is the primary mechanism of rapid escape in fishes and is a model system for examining neural circuit design and musculoskeletal function. To develop a dataset for evolutionary analysis of the startle response, the kinematics and muscle activity patterns of the fast-start were analyzed for four fish species at key branches in the phylogeny of vertebrates. Three of these species (Polypterus palmas, Lepisosteus osseus, and Amia calva) represent the base of the actinopterygian radiation. A fourth species (Oncorhynchus mykiss) provided data for a species in the central region of the teleost phylogeny. Using these data, we explored the evolution of this behavior within the phylogeny of vertebrates. To test the hypothesis that startle features are evolutionarily conservative, the variability of motor patterns and kinematics in fast-starts was described. Results show that the evolution of the startle behavior in fishes, and more broadly among vertebrates, is not conservative. The fast-start has undergone substantial change in suites of kinematics and electromyogram features, including the presence of either a one- or a two-stage kinematic response and change in the extent of bilateral muscle activity. Comparative methods were used to test the evolutionary hypothesis that changes in motor control are correlated with key differences in the kinematics and behavior of the fast-start. Significant evolutionary correlations were found between several motor pattern and behavioral characters. These results suggest that the startle neural circuit itself is not conservative. By tracing the evolution of motor pattern and kinematics on a phylogeny, it is shown that major changes in the neural circuit of the startle behavior occur at several levels in the phylogeny of vertebrates.

Standard cell-based implementation of a digital optoelectronic neural-network hardware.

PubMed

Maier, K D; Beckstein, C; Blickhan, R; Erhard, W

2001-03-10

A standard cell-based implementation of a digital optoelectronic neural-network architecture is presented. The overall structure of the multilayer perceptron network that was used, the optoelectronic interconnection system between the layers, and all components required in each layer are defined. The design process from VHDL-based modeling from synthesis and partly automatic placing and routing to the final editing of one layer of the circuit of the multilayer perceptrons are described. A suitable approach for the standard cell-based design of optoelectronic systems is presented, and shortcomings of the design tool that was used are pointed out. The layout for the microelectronic circuit of one layer in a multilayer perceptron neural network with a performance potential 1 magnitude higher than neural networks that are purely electronic based has been successfully designed.

Plasticity in single neuron and circuit computations

NASA Astrophysics Data System (ADS)

Destexhe, Alain; Marder, Eve

2004-10-01

Plasticity in neural circuits can result from alterations in synaptic strength or connectivity, as well as from changes in the excitability of the neurons themselves. To better understand the role of plasticity in the brain, we need to establish how brain circuits work and the kinds of computations that different circuit structures achieve. By linking theoretical and experimental studies, we are beginning to reveal the consequences of plasticity mechanisms for network dynamics, in both simple invertebrate circuits and the complex circuits of mammalian cerebral cortex.

The neuropsychology of self-reflection in psychiatric illness

PubMed Central

Philippi, Carissa L.; Koenigs, Michael

2014-01-01

The development of robust neuropsychological measures of social and affective function—which link critical dimensions of mental health to their underlying neural circuitry—could be a key step in achieving a more pathophysiologically-based approach to psychiatric medicine. In this article, we summarize research indicating that self-reflection (the inward attention to personal thoughts, memories, feelings, and actions) may be a useful model for developing such a paradigm, as there is evidence that self-reflection is (1) measurable with self-report scales and performance-based tests, (2) linked to the activity of a specific neural circuit, and (3) dimensionally related to mental health and various forms of psychopathology. PMID:24685311

The impact of neurotechnology on rehabilitation.

PubMed

Berger, Theodore W; Gerhardt, Greg; Liker, Mark A; Soussou, Walid

2008-01-01

This paper present results of a multi-disciplinary project that is developing a microchip-based neural prosthesis for the hippocampus, a region of the brain responsible for the formation of long-term memories. Damage to the hippocampus is frequently associated with epilepsy, stroke, and dementia (Alzheimer's disease) and is considered to underlie the memory deficits related to these neurological conditions. The essential goals of the multi-laboratory effort include: (1) experimental study of neuron and neural network function--how does the hippocampus encode information? (2) formulation of biologically realistic models of neural system dynamics--can that encoding process be described mathematically to realize a predictive model of how the hippocampus responds to any event? (3) microchip implementation of neural system models--can the mathematical model be realized as a set of electronic circuits to achieve parallel processing, rapid computational speed, and miniaturization? and (4) creation of hybrid neuron-silicon interfaces-can structural and functional connections between electronic devices and neural tissue be achieved for long-term, bi-directional communication with the brain? By integrating solutions to these component problems, we are realizing a microchip-based model of hippocampal nonlinear dynamics that can perform the same function as part of the hippocampus. Through bi-directional communication with other neural tissue that normally provides the inputs and outputs to/from a damaged hippocampal area, the biomimetic model could serve as a neural prosthesis. A proof-of-concept will be presented in which the CA3 region of the hippocampal slice is surgically removed and is replaced by a microchip model of CA3 nonlinear dynamics--the "hybrid" hippocampal circuit displays normal physiological properties. How the work in brain slices is being extended to behaving animals also will be described.

Energy-efficient neural information processing in individual neurons and neuronal networks.

PubMed

Yu, Lianchun; Yu, Yuguo

2017-11-01

Brains are composed of networks of an enormous number of neurons interconnected with synapses. Neural information is carried by the electrical signals within neurons and the chemical signals among neurons. Generating these electrical and chemical signals is metabolically expensive. The fundamental issue raised here is whether brains have evolved efficient ways of developing an energy-efficient neural code from the molecular level to the circuit level. Here, we summarize the factors and biophysical mechanisms that could contribute to the energy-efficient neural code for processing input signals. The factors range from ion channel kinetics, body temperature, axonal propagation of action potentials, low-probability release of synaptic neurotransmitters, optimal input and noise, the size of neurons and neuronal clusters, excitation/inhibition balance, coding strategy, cortical wiring, and the organization of functional connectivity. Both experimental and computational evidence suggests that neural systems may use these factors to maximize the efficiency of energy consumption in processing neural signals. Studies indicate that efficient energy utilization may be universal in neuronal systems as an evolutionary consequence of the pressure of limited energy. As a result, neuronal connections may be wired in a highly economical manner to lower energy costs and space. Individual neurons within a network may encode independent stimulus components to allow a minimal number of neurons to represent whole stimulus characteristics efficiently. This basic principle may fundamentally change our view of how billions of neurons organize themselves into complex circuits to operate and generate the most powerful intelligent cognition in nature. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Insect Responses to Linearly Polarized Reflections: Orphan Behaviors Without Neural Circuits.

PubMed

Heinloth, Tanja; Uhlhorn, Juliane; Wernet, Mathias F

2018-01-01

The e-vector orientation of linearly polarized light represents an important visual stimulus for many insects. Especially the detection of polarized skylight by many navigating insect species is known to improve their orientation skills. While great progress has been made towards describing both the anatomy and function of neural circuit elements mediating behaviors related to navigation, relatively little is known about how insects perceive non-celestial polarized light stimuli, like reflections off water, leaves, or shiny body surfaces. Work on different species suggests that these behaviors are not mediated by the "Dorsal Rim Area" (DRA), a specialized region in the dorsal periphery of the adult compound eye, where ommatidia contain highly polarization-sensitive photoreceptor cells whose receptive fields point towards the sky. So far, only few cases of polarization-sensitive photoreceptors have been described in the ventral periphery of the insect retina. Furthermore, both the structure and function of those neural circuits connecting to these photoreceptor inputs remain largely uncharacterized. Here we review the known data on non-celestial polarization vision from different insect species (dragonflies, butterflies, beetles, bugs and flies) and present three well-characterized examples for functionally specialized non-DRA detectors from different insects that seem perfectly suited for mediating such behaviors. Finally, using recent advances from circuit dissection in Drosophila melanogaster , we discuss what types of potential candidate neurons could be involved in forming the underlying neural circuitry mediating non-celestial polarization vision.

Thalamo-cortical communication, glutamatergic neurotransmission and neural oscillations: A unique window into the origins of ScZ?

PubMed

Pratt, Judith; Dawson, Neil; Morris, Brain J; Grent-'t-Jong, Tineke; Roux, Frederic; Uhlhaas, Peter J

2017-02-01

The thalamus has recently received renewed interest in systems-neuroscience and schizophrenia (ScZ) research because of emerging evidence highlighting its important role in coordinating functional interactions in cortical-subcortical circuits. Moreover, higher cognitive functions, such as working memory and attention, have been related to thalamo-cortical interactions, providing a novel perspective for the understanding of the neural substrate of cognition. The current review will support this perspective by summarizing evidence on the crucial role of neural oscillations in facilitating thalamo-cortical (TC) interactions during normal brain functioning and their potential impairment in ScZ. Specifically, we will focus on the relationship between NMDA-R mediated (glutamatergic) neurotransmission in TC-interactions. To this end, we will first review the functional anatomy and neurotransmitters in thalamic circuits, followed by a review of the oscillatory signatures and cognitive processes supported by TC-circuits. In the second part of the paper, data from preclinical research as well as human studies will be summarized that have implicated TC-interactions as a crucial target for NMDA-receptor hypofunctioning. Finally, we will compare these neural signatures with current evidence from ScZ-research, suggesting a potential overlap between alterations in TC-circuits as the result of NMDA-R deficits and stage-specific alterations in large-scale networks in ScZ. Copyright © 2016 Elsevier B.V. All rights reserved.

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder.

PubMed

Boi, Fabio; Moraitis, Timoleon; De Feo, Vito; Diotalevi, Francesco; Bartolozzi, Chiara; Indiveri, Giacomo; Vato, Alessandro

2016-01-01

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive.

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder

PubMed Central

Boi, Fabio; Moraitis, Timoleon; De Feo, Vito; Diotalevi, Francesco; Bartolozzi, Chiara; Indiveri, Giacomo; Vato, Alessandro

2016-01-01

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive. PMID:28018162

Satisfiability of logic programming based on radial basis function neural networks

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

Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged

2014-07-10

In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We appliedmore » the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems.« less

Sensitive periods in affective development: nonlinear maturation of fear learning.

PubMed

Hartley, Catherine A; Lee, Francis S

2015-01-01

At specific maturational stages, neural circuits enter sensitive periods of heightened plasticity, during which the development of both brain and behavior are highly receptive to particular experiential information. A relatively advanced understanding of the regulatory mechanisms governing the initiation, closure, and reinstatement of sensitive period plasticity has emerged from extensive research examining the development of the visual system. In this article, we discuss a large body of work characterizing the pronounced nonlinear changes in fear learning and extinction that occur from childhood through adulthood, and their underlying neural substrates. We draw upon the model of sensitive period regulation within the visual system, and present burgeoning evidence suggesting that parallel mechanisms may regulate the qualitative changes in fear learning across development.

Sensitive Periods in Affective Development: Nonlinear Maturation of Fear Learning

PubMed Central

Hartley, Catherine A; Lee, Francis S

2015-01-01

At specific maturational stages, neural circuits enter sensitive periods of heightened plasticity, during which the development of both brain and behavior are highly receptive to particular experiential information. A relatively advanced understanding of the regulatory mechanisms governing the initiation, closure, and reinstatement of sensitive period plasticity has emerged from extensive research examining the development of the visual system. In this article, we discuss a large body of work characterizing the pronounced nonlinear changes in fear learning and extinction that occur from childhood through adulthood, and their underlying neural substrates. We draw upon the model of sensitive period regulation within the visual system, and present burgeoning evidence suggesting that parallel mechanisms may regulate the qualitative changes in fear learning across development. PMID:25035083

Document analysis with neural net circuits

NASA Technical Reports Server (NTRS)

Graf, Hans Peter

1994-01-01

Document analysis is one of the main applications of machine vision today and offers great opportunities for neural net circuits. Despite more and more data processing with computers, the number of paper documents is still increasing rapidly. A fast translation of data from paper into electronic format is needed almost everywhere, and when done manually, this is a time consuming process. Markets range from small scanners for personal use to high-volume document analysis systems, such as address readers for the postal service or check processing systems for banks. A major concern with present systems is the accuracy of the automatic interpretation. Today's algorithms fail miserably when noise is present, when print quality is poor, or when the layout is complex. A common approach to circumvent these problems is to restrict the variations of the documents handled by a system. In our laboratory, we had the best luck with circuits implementing basic functions, such as convolutions, that can be used in many different algorithms. To illustrate the flexibility of this approach, three applications of the NET32K circuit are described in this short viewgraph presentation: locating address blocks, cleaning document images by removing noise, and locating areas of interest in personal checks to improve image compression. Several of the ideas realized in this circuit that were inspired by neural nets, such as analog computation with a low resolution, resulted in a chip that is well suited for real-world document analysis applications and that compares favorably with alternative, 'conventional' circuits.

The Pleiotropic MET Receptor Network: Circuit Development and the Neural-Medical Interface of Autism

PubMed Central

Eagleson, Kathie L.; Xie, Zhihui; Levitt, Pat

2016-01-01

People with autism spectrum disorder (ASD) and other neurodevelopmental disorders (NDDs) are behaviorally and medically heterogeneous. The combination of polygenicity and gene pleiotropy - the influence of one gene on distinct phenotypes - raises questions of how specific genes and their protein products interact to contribute to NDDs. A preponderance of evidence supports developmental and pathophysiological roles for the MET receptor tyrosine kinase, a multi-functional receptor that mediates distinct biological responses depending upon cell context. MET influences neuron architecture and synapse maturation in the forebrain, and regulates homeostasis in gastrointestinal and immune systems, both commonly disrupted in NDDs. Peak expression of synapse-enriched MET is conserved across rodent and primate forebrain, yet regional differences in primate neocortex are pronounced, with enrichment in circuits that participate in social information processing. A functional risk allele in the MET promoter, enriched in subgroups of children with ASD, reduces transcription and disrupts socially-relevant neural circuits structurally and functionally. In mice, circuit-specific deletion of Met causes distinct atypical behaviors. MET activation increases dendritic complexity and nascent synapse number, but synapse maturation requires reductions in MET. MET mediates its specific biological effects through different intracellular signaling pathways, and has a complex protein interactome that is enriched in ASD and other NDD candidates. The interactome is co-regulated in developing human neocortex. We suggest that a gene as pleiotropic and highly regulated as MET, together with its interactome, is biologically relevant in normal and pathophysiological contexts, impacting central and peripheral phenotypes that contribute to NDD risk and clinical symptoms. PMID:27837921

An Implantable Wireless Neural Interface for Recording Cortical Circuit Dynamics in Moving Primates

PubMed Central

Borton, David A.; Yin, Ming; Aceros, Juan; Nurmikko, Arto

2013-01-01

Objective Neural interface technology suitable for clinical translation has the potential to significantly impact the lives of amputees, spinal cord injury victims, and those living with severe neuromotor disease. Such systems must be chronically safe, durable, and effective. Approach We have designed and implemented a neural interface microsystem, housed in a compact, subcutaneous, and hermetically sealed titanium enclosure. The implanted device interfaces the brain with a 510k-approved, 100-element silicon-based MEA via a custom hermetic feedthrough design. Full spectrum neural signals were amplified (0.1Hz to 7.8kHz, ×200 gain) and multiplexed by a custom application specific integrated circuit, digitized, and then packaged for transmission. The neural data (24 Mbps) was transmitted by a wireless data link carried on an frequency shift key modulated signal at 3.2GHz and 3.8GHz to a receiver 1 meter away by design as a point-to-point communication link for human clinical use. The system was powered by an embedded medical grade rechargeable Li-ion battery for 7-hour continuous operation between recharge via an inductive transcutaneous wireless power link at 2MHz. Main results Device verification and early validation was performed in both swine and non-human primate freely-moving animal models and showed that the wireless implant was electrically stable, effective in capturing and delivering broadband neural data, and safe for over one year of testing. In addition, we have used the multichannel data from these mobile animal models to demonstrate the ability to decode neural population dynamics associated with motor activity. Significance We have developed an implanted wireless broadband neural recording device evaluated in non-human primate and swine. The use of this new implantable neural interface technology can provide insight on how to advance human neuroprostheses beyond the present early clinical trials. Further, such tools enable mobile patient use, have the potential for wider diagnosis of neurological conditions, and will advance brain research. PMID:23428937

An implantable wireless neural interface for recording cortical circuit dynamics in moving primates

NASA Astrophysics Data System (ADS)

Borton, David A.; Yin, Ming; Aceros, Juan; Nurmikko, Arto

2013-04-01

Objective. Neural interface technology suitable for clinical translation has the potential to significantly impact the lives of amputees, spinal cord injury victims and those living with severe neuromotor disease. Such systems must be chronically safe, durable and effective. Approach. We have designed and implemented a neural interface microsystem, housed in a compact, subcutaneous and hermetically sealed titanium enclosure. The implanted device interfaces the brain with a 510k-approved, 100-element silicon-based microelectrode array via a custom hermetic feedthrough design. Full spectrum neural signals were amplified (0.1 Hz to 7.8 kHz, 200× gain) and multiplexed by a custom application specific integrated circuit, digitized and then packaged for transmission. The neural data (24 Mbps) were transmitted by a wireless data link carried on a frequency-shift-key-modulated signal at 3.2 and 3.8 GHz to a receiver 1 m away by design as a point-to-point communication link for human clinical use. The system was powered by an embedded medical grade rechargeable Li-ion battery for 7 h continuous operation between recharge via an inductive transcutaneous wireless power link at 2 MHz. Main results. Device verification and early validation were performed in both swine and non-human primate freely-moving animal models and showed that the wireless implant was electrically stable, effective in capturing and delivering broadband neural data, and safe for over one year of testing. In addition, we have used the multichannel data from these mobile animal models to demonstrate the ability to decode neural population dynamics associated with motor activity. Significance. We have developed an implanted wireless broadband neural recording device evaluated in non-human primate and swine. The use of this new implantable neural interface technology can provide insight into how to advance human neuroprostheses beyond the present early clinical trials. Further, such tools enable mobile patient use, have the potential for wider diagnosis of neurological conditions and will advance brain research.

An Integrated Circuit for Simultaneous Extracellular Electrophysiology Recording and Optogenetic Neural Manipulation.

PubMed

Chen, Chang Hao; McCullagh, Elizabeth A; Pun, Sio Hang; Mak, Peng Un; Vai, Mang I; Mak, Pui In; Klug, Achim; Lei, Tim C

2017-03-01

The ability to record and to control action potential firing in neuronal circuits is critical to understand how the brain functions. The objective of this study is to develop a monolithic integrated circuit (IC) to record action potentials and simultaneously control action potential firing using optogenetics. A low-noise and high input impedance (or low input capacitance) neural recording amplifier is combined with a high current laser/light-emitting diode (LED) driver in a single IC. The low input capacitance of the amplifier (9.7 pF) was achieved by adding a dedicated unity gain stage optimized for high impedance metal electrodes. The input referred noise of the amplifier is [Formula: see text], which is lower than the estimated thermal noise of the metal electrode. Thus, the action potentials originating from a single neuron can be recorded with a signal-to-noise ratio of at least 6.6. The LED/laser current driver delivers a maximum current of 330 mA, which is adequate for optogenetic control. The functionality of the IC was tested with an anesthetized Mongolian gerbil and auditory stimulated action potentials were recorded from the inferior colliculus. Spontaneous firings of fifth (trigeminal) nerve fibers were also inhibited using the optogenetic protein Halorhodopsin. Moreover, a noise model of the system was derived to guide the design. A single IC to measure and control action potentials using optogenetic proteins is realized so that more complicated behavioral neuroscience research and the translational neural disorder treatments become possible in the future.

Connecting a Connectome to Behavior: An Ensemble of Neuroanatomical Models of C. elegans Klinotaxis

PubMed Central

Izquierdo, Eduardo J.; Beer, Randall D.

2013-01-01

Increased efforts in the assembly and analysis of connectome data are providing new insights into the principles underlying the connectivity of neural circuits. However, despite these considerable advances in connectomics, neuroanatomical data must be integrated with neurophysiological and behavioral data in order to obtain a complete picture of neural function. Due to its nearly complete wiring diagram and large behavioral repertoire, the nematode worm Caenorhaditis elegans is an ideal organism in which to explore in detail this link between neural connectivity and behavior. In this paper, we develop a neuroanatomically-grounded model of salt klinotaxis, a form of chemotaxis in which changes in orientation are directed towards the source through gradual continual adjustments. We identify a minimal klinotaxis circuit by systematically searching the C. elegans connectome for pathways linking chemosensory neurons to neck motor neurons, and prune the resulting network based on both experimental considerations and several simplifying assumptions. We then use an evolutionary algorithm to find possible values for the unknown electrophsyiological parameters in the network such that the behavioral performance of the entire model is optimized to match that of the animal. Multiple runs of the evolutionary algorithm produce an ensemble of such models. We analyze in some detail the mechanisms by which one of the best evolved circuits operates and characterize the similarities and differences between this mechanism and other solutions in the ensemble. Finally, we propose a series of experiments to determine which of these alternatives the worm may be using. PMID:23408877

Lighting up the brain's reward circuitry.

PubMed

Lobo, Mary Kay

2012-07-01

The brain's reward circuit is critical for mediating natural reward behaviors including food, sex, and social interaction. Drugs of abuse take over this circuit and produce persistent molecular and cellular alterations in the brain regions and their neural circuitry that make up the reward pathway. Recent use of optogenetic technologies has provided novel insights into the functional and molecular role of the circuitry and cell subtypes within these circuits that constitute this pathway. This perspective will address the current and future use of light-activated proteins, including those involved in modulating neuronal activity, cellular signaling, and molecular properties in the neural circuitry mediating rewarding stimuli and maladaptive responses to drugs of abuse. © 2012 New York Academy of Sciences.

Large scale in vivo recordings to study neuronal biophysics.

PubMed

Giocomo, Lisa M

2015-06-01

Over the last several years, technological advances have enabled researchers to more readily observe single-cell membrane biophysics in awake, behaving animals. Studies utilizing these technologies have provided important insights into the mechanisms generating functional neural codes in both sensory and non-sensory cortical circuits. Crucial for a deeper understanding of how membrane biophysics control circuit dynamics however, is a continued effort to move toward large scale studies of membrane biophysics, in terms of the numbers of neurons and ion channels examined. Future work faces a number of theoretical and technical challenges on this front but recent technological developments hold great promise for a larger scale understanding of how membrane biophysics contribute to circuit coding and computation. Copyright © 2014 Elsevier Ltd. All rights reserved.

Integrated circuits and molecular components for stress and feeding: implications for eating disorders

PubMed Central

Hardaway, J. A.; Crowley, N. A.; Bulik, C. M.; Kash, T. L.

2015-01-01

Eating disorders are complex brain disorders that afflict millions of individuals worldwide. The etiology of these diseases is not fully understood, but a growing body of literature suggests that stress and anxiety may play a critical role in their development. As our understanding of the genetic and environmental factors that contribute to disease in clinical populations like anorexia nervosa, bulimia nervosa and binge eating disorder continue to grow, neuroscientists are using animal models to understand the neurobiology of stress and feeding. We hypothesize that eating disorder clinical phenotypes may result from stress-induced maladaptive alterations in neural circuits that regulate feeding, and that these circuits can be neurochemically isolated using animal model of eating disorders. PMID:25366309

Cascade Back-Propagation Learning in Neural Networks

NASA Technical Reports Server (NTRS)

Duong, Tuan A.

2003-01-01

The cascade back-propagation (CBP) algorithm is the basis of a conceptual design for accelerating learning in artificial neural networks. The neural networks would be implemented as analog very-large-scale integrated (VLSI) circuits, and circuits to implement the CBP algorithm would be fabricated on the same VLSI circuit chips with the neural networks. Heretofore, artificial neural networks have learned slowly because it has been necessary to train them via software, for lack of a good on-chip learning technique. The CBP algorithm is an on-chip technique that provides for continuous learning in real time. Artificial neural networks are trained by example: A network is presented with training inputs for which the correct outputs are known, and the algorithm strives to adjust the weights of synaptic connections in the network to make the actual outputs approach the correct outputs. The input data are generally divided into three parts. Two of the parts, called the "training" and "cross-validation" sets, respectively, must be such that the corresponding input/output pairs are known. During training, the cross-validation set enables verification of the status of the input-to-output transformation learned by the network to avoid over-learning. The third part of the data, termed the "test" set, consists of the inputs that are required to be transformed into outputs; this set may or may not include the training set and/or the cross-validation set. Proposed neural-network circuitry for on-chip learning would be divided into two distinct networks; one for training and one for validation. Both networks would share the same synaptic weights.

The Role of the Medial Prefrontal Cortex in Trace Fear Extinction

ERIC Educational Resources Information Center

Kwapis, Janine L.; Jarome, Timothy J.; Helmstetter, Fred J.

2015-01-01

The extinction of delay fear conditioning relies on a neural circuit that has received much attention and is relatively well defined. Whether this established circuit also supports the extinction of more complex associations, however, is unclear. Trace fear conditioning is a better model of complex relational learning, yet the circuit that…

A computational model for epidural electrical stimulation of spinal sensorimotor circuits.

PubMed

Capogrosso, Marco; Wenger, Nikolaus; Raspopovic, Stanisa; Musienko, Pavel; Beauparlant, Janine; Bassi Luciani, Lorenzo; Courtine, Grégoire; Micera, Silvestro

2013-12-04

Epidural electrical stimulation (EES) of lumbosacral segments can restore a range of movements after spinal cord injury. However, the mechanisms and neural structures through which EES facilitates movement execution remain unclear. Here, we designed a computational model and performed in vivo experiments to investigate the type of fibers, neurons, and circuits recruited in response to EES. We first developed a realistic finite element computer model of rat lumbosacral segments to identify the currents generated by EES. To evaluate the impact of these currents on sensorimotor circuits, we coupled this model with an anatomically realistic axon-cable model of motoneurons, interneurons, and myelinated afferent fibers for antagonistic ankle muscles. Comparisons between computer simulations and experiments revealed the ability of the model to predict EES-evoked motor responses over multiple intensities and locations. Analysis of the recruited neural structures revealed the lack of direct influence of EES on motoneurons and interneurons. Simulations and pharmacological experiments demonstrated that EES engages spinal circuits trans-synaptically through the recruitment of myelinated afferent fibers. The model also predicted the capacity of spatially distinct EES to modulate side-specific limb movements and, to a lesser extent, extension versus flexion. These predictions were confirmed during standing and walking enabled by EES in spinal rats. These combined results provide a mechanistic framework for the design of spinal neuroprosthetic systems to improve standing and walking after neurological disorders.

Information Flow through a Model of the C. elegans Klinotaxis Circuit

PubMed Central

Izquierdo, Eduardo J.; Williams, Paul L.; Beer, Randall D.

2015-01-01

Understanding how information about external stimuli is transformed into behavior is one of the central goals of neuroscience. Here we characterize the information flow through a complete sensorimotor circuit: from stimulus, to sensory neurons, to interneurons, to motor neurons, to muscles, to motion. Specifically, we apply a recently developed framework for quantifying information flow to a previously published ensemble of models of salt klinotaxis in the nematode worm Caenorhabditis elegans. Despite large variations in the neural parameters of individual circuits, we found that the overall information flow architecture circuit is remarkably consistent across the ensemble. This suggests structural connectivity is not necessarily predictive of effective connectivity. It also suggests information flow analysis captures general principles of operation for the klinotaxis circuit. In addition, information flow analysis reveals several key principles underlying how the models operate: (1) Interneuron class AIY is responsible for integrating information about positive and negative changes in concentration, and exhibits a strong left/right information asymmetry. (2) Gap junctions play a crucial role in the transfer of information responsible for the information symmetry observed in interneuron class AIZ. (3) Neck motor neuron class SMB implements an information gating mechanism that underlies the circuit’s state-dependent response. (4) The neck carries more information about small changes in concentration than about large ones, and more information about positive changes in concentration than about negative ones. Thus, not all directions of movement are equally informative for the worm. Each of these findings corresponds to hypotheses that could potentially be tested in the worm. Knowing the results of these experiments would greatly refine our understanding of the neural circuit underlying klinotaxis. PMID:26465883

Encoding neural and synaptic functionalities in electron spin: A pathway to efficient neuromorphic computing

NASA Astrophysics Data System (ADS)

Sengupta, Abhronil; Roy, Kaushik

2017-12-01

Present day computers expend orders of magnitude more computational resources to perform various cognitive and perception related tasks that humans routinely perform every day. This has recently resulted in a seismic shift in the field of computation where research efforts are being directed to develop a neurocomputer that attempts to mimic the human brain by nanoelectronic components and thereby harness its efficiency in recognition problems. Bridging the gap between neuroscience and nanoelectronics, this paper attempts to provide a review of the recent developments in the field of spintronic device based neuromorphic computing. Description of various spin-transfer torque mechanisms that can be potentially utilized for realizing device structures mimicking neural and synaptic functionalities is provided. A cross-layer perspective extending from the device to the circuit and system level is presented to envision the design of an All-Spin neuromorphic processor enabled with on-chip learning functionalities. Device-circuit-algorithm co-simulation framework calibrated to experimental results suggest that such All-Spin neuromorphic systems can potentially achieve almost two orders of magnitude energy improvement in comparison to state-of-the-art CMOS implementations.

Using high-throughput barcode sequencing to efficiently map connectomes.

PubMed

Peikon, Ian D; Kebschull, Justus M; Vagin, Vasily V; Ravens, Diana I; Sun, Yu-Chi; Brouzes, Eric; Corrêa, Ivan R; Bressan, Dario; Zador, Anthony M

2017-07-07

The function of a neural circuit is determined by the details of its synaptic connections. At present, the only available method for determining a neural wiring diagram with single synapse precision-a 'connectome'-is based on imaging methods that are slow, labor-intensive and expensive. Here, we present SYNseq, a method for converting the connectome into a form that can exploit the speed and low cost of modern high-throughput DNA sequencing. In SYNseq, each neuron is labeled with a unique random nucleotide sequence-an RNA 'barcode'-which is targeted to the synapse using engineered proteins. Barcodes in pre- and postsynaptic neurons are then associated through protein-protein crosslinking across the synapse, extracted from the tissue, and joined into a form suitable for sequencing. Although our failure to develop an efficient barcode joining scheme precludes the widespread application of this approach, we expect that with further development SYNseq will enable tracing of complex circuits at high speed and low cost. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Computational modeling of stuttering caused by impairments in a basal ganglia thalamo-cortical circuit involved in syllable selection and initiation

PubMed Central

Civier, Oren; Bullock, Daniel; Max, Ludo; Guenther, Frank H.

2013-01-01

A typical white-matter integrity and elevated dopamine levels have been reported for individuals who stutter. We investigated how such abnormalities may lead to speech dysfluencies due to their effects on a syllable-sequencing circuit that consists of basal ganglia (BG), thalamus, and left ventral premotor cortex (vPMC). “Neurally impaired” versions of the neurocomputational speech production model GODIVA were utilized to test two hypotheses: (1) that white-matter abnormalities disturb the circuit via corticostriatal projections carrying copies of executed motor commands, and (2) that dopaminergic abnormalities disturb the circuit via the striatum. Simulation results support both hypotheses: in both scenarios, the neural abnormalities delay readout of the next syllable’s motor program, leading to dysfluency. The results also account for brain imaging findings during dysfluent speech. It is concluded that each of the two abnormality types can cause stuttering moments, probably by affecting the same BG-thalamus-vPMC circuit. PMID:23872286

From Whole-Brain Data to Functional Circuit Models: The Zebrafish Optomotor Response.

PubMed

Naumann, Eva A; Fitzgerald, James E; Dunn, Timothy W; Rihel, Jason; Sompolinsky, Haim; Engert, Florian

2016-11-03

Detailed descriptions of brain-scale sensorimotor circuits underlying vertebrate behavior remain elusive. Recent advances in zebrafish neuroscience offer new opportunities to dissect such circuits via whole-brain imaging, behavioral analysis, functional perturbations, and network modeling. Here, we harness these tools to generate a brain-scale circuit model of the optomotor response, an orienting behavior evoked by visual motion. We show that such motion is processed by diverse neural response types distributed across multiple brain regions. To transform sensory input into action, these regions sequentially integrate eye- and direction-specific sensory streams, refine representations via interhemispheric inhibition, and demix locomotor instructions to independently drive turning and forward swimming. While experiments revealed many neural response types throughout the brain, modeling identified the dimensions of functional connectivity most critical for the behavior. We thus reveal how distributed neurons collaborate to generate behavior and illustrate a paradigm for distilling functional circuit models from whole-brain data. Copyright © 2016 Elsevier Inc. All rights reserved.

Delineating the Diversity of Spinal Interneurons in Locomotor Circuits.

PubMed

Gosgnach, Simon; Bikoff, Jay B; Dougherty, Kimberly J; El Manira, Abdeljabbar; Lanuza, Guillermo M; Zhang, Ying

2017-11-08

Locomotion is common to all animals and is essential for survival. Neural circuits located in the spinal cord have been shown to be necessary and sufficient for the generation and control of the basic locomotor rhythm by activating muscles on either side of the body in a specific sequence. Activity in these neural circuits determines the speed, gait pattern, and direction of movement, so the specific locomotor pattern generated relies on the diversity of the neurons within spinal locomotor circuits. Here, we review findings demonstrating that developmental genetics can be used to identify populations of neurons that comprise these circuits and focus on recent work indicating that many of these populations can be further subdivided into distinct subtypes, with each likely to play complementary functions during locomotion. Finally, we discuss data describing the manner in which these populations interact with each other to produce efficient, task-dependent locomotion. Copyright © 2017 the authors 0270-6474/17/3710835-07$15.00/0.

Advances in color science: from retina to behavior

PubMed Central

Chatterjee, Soumya; Field, Greg D.; Horwitz, Gregory D.; Johnson, Elizabeth N.; Koida, Kowa; Mancuso, Katherine

2010-01-01

Color has become a premier model system for understanding how information is processed by neural circuits, and for investigating the relationships among genes, neural circuits and perception. Both the physical stimulus for color and the perceptual output experienced as color are quite well characterized, but the neural mechanisms that underlie the transformation from stimulus to perception are incompletely understood. The past several years have seen important scientific and technical advances that are changing our understanding of these mechanisms. Here, and in the accompanying minisymposium, we review the latest findings and hypotheses regarding color computations in the retina, primary visual cortex and higher-order visual areas, focusing on non-human primates, a model of human color vision. PMID:21068298

Doubly stochastic Poisson processes in artificial neural learning.

PubMed

Card, H C

1998-01-01

This paper investigates neuron activation statistics in artificial neural networks employing stochastic arithmetic. It is shown that a doubly stochastic Poisson process is an appropriate model for the signals in these circuits.

The neural basis of financial risk taking.

PubMed

Kuhnen, Camelia M; Knutson, Brian

2005-09-01

Investors systematically deviate from rationality when making financial decisions, yet the mechanisms responsible for these deviations have not been identified. Using event-related fMRI, we examined whether anticipatory neural activity would predict optimal and suboptimal choices in a financial decision-making task. We characterized two types of deviations from the optimal investment strategy of a rational risk-neutral agent as risk-seeking mistakes and risk-aversion mistakes. Nucleus accumbens activation preceded risky choices as well as risk-seeking mistakes, while anterior insula activation preceded riskless choices as well as risk-aversion mistakes. These findings suggest that distinct neural circuits linked to anticipatory affect promote different types of financial choices and indicate that excessive activation of these circuits may lead to investing mistakes. Thus, consideration of anticipatory neural mechanisms may add predictive power to the rational actor model of economic decision making.

Digital micromirror based near-infrared illumination system for plasmonic photothermal neuromodulation.

PubMed

Jung, Hyunjun; Kang, Hongki; Nam, Yoonkey

2017-06-01

Light-mediated neuromodulation techniques provide great advantages to investigate neuroscience due to its high spatial and temporal resolution. To generate a spatial pattern of neural activity, it is necessary to develop a system for patterned-light illumination to a specific area. Digital micromirror device (DMD) based patterned illumination system have been used for neuromodulation due to its simple configuration and design flexibility. In this paper, we developed a patterned near-infrared (NIR) illumination system for region specific photothermal manipulation of neural activity using NIR-sensitive plasmonic gold nanorods (GNRs). The proposed system had high power transmission efficiency for delivering power density up to 19 W/mm 2 . We used a GNR-coated microelectrode array (MEA) to perform biological experiments using E18 rat hippocampal neurons and showed that it was possible to inhibit neural spiking activity of specific area in neural circuits with the patterned NIR illumination. This patterned NIR illumination system can serve as a promising neuromodulation tool to investigate neuroscience in a wide range of physiological and clinical applications.

Community-based benchmarking improves spike rate inference from two-photon calcium imaging data.

PubMed

Berens, Philipp; Freeman, Jeremy; Deneux, Thomas; Chenkov, Nikolay; McColgan, Thomas; Speiser, Artur; Macke, Jakob H; Turaga, Srinivas C; Mineault, Patrick; Rupprecht, Peter; Gerhard, Stephan; Friedrich, Rainer W; Friedrich, Johannes; Paninski, Liam; Pachitariu, Marius; Harris, Kenneth D; Bolte, Ben; Machado, Timothy A; Ringach, Dario; Stone, Jasmine; Rogerson, Luke E; Sofroniew, Nicolas J; Reimer, Jacob; Froudarakis, Emmanouil; Euler, Thomas; Román Rosón, Miroslav; Theis, Lucas; Tolias, Andreas S; Bethge, Matthias

2018-05-01

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

A comparative examination of neural circuit and brain patterning between the lamprey and amphioxus reveals the evolutionary origin of the vertebrate visual center.

PubMed

Suzuki, Daichi G; Murakami, Yasunori; Escriva, Hector; Wada, Hiroshi

2015-02-01

Vertebrates are equipped with so-called camera eyes, which provide them with image-forming vision. Vertebrate image-forming vision evolved independently from that of other animals and is regarded as a key innovation for enhancing predatory ability and ecological success. Evolutionary changes in the neural circuits, particularly the visual center, were central for the acquisition of image-forming vision. However, the evolutionary steps, from protochordates to jaw-less primitive vertebrates and then to jawed vertebrates, remain largely unknown. To bridge this gap, we present the detailed development of retinofugal projections in the lamprey, the neuroarchitecture in amphioxus, and the brain patterning in both animals. Both the lateral eye in larval lamprey and the frontal eye in amphioxus project to a light-detecting visual center in the caudal prosencephalic region marked by Pax6, which possibly represents the ancestral state of the chordate visual system. Our results indicate that the visual system of the larval lamprey represents an evolutionarily primitive state, forming a link from protochordates to vertebrates and providing a new perspective of brain evolution based on developmental mechanisms and neural functions. © 2014 Wiley Periodicals, Inc.

Neural net diagnostics for VLSI test

NASA Technical Reports Server (NTRS)

Lin, T.; Tseng, H.; Wu, A.; Dogan, N.; Meador, J.

1990-01-01

This paper discusses the application of neural network pattern analysis algorithms to the IC fault diagnosis problem. A fault diagnostic is a decision rule combining what is known about an ideal circuit test response with information about how it is distorted by fabrication variations and measurement noise. The rule is used to detect fault existence in fabricated circuits using real test equipment. Traditional statistical techniques may be used to achieve this goal, but they can employ unrealistic a priori assumptions about measurement data. Our approach to this problem employs an adaptive pattern analysis technique based on feedforward neural networks. During training, a feedforward network automatically captures unknown sample distributions. This is important because distributions arising from the nonlinear effects of process variation can be more complex than is typically assumed. A feedforward network is also able to extract measurement features which contribute significantly to making a correct decision. Traditional feature extraction techniques employ matrix manipulations which can be particularly costly for large measurement vectors. In this paper we discuss a software system which we are developing that uses this approach. We also provide a simple example illustrating the use of the technique for fault detection in an operational amplifier.

The Principle of the Micro-Electronic Neural Bridge and a Prototype System Design.

PubMed

Huang, Zong-Hao; Wang, Zhi-Gong; Lu, Xiao-Ying; Li, Wen-Yuan; Zhou, Yu-Xuan; Shen, Xiao-Yan; Zhao, Xin-Tai

2016-01-01

The micro-electronic neural bridge (MENB) aims to rebuild lost motor function of paralyzed humans by routing movement-related signals from the brain, around the damage part in the spinal cord, to the external effectors. This study focused on the prototype system design of the MENB, including the principle of the MENB, the neural signal detecting circuit and the functional electrical stimulation (FES) circuit design, and the spike detecting and sorting algorithm. In this study, we developed a novel improved amplitude threshold spike detecting method based on variable forward difference threshold for both training and bridging phase. The discrete wavelet transform (DWT), a new level feature coefficient selection method based on Lilliefors test, and the k-means clustering method based on Mahalanobis distance were used for spike sorting. A real-time online spike detecting and sorting algorithm based on DWT and Euclidean distance was also implemented for the bridging phase. Tested by the data sets available at Caltech, in the training phase, the average sensitivity, specificity, and clustering accuracies are 99.43%, 97.83%, and 95.45%, respectively. Validated by the three-fold cross-validation method, the average sensitivity, specificity, and classification accuracy are 99.43%, 97.70%, and 96.46%, respectively.

Structural and functional maturation of the developing primate brain.

PubMed

Levitt, Pat

2003-10-01

Descriptive studies have established that the developmental events responsible for the assembly of neural systems and circuitry are conserved across mammalian species. However, primates are unique regarding the time during which histogenesis occurs and the extended postnatal period during which myelination of pathways and circuitry formation occur and are then subsequently modified, particularly in the cerebral cortex. As in lower mammals, the framework for subcortical-cortical connectivity in primates is established before midgestation and already begins to remodel before birth. Association systems, responsible for modulating intracortical circuits that integrate information across functional domains, also form before birth, but their growth and reorganization extend into puberty. There are substantial differences across species in the patterns of development of specific neurochemical systems. The complexity is even greater when considering that the development of any particular cellular component may differ among cortical areas in the same primate species. Developmental and behavioral neurobiologists, psychologists, and pediatricians are challenged with understanding how functional maturation relates to the evolving anatomical organization of the human brain during childhood, and moreover, how genetic and environmental perturbations affect the adaptive changes exhibited by neural circuits in response to developmental disruption.

Network architectures and circuit function: testing alternative hypotheses in multifunctional networks.

PubMed

Leonard, J L

2000-05-01

Understanding how species-typical movement patterns are organized in the nervous system is a central question in neurobiology. The current explanations involve 'alphabet' models in which an individual neuron may participate in the circuit for several behaviors but each behavior is specified by a specific neural circuit. However, not all of the well-studied model systems fit the 'alphabet' model. The 'equation' model provides an alternative possibility, whereby a system of parallel motor neurons, each with a unique (but overlapping) field of innervation, can account for the production of stereotyped behavior patterns by variable circuits. That is, it is possible for such patterns to arise as emergent properties of a generalized neural network in the absence of feedback, a simple version of a 'self-organizing' behavioral system. Comparison of systems of identified neurons suggest that the 'alphabet' model may account for most observations where CPGs act to organize motor patterns. Other well-known model systems, involving architectures corresponding to feed-forward neural networks with a hidden layer, may organize patterned behavior in a manner consistent with the 'equation' model. Such architectures are found in the Mauthner and reticulospinal circuits, 'escape' locomotion in cockroaches, CNS control of Aplysia gill, and may also be important in the coordination of sensory information and motor systems in insect mushroom bodies and the vertebrate hippocampus. The hidden layer of such networks may serve as an 'internal representation' of the behavioral state and/or body position of the animal, allowing the animal to fine-tune oriented, or particularly context-sensitive, movements to the prevalent conditions. Experiments designed to distinguish between the two models in cases where they make mutually exclusive predictions provide an opportunity to elucidate the neural mechanisms by which behavior is organized in vivo and in vitro. Copyright 2000 S. Karger AG, Basel

A simple structure wavelet transform circuit employing function link neural networks and SI filters

NASA Astrophysics Data System (ADS)

Mu, Li; Yigang, He

2016-12-01

Signal processing by means of analog circuits offers advantages from a power consumption viewpoint. Implementing wavelet transform (WT) using analog circuits is of great interest when low-power consumption becomes an important issue. In this article, a novel simple structure WT circuit in analog domain is presented by employing functional link neural network (FLNN) and switched-current (SI) filters. First, the wavelet base is approximated using FLNN algorithms for giving a filter transfer function that is suitable for simple structure WT circuit implementation. Next, the WT circuit is constructed with the wavelet filter bank, whose impulse response is the approximated wavelet and its dilations. The filter design that follows is based on a follow-the-leader feedback (FLF) structure with multiple output bilinear SI integrators and current mirrors as the main building blocks. SI filter is well suited for this application since the dilation constant across different scales of the transform can be precisely implemented and controlled by the clock frequency of the circuit with the same system architecture. Finally, to illustrate the design procedure, a seventh-order FLNN-approximated Gaussian wavelet is implemented as an example. Simulations have successfully verified that the designed simple structure WT circuit has low sensitivity, low-power consumption and litter effect to the imperfections.

Progress in understanding mood disorders: optogenetic dissection of neural circuits.

PubMed

Lammel, S; Tye, K M; Warden, M R

2014-01-01

Major depression is characterized by a cluster of symptoms that includes hopelessness, low mood, feelings of worthlessness and inability to experience pleasure. The lifetime prevalence of major depression approaches 20%, yet current treatments are often inadequate both because of associated side effects and because they are ineffective for many people. In basic research, animal models are often used to study depression. Typically, experimental animals are exposed to acute or chronic stress to generate a variety of depression-like symptoms. Despite its clinical importance, very little is known about the cellular and neural circuits that mediate these symptoms. Recent advances in circuit-targeted approaches have provided new opportunities to study the neuropathology of mood disorders such as depression and anxiety. We review recent progress and highlight some studies that have begun tracing a functional neuronal circuit diagram that may prove essential in establishing novel treatment strategies in mood disorders. First, we shed light on the complexity of mesocorticolimbic dopamine (DA) responses to stress by discussing two recent studies reporting that optogenetic activation of midbrain DA neurons can induce or reverse depression-related behaviors. Second, we describe the role of the lateral habenula circuitry in the pathophysiology of depression. Finally, we discuss how the prefrontal cortex controls limbic and neuromodulatory circuits in mood disorders. © 2013 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

Dysfunctional Autism Risk Genes Cause Circuit-Specific Connectivity Deficits With Distinct Developmental Trajectories

PubMed Central

Ielacqua, Giovanna D; Markicevic, Marija; Haberl, Matthias Georg; Ellisman, Mark H; A-Bhaskaran, Arjun; Frick, Andreas; Rudin, Markus; Wenderoth, Nicole

2018-01-01

Abstract Autism spectrum disorders (ASD) are a set of complex neurodevelopmental disorders for which there is currently no targeted therapeutic approach. It is thought that alterations of genes regulating migration and synapse formation during development affect neural circuit formation and result in aberrant connectivity within distinct circuits that underlie abnormal behaviors. However, it is unknown whether deviant developmental trajectories are circuit-specific for a given autism risk-gene. We used MRI to probe changes in functional and structural connectivity from childhood to adulthood in Fragile-X (Fmr1−/y) and contactin-associated (CNTNAP2−/−) knockout mice. Young Fmr1−/y mice (30 days postnatal) presented with a robust hypoconnectivity phenotype in corticocortico and corticostriatal circuits in areas associated with sensory information processing, which was maintained until adulthood. Conversely, only small differences in hippocampal and striatal areas were present during early postnatal development in CNTNAP2−/− mice, while major connectivity deficits in prefrontal and limbic pathways developed between adolescence and adulthood. These findings are supported by viral tracing and electron micrograph approaches and define 2 clearly distinct connectivity endophenotypes within the autism spectrum. We conclude that the genetic background of ASD strongly influences which circuits are most affected, the nature of the phenotype, and the developmental time course of the associated changes. PMID:29901787

Dendritic nonlinearities are tuned for efficient spike-based computations in cortical circuits.

PubMed

Ujfalussy, Balázs B; Makara, Judit K; Branco, Tiago; Lengyel, Máté

2015-12-24

Cortical neurons integrate thousands of synaptic inputs in their dendrites in highly nonlinear ways. It is unknown how these dendritic nonlinearities in individual cells contribute to computations at the level of neural circuits. Here, we show that dendritic nonlinearities are critical for the efficient integration of synaptic inputs in circuits performing analog computations with spiking neurons. We developed a theory that formalizes how a neuron's dendritic nonlinearity that is optimal for integrating synaptic inputs depends on the statistics of its presynaptic activity patterns. Based on their in vivo preynaptic population statistics (firing rates, membrane potential fluctuations, and correlations due to ensemble dynamics), our theory accurately predicted the responses of two different types of cortical pyramidal cells to patterned stimulation by two-photon glutamate uncaging. These results reveal a new computational principle underlying dendritic integration in cortical neurons by suggesting a functional link between cellular and systems--level properties of cortical circuits.

Spatial gradients and multidimensional dynamics in a neural integrator circuit

PubMed Central

Miri, Andrew; Daie, Kayvon; Arrenberg, Aristides B.; Baier, Herwig; Aksay, Emre; Tank, David W.

2011-01-01

In a neural integrator, the variability and topographical organization of neuronal firing rate persistence can provide information about the circuit’s functional architecture. Here we use optical recording to measure the time constant of decay of persistent firing (“persistence time”) across a population of neurons comprising the larval zebrafish oculomotor velocity-to-position neural integrator. We find extensive persistence time variation (10-fold; coefficients of variation 0.58–1.20) across cells within individual larvae. We also find that the similarity in firing between two neurons decreased as the distance between them increased and that a gradient in persistence time was mapped along the rostrocaudal and dorsoventral axes. This topography is consistent with the emergence of persistence time heterogeneity from a circuit architecture in which nearby neurons are more strongly interconnected than distant ones. Collectively, our results can be accounted for by integrator circuit models characterized by multiple dimensions of slow firing rate dynamics. PMID:21857656

Engagement of neural circuits underlying 2D spatial navigation in a rodent virtual reality system.

PubMed

Aronov, Dmitriy; Tank, David W

2014-10-22

Virtual reality (VR) enables precise control of an animal's environment and otherwise impossible experimental manipulations. Neural activity in rodents has been studied on virtual 1D tracks. However, 2D navigation imposes additional requirements, such as the processing of head direction and environment boundaries, and it is unknown whether the neural circuits underlying 2D representations can be sufficiently engaged in VR. We implemented a VR setup for rats, including software and large-scale electrophysiology, that supports 2D navigation by allowing rotation and walking in any direction. The entorhinal-hippocampal circuit, including place, head direction, and grid cells, showed 2D activity patterns similar to those in the real world. Furthermore, border cells were observed, and hippocampal remapping was driven by environment shape, suggesting functional processing of virtual boundaries. These results illustrate that 2D spatial representations can be engaged by visual and rotational vestibular stimuli alone and suggest a novel VR tool for studying rat navigation.

The participation of cortical amygdala in innate, odour-driven behaviour.

PubMed

Root, Cory M; Denny, Christine A; Hen, René; Axel, Richard

2014-11-13

Innate behaviours are observed in naive animals without prior learning or experience, suggesting that the neural circuits that mediate these behaviours are genetically determined and stereotyped. The neural circuits that convey olfactory information from the sense organ to the cortical and subcortical olfactory centres have been anatomically defined, but the specific pathways responsible for innate responses to volatile odours have not been identified. Here we devise genetic strategies that demonstrate that a stereotyped neural circuit that transmits information from the olfactory bulb to cortical amygdala is necessary for innate aversive and appetitive behaviours. Moreover, we use the promoter of the activity-dependent gene arc to express the photosensitive ion channel, channelrhodopsin, in neurons of the cortical amygdala activated by odours that elicit innate behaviours. Optical activation of these neurons leads to appropriate behaviours that recapitulate the responses to innate odours. These data indicate that the cortical amygdala plays a critical role in generating innate odour-driven behaviours but do not preclude its participation in learned olfactory behaviours.

An ultra low-power CMOS automatic action potential detector.

PubMed

Gosselin, Benoit; Sawan, Mohamad

2009-08-01

We present a low-power complementary metal-oxide semiconductor (CMOS) analog integrated biopotential detector intended for neural recording in wireless multichannel implants. The proposed detector can achieve accurate automatic discrimination of action potential (APs) from the background activity by means of an energy-based preprocessor and a linear delay element. This strategy improves detected waveforms integrity and prompts for better performance in neural prostheses. The delay element is implemented with a low-power continuous-time filter using a ninth-order equiripple allpass transfer function. All circuit building blocks use subthreshold OTAs employing dedicated circuit techniques for achieving ultra low-power and high dynamic range. The proposed circuit function in the submicrowatt range as the implemented CMOS 0.18- microm chip dissipates 780 nW, and it features a size of 0.07 mm(2). So it is suitable for massive integration in a multichannel device with modest overhead. The fabricated detector succeeds to automatically detect APs from underlying background activity. Testbench validation results obtained with synthetic neural waveforms are presented.

Marginalization in neural circuits with divisive normalization

PubMed Central

Beck, J.M.; Latham, P.E.; Pouget, A.

2011-01-01

A wide range of computations performed by the nervous system involves a type of probabilistic inference known as marginalization. This computation comes up in seemingly unrelated tasks, including causal reasoning, odor recognition, motor control, visual tracking, coordinate transformations, visual search, decision making, and object recognition, to name just a few. The question we address here is: how could neural circuits implement such marginalizations? We show that when spike trains exhibit a particular type of statistics – associated with constant Fano factors and gain-invariant tuning curves, as is often reported in vivo – some of the more common marginalizations can be achieved with networks that implement a quadratic nonlinearity and divisive normalization, the latter being a type of nonlinear lateral inhibition that has been widely reported in neural circuits. Previous studies have implicated divisive normalization in contrast gain control and attentional modulation. Our results raise the possibility that it is involved in yet another, highly critical, computation: near optimal marginalization in a remarkably wide range of tasks. PMID:22031877

Changed Synaptic Plasticity in Neural Circuits of Depressive-Like and Escitalopram-Treated Rats

PubMed Central

Li, Xiao-Li; Yuan, Yong-Gui; Xu, Hua; Wu, Di; Gong, Wei-Gang; Geng, Lei-Yu; Wu, Fang-Fang; Tang, Hao; Xu, Lin

2015-01-01

Background: Although progress has been made in the detection and characterization of neural plasticity in depression, it has not been fully understood in individual synaptic changes in the neural circuits under chronic stress and antidepressant treatment. Methods: Using electron microscopy and Western-blot analyses, the present study quantitatively examined the changes in the Gray’s Type I synaptic ultrastructures and the expression of synapse-associated proteins in the key brain regions of rats’ depressive-related neural circuit after chronic unpredicted mild stress and/or escitalopram administration. Meanwhile, their depressive behaviors were also determined by several tests. Results: The Type I synapses underwent considerable remodeling after chronic unpredicted mild stress, which resulted in the changed width of the synaptic cleft, length of the active zone, postsynaptic density thickness, and/or synaptic curvature in the subregions of medial prefrontal cortex and hippocampus, as well as the basolateral amygdaloid nucleus of the amygdala, accompanied by changed expression of several synapse-associated proteins. Chronic escitalopram administration significantly changed the above alternations in the chronic unpredicted mild stress rats but had little effect on normal controls. Also, there was a positive correlation between the locomotor activity and the maximal synaptic postsynaptic density thickness in the stratum radiatum of the Cornu Ammonis 1 region and a negative correlation between the sucrose preference and the length of the active zone in the basolateral amygdaloid nucleus region in chronic unpredicted mild stress rats. Conclusion: These findings strongly indicate that chronic stress and escitalopram can alter synaptic plasticity in the neural circuits, and the remodeled synaptic ultrastructure was correlated with the rats’ depressive behaviors, suggesting a therapeutic target for further exploration. PMID:25899067

Sensori-motor experience leads to changes in visual processing in the developing brain.

PubMed

James, Karin Harman

2010-03-01

Since Broca's studies on language processing, cortical functional specialization has been considered to be integral to efficient neural processing. A fundamental question in cognitive neuroscience concerns the type of learning that is required for functional specialization to develop. To address this issue with respect to the development of neural specialization for letters, we used functional magnetic resonance imaging (fMRI) to compare brain activation patterns in pre-school children before and after different letter-learning conditions: a sensori-motor group practised printing letters during the learning phase, while the control group practised visual recognition. Results demonstrated an overall left-hemisphere bias for processing letters in these pre-literate participants, but, more interestingly, showed enhanced blood oxygen-level-dependent activation in the visual association cortex during letter perception only after sensori-motor (printing) learning. It is concluded that sensori-motor experience augments processing in the visual system of pre-school children. The change of activation in these neural circuits provides important evidence that 'learning-by-doing' can lay the foundation for, and potentially strengthen, the neural systems used for visual letter recognition.

Adolescence and Reward: Making Sense of Neural and Behavioral Changes Amid the Chaos

PubMed Central

Walker, Deena M.; Willing, Jari

2017-01-01

Adolescence is a time of significant neural and behavioral change with remarkable development in social, emotional, and cognitive skills. It is also a time of increased exploration and risk-taking (e.g., drug use). Many of these changes are thought to be the result of increased reward-value coupled with an underdeveloped inhibitory control, and thus a hypersensitivity to reward. Perturbations during adolescence can alter the developmental trajectory of the brain, resulting in long-term alterations in reward-associated behaviors. This review highlights recent developments in our understanding of how neural circuits, pubertal hormones, and environmental factors contribute to adolescent-typical reward-associated behaviors with a particular focus on sex differences, the medial prefrontal cortex, social reward, social isolation, and drug use. We then introduce a new approach that makes use of natural adaptations of seasonally breeding species to investigate the role of pubertal hormones in adolescent development. This research has only begun to parse out contributions of the many neural, endocrine, and environmental changes to the heightened reward sensitivity and increased vulnerability to mental health disorders that characterize this life stage. PMID:29118215

The cholinergic anti-inflammatory pathway revisited.

PubMed

Murray, K; Reardon, C

2018-03-01

Inflammatory bowel disease negatively affects the quality of life of millions of patients around the world. Although the precise etiology of the disease remains elusive, aberrant immune system activation is an underlying cause. As such, therapies that selectively inhibit immune cell activation without broad immunosuppression are desired. Inhibition of immune cell activation preventing pro-inflammatory cytokine production through neural stimulation has emerged as one such treatment. These therapeutics are based on the discovery of the cholinergic anti-inflammatory pathway, a reflex arc that induces efferent vagal nerve signaling to reduce immune cell activation and consequently mortality during septic shock. Despite the success of preclinical and clinical trials, the neural circuitry and mechanisms of action of these immune-regulatory circuits are controversial. At the heart of this controversy is the protective effect of vagal nerve stimulation despite an apparent lack of neuroanatomical connections between the vagus and target organs. Additional studies have further emphasized the importance of sympathetic innervation of these organs, and that alternative neural circuits could be involved in neural regulation of the immune system. Such controversies also extend to the regulation of intestinal inflammation, with the importance of efferent vagus nerve signals in question. Experiments that better characterize these pathways have now been performed by Willemze et al. in this issue of Neurogastroenterology & Motility. These continued efforts will be critical to the development of better neurostimulator based therapeutics for inflammatory bowel disease. © 2018 John Wiley & Sons Ltd.

An integrated modelling framework for neural circuits with multiple neuromodulators.

PubMed

Joshi, Alok; Youssofzadeh, Vahab; Vemana, Vinith; McGinnity, T M; Prasad, Girijesh; Wong-Lin, KongFatt

2017-01-01

Neuromodulators are endogenous neurochemicals that regulate biophysical and biochemical processes, which control brain function and behaviour, and are often the targets of neuropharmacological drugs. Neuromodulator effects are generally complex partly owing to the involvement of broad innervation, co-release of neuromodulators, complex intra- and extrasynaptic mechanism, existence of multiple receptor subtypes and high interconnectivity within the brain. In this work, we propose an efficient yet sufficiently realistic computational neural modelling framework to study some of these complex behaviours. Specifically, we propose a novel dynamical neural circuit model that integrates the effective neuromodulator-induced currents based on various experimental data (e.g. electrophysiology, neuropharmacology and voltammetry). The model can incorporate multiple interacting brain regions, including neuromodulator sources, simulate efficiently and easily extendable to large-scale brain models, e.g. for neuroimaging purposes. As an example, we model a network of mutually interacting neural populations in the lateral hypothalamus, dorsal raphe nucleus and locus coeruleus, which are major sources of neuromodulator orexin/hypocretin, serotonin and norepinephrine/noradrenaline, respectively, and which play significant roles in regulating many physiological functions. We demonstrate that such a model can provide predictions of systemic drug effects of the popular antidepressants (e.g. reuptake inhibitors), neuromodulator antagonists or their combinations. Finally, we developed user-friendly graphical user interface software for model simulation and visualization for both fundamental sciences and pharmacological studies. © 2017 The Authors.

An integrated modelling framework for neural circuits with multiple neuromodulators

PubMed Central

Vemana, Vinith

2017-01-01

Neuromodulators are endogenous neurochemicals that regulate biophysical and biochemical processes, which control brain function and behaviour, and are often the targets of neuropharmacological drugs. Neuromodulator effects are generally complex partly owing to the involvement of broad innervation, co-release of neuromodulators, complex intra- and extrasynaptic mechanism, existence of multiple receptor subtypes and high interconnectivity within the brain. In this work, we propose an efficient yet sufficiently realistic computational neural modelling framework to study some of these complex behaviours. Specifically, we propose a novel dynamical neural circuit model that integrates the effective neuromodulator-induced currents based on various experimental data (e.g. electrophysiology, neuropharmacology and voltammetry). The model can incorporate multiple interacting brain regions, including neuromodulator sources, simulate efficiently and easily extendable to large-scale brain models, e.g. for neuroimaging purposes. As an example, we model a network of mutually interacting neural populations in the lateral hypothalamus, dorsal raphe nucleus and locus coeruleus, which are major sources of neuromodulator orexin/hypocretin, serotonin and norepinephrine/noradrenaline, respectively, and which play significant roles in regulating many physiological functions. We demonstrate that such a model can provide predictions of systemic drug effects of the popular antidepressants (e.g. reuptake inhibitors), neuromodulator antagonists or their combinations. Finally, we developed user-friendly graphical user interface software for model simulation and visualization for both fundamental sciences and pharmacological studies. PMID:28100828

Reading acceleration training changes brain circuitry in children with reading difficulties

PubMed Central

Horowitz-Kraus, Tzipi; Vannest, Jennifer J; Kadis, Darren; Cicchino, Nicole; Wang, Yingying Y; Holland, Scott K

2014-01-01

Introduction Dyslexia is characterized by slow, inaccurate reading. Previous studies have shown that the Reading Acceleration Program (RAP) improves reading speed and accuracy in children and adults with dyslexia and in typical readers across different orthographies. However, the effect of the RAP on the neural circuitry of reading has not been established. In the current study, we examined the effect of the RAP training on regions of interest in the neural circuitry for reading using a lexical decision task during fMRI in children with reading difficulties and typical readers. Methods Children (8–12 years old) with reading difficulties and typical readers were studied before and after 4 weeks of training with the RAP in both groups. Results In addition to improvements in oral and silent contextual reading speed, training-related gains were associated with increased activation of the left hemisphere in both children with reading difficulties and typical readers. However, only children with reading difficulties showed improvements in reading comprehension, which were associated with significant increases in right frontal lobe activation. Conclusions Our results demonstrate differential effects of the RAP on neural circuits supporting reading in both children with reading difficulties and typical readers and suggest that the intervention may stimulate use of typical neural circuits for reading and engage compensatory pathways to support reading in the developing brain of children with reading difficulties. PMID:25365797

A conserved juxtacrine signal regulates synaptic partner recognition in Caenorhabditis elegans

PubMed Central

2011-01-01

Background An essential stage of neural development involves the assembly of neural circuits via formation of inter-neuronal connections. Early steps in neural circuit formation, including cell migration, axon guidance, and the localization of synaptic components, are well described. However, upon reaching their target region, most neurites still contact many potential partners. In order to assemble functional circuits, it is critical that within this group of cells, neurons identify and form connections only with their appropriate partners, a process we call synaptic partner recognition (SPR). To understand how SPR is mediated, we previously developed a genetically encoded fluorescent trans-synaptic marker called NLG-1 GRASP, which labels synaptic contacts between individual neurons of interest in dense cellular environments in the genetic model organism Caenorhabditis elegans. Results Here, we describe the first use of NLG-1 GRASP technology, to identify SPR genes that function in this critical process. The NLG-1 GRASP system allows us to assess synaptogenesis between PHB sensory neurons and AVA interneurons instantly in live animals, making genetic analysis feasible. Additionally, we employ a behavioral assay to specifically test PHB sensory circuit function. Utilizing this approach, we reveal a new role for the secreted UNC-6/Netrin ligand and its transmembrane receptor UNC-40/Deleted in colorectal cancer (DCC) in SPR. Synapses between PHB and AVA are severely reduced in unc-6 and unc-40 animals despite normal axon guidance and subcellular localization of synaptic components. Additionally, behavioral defects indicate a complete disruption of PHB circuit function in unc-40 mutants. Our data indicate that UNC-40 and UNC-6 function in PHB and AVA, respectively, to specify SPR. Strikingly, overexpression of UNC-6 in postsynaptic neurons is sufficient to promote increased PHB-AVA synaptogenesis and to potentiate the behavioral response beyond wild-type levels. Furthermore, an artificially membrane-tethered UNC-6 expressed in the postsynaptic neurons promotes SPR, consistent with a short-range signal between adjacent synaptic partners. Conclusions These results indicate that the conserved UNC-6/Netrin-UNC-40/DCC ligand-receptor pair has a previously unknown function, acting in a juxtacrine manner to specify recognition of individual postsynaptic neurons. Furthermore, they illustrate the potential of this new approach, combining NLG-1 GRASP and behavioral analysis, in gene discovery and characterization. PMID:21663630

A delicate balance: role of MMP-9 in brain development and pathophysiology of neurodevelopmental disorders.

PubMed

Reinhard, Sarah M; Razak, Khaleel; Ethell, Iryna M

2015-01-01

The extracellular matrix (ECM) is a critical regulator of neural network development and plasticity. As neuronal circuits develop, the ECM stabilizes synaptic contacts, while its cleavage has both permissive and active roles in the regulation of plasticity. Matrix metalloproteinase 9 (MMP-9) is a member of a large family of zinc-dependent endopeptidases that can cleave ECM and several cell surface receptors allowing for synaptic and circuit level reorganization. It is becoming increasingly clear that the regulated activity of MMP-9 is critical for central nervous system (CNS) development. In particular, MMP-9 has a role in the development of sensory circuits during early postnatal periods, called 'critical periods.' MMP-9 can regulate sensory-mediated, local circuit reorganization through its ability to control synaptogenesis, axonal pathfinding and myelination. Although activity-dependent activation of MMP-9 at specific synapses plays an important role in multiple plasticity mechanisms throughout the CNS, misregulated activation of the enzyme is implicated in a number of neurodegenerative disorders, including traumatic brain injury, multiple sclerosis, and Alzheimer's disease. Growing evidence also suggests a role for MMP-9 in the pathophysiology of neurodevelopmental disorders including Fragile X Syndrome. This review outlines the various actions of MMP-9 during postnatal brain development, critical for future studies exploring novel therapeutic strategies for neurodevelopmental disorders.

Synaptic up-scaling preserves motor circuit output after chronic, natural inactivity

PubMed Central

Vallejo, Mauricio; Hartzler, Lynn K

2017-01-01

Neural systems use homeostatic plasticity to maintain normal brain functions and to prevent abnormal activity. Surprisingly, homeostatic mechanisms that regulate circuit output have mainly been demonstrated during artificial and/or pathological perturbations. Natural, physiological scenarios that activate these stabilizing mechanisms in neural networks of mature animals remain elusive. To establish the extent to which a naturally inactive circuit engages mechanisms of homeostatic plasticity, we utilized the respiratory motor circuit in bullfrogs that normally remains inactive for several months during the winter. We found that inactive respiratory motoneurons exhibit a classic form of homeostatic plasticity, up-scaling of AMPA-glutamate receptors. Up-scaling increased the synaptic strength of respiratory motoneurons and acted to boost motor amplitude from the respiratory network following months of inactivity. Our results show that synaptic scaling sustains strength of the respiratory motor output following months of inactivity, thereby supporting a major neuroscience hypothesis in a normal context for an adult animal. PMID:28914603

Learning multiple variable-speed sequences in striatum via cortical tutoring.

PubMed

Murray, James M; Escola, G Sean

2017-05-08

Sparse, sequential patterns of neural activity have been observed in numerous brain areas during timekeeping and motor sequence tasks. Inspired by such observations, we construct a model of the striatum, an all-inhibitory circuit where sequential activity patterns are prominent, addressing the following key challenges: (i) obtaining control over temporal rescaling of the sequence speed, with the ability to generalize to new speeds; (ii) facilitating flexible expression of distinct sequences via selective activation, concatenation, and recycling of specific subsequences; and (iii) enabling the biologically plausible learning of sequences, consistent with the decoupling of learning and execution suggested by lesion studies showing that cortical circuits are necessary for learning, but that subcortical circuits are sufficient to drive learned behaviors. The same mechanisms that we describe can also be applied to circuits with both excitatory and inhibitory populations, and hence may underlie general features of sequential neural activity pattern generation in the brain.

Direction-selective circuits shape noise to ensure a precise population code

PubMed Central

Zylberberg, Joel; Cafaro, Jon; Turner, Maxwell H

2016-01-01

Summary Neural responses are noisy, and circuit structure can correlate this noise across neurons. Theoretical studies show that noise correlations can have diverse effects on population coding, but these studies rarely explore stimulus dependence of noise correlations. Here, we show that noise correlations in responses of ON-OFF direction-selective retinal ganglion cells are strongly stimulus dependent and we uncover the circuit mechanisms producing this stimulus dependence. A population model based on these mechanistic studies shows that stimulus-dependent noise correlations improve the encoding of motion direction two-fold compared to independent noise. This work demonstrates a mechanism by which a neural circuit effectively shapes its signal and noise in concert, minimizing corruption of signal by noise. Finally, we generalize our findings beyond direction coding in the retina and show that stimulus-dependent correlations will generally enhance information coding in populations of diversely tuned neurons. PMID:26796691

Insect Responses to Linearly Polarized Reflections: Orphan Behaviors Without Neural Circuits

PubMed Central

Heinloth, Tanja; Uhlhorn, Juliane; Wernet, Mathias F.

2018-01-01

The e-vector orientation of linearly polarized light represents an important visual stimulus for many insects. Especially the detection of polarized skylight by many navigating insect species is known to improve their orientation skills. While great progress has been made towards describing both the anatomy and function of neural circuit elements mediating behaviors related to navigation, relatively little is known about how insects perceive non-celestial polarized light stimuli, like reflections off water, leaves, or shiny body surfaces. Work on different species suggests that these behaviors are not mediated by the “Dorsal Rim Area” (DRA), a specialized region in the dorsal periphery of the adult compound eye, where ommatidia contain highly polarization-sensitive photoreceptor cells whose receptive fields point towards the sky. So far, only few cases of polarization-sensitive photoreceptors have been described in the ventral periphery of the insect retina. Furthermore, both the structure and function of those neural circuits connecting to these photoreceptor inputs remain largely uncharacterized. Here we review the known data on non-celestial polarization vision from different insect species (dragonflies, butterflies, beetles, bugs and flies) and present three well-characterized examples for functionally specialized non-DRA detectors from different insects that seem perfectly suited for mediating such behaviors. Finally, using recent advances from circuit dissection in Drosophila melanogaster, we discuss what types of potential candidate neurons could be involved in forming the underlying neural circuitry mediating non-celestial polarization vision. PMID:29615868

NT3-chitosan elicits robust endogenous neurogenesis to enable functional recovery after spinal cord injury

PubMed Central

Yang, Zhaoyang; Zhang, Aifeng; Duan, Hongmei; Zhang, Sa; Hao, Peng; Ye, Keqiang; Sun, Yi E.; Li, Xiaoguang

2015-01-01

Neural stem cells (NSCs) in the adult mammalian central nervous system (CNS) hold the key to neural regeneration through proper activation, differentiation, and maturation, to establish nascent neural networks, which can be integrated into damaged neural circuits to repair function. However, the CNS injury microenvironment is often inhibitory and inflammatory, limiting the ability of activated NSCs to differentiate into neurons and form nascent circuits. Here we report that neurotrophin-3 (NT3)-coupled chitosan biomaterial, when inserted into a 5-mm gap of completely transected and excised rat thoracic spinal cord, elicited robust activation of endogenous NSCs in the injured spinal cord. Through slow release of NT3, the biomaterial attracted NSCs to migrate into the lesion area, differentiate into neurons, and form functional neural networks, which interconnected severed ascending and descending axons, resulting in sensory and motor behavioral recovery. Our study suggests that enhancing endogenous neurogenesis could be a novel strategy for treatment of spinal cord injury. PMID:26460015

A theory of neural dimensionality, dynamics, and measurement

NASA Astrophysics Data System (ADS)

Ganguli, Surya

In many experiments, neuroscientists tightly control behavior, record many trials, and obtain trial-averaged firing rates from hundreds of neurons in circuits containing millions of behaviorally relevant neurons. Dimensionality reduction has often shown that such datasets are strikingly simple; they can be described using a much smaller number of dimensions than the number of recorded neurons, and the resulting projections onto these dimensions yield a remarkably insightful dynamical portrait of circuit computation. This ubiquitous simplicity raises several profound and timely conceptual questions. What is the origin of this simplicity and its implications for the complexity of brain dynamics? Would neuronal datasets become more complex if we recorded more neurons? How and when can we trust dynamical portraits obtained from only hundreds of neurons in circuits containing millions of neurons? We present a theory that answers these questions, and test it using neural data recorded from reaching monkeys. Overall, this theory yields a picture of the neural measurement process as a random projection of neural dynamics, conceptual insights into how we can reliably recover dynamical portraits in such under-sampled measurement regimes, and quantitative guidelines for the design of future experiments. Moreover, it reveals the existence of phase transition boundaries in our ability to successfully decode cognition and behavior as a function of the number of recorded neurons, the complexity of the task, and the smoothness of neural dynamics. membership pending.

New Insights on Neurobiological Mechanisms underlying Alcohol Addiction

PubMed Central

Cui, Changhai; Noronha, Antonio; Morikawa, Hitoshi; Alvarez, Veronica A.; Stuber, Garret D.; Szumlinski, Karen K.; Kash, Thomas L.; Roberto, Marisa; Wilcox, Mark V.

2012-01-01

Alcohol dependence/addiction is mediated by complex neural mechanisms that involve multiple brain circuits and neuroadaptive changes in a variety of neurotransmitter and neuropeptide systems. Although recent studies have provided substantial information on the neurobiological mechanisms that drive alcohol drinking behavior, significant challenges remain in understanding how alcohol-induced neuroadaptations occur and how different neurocircuits and pathways cross-talk. This review article highlights recent progress in understanding neural mechanisms of alcohol addiction from the perspectives of the development and maintenance of alcohol dependence. It provides insights on cross talks of different mechanisms and reviews the latest studies on metaplasticity, structural plasticity, interface of reward and stress pathways, and cross-talk of different neural signaling systems involved in binge-like drinking and alcohol dependence. PMID:23159531

Mdm2 mediates FMRP- and Gp1 mGluR-dependent protein translation and neural network activity.

PubMed

Liu, Dai-Chi; Seimetz, Joseph; Lee, Kwan Young; Kalsotra, Auinash; Chung, Hee Jung; Lu, Hua; Tsai, Nien-Pei

2017-10-15

Activating Group 1 (Gp1) metabotropic glutamate receptors (mGluRs), including mGluR1 and mGluR5, elicits translation-dependent neural plasticity mechanisms that are crucial to animal behavior and circuit development. Dysregulated Gp1 mGluR signaling has been observed in numerous neurological and psychiatric disorders. However, the molecular pathways underlying Gp1 mGluR-dependent plasticity mechanisms are complex and have been elusive. In this study, we identified a novel mechanism through which Gp1 mGluR mediates protein translation and neural plasticity. Using a multi-electrode array (MEA) recording system, we showed that activating Gp1 mGluR elevates neural network activity, as demonstrated by increased spontaneous spike frequency and burst activity. Importantly, we validated that elevating neural network activity requires protein translation and is dependent on fragile X mental retardation protein (FMRP), the protein that is deficient in the most common inherited form of mental retardation and autism, fragile X syndrome (FXS). In an effort to determine the mechanism by which FMRP mediates protein translation and neural network activity, we demonstrated that a ubiquitin E3 ligase, murine double minute-2 (Mdm2), is required for Gp1 mGluR-induced translation and neural network activity. Our data showed that Mdm2 acts as a translation suppressor, and FMRP is required for its ubiquitination and down-regulation upon Gp1 mGluR activation. These data revealed a novel mechanism by which Gp1 mGluR and FMRP mediate protein translation and neural network activity, potentially through de-repressing Mdm2. Our results also introduce an alternative way for understanding altered protein translation and brain circuit excitability associated with Gp1 mGluR in neurological diseases such as FXS. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

The neuropsychology of self-reflection in psychiatric illness.

PubMed

Philippi, Carissa L; Koenigs, Michael

2014-07-01

The development of robust neuropsychological measures of social and affective function-which link critical dimensions of mental health to their underlying neural circuitry-could be a key step in achieving a more pathophysiologically-based approach to psychiatric medicine. In this article, we summarize research indicating that self-reflection (the inward attention to personal thoughts, memories, feelings, and actions) may be a useful model for developing such a paradigm, as there is evidence that self-reflection is (1) measurable with self-report scales and performance-based tests, (2) linked to the activity of a specific neural circuit, and (3) dimensionally related to mental health and various forms of psychopathology. Copyright © 2014 Elsevier Ltd. All rights reserved.

CMOS-micromachined, two-dimenisional transistor arrays for neural recording and stimulation.

PubMed

Lin, J S; Chang, S R; Chang, C H; Lu, S C; Chen, H

2007-01-01

In-plane microelectrode arrays have proven to be useful tools for studying the connectivities and the functions of neural tissues. However, seldom microelectrode arrays are monolithically-integrated with signal-processing circuits, without which the maximum number of electrodes is limited by the compromise with routing complexity and interferences. This paper proposes a CMOS-compatible, two-dimensional array of oxide-semiconductor field-effect transistors(OSFETs), capable of both recording and stimulating neuronal activities. The fabrication of the OSFETs not only requires simply die-level, post-CMOS micromachining process, but also retains metal layers for monolithic integration with signal-processing circuits. A CMOS microsystem containing the OSFET arrays and gain-programmable recording circuits has been fabricated and tested. The preliminary testing results are presented and discussed.

Model-based evaluation of the short-circuited tripolar cuff configuration.

PubMed

Andreasen, Lotte N S; Struijk, Johannes J

2006-05-01

Recordings of neural information for use as feedback in functional electrical stimulation are often contaminated with interfering signals from muscles and from stimulus pulses. The cuff electrode used for the neural recording can be optimized to improve the S/I ratio. In this work, we evaluate a model of both the nerve signal and the interfering signals recorded by a cuff, and subsequently use this model to study the signal to interference ratio of different cuff designs and to evaluate a recently introduced short-circuited tripolar cuff configuration. The results of the model showed good agreement with results from measurements in rabbits and confirmed the superior performance of the short-circuited tripolar configuration as compared with the traditionally used tripolar configuration.

A scalable neural chip with synaptic electronics using CMOS integrated memristors.

PubMed

Cruz-Albrecht, Jose M; Derosier, Timothy; Srinivasa, Narayan

2013-09-27

The design and simulation of a scalable neural chip with synaptic electronics using nanoscale memristors fully integrated with complementary metal-oxide-semiconductor (CMOS) is presented. The circuit consists of integrate-and-fire neurons and synapses with spike-timing dependent plasticity (STDP). The synaptic conductance values can be stored in memristors with eight levels, and the topology of connections between neurons is reconfigurable. The circuit has been designed using a 90 nm CMOS process with via connections to on-chip post-processed memristor arrays. The design has about 16 million CMOS transistors and 73 728 integrated memristors. We provide circuit level simulations of the entire chip performing neuronal and synaptic computations that result in biologically realistic functional behavior.

Neural CMOS-integrated circuit and its application to data classification.

PubMed

Göknar, Izzet Cem; Yildiz, Merih; Minaei, Shahram; Deniz, Engin

2012-05-01

Implementation and new applications of a tunable complementary metal-oxide-semiconductor-integrated circuit (CMOS-IC) of a recently proposed classifier core-cell (CC) are presented and tested with two different datasets. With two algorithms-one based on Fisher's linear discriminant analysis and the other based on perceptron learning, used to obtain CCs' tunable parameters-the Haberman and Iris datasets are classified. The parameters so obtained are used for hard-classification of datasets with a neural network structured circuit. Classification performance and coefficient calculation times for both algorithms are given. The CC has 6-ns response time and 1.8-mW power consumption. The fabrication parameters used for the IC are taken from CMOS AMS 0.35-μm technology.

Finding the beat: a neural perspective across humans and non-human primates.

PubMed

Merchant, Hugo; Grahn, Jessica; Trainor, Laurel; Rohrmeier, Martin; Fitch, W Tecumseh

2015-03-19

Humans possess an ability to perceive and synchronize movements to the beat in music ('beat perception and synchronization'), and recent neuroscientific data have offered new insights into this beat-finding capacity at multiple neural levels. Here, we review and compare behavioural and neural data on temporal and sequential processing during beat perception and entrainment tasks in macaques (including direct neural recording and local field potential (LFP)) and humans (including fMRI, EEG and MEG). These abilities rest upon a distributed set of circuits that include the motor cortico-basal-ganglia-thalamo-cortical (mCBGT) circuit, where the supplementary motor cortex (SMA) and the putamen are critical cortical and subcortical nodes, respectively. In addition, a cortical loop between motor and auditory areas, connected through delta and beta oscillatory activity, is deeply involved in these behaviours, with motor regions providing the predictive timing needed for the perception of, and entrainment to, musical rhythms. The neural discharge rate and the LFP oscillatory activity in the gamma- and beta-bands in the putamen and SMA of monkeys are tuned to the duration of intervals produced during a beat synchronization-continuation task (SCT). Hence, the tempo during beat synchronization is represented by different interval-tuned cells that are activated depending on the produced interval. In addition, cells in these areas are tuned to the serial-order elements of the SCT. Thus, the underpinnings of beat synchronization are intrinsically linked to the dynamics of cell populations tuned for duration and serial order throughout the mCBGT. We suggest that a cross-species comparison of behaviours and the neural circuits supporting them sets the stage for a new generation of neurally grounded computational models for beat perception and synchronization. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Finding the beat: a neural perspective across humans and non-human primates

PubMed Central

Merchant, Hugo; Grahn, Jessica; Trainor, Laurel; Rohrmeier, Martin; Fitch, W. Tecumseh

2015-01-01

Humans possess an ability to perceive and synchronize movements to the beat in music (‘beat perception and synchronization’), and recent neuroscientific data have offered new insights into this beat-finding capacity at multiple neural levels. Here, we review and compare behavioural and neural data on temporal and sequential processing during beat perception and entrainment tasks in macaques (including direct neural recording and local field potential (LFP)) and humans (including fMRI, EEG and MEG). These abilities rest upon a distributed set of circuits that include the motor cortico-basal-ganglia–thalamo-cortical (mCBGT) circuit, where the supplementary motor cortex (SMA) and the putamen are critical cortical and subcortical nodes, respectively. In addition, a cortical loop between motor and auditory areas, connected through delta and beta oscillatory activity, is deeply involved in these behaviours, with motor regions providing the predictive timing needed for the perception of, and entrainment to, musical rhythms. The neural discharge rate and the LFP oscillatory activity in the gamma- and beta-bands in the putamen and SMA of monkeys are tuned to the duration of intervals produced during a beat synchronization–continuation task (SCT). Hence, the tempo during beat synchronization is represented by different interval-tuned cells that are activated depending on the produced interval. In addition, cells in these areas are tuned to the serial-order elements of the SCT. Thus, the underpinnings of beat synchronization are intrinsically linked to the dynamics of cell populations tuned for duration and serial order throughout the mCBGT. We suggest that a cross-species comparison of behaviours and the neural circuits supporting them sets the stage for a new generation of neurally grounded computational models for beat perception and synchronization. PMID:25646516

Decision making: the neuroethological turn

PubMed Central

Pearson, John M.; Watson, Karli K.; Platt, Michael L.

2014-01-01

Neuroeconomics applies models from economics and psychology to inform neurobiological studies of choice. This approach has revealed neural signatures of concepts like value, risk, and ambiguity, which are known to influence decision-making. Such observations have led theorists to hypothesize a single, unified decision process that mediates choice behavior via a common neural currency for outcomes like food, money, or social praise. In parallel, recent neuroethological studies of decision-making have focused on natural behaviors like foraging, mate choice, and social interactions. These decisions strongly impact evolutionary fitness and thus are likely to have played a key role in shaping the neural circuits that mediate decision-making. This approach has revealed a suite of computational motifs that appear to be shared across a wide variety of organisms. We argue that the existence of deep homologies in the neural circuits mediating choice may have profound implications for understanding human decision-making in health and disease. PMID:24908481

A neural circuit encoding sexual preference in humans

PubMed Central

Poeppl, Timm B.; Langguth, Berthold; Rupprecht, Rainer; Laird, Angela R; Eickhoff, Simon B.

2016-01-01

Sexual preference determines mate choice for reproduction and hence guarantees conservation of species in mammals. Despite this fundamental role in human behavior, current knowledge on its target-specific neurofunctional substrate is based on lesion studies and therefore limited. We used meta-analytic remodeling of neuroimaging data from 364 human subjects with diverse sexual interests during sexual stimulation to quantify neural regions associated with sexual preference manipulations. We found that sexual preference is encoded by four phylogenetically old, subcortical brain structures. More specifically, sexual preference is controlled by the anterior and preoptic area of the hypothalamus, the anterior and mediodorsal thalamus, the septal area, and the perirhinal parahippocampus including the dentate gyrus. In contrast, sexual non-preference is regulated by the substantia innominata. We anticipate the identification of a core neural circuit for sexual preferences to be a starting point for further sophisticated investigations into the neural principles of sexual behavior and particularly of its aberrations. PMID:27339689

Viral-genetic tracing of the input-output organization of a central noradrenaline circuit.

PubMed

Schwarz, Lindsay A; Miyamichi, Kazunari; Gao, Xiaojing J; Beier, Kevin T; Weissbourd, Brandon; DeLoach, Katherine E; Ren, Jing; Ibanes, Sandy; Malenka, Robert C; Kremer, Eric J; Luo, Liqun

2015-08-06

Deciphering how neural circuits are anatomically organized with regard to input and output is instrumental in understanding how the brain processes information. For example, locus coeruleus noradrenaline (also known as norepinephrine) (LC-NE) neurons receive input from and send output to broad regions of the brain and spinal cord, and regulate diverse functions including arousal, attention, mood and sensory gating. However, it is unclear how LC-NE neurons divide up their brain-wide projection patterns and whether different LC-NE neurons receive differential input. Here we developed a set of viral-genetic tools to quantitatively analyse the input-output relationship of neural circuits, and applied these tools to dissect the LC-NE circuit in mice. Rabies-virus-based input mapping indicated that LC-NE neurons receive convergent synaptic input from many regions previously identified as sending axons to the locus coeruleus, as well as from newly identified presynaptic partners, including cerebellar Purkinje cells. The 'tracing the relationship between input and output' method (or TRIO method) enables trans-synaptic input tracing from specific subsets of neurons based on their projection and cell type. We found that LC-NE neurons projecting to diverse output regions receive mostly similar input. Projection-based viral labelling revealed that LC-NE neurons projecting to one output region also project to all brain regions we examined. Thus, the LC-NE circuit overall integrates information from, and broadcasts to, many brain regions, consistent with its primary role in regulating brain states. At the same time, we uncovered several levels of specificity in certain LC-NE sub-circuits. These tools for mapping output architecture and input-output relationship are applicable to other neuronal circuits and organisms. More broadly, our viral-genetic approaches provide an efficient intersectional means to target neuronal populations based on cell type and projection pattern.

Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience.

PubMed

Paninski, L; Cunningham, J P

2018-06-01

Modern large-scale multineuronal recording methodologies, including multielectrode arrays, calcium imaging, and optogenetic techniques, produce single-neuron resolution data of a magnitude and precision that were the realm of science fiction twenty years ago. The major bottlenecks in systems and circuit neuroscience no longer lie in simply collecting data from large neural populations, but also in understanding this data: developing novel scientific questions, with corresponding analysis techniques and experimental designs to fully harness these new capabilities and meaningfully interrogate these questions. Advances in methods for signal processing, network analysis, dimensionality reduction, and optimal control-developed in lockstep with advances in experimental neurotechnology-promise major breakthroughs in multiple fundamental neuroscience problems. These trends are clear in a broad array of subfields of modern neuroscience; this review focuses on recent advances in methods for analyzing neural time-series data with single-neuronal precision. Copyright © 2018 Elsevier Ltd. All rights reserved.

Language and reading development in the brain today: neuromarkers and the case for prediction.

PubMed

Buchweitz, Augusto

2016-01-01

The goal of this article is to provide an account of language development in the brain using the new information about brain function gleaned from cognitive neuroscience. This account goes beyond describing the association between language and specific brain areas to advocate the possibility of predicting language outcomes using brain-imaging data. The goal is to address the current evidence about language development in the brain and prediction of language outcomes. Recent studies will be discussed in the light of the evidence generated for predicting language outcomes and using new methods of analysis of brain data. The present account of brain behavior will address: (1) the development of a hardwired brain circuit for spoken language; (2) the neural adaptation that follows reading instruction and fosters the "grafting" of visual processing areas of the brain onto the hardwired circuit of spoken language; and (3) the prediction of language development and the possibility of translational neuroscience. Brain imaging has allowed for the identification of neural indices (neuromarkers) that reflect typical and atypical language development; the possibility of predicting risk for language disorders has emerged. A mandate to develop a bridge between neuroscience and health and cognition-related outcomes may pave the way for translational neuroscience. Copyright © 2016 Sociedade Brasileira de Pediatria. Published by Elsevier Editora Ltda. All rights reserved.

Understand the cogs to understand cognition.

PubMed

Marblestone, Adam H; Wayne, Greg; Kording, Konrad P

2017-01-01

Lake et al. suggest that current AI systems lack the inductive biases that enable human learning. However, Lake et al.'s proposed biases may not directly map onto mechanisms in the developing brain. A convergence of fields may soon create a correspondence between biological neural circuits and optimization in structured architectures, allowing us to systematically dissect how brains learn.

A subthreshold aVLSI implementation of the Izhikevich simple neuron model.

PubMed

Rangan, Venkat; Ghosh, Abhishek; Aparin, Vladimir; Cauwenberghs, Gert

2010-01-01

We present a circuit architecture for compact analog VLSI implementation of the Izhikevich neuron model, which efficiently describes a wide variety of neuron spiking and bursting dynamics using two state variables and four adjustable parameters. Log-domain circuit design utilizing MOS transistors in subthreshold results in high energy efficiency, with less than 1pJ of energy consumed per spike. We also discuss the effects of parameter variations on the dynamics of the equations, and present simulation results that replicate several types of neural dynamics. The low power operation and compact analog VLSI realization make the architecture suitable for human-machine interface applications in neural prostheses and implantable bioelectronics, as well as large-scale neural emulation tools for computational neuroscience.

Spikes alone do not behavior make: Why neuroscience needs biomechanics

PubMed Central

Tytell, E.D.; Holmes, P.; Cohen, A.H.

2011-01-01

Neural circuits do not function in isolation; they interact with the physical world, accepting sensory inputs and producing outputs via muscles. Since both these pathways are constrained by physics, the activity of neural circuits can only be understood by considering biomechanics of muscles, bodies, and the exterior world. We discuss how animal bodies have natural stable motions that require relatively little activation or control from the nervous system. The nervous system can substantially alter these motions, by subtly changing mechanical properties such as leg sti ness. Mechanics can also provide robustness to perturbations without sensory reflexes. By considering a complete neuromechanical system, neuroscientists and biomechanicians together can provide a more integrated view of neural circuitry and behavior. PMID:21683575

Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

USGS Publications Warehouse

Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

1997-01-01

One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

The relation between finger gnosis and mathematical ability: why redeployment of neural circuits best explains the finding

PubMed Central

Penner-Wilger, Marcie; Anderson, Michael L.

2013-01-01

This paper elaborates a novel hypothesis regarding the observed predictive relation between finger gnosis and mathematical ability. In brief, we suggest that these two cognitive phenomena have overlapping neural substrates, as the result of the re-use (“redeployment”) of part of the finger gnosis circuit for the purpose of representing numbers. We offer some background on the relation and current explanations for it; an outline of our alternate hypothesis; some evidence supporting redeployment over current views; and a plan for further research. PMID:24367341

The relation between finger gnosis and mathematical ability: why redeployment of neural circuits best explains the finding.

PubMed

Penner-Wilger, Marcie; Anderson, Michael L

2013-01-01

This paper elaborates a novel hypothesis regarding the observed predictive relation between finger gnosis and mathematical ability. In brief, we suggest that these two cognitive phenomena have overlapping neural substrates, as the result of the re-use ("redeployment") of part of the finger gnosis circuit for the purpose of representing numbers. We offer some background on the relation and current explanations for it; an outline of our alternate hypothesis; some evidence supporting redeployment over current views; and a plan for further research.

Schaffer Collateral Inputs to CA1 Excitatory and Inhibitory Neurons Follow Different Connectivity Rules.

PubMed

Kwon, Osung; Feng, Linqing; Druckmann, Shaul; Kim, Jinhyun

2018-05-30

Neural circuits, governed by a complex interplay between excitatory and inhibitory neurons, are the substrate for information processing, and the organization of synaptic connectivity in neural network is an important determinant of circuit function. Here, we analyzed the fine structure of connectivity in hippocampal CA1 excitatory and inhibitory neurons innervated by Schaffer collaterals (SCs) using mGRASP in male mice. Our previous study revealed spatially structured synaptic connectivity between CA3 and CA1 pyramidal cells (PCs). Surprisingly, parvalbumin-positive interneurons (PVs) showed a significantly more random pattern spatial structure. Notably, application of Peters' rule for synapse prediction by random overlap between axons and dendrites enhanced structured connectivity in PCs, but, by contrast, made the connectivity pattern in PVs more random. In addition, PCs in a deep sublayer of striatum pyramidale appeared more highly structured than PCs in superficial layers, and little or no sublayer specificity was found in PVs. Our results show that CA1 excitatory PCs and inhibitory PVs innervated by the same SC inputs follow different connectivity rules. The different organizations of fine scale structured connectivity in hippocampal excitatory and inhibitory neurons provide important insights into the development and functions of neural networks. SIGNIFICANCE STATEMENT Understanding how neural circuits generate behavior is one of the central goals of neuroscience. An important component of this endeavor is the mapping of fine-scale connection patterns that underlie, and help us infer, signal processing in the brain. Here, using our recently developed synapse detection technology (mGRASP and neuTube), we provide detailed profiles of synaptic connectivity in excitatory (CA1 pyramidal) and inhibitory (CA1 parvalbumin-positive) neurons innervated by the same presynaptic inputs (CA3 Schaffer collaterals). Our results reveal that these two types of CA1 neurons follow different connectivity patterns. Our new evidence for differently structured connectivity at a fine scale in hippocampal excitatory and inhibitory neurons provides a better understanding of hippocampal networks and will guide theoretical and experimental studies. Copyright © 2018 the authors 0270-6474/18/385140-13$15.00/0.

Multiple conserved cell adhesion protein interactions mediate neural wiring of a sensory circuit in C. elegans.

PubMed

Kim, Byunghyuk; Emmons, Scott W

2017-09-13

Nervous system function relies on precise synaptic connections. A number of widely-conserved cell adhesion proteins are implicated in cell recognition between synaptic partners, but how these proteins act as a group to specify a complex neural network is poorly understood. Taking advantage of known connectivity in C. elegans , we identified and studied cell adhesion genes expressed in three interacting neurons in the mating circuits of the adult male. Two interacting pairs of cell surface proteins independently promote fasciculation between sensory neuron HOA and its postsynaptic target interneuron AVG: BAM-2/neurexin-related in HOA binds to CASY-1/calsyntenin in AVG; SAX-7/L1CAM in sensory neuron PHC binds to RIG-6/contactin in AVG. A third, basal pathway results in considerable HOA-AVG fasciculation and synapse formation in the absence of the other two. The features of this multiplexed mechanism help to explain how complex connectivity is encoded and robustly established during nervous system development.

Shaping vulnerability to addiction - the contribution of behavior, neural circuits and molecular mechanisms.

PubMed

Egervari, Gabor; Ciccocioppo, Roberto; Jentsch, J David; Hurd, Yasmin L

2018-02-01

Substance use disorders continue to impose increasing medical, financial and emotional burdens on society in the form of morbidity and overdose, family disintegration, loss of employment and crime, while advances in prevention and treatment options remain limited. Importantly, not all individuals exposed to abused substances effectively develop the disease. Genetic factors play a significant role in determining addiction vulnerability and interactions between innate predisposition, environmental factors and personal experiences are also critical. Thus, understanding individual differences that contribute to the initiation of substance use as well as on long-term maladaptations driving compulsive drug use and relapse propensity is of critical importance to reduce this devastating disorder. In this paper, we discuss current topics in the field of addiction regarding individual vulnerability related to behavioral endophenotypes, neural circuits, as well as genetics and epigenetic mechanisms. Expanded knowledge of these factors is of importance to improve and personalize prevention and treatment interventions in the future. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Pleiotropic MET Receptor Network: Circuit Development and the Neural-Medical Interface of Autism.

PubMed

Eagleson, Kathie L; Xie, Zhihui; Levitt, Pat

2017-03-01

People with autism spectrum disorder and other neurodevelopmental disorders (NDDs) are behaviorally and medically heterogeneous. The combination of polygenicity and gene pleiotropy-the influence of one gene on distinct phenotypes-raises questions of how specific genes and their protein products interact to contribute to NDDs. A preponderance of evidence supports developmental and pathophysiological roles for the MET receptor tyrosine kinase, a multifunctional receptor that mediates distinct biological responses depending upon cell context. MET influences neuron architecture and synapse maturation in the forebrain and regulates homeostasis in gastrointestinal and immune systems, both commonly disrupted in NDDs. Peak expression of synapse-enriched MET is conserved across rodent and primate forebrain, yet regional differences in primate neocortex are pronounced, with enrichment in circuits that participate in social information processing. A functional risk allele in the MET promoter, enriched in subgroups of children with autism spectrum disorder, reduces transcription and disrupts socially relevant neural circuits structurally and functionally. In mice, circuit-specific deletion of Met causes distinct atypical behaviors. MET activation increases dendritic complexity and nascent synapse number, but synapse maturation requires reductions in MET. MET mediates its specific biological effects through different intracellular signaling pathways and has a complex protein interactome that is enriched in autism spectrum disorder and other NDD candidates. The interactome is coregulated in developing human neocortex. We suggest that a gene as pleiotropic and highly regulated as MET, together with its interactome, is biologically relevant in normal and pathophysiological contexts, affecting central and peripheral phenotypes that contribute to NDD risk and clinical symptoms. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Analog hardware for delta-backpropagation neural networks

NASA Technical Reports Server (NTRS)

Eberhardt, Silvio P. (Inventor)

1992-01-01

This is a fully parallel analog backpropagation learning processor which comprises a plurality of programmable resistive memory elements serving as synapse connections whose values can be weighted during learning with buffer amplifiers, summing circuits, and sample-and-hold circuits arranged in a plurality of neuron layers in accordance with delta-backpropagation algorithms modified so as to control weight changes due to circuit drift.

CRHR1 genotypes, neural circuits and the diathesis for anxiety and depression.

PubMed

Rogers, J; Raveendran, M; Fawcett, G L; Fox, A S; Shelton, S E; Oler, J A; Cheverud, J; Muzny, D M; Gibbs, R A; Davidson, R J; Kalin, N H

2013-06-01

The corticotrophin-releasing hormone (CRH) system integrates the stress response and is associated with stress-related psychopathology. Previous reports have identified interactions between childhood trauma and sequence variation in the CRH receptor 1 gene (CRHR1) that increase risk for affective disorders. However, the underlying mechanisms that connect variation in CRHR1 to psychopathology are unknown. To explore potential mechanisms, we used a validated rhesus macaque model to investigate association between genetic variation in CRHR1, anxious temperament (AT) and brain metabolic activity. In young rhesus monkeys, AT is analogous to the childhood risk phenotype that predicts the development of human anxiety and depressive disorders. Regional brain metabolism was assessed with (18)F-labeled fluoro-2-deoxyglucose (FDG) positron emission tomography in 236 young, normally reared macaques that were also characterized for AT. We show that single nucleotide polymorphisms (SNPs) affecting exon 6 of CRHR1 influence both AT and metabolic activity in the anterior hippocampus and amygdala, components of the neural circuit underlying AT. We also find evidence for association between SNPs in CRHR1 and metabolism in the intraparietal sulcus and precuneus. These translational data suggest that genetic variation in CRHR1 affects the risk for affective disorders by influencing the function of the neural circuit underlying AT and that differences in gene expression or the protein sequence involving exon 6 may be important. These results suggest that variation in CRHR1 may influence brain function before any childhood adversity and may be a diathesis for the interaction between CRHR1 genotypes and childhood trauma reported to affect human psychopathology.

Fast Neural Solution Of A Nonlinear Wave Equation

NASA Technical Reports Server (NTRS)

Barhen, Jacob; Toomarian, Nikzad

1996-01-01

Neural algorithm for simulation of class of nonlinear wave phenomena devised. Numerically solves special one-dimensional case of Korteweg-deVries equation. Intended to be executed rapidly by neural network implemented as charge-coupled-device/charge-injection device, very-large-scale integrated-circuit analog data processor of type described in "CCD/CID Processors Would Offer Greater Precision" (NPO-18972).

Neural Plasticity and Neurorehabilitation: Teaching the New Brain Old Tricks

ERIC Educational Resources Information Center

Kleim, Jeffrey A.

2011-01-01

Following brain injury or disease there are widespread biochemical, anatomical and physiological changes that result in what might be considered a new, very different brain. This adapted brain is forced to reacquire behaviors lost as a result of the injury or disease and relies on neural plasticity within the residual neural circuits. The same…

Neuromorphic Implementation of Attractor Dynamics in a Two-Variable Winner-Take-All Circuit with NMDARs: A Simulation Study

PubMed Central

You, Hongzhi; Wang, Da-Hui

2017-01-01

Neural networks configured with winner-take-all (WTA) competition and N-methyl-D-aspartate receptor (NMDAR)-mediated synaptic dynamics are endowed with various dynamic characteristics of attractors underlying many cognitive functions. This paper presents a novel method for neuromorphic implementation of a two-variable WTA circuit with NMDARs aimed at implementing decision-making, working memory and hysteresis in visual perceptions. The method proposed is a dynamical system approach of circuit synthesis based on a biophysically plausible WTA model. Notably, slow and non-linear temporal dynamics of NMDAR-mediated synapses was generated. Circuit simulations in Cadence reproduced ramping neural activities observed in electrophysiological recordings in experiments of decision-making, the sustained activities observed in the prefrontal cortex during working memory, and classical hysteresis behavior during visual discrimination tasks. Furthermore, theoretical analysis of the dynamical system approach illuminated the underlying mechanisms of decision-making, memory capacity and hysteresis loops. The consistence between the circuit simulations and theoretical analysis demonstrated that the WTA circuit with NMDARs was able to capture the attractor dynamics underlying these cognitive functions. Their physical implementations as elementary modules are promising for assembly into integrated neuromorphic cognitive systems. PMID:28223913

Neuromorphic Implementation of Attractor Dynamics in a Two-Variable Winner-Take-All Circuit with NMDARs: A Simulation Study.

PubMed

You, Hongzhi; Wang, Da-Hui

2017-01-01

Neural networks configured with winner-take-all (WTA) competition and N-methyl-D-aspartate receptor (NMDAR)-mediated synaptic dynamics are endowed with various dynamic characteristics of attractors underlying many cognitive functions. This paper presents a novel method for neuromorphic implementation of a two-variable WTA circuit with NMDARs aimed at implementing decision-making, working memory and hysteresis in visual perceptions. The method proposed is a dynamical system approach of circuit synthesis based on a biophysically plausible WTA model. Notably, slow and non-linear temporal dynamics of NMDAR-mediated synapses was generated. Circuit simulations in Cadence reproduced ramping neural activities observed in electrophysiological recordings in experiments of decision-making, the sustained activities observed in the prefrontal cortex during working memory, and classical hysteresis behavior during visual discrimination tasks. Furthermore, theoretical analysis of the dynamical system approach illuminated the underlying mechanisms of decision-making, memory capacity and hysteresis loops. The consistence between the circuit simulations and theoretical analysis demonstrated that the WTA circuit with NMDARs was able to capture the attractor dynamics underlying these cognitive functions. Their physical implementations as elementary modules are promising for assembly into integrated neuromorphic cognitive systems.

Artificial immune system algorithm in VLSI circuit configuration

NASA Astrophysics Data System (ADS)

Mansor, Mohd. Asyraf; Sathasivam, Saratha; Kasihmuddin, Mohd Shareduwan Mohd

2017-08-01

In artificial intelligence, the artificial immune system is a robust bio-inspired heuristic method, extensively used in solving many constraint optimization problems, anomaly detection, and pattern recognition. This paper discusses the implementation and performance of artificial immune system (AIS) algorithm integrated with Hopfield neural networks for VLSI circuit configuration based on 3-Satisfiability problems. Specifically, we emphasized on the clonal selection technique in our binary artificial immune system algorithm. We restrict our logic construction to 3-Satisfiability (3-SAT) clauses in order to outfit with the transistor configuration in VLSI circuit. The core impetus of this research is to find an ideal hybrid model to assist in the VLSI circuit configuration. In this paper, we compared the artificial immune system (AIS) algorithm (HNN-3SATAIS) with the brute force algorithm incorporated with Hopfield neural network (HNN-3SATBF). Microsoft Visual C++ 2013 was used as a platform for training, simulating and validating the performances of the proposed network. The results depict that the HNN-3SATAIS outperformed HNN-3SATBF in terms of circuit accuracy and CPU time. Thus, HNN-3SATAIS can be used to detect an early error in the VLSI circuit design.

Disrupted reward circuits is associated with cognitive deficits and depression severity in major depressive disorder.

PubMed

Gong, Liang; Yin, Yingying; He, Cancan; Ye, Qing; Bai, Feng; Yuan, Yonggui; Zhang, Haisan; Lv, Luxian; Zhang, Hongxing; Xie, Chunming; Zhang, Zhijun

2017-01-01

Neuroimaging studies have demonstrated that major depressive disorder (MDD) patients show blunted activity responses to reward-related tasks. However, whether abnormal reward circuits affect cognition and depression in MDD patients remains unclear. Seventy-five drug-naive MDD patients and 42 cognitively normal (CN) subjects underwent a resting-state functional magnetic resonance imaging scan. The bilateral nucleus accumbens (NAc) were selected as seeds to construct reward circuits across all subjects. A multivariate linear regression analysis was employed to investigate the neural substrates of cognitive function and depression severity on the reward circuits in MDD patients. The common pathway underlying cognitive deficits and depression was identified with conjunction analysis. Compared with CN subjects, MDD patients showed decreased reward network connectivity that was primarily located in the prefrontal-striatal regions. Importantly, distinct and common neural pathways underlying cognition and depression were identified, implying the independent and synergistic effects of cognitive deficits and depression severity on reward circuits. This study demonstrated that disrupted topological organization within reward circuits was significantly associated with cognitive deficits and depression severity in MDD patients. These findings suggest that in addition to antidepressant treatment, normalized reward circuits should be a focus and a target for improving depression and cognitive deficits in MDD patients. Copyright © 2016 Elsevier Ltd. All rights reserved.

What is CAR doing in the middle of the adult neurogenic road?

PubMed Central

Junyent, Felix; Coré, Nathalie; Cremer, Harold

2017-01-01

ABSTRACT The molecular and cellular basis of adult neurogenesis has attracted considerable attention for fundamental and clinical applications because neural stem cells and newborn neurons may, one day, be harnessed to replace neurons and allow cognitive improvement in the diseased brain. In rodents, neural progenitors are located in the dentate gyrus and the sub/periventricular zone. In the dentate gyrus the generation of newborn neurons is associated with plasticity, including regulation of memory. The role of subventricular zone neural precursors that migrate to the olfactory bulb is less characterized. Identifying factors that impact neural stem cell proliferation, migration and differentiation is therefore sine qua non before we can harness their potential. Here, we expand upon our recent results showing that CAR, the coxsackievirus and adenovirus receptor, is among the developing list of key players when it comes to the complex process of integrating newborn neurons into existing circuits in the mature brain. PMID:28516108

Oscillation-Induced Signal Transmission and Gating in Neural Circuits

PubMed Central

Jahnke, Sven; Memmesheimer, Raoul-Martin; Timme, Marc

2014-01-01

Reliable signal transmission constitutes a key requirement for neural circuit function. The propagation of synchronous pulse packets through recurrent circuits is hypothesized to be one robust form of signal transmission and has been extensively studied in computational and theoretical works. Yet, although external or internally generated oscillations are ubiquitous across neural systems, their influence on such signal propagation is unclear. Here we systematically investigate the impact of oscillations on propagating synchrony. We find that for standard, additive couplings and a net excitatory effect of oscillations, robust propagation of synchrony is enabled in less prominent feed-forward structures than in systems without oscillations. In the presence of non-additive coupling (as mediated by fast dendritic spikes), even balanced oscillatory inputs may enable robust propagation. Here, emerging resonances create complex locking patterns between oscillations and spike synchrony. Interestingly, these resonances make the circuits capable of selecting specific pathways for signal transmission. Oscillations may thus promote reliable transmission and, in co-action with dendritic nonlinearities, provide a mechanism for information processing by selectively gating and routing of signals. Our results are of particular interest for the interpretation of sharp wave/ripple complexes in the hippocampus, where previously learned spike patterns are replayed in conjunction with global high-frequency oscillations. We suggest that the oscillations may serve to stabilize the replay. PMID:25503492

Adaptive Neurotechnology for Making Neural Circuits Functional .

NASA Astrophysics Data System (ADS)

Jung, Ranu

2008-03-01

Two of the most important trends in recent technological developments are that technology is increasingly integrated with biological systems and that it is increasingly adaptive in its capabilities. Neuroprosthetic systems that provide lost sensorimotor function after a neural disability offer a platform to investigate this interplay between biological and engineered systems. Adaptive neurotechnology (hardware and software) could be designed to be biomimetic, guided by the physical and programmatic constraints observed in biological systems, and allow for real-time learning, stability, and error correction. An example will present biomimetic neural-network hardware that can be interfaced with the isolated spinal cord of a lower vertebrate to allow phase-locked real-time neural control. Another will present adaptive neural network control algorithms for functional electrical stimulation of the peripheral nervous system to provide desired movements of paralyzed limbs in rodents or people. Ultimately, the frontier lies in being able to utilize the adaptive neurotechnology to promote neuroplasticity in the living system on a long-time scale under co-adaptive conditions.

Role of neurotrophins in the development and function of neural circuits that regulate energy homeostasis.

PubMed

Fargali, Samira; Sadahiro, Masato; Jiang, Cheng; Frick, Amy L; Indall, Tricia; Cogliani, Valeria; Welagen, Jelle; Lin, Wei-Jye; Salton, Stephen R

2012-11-01

Members of the neurotrophin family, including nerve growth factor, brain-derived neurotrophic factor, neurotrophin-3, and neurotrophin-4/5, and other neurotrophic growth factors such as ciliary neurotrophic factor and artemin, regulate peripheral and central nervous system development and function. A subset of the neurotrophin-dependent pathways in the hypothalamus, brainstem, and spinal cord, and those that project via the sympathetic nervous system to peripheral metabolic tissues including brown and white adipose tissue, muscle and liver, regulate feeding, energy storage, and energy expenditure. We briefly review the role that neurotrophic growth factors play in energy balance, as regulators of neuronal survival and differentiation, neurogenesis, and circuit formation and function, and as inducers of critical gene products that control energy homeostasis.

Reward from bugs to bipeds: a comparative approach to understanding how reward circuits function

PubMed Central

Scaplen, Kristin M.; Kaun, Karla R.

2016-01-01

Abstract In a complex environment, animals learn from their responses to stimuli and events. Appropriate response to reward and punishment can promote survival, reproduction and increase evolutionary fitness. Interestingly, the neural processes underlying these responses are remarkably similar across phyla. In all species, dopamine is central to encoding reward and directing motivated behaviors, however, a comprehensive understanding of how circuits encode reward and direct motivated behaviors is still lacking. In part, this is a result of the sheer diversity of neurons, the heterogeneity of their responses and the complexity of neural circuits within which they are found. We argue that general features of reward circuitry are common across model organisms, and thus principles learned from invertebrate model organisms can inform research across species. In particular, we discuss circuit motifs that appear to be functionally equivalent from flies to primates. We argue that a comparative approach to studying and understanding reward circuit function provides a more comprehensive understanding of reward circuitry, and informs disorders that affect the brain’s reward circuitry. PMID:27328845

Novel Roles for Immune Molecules in Neural Development: Implications for Neurodevelopmental Disorders

PubMed Central

Garay, Paula A.; McAllister, A. Kimberley

2010-01-01

Although the brain has classically been considered “immune-privileged”, current research suggests an extensive communication between the immune and nervous systems in both health and disease. Recent studies demonstrate that immune molecules are present at the right place and time to modulate the development and function of the healthy and diseased central nervous system (CNS). Indeed, immune molecules play integral roles in the CNS throughout neural development, including affecting neurogenesis, neuronal migration, axon guidance, synapse formation, activity-dependent refinement of circuits, and synaptic plasticity. Moreover, the roles of individual immune molecules in the nervous system may change over development. This review focuses on the effects of immune molecules on neuronal connections in the mammalian central nervous system – specifically the roles for MHCI and its receptors, complement, and cytokines on the function, refinement, and plasticity of geniculate, cortical and hippocampal synapses, and their relationship to neurodevelopmental disorders. These functions for immune molecules during neural development suggest that they could also mediate pathological responses to chronic elevations of cytokines in neurodevelopmental disorders, including autism spectrum disorders (ASD) and schizophrenia. PMID:21423522

Integration of nanoscale memristor synapses in neuromorphic computing architectures

NASA Astrophysics Data System (ADS)

Indiveri, Giacomo; Linares-Barranco, Bernabé; Legenstein, Robert; Deligeorgis, George; Prodromakis, Themistoklis

2013-09-01

Conventional neuro-computing architectures and artificial neural networks have often been developed with no or loose connections to neuroscience. As a consequence, they have largely ignored key features of biological neural processing systems, such as their extremely low-power consumption features or their ability to carry out robust and efficient computation using massively parallel arrays of limited precision, highly variable, and unreliable components. Recent developments in nano-technologies are making available extremely compact and low power, but also variable and unreliable solid-state devices that can potentially extend the offerings of availing CMOS technologies. In particular, memristors are regarded as a promising solution for modeling key features of biological synapses due to their nanoscale dimensions, their capacity to store multiple bits of information per element and the low energy required to write distinct states. In this paper, we first review the neuro- and neuromorphic computing approaches that can best exploit the properties of memristor and scale devices, and then propose a novel hybrid memristor-CMOS neuromorphic circuit which represents a radical departure from conventional neuro-computing approaches, as it uses memristors to directly emulate the biophysics and temporal dynamics of real synapses. We point out the differences between the use of memristors in conventional neuro-computing architectures and the hybrid memristor-CMOS circuit proposed, and argue how this circuit represents an ideal building block for implementing brain-inspired probabilistic computing paradigms that are robust to variability and fault tolerant by design.

Neurodynamic oscillators

NASA Technical Reports Server (NTRS)

Espinosa, Ismael; Gonzalez, Hortensia; Quiza, Jorge; Gonazalez, J. Jesus; Arroyo, Ruben; Lara, Ritaluz

1995-01-01

Oscillation of electrical activity has been found in many nervous systems, from invertebrates to vertebrates including man. There exists experimental evidence of very simple circuits with the capability of oscillation. Neurons with intrinsic oscillation have been found and also neural circuits where oscillation is a property of the network. These two types of oscillations coexist in many instances. It is nowadays hypothesized that behind synchronization and oscillation there is a system of coupled oscillators responsible for activities that range from locomotion and feature binding in vision to control of sleep and circadian rhythms. The huge knowledge that has been acquired on oscillators from the times of Lord Rayleigh has made the simulation of neural oscillators a very active endeavor. This has been enhanced with more recent physiological findings about small neural circuits by means of intracellular and extracellular recordings as well as imaging methods. The future of this interdisciplinary field looks very promising; some researchers are going into quantum mechanics with the idea of trying to provide a quantum description of the brain. In this work we describe some simulations using neuron models by means of which we form simple neural networks that have the capability of oscillation. We analyze the oscillatory activity with root locus method, cross-correlation histograms, and phase planes. In the more complicated neural network models there is the possibility of chaotic oscillatory activity and we study that by means of Lyapunov exponents. The companion paper shows an example of that kind.

Neural Control of the Lower Urinary Tract

PubMed Central

de Groat, William C.; Griffiths, Derek; Yoshimura, Naoki

2015-01-01

This article summarizes anatomical, neurophysiological, pharmacological, and brain imaging studies in humans and animals that have provided insights into the neural circuitry and neurotransmitter mechanisms controlling the lower urinary tract. The functions of the lower urinary tract to store and periodically eliminate urine are regulated by a complex neural control system in the brain, spinal cord, and peripheral autonomic ganglia that coordinates the activity of smooth and striated muscles of the bladder and urethral outlet. The neural control of micturition is organized as a hierarchical system in which spinal storage mechanisms are in turn regulated by circuitry in the rostral brain stem that initiates reflex voiding. Input from the forebrain triggers voluntary voiding by modulating the brain stem circuitry. Many neural circuits controlling the lower urinary tract exhibit switch-like patterns of activity that turn on and off in an all-or-none manner. The major component of the micturition switching circuit is a spinobulbospinal parasympathetic reflex pathway that has essential connections in the periaqueductal gray and pontine micturition center. A computer model of this circuit that mimics the switching functions of the bladder and urethra at the onset of micturition is described. Micturition occurs involuntarily in infants and young children until the age of 3 to 5 years, after which it is regulated voluntarily. Diseases or injuries of the nervous system in adults can cause the re-emergence of involuntary micturition, leading to urinary incontinence. Neuroplasticity underlying these developmental and pathological changes in voiding function is discussed. PMID:25589273

Navigating the Neural Space in Search of the Neural Code.

PubMed

Jazayeri, Mehrdad; Afraz, Arash

2017-03-08

The advent of powerful perturbation tools, such as optogenetics, has created new frontiers for probing causal dependencies in neural and behavioral states. These approaches have significantly enhanced the ability to characterize the contribution of different cells and circuits to neural function in health and disease. They have shifted the emphasis of research toward causal interrogations and increased the demand for more precise and powerful tools to control and manipulate neural activity. Here, we clarify the conditions under which measurements and perturbations support causal inferences. We note that the brain functions at multiple scales and that causal dependencies may be best inferred with perturbation tools that interface with the system at the appropriate scale. Finally, we develop a geometric framework to facilitate the interpretation of causal experiments when brain perturbations do or do not respect the intrinsic patterns of brain activity. We describe the challenges and opportunities of applying perturbations in the presence of dynamics, and we close with a general perspective on navigating the activity space of neurons in the search for neural codes. Copyright © 2017 Elsevier Inc. All rights reserved.

Optogenetics and computer vision for Caenorhabditis elegans neuroscience and other biophysical applications

NASA Astrophysics Data System (ADS)

Leifer, Andrew Michael

2011-07-01

This work presents optogenetics and real-time computer vision techniques to non-invasively manipulate and monitor neural activity with high spatiotemporal resolution in awake behaving Caenorhabditis elegans. These methods were employed to dissect the nematode's mechanosensory and motor circuits and to elucidate the neural control of wave propagation during forward locomotion. Additionally, similar computer vision methods were used to automatically detect and decode fluorescing DNA origami nanobarcodes, a new class of fluorescent reporter constructs. An optogenetic instrument capable of real-time light delivery with high spatiotemporal resolution to specified targets in freely moving C. elegans, the first such instrument of its kind, was developed. The instrument was used to probe the nematode's mechanosensory circuit, demonstrating that stimulation of a single mechanosensory neuron suffices to induce reversals. The instrument was also used to probe the motor circuit, demonstrating that inhibition of regions of cholinergic motor neurons blocks undulatory wave propagation and that muscle contractions can persist even without inputs from the motor neurons. The motor circuit was further probed using optogenetics and microfluidic techniques. Undulatory wave propagation during forward locomotion was observed to depend on stretch-sensitive signaling mediated by cholinergic motor neurons. Specifically, posterior body segments are compelled, through stretch-sensitive feedback, to bend in the same direction as anterior segments. This is the first explicit demonstration of such feedback and serves as a foundation for understanding motor circuits in other organisms. A real-time tracking system was developed to record intracellular calcium transients in single neurons while simultaneously monitoring macroscopic behavior of freely moving C. elegans. This was used to study the worm's stereotyped reversal behavior, the omega turn. Calcium transients corresponding to temporal features of the omega turn were observed in interneurons AVA and AVB. Optics and computer vision techniques similar to those developed for the C. elegans experiments were also used to detect DNA origami nanorod barcodes. An optimal Bayesian multiple hypothesis test was deployed to unambiguously classify each barcode as a member of one of 216 distinct barcode species. Overall, this set of experiments demonstrates the powerful role that optogenetics and computer vision can play in behavioral neuroscience and quantitative biophysics.

Developmental insights into mature cognition.

PubMed

Keil, Frank C

2015-02-01

Three cases are described that illustrate new ways in which developmental research is informing the study of cognition in adults: statistical learning, neural substrates of cognition, and extended concepts. Developmental research has made clear the ubiquity of statistical learning while also revealing is limitations as a stand-alone way to acquire knowledge. With respect to neural substrates, development has uncovered links between executive processing and fronto-striatal circuits while also pointing to many aspects of high-level cognition that may not be neatly reducible to coherent neural descriptions. For extended concepts, children have made especially clear the weaknesses of intuitive theories in both children and adults while also illustrating other cognitive capacities that are used at all ages to navigate the socially distributed aspects of knowledge. Copyright © 2014 Elsevier B.V. All rights reserved.

(abstract) A High Throughput 3-D Inner Product Processor

NASA Technical Reports Server (NTRS)

Daud, Tuan

1996-01-01

A particularily challenging image processing application is the real time scene acquisition and object discrimination. It requires spatio-temporal recognition of point and resolved objects at high speeds with parallel processing algorithms. Neural network paradigms provide fine grain parallism and, when implemented in hardware, offer orders of magnitude speed up. However, neural networks implemented on a VLSI chip are planer architectures capable of efficient processing of linear vector signals rather than 2-D images. Therefore, for processing of images, a 3-D stack of neural-net ICs receiving planar inputs and consuming minimal power are required. Details of the circuits with chip architectures will be described with need to develop ultralow-power electronics. Further, use of the architecture in a system for high-speed processing will be illustrated.

Concise review: Pax6 transcription factor contributes to both embryonic and adult neurogenesis as a multifunctional regulator.

PubMed

Osumi, Noriko; Shinohara, Hiroshi; Numayama-Tsuruta, Keiko; Maekawa, Motoko

2008-07-01

Pax6 is a highly conserved transcription factor among vertebrates and is important in various developmental processes in the central nervous system (CNS), including patterning of the neural tube, migration of neurons, and formation of neural circuits. In this review, we focus on the role of Pax6 in embryonic and postnatal neurogenesis, namely, production of new neurons from neural stem/progenitor cells, because Pax6 is intensely expressed in these cells from the initial stage of CNS development and in neurogenic niches (the subgranular zone of the hippocampal dentate gyrus and the subventricular zone of the lateral ventricle) throughout life. Pax6 is a multifunctional player regulating proliferation and differentiation through the control of expression of different downstream molecules in a highly context-dependent manner.

Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks

PubMed Central

2017-01-01

Decoding neural activities related to voluntary and involuntary movements is fundamental to understanding human brain motor circuits and neuromotor disorders and can lead to the development of neuromotor prosthetic devices for neurorehabilitation. This study explores using recorded deep brain local field potentials (LFPs) for robust movement decoding of Parkinson's disease (PD) and Dystonia patients. The LFP data from voluntary movement activities such as left and right hand index finger clicking were recorded from patients who underwent surgeries for implantation of deep brain stimulation electrodes. Movement-related LFP signal features were extracted by computing instantaneous power related to motor response in different neural frequency bands. An innovative neural network ensemble classifier has been proposed and developed for accurate prediction of finger movement and its forthcoming laterality. The ensemble classifier contains three base neural network classifiers, namely, feedforward, radial basis, and probabilistic neural networks. The majority voting rule is used to fuse the decisions of the three base classifiers to generate the final decision of the ensemble classifier. The overall decoding performance reaches a level of agreement (kappa value) at about 0.729 ± 0.16 for decoding movement from the resting state and about 0.671 ± 0.14 for decoding left and right visually cued movements. PMID:29201041

A model study on the circuit mechanism underlying decision-making in Drosophila.

PubMed

Wu, Zhihua; Guo, Aike

2011-05-01

Previous elegant experiments in a flight simulator showed that conditioned Drosophila is able to make a clear-cut decision to avoid potential danger. When confronted with conflicting visual cues, the relative saliency of two competing cues is found to be a sensory ruler for flies to judge which cue should be used for decision-making. Further genetic manipulations and immunohistological analysis revealed that the dopamine system and mushroom bodies are indispensable for such a clear-cut or nonlinear decision. The neural circuit mechanism, however, is far from being clear. In this paper, we adopt a computational modeling approach to investigate how different brain areas and the dopamine system work together to drive a fly to make a decision. By developing a systems-level neural network, a two-pathway circuit is proposed. Besides a direct pathway from a feature binding area to the motor center, another connects two areas via the mushroom body, a target of dopamine release. A raised dopamine level is hypothesized to be induced by complex choice tasks and to enhance lateral inhibition and steepen the units' response gain in the mushroom body. Simulations show that training helps to assign values to formerly neutral features. For a circuit model with a blocked mushroom body, the direct pathway passes all alternatives to the motor center without changing original values, giving rise to a simple choice characterized by a linear choice curve. With respect to an intact circuit, enhanced lateral inhibition dependent on dopamine critically promotes competition between alternatives, turning the linear- into nonlinear choice behavior. Results account well for experimental data, supporting the reasonableness of model working hypotheses. Several testable predictions are made for future studies. Copyright © 2011 Elsevier Ltd. All rights reserved.

Insights into Rapid Modulation of Neuroplasticity by Brain Estrogens

PubMed Central

Woolfrey, Kevin M.; Penzes, Peter

2013-01-01

Converging evidence from cellular, electrophysiological, anatomic, and behavioral studies suggests that the remodeling of synapse structure and function is a critical component of cognition. This modulation of neuroplasticity can be achieved through the actions of numerous extracellular signals. Moreover, it is thought that it is the integration of different extracellular signals regulation of neuroplasticity that greatly influences cognitive function. One group of signals that exerts powerful effects on multiple neurologic processes is estrogens. Classically, estrogens have been described to exert their effects over a period of hours to days. However, there is now increasing evidence that estrogens can rapidly influence multiple behaviors, including those that require forebrain neural circuitry. Moreover, these effects are found in both sexes. Critically, it is now emerging that the modulation of cognition by rapid estrogenic signaling is achieved by activation of specific signaling cascades and regulation of synapse structure and function, cumulating in the rewiring of neural circuits. The importance of understanding the rapid effects of estrogens on forebrain function and circuitry is further emphasized as investigations continue to consider the potential of estrogenic-based therapies for neuropathologies. This review focuses on how estrogens can rapidly influence cognition and the emerging mechanisms that underlie these effects. We discuss the potential sources and the biosynthesis of estrogens within the brain and the consequences of rapid estrogenic-signaling on the remodeling of neural circuits. Furthermore, we argue that estrogens act via distinct signaling pathways to modulate synapse structure and function in a manner that may vary with cell type, developmental stage, and sex. Finally, we present a model in which the coordination of rapid estrogenic-signaling and activity-dependent stimuli can result in long-lasting changes in neural circuits, contributing to cognition, with potential relevance for the development of novel estrogenic-based therapies for neurodevelopmental or neurodegenerative disorders. PMID:24076546

Is social attachment an addictive disorder?

PubMed

Insel, Thomas R

2003-08-01

There is a considerable literature on the neurobiology of reward, based largely on studies of addiction or substance abuse. This review considers the possibility that the neural circuits that mediate reward evolved for ethologically relevant cues, such as social attachment. Specifically, mesocorticolimbic dopamine appears important for maternal behavior in rats and pair bonding in monogamous voles. It is not yet clear that dopamine in this pathway mediates the hedonic properties of social bond formation or whether dopamine's role is more relevant to developing associative networks or assigning salience to social stimuli. The neuropeptides oxytocin (OT) and vasopressin (AVP) appear to be critical for linking social signals to the mesocorticolimbic circuit.

Neuroendocrine circuits governing energy balance and stress regulation: functional overlap and therapeutic implications

PubMed Central

Ulrich-Lai, Yvonne M.; Ryan, Karen K.

2014-01-01

Significant co-morbidities between obesity-related metabolic disease and stress-related psychological disorders suggest important functional interactions between energy balance and brain stress integration. Largely overlapping neural circuits control these systems, and this anatomical arrangement optimizes opportunities for mutual influence. Here we first review the current literature identifying effects of metabolic neuroendocrine signals on stress regulation, and vice versa. Next, the contributions of reward driven food intake to these metabolic and stress interactions are discussed. Lastly, we consider the inter-relationships among metabolism, stress and reward in light of their important implications in the development of therapies for metabolism- or stress-related disease. PMID:24630812

The participation of cortical amygdala in innate, odor-driven behavior

PubMed Central

Root, Cory M.; Denny, Christine A.; Hen, René; Axel, Richard

2014-01-01

Innate behaviors are observed in naïve animals without prior learning or experience, suggesting that the neural circuits that mediate these behaviors are genetically determined and stereotyped. The neural circuits that convey olfactory information from the sense organ to the cortical and subcortical olfactory centers have been anatomically defined1-3 but the specific pathways responsible for innate responses to volatile odors have not been identified. We have devised genetic strategies that demonstrate that a stereotyped neural circuit that transmits information from the olfactory bulb to cortical amygdala is necessary for innate aversive and appetitive behaviors. Moreover, we have employed the promoter of the activity-dependent gene, arc, to express the photosensitive ion channel, channelrhodopsin, in neurons of the cortical amygdala activated by odors that elicit innate behaviors. Optical activation of these neurons leads to appropriate behaviors that recapitulate the responses to innate odors. These data indicate that the cortical amygdala plays a critical role in the generation of innate odor-driven behaviors but do not preclude the participation of cortical amygdala in learned olfactory behaviors. PMID:25383519

Tonic signaling from O2 sensors sets neural circuit activity and behavioral state

PubMed Central

Busch, Karl Emanuel; Laurent, Patrick; Soltesz, Zoltan; Murphy, Robin Joseph; Faivre, Olivier; Hedwig, Berthold; Thomas, Martin; Smith, Heather L.; de Bono, Mario

2012-01-01

Tonic receptors convey stimulus duration and intensity and are implicated in homeostatic control. However, how tonic homeostatic signals are generated, and how they reconfigure neural circuits and modify animal behavior is poorly understood. Here we show that C. elegans O2-sensing neurons are tonic receptors that continuously signal ambient [O2] to set the animal’s behavioral state. Sustained signalling relies on a Ca2+ relay involving L-type voltage-gated Ca2+ channels, the ryanodine and the IP3 receptors. Tonic activity evokes continuous neuropeptide release, which helps elicit the enduring behavioral state associated with high [O2]. Sustained O2 receptor signalling is propagated to downstream neural circuits, including the hub interneuron RMG. O2 receptors evoke similar locomotory states at particular [O2], regardless of previous d[O2]/dt. However, a phasic component of the URX receptors’ response to high d[O2]/dt, as well as tonic-to-phasic transformations in downstream interneurons, enable transient reorientation movements shaped by d[O2]/dt. Our results highlight how tonic homeostatic signals can generate both transient and enduring behavioral change. PMID:22388961

Tunable Low Energy, Compact and High Performance Neuromorphic Circuit for Spike-Based Synaptic Plasticity

PubMed Central

Rahimi Azghadi, Mostafa; Iannella, Nicolangelo; Al-Sarawi, Said; Abbott, Derek

2014-01-01

Cortical circuits in the brain have long been recognised for their information processing capabilities and have been studied both experimentally and theoretically via spiking neural networks. Neuromorphic engineers are primarily concerned with translating the computational capabilities of biological cortical circuits, using the Spiking Neural Network (SNN) paradigm, into in silico applications that can mimic the behaviour and capabilities of real biological circuits/systems. These capabilities include low power consumption, compactness, and relevant dynamics. In this paper, we propose a new accelerated-time circuit that has several advantages over its previous neuromorphic counterparts in terms of compactness, power consumption, and capability to mimic the outcomes of biological experiments. The presented circuit simulation results demonstrate that, in comparing the new circuit to previous published synaptic plasticity circuits, reduced silicon area and lower energy consumption for processing each spike is achieved. In addition, it can be tuned in order to closely mimic the outcomes of various spike timing- and rate-based synaptic plasticity experiments. The proposed circuit is also investigated and compared to other designs in terms of tolerance to mismatch and process variation. Monte Carlo simulation results show that the proposed design is much more stable than its previous counterparts in terms of vulnerability to transistor mismatch, which is a significant challenge in analog neuromorphic design. All these features make the proposed design an ideal circuit for use in large scale SNNs, which aim at implementing neuromorphic systems with an inherent capability that can adapt to a continuously changing environment, thus leading to systems with significant learning and computational abilities. PMID:24551089

Tunable low energy, compact and high performance neuromorphic circuit for spike-based synaptic plasticity.

PubMed

Rahimi Azghadi, Mostafa; Iannella, Nicolangelo; Al-Sarawi, Said; Abbott, Derek

2014-01-01

Cortical circuits in the brain have long been recognised for their information processing capabilities and have been studied both experimentally and theoretically via spiking neural networks. Neuromorphic engineers are primarily concerned with translating the computational capabilities of biological cortical circuits, using the Spiking Neural Network (SNN) paradigm, into in silico applications that can mimic the behaviour and capabilities of real biological circuits/systems. These capabilities include low power consumption, compactness, and relevant dynamics. In this paper, we propose a new accelerated-time circuit that has several advantages over its previous neuromorphic counterparts in terms of compactness, power consumption, and capability to mimic the outcomes of biological experiments. The presented circuit simulation results demonstrate that, in comparing the new circuit to previous published synaptic plasticity circuits, reduced silicon area and lower energy consumption for processing each spike is achieved. In addition, it can be tuned in order to closely mimic the outcomes of various spike timing- and rate-based synaptic plasticity experiments. The proposed circuit is also investigated and compared to other designs in terms of tolerance to mismatch and process variation. Monte Carlo simulation results show that the proposed design is much more stable than its previous counterparts in terms of vulnerability to transistor mismatch, which is a significant challenge in analog neuromorphic design. All these features make the proposed design an ideal circuit for use in large scale SNNs, which aim at implementing neuromorphic systems with an inherent capability that can adapt to a continuously changing environment, thus leading to systems with significant learning and computational abilities.

Wireless neural recording with single low-power integrated circuit.

PubMed

Harrison, Reid R; Kier, Ryan J; Chestek, Cynthia A; Gilja, Vikash; Nuyujukian, Paul; Ryu, Stephen; Greger, Bradley; Solzbacher, Florian; Shenoy, Krishna V

2009-08-01

We present benchtop and in vivo experimental results from an integrated circuit designed for wireless implantable neural recording applications. The chip, which was fabricated in a commercially available 0.6- mum 2P3M BiCMOS process, contains 100 amplifiers, a 10-bit analog-to-digital converter (ADC), 100 threshold-based spike detectors, and a 902-928 MHz frequency-shift-keying (FSK) transmitter. Neural signals from a selected amplifier are sampled by the ADC at 15.7 kSps and telemetered over the FSK wireless data link. Power, clock, and command signals are sent to the chip wirelessly over a 2.765-MHz inductive (coil-to-coil) link. The chip is capable of operating with only two off-chip components: a power/command receiving coil and a 100-nF capacitor.

Competing dopamine neurons drive oviposition choice for ethanol in Drosophila.

PubMed

Azanchi, Reza; Kaun, Karla R; Heberlein, Ulrike

2013-12-24

The neural circuits that mediate behavioral choice evaluate and integrate information from the environment with internal demands and then initiate a behavioral response. Even circuits that support simple decisions remain poorly understood. In Drosophila melanogaster, oviposition on a substrate containing ethanol enhances fitness; however, little is known about the neural mechanisms mediating this important choice behavior. Here, we characterize the neural modulation of this simple choice and show that distinct subsets of dopaminergic neurons compete to either enhance or inhibit egg-laying preference for ethanol-containing food. Moreover, activity in α'β' neurons of the mushroom body and a subset of ellipsoid body ring neurons (R2) is required for this choice. We propose a model where competing dopaminergic systems modulate oviposition preference to adjust to changes in natural oviposition substrates.

Closed-Loop and Activity-Guided Optogenetic Control

PubMed Central

Grosenick, Logan; Marshel, James H.; Deisseroth, Karl

2016-01-01

Advances in optical manipulation and observation of neural activity have set the stage for widespread implementation of closed-loop and activity-guided optical control of neural circuit dynamics. Closing the loop optogenetically (i.e., basing optogenetic stimulation on simultaneously observed dynamics in a principled way) is a powerful strategy for causal investigation of neural circuitry. In particular, observing and feeding back the effects of circuit interventions on physiologically relevant timescales is valuable for directly testing whether inferred models of dynamics, connectivity, and causation are accurate in vivo. Here we highlight technical and theoretical foundations as well as recent advances and opportunities in this area, and we review in detail the known caveats and limitations of optogenetic experimentation in the context of addressing these challenges with closed-loop optogenetic control in behaving animals. PMID:25856490

A canonical neural mechanism for behavioral variability

NASA Astrophysics Data System (ADS)

Darshan, Ran; Wood, William E.; Peters, Susan; Leblois, Arthur; Hansel, David

2017-05-01

The ability to generate variable movements is essential for learning and adjusting complex behaviours. This variability has been linked to the temporal irregularity of neuronal activity in the central nervous system. However, how neuronal irregularity actually translates into behavioural variability is unclear. Here we combine modelling, electrophysiological and behavioural studies to address this issue. We demonstrate that a model circuit comprising topographically organized and strongly recurrent neural networks can autonomously generate irregular motor behaviours. Simultaneous recordings of neurons in singing finches reveal that neural correlations increase across the circuit driving song variability, in agreement with the model predictions. Analysing behavioural data, we find remarkable similarities in the babbling statistics of 5-6-month-old human infants and juveniles from three songbird species and show that our model naturally accounts for these `universal' statistics.

Feed Your Head: Neurodevelopmental Control of Feeding and Metabolism

PubMed Central

Lee, Daniel A.; Blackshaw, Seth

2014-01-01

During critical periods of development early in life, excessive or scarce nutritional environments can disrupt the development of central feeding and metabolic neural circuitry, leading to obesity and metabolic disorders in adulthood. A better understanding of the genetic networks that control the development of feeding and metabolic neural circuits, along with knowledge of how and where dietary signals disrupt this process, can serve as the basis for future therapies aimed at reversing the public health crisis that is now building as a result of the global obesity epidemic. This review of animal and human studies highlights recent insights into the molecular mechanisms that regulate the development of central feeding circuitries, the mechanisms by which gestational and early postnatal nutritional status affects this process, and approaches aimed at counteracting the deleterious effects of early over- and underfeeding. PMID:24274739

Emerging Structure–Function Relations in the Developing Face Processing System

PubMed Central

Suzanne Scherf, K.; Thomas, Cibu; Doyle, Jaime; Behrmann, Marlene

2014-01-01

To evaluate emerging structure–function relations in a neural circuit that mediates complex behavior, we investigated age-related differences among cortical regions that support face recognition behavior and the fiber tracts through which they transmit and receive signals using functional neuroimaging and diffusion tensor imaging. In a large sample of human participants (aged 6–23 years), we derived the microstructural and volumetric properties of the inferior longitudinal fasciculus (ILF), the inferior fronto-occipital fasciculus, and control tracts, using independently defined anatomical markers. We also determined the functional characteristics of core face- and place-selective regions that are distributed along the trajectory of the pathways of interest. We observed disproportionately large age-related differences in the volume, fractional anisotropy, and mean and radial, but not axial, diffusivities of the ILF. Critically, these differences in the structural properties of the ILF were tightly and specifically linked with an age-related increase in the size of a key face-selective functional region, the fusiform face area. This dynamic association between emerging structural and functional architecture in the developing brain may provide important clues about the mechanisms by which neural circuits become organized and optimized in the human cortex. PMID:23765156

Temperature-Robust Neural Function from Activity-Dependent Ion Channel Regulation.

PubMed

O'Leary, Timothy; Marder, Eve

2016-11-07

Many species of cold-blooded animals experience substantial and rapid fluctuations in body temperature. Because biological processes are differentially temperature dependent, it is difficult to understand how physiological processes in such animals can be temperature robust [1-8]. Experiments have shown that core neural circuits, such as the pyloric circuit of the crab stomatogastric ganglion (STG), exhibit robust neural activity in spite of large (20°C) temperature fluctuations [3, 5, 7, 8]. This robustness is surprising because (1) each neuron has many different kinds of ion channels with different temperature dependencies (Q 10 s) that interact in a highly nonlinear way to produce firing patterns and (2) across animals there is substantial variability in conductance densities that nonetheless produce almost identical firing properties. The high variability in conductance densities in these neurons [9, 10] appears to contradict the possibility that robustness is achieved through precise tuning of key temperature-dependent processes. In this paper, we develop a theoretical explanation for how temperature robustness can emerge from a simple regulatory control mechanism that is compatible with highly variable conductance densities [11-13]. The resulting model suggests a general mechanism for how nervous systems and excitable tissues can exploit degenerate relationships among temperature-sensitive processes to achieve robust function. Copyright © 2016 Elsevier Ltd. All rights reserved.

Evolutionary mechanisms that generate morphology and neural-circuit diversity of the cerebellum.

PubMed

Hibi, Masahiko; Matsuda, Koji; Takeuchi, Miki; Shimizu, Takashi; Murakami, Yasunori

2017-05-01

The cerebellum is derived from the dorsal part of the anterior-most hindbrain. The vertebrate cerebellum contains glutamatergic granule cells (GCs) and gamma-aminobutyric acid (GABA)ergic Purkinje cells (PCs). These cerebellar neurons are generated from neuronal progenitors or neural stem cells by mechanisms that are conserved among vertebrates. However, vertebrate cerebella are widely diverse with respect to their gross morphology and neural circuits. The cerebellum of cyclostomes, the basal vertebrates, has a negligible structure. Cartilaginous fishes have a cerebellum containing GCs, PCs, and deep cerebellar nuclei (DCNs), which include projection neurons. Ray-finned fish lack DCNs but have projection neurons termed eurydendroid cells (ECs) in the vicinity of the PCs. Among ray-finned fishes, the cerebellum of teleost zebrafish has a simple lobular structure, whereas that of weakly electric mormyrid fish is large and foliated. Amniotes, which include mammals, independently evolved a large, foliated cerebellum, which contains massive numbers of GCs and has functional connections with the dorsal telencephalon (neocortex). Recent studies of cyclostomes and cartilaginous fish suggest that the genetic program for cerebellum development was already encoded in the genome of ancestral vertebrates. In this review, we discuss how alterations of the genetic and cellular programs generated diversity of the cerebellum during evolution. © 2017 Japanese Society of Developmental Biologists.

Neuromodulation: Selected approaches and challenges

PubMed Central

Parpura, Vladimir; Silva, Gabriel A.; Tass, Peter A.; Bennet, Kevin E.; Meyyappan, Meyya; Koehne, Jessica; Lee, Kendall H.; Andrews, Russell J.

2012-01-01

The brain operates through complex interactions in the flow of information and signal processing within neural networks. The “wiring” of such networks, being neuronal or glial, can physically and/or functionally go rogue in various pathological states. Neuromodulation, as a multidisciplinary venture, attempts to correct such faulty nets. In this review, selected approaches and challenges in neuromoduation are discussed. The use of water-dispersible carbon nanotubes have proven effective in modulation of neurite outgrowth in culture as well as in aiding regeneration after spinal cord injury in vivo. Studying neural circuits using computational biology and analytical engineering approaches brings to light geometrical mapping of dynamics within neural networks, much needed information for stimulation interventions in medical practice. Indeed, sophisticated desynchronization approaches used for brain stimulation have been successful in coaxing “misfiring” neuronal circuits to resume productive firing patterns in various human disorders. Devices have been developed for the real time measurement of various neurotransmitters as well as electrical activity in the human brain during electrical deep brain stimulation. Such devices can establish the dynamics of electrochemical changes in the brain during stimulation. With increasing application of nanomaterials in devices for electrical and chemical recording and stimulating in the brain, the era of cellular, and even intracellular, precision neuromodulation will soon be upon us. PMID:23190025

An improved fault detection classification and location scheme based on wavelet transform and artificial neural network for six phase transmission line using single end data only.

PubMed

Koley, Ebha; Verma, Khushaboo; Ghosh, Subhojit

2015-01-01

Restrictions on right of way and increasing power demand has boosted development of six phase transmission. It offers a viable alternative for transmitting more power, without major modification in existing structure of three phase double circuit transmission system. Inspite of the advantages, low acceptance of six phase system is attributed to the unavailability of a proper protection scheme. The complexity arising from large number of possible faults in six phase lines makes the protection quite challenging. The proposed work presents a hybrid wavelet transform and modular artificial neural network based fault detector, classifier and locator for six phase lines using single end data only. The standard deviation of the approximate coefficients of voltage and current signals obtained using discrete wavelet transform are applied as input to the modular artificial neural network for fault classification and location. The proposed scheme has been tested for all 120 types of shunt faults with variation in location, fault resistance, fault inception angles. The variation in power system parameters viz. short circuit capacity of the source and its X/R ratio, voltage, frequency and CT saturation has also been investigated. The result confirms the effectiveness and reliability of the proposed protection scheme which makes it ideal for real time implementation.

Neuronal pathway finding: from neurons to initial neural networks.

PubMed

Roscigno, Cecelia I

2004-10-01

Neuronal pathway finding is crucial for structured cellular organization and development of neural circuits within the nervous system. Neuronal pathway finding within the visual system has been extensively studied and therefore is used as a model to review existing knowledge regarding concepts of this developmental process. General principles of neuron pathway finding throughout the nervous system exist. Comprehension of these concepts guides neuroscience nurses in gaining an understanding of the developmental course of action, the implications of different anomalies, as well as the theoretical basis and nursing implications of some provocative new therapies being proposed to treat neurodegenerative diseases and neurologic injuries. These therapies have limitations in light of current ethical, developmental, and delivery modes and what is known about the development of neuronal pathways.

Optical implementation of neural learning algorithms based on cross-gain modulation in a semiconductor optical amplifier

NASA Astrophysics Data System (ADS)

Li, Qiang; Wang, Zhi; Le, Yansi; Sun, Chonghui; Song, Xiaojia; Wu, Chongqing

2016-10-01

Neuromorphic engineering has a wide range of applications in the fields of machine learning, pattern recognition, adaptive control, etc. Photonics, characterized by its high speed, wide bandwidth, low power consumption and massive parallelism, is an ideal way to realize ultrafast spiking neural networks (SNNs). Synaptic plasticity is believed to be critical for learning, memory and development in neural circuits. Experimental results have shown that changes of synapse are highly dependent on the relative timing of pre- and postsynaptic spikes. Synaptic plasticity in which presynaptic spikes preceding postsynaptic spikes results in strengthening, while the opposite timing results in weakening is called antisymmetric spike-timing-dependent plasticity (STDP) learning rule. And synaptic plasticity has the opposite effect under the same conditions is called antisymmetric anti-STDP learning rule. We proposed and experimentally demonstrated an optical implementation of neural learning algorithms, which can achieve both of antisymmetric STDP and anti-STDP learning rule, based on the cross-gain modulation (XGM) within a single semiconductor optical amplifier (SOA). The weight and height of the potentitation and depression window can be controlled by adjusting the injection current of the SOA, to mimic the biological antisymmetric STDP and anti-STDP learning rule more realistically. As the injection current increases, the width of depression and potentitation window decreases and height increases, due to the decreasing of recovery time and increasing of gain under a stronger injection current. Based on the demonstrated optical STDP circuit, ultrafast learning in optical SNNs can be realized.

A delicate balance: role of MMP-9 in brain development and pathophysiology of neurodevelopmental disorders

PubMed Central

Reinhard, Sarah M.; Razak, Khaleel; Ethell, Iryna M.

2015-01-01

The extracellular matrix (ECM) is a critical regulator of neural network development and plasticity. As neuronal circuits develop, the ECM stabilizes synaptic contacts, while its cleavage has both permissive and active roles in the regulation of plasticity. Matrix metalloproteinase 9 (MMP-9) is a member of a large family of zinc-dependent endopeptidases that can cleave ECM and several cell surface receptors allowing for synaptic and circuit level reorganization. It is becoming increasingly clear that the regulated activity of MMP-9 is critical for central nervous system (CNS) development. In particular, MMP-9 has a role in the development of sensory circuits during early postnatal periods, called ‘critical periods.’ MMP-9 can regulate sensory-mediated, local circuit reorganization through its ability to control synaptogenesis, axonal pathfinding and myelination. Although activity-dependent activation of MMP-9 at specific synapses plays an important role in multiple plasticity mechanisms throughout the CNS, misregulated activation of the enzyme is implicated in a number of neurodegenerative disorders, including traumatic brain injury, multiple sclerosis, and Alzheimer’s disease. Growing evidence also suggests a role for MMP-9 in the pathophysiology of neurodevelopmental disorders including Fragile X Syndrome. This review outlines the various actions of MMP-9 during postnatal brain development, critical for future studies exploring novel therapeutic strategies for neurodevelopmental disorders. PMID:26283917

Toward an Integration of Deep Learning and Neuroscience

PubMed Central

Marblestone, Adam H.; Wayne, Greg; Kording, Konrad P.

2016-01-01

Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively uniform initial architectures. Two recent developments have emerged within machine learning that create an opportunity to connect these seemingly divergent perspectives. First, structured architectures are used, including dedicated systems for attention, recursion and various forms of short- and long-term memory storage. Second, cost functions and training procedures have become more complex and are varied across layers and over time. Here we think about the brain in terms of these ideas. We hypothesize that (1) the brain optimizes cost functions, (2) the cost functions are diverse and differ across brain locations and over development, and (3) optimization operates within a pre-structured architecture matched to the computational problems posed by behavior. In support of these hypotheses, we argue that a range of implementations of credit assignment through multiple layers of neurons are compatible with our current knowledge of neural circuitry, and that the brain's specialized systems can be interpreted as enabling efficient optimization for specific problem classes. Such a heterogeneously optimized system, enabled by a series of interacting cost functions, serves to make learning data-efficient and precisely targeted to the needs of the organism. We suggest directions by which neuroscience could seek to refine and test these hypotheses. PMID:27683554

Identifying Predictors, Moderators, and Mediators of Antidepressant Response in Major Depressive Disorder: Neuroimaging Approaches

PubMed Central

Phillips, Mary L.; Chase, Henry W.; Sheline, Yvette I.; Etkin, Amit; Almeida, Jorge R.C.; Deckersbach, Thilo; Trivedi, Madhukar H.

2015-01-01

Objective Despite significant advances in neuroscience and treatment development, no widely accepted biomarkers are available to inform diagnostics or identify preferred treatments for individuals with major depressive disorder. Method In this critical review, the authors examine the extent to which multimodal neuroimaging techniques can identify biomarkers reflecting key pathophysiologic processes in depression and whether such biomarkers may act as predictors, moderators, and mediators of treatment response that might facilitate development of personalized treatments based on a better understanding of these processes. Results The authors first highlight the most consistent findings from neuroimaging studies using different techniques in depression, including structural and functional abnormalities in two parallel neural circuits: serotonergically modulated implicit emotion regulation circuitry, centered on the amygdala and different regions in the medial prefrontal cortex; and dopaminergically modulated reward neural circuitry, centered on the ventral striatum and medial prefrontal cortex. They then describe key findings from the relatively small number of studies indicating that specific measures of regional function and, to a lesser extent, structure in these neural circuits predict treatment response in depression. Conclusions Limitations of existing studies include small sample sizes, use of only one neuroimaging modality, and a focus on identifying predictors rather than moderators and mediators of differential treatment response. By addressing these limitations and, most importantly, capitalizing on the benefits of multimodal neuroimaging, future studies can yield moderators and mediators of treatment response in depression to facilitate significant improvements in shorter- and longer-term clinical and functional outcomes. PMID:25640931

Digital micromirror based near-infrared illumination system for plasmonic photothermal neuromodulation

PubMed Central

Jung, Hyunjun; Kang, Hongki; Nam, Yoonkey

2017-01-01

Light-mediated neuromodulation techniques provide great advantages to investigate neuroscience due to its high spatial and temporal resolution. To generate a spatial pattern of neural activity, it is necessary to develop a system for patterned-light illumination to a specific area. Digital micromirror device (DMD) based patterned illumination system have been used for neuromodulation due to its simple configuration and design flexibility. In this paper, we developed a patterned near-infrared (NIR) illumination system for region specific photothermal manipulation of neural activity using NIR-sensitive plasmonic gold nanorods (GNRs). The proposed system had high power transmission efficiency for delivering power density up to 19 W/mm2. We used a GNR-coated microelectrode array (MEA) to perform biological experiments using E18 rat hippocampal neurons and showed that it was possible to inhibit neural spiking activity of specific area in neural circuits with the patterned NIR illumination. This patterned NIR illumination system can serve as a promising neuromodulation tool to investigate neuroscience in a wide range of physiological and clinical applications. PMID:28663912

Photovoltaic Pixels for Neural Stimulation: Circuit Models and Performance.

PubMed

Boinagrov, David; Lei, Xin; Goetz, Georges; Kamins, Theodore I; Mathieson, Keith; Galambos, Ludwig; Harris, James S; Palanker, Daniel

2016-02-01

Photovoltaic conversion of pulsed light into pulsed electric current enables optically-activated neural stimulation with miniature wireless implants. In photovoltaic retinal prostheses, patterns of near-infrared light projected from video goggles onto subretinal arrays of photovoltaic pixels are converted into patterns of current to stimulate the inner retinal neurons. We describe a model of these devices and evaluate the performance of photovoltaic circuits, including the electrode-electrolyte interface. Characteristics of the electrodes measured in saline with various voltages, pulse durations, and polarities were modeled as voltage-dependent capacitances and Faradaic resistances. The resulting mathematical model of the circuit yielded dynamics of the electric current generated by the photovoltaic pixels illuminated by pulsed light. Voltages measured in saline with a pipette electrode above the pixel closely matched results of the model. Using the circuit model, our pixel design was optimized for maximum charge injection under various lighting conditions and for different stimulation thresholds. To speed discharge of the electrodes between the pulses of light, a shunt resistor was introduced and optimized for high frequency stimulation.

An integrated multichannel neural recording analog front-end ASIC with area-efficient driven right leg circuit.

PubMed

Tao Tang; Wang Ling Goh; Lei Yao; Jia Hao Cheong; Yuan Gao

2017-07-01

This paper describes an integrated multichannel neural recording analog front end (AFE) with a novel area-efficient driven right leg (DRL) circuit to improve the system common mode rejection ratio (CMRR). The proposed AFE consists of an AC-coupled low-noise programmable-gain amplifier, an area-efficient DRL block and a 10-bit SAR ADC. Compared to conventional DRL circuit, the proposed capacitor-less DRL design achieves 90% chip area reduction with enhanced CMRR performance, making it ideal for multichannel biomedical recording applications. The AFE circuit has been designed in a standard 0.18-μm CMOS process. Post-layout simulation results show that the AFE provides two gain settings of 54dB/60dB while consuming 1 μA per channel under a supply voltage of 1 V. The input-referred noise of the AFE integrated from 1 Hz to 10k Hz is only 4 μVrms and the CMRR is 110 dB.

Hunger and Satiety Signaling: Modeling Two Hypothalamomedullary Pathways for Energy Homeostasis.

PubMed

Nakamura, Kazuhiro; Nakamura, Yoshiko

2018-06-04

The recent discovery of the medullary circuit driving "hunger responses" - reduced thermogenesis and promoted feeding - has greatly expanded our knowledge on the central neural networks for energy homeostasis. However, how hypothalamic hunger and satiety signals generated under fasted and fed conditions, respectively, control the medullary autonomic and somatic motor mechanisms remains unknown. Here, in reviewing this field, we propose two hypothalamomedullary neural pathways for hunger and satiety signaling. To trigger hunger signaling, neuropeptide Y activates a group of neurons in the paraventricular hypothalamic nucleus (PVH), which then stimulate an excitatory pathway to the medullary circuit to drive the hunger responses. In contrast, melanocortin-mediated satiety signaling activates a distinct group of PVH neurons, which then stimulate a putatively inhibitory pathway to the medullary circuit to counteract the hunger signaling. The medullary circuit likely contains inhibitory and excitatory premotor neurons whose alternate phasic activation generates the coordinated masticatory motor rhythms to promote feeding. © 2018 The Authors. BioEssays Published by WILEY Periodicals, Inc.

A SERIES OF SUPPRESSIVE SIGNALS WITHIN THE DROSOPHILA CIRCADIAN NEURAL CIRCUIT GENERATES SEQUENTIAL DAILY OUTPUTS

PubMed Central

Liang, Xitong; Holy, Timothy E; Taghert, Paul H

2017-01-01

Summary We studied the Drosophila circadian neural circuit using whole brain imaging in vivo. Five major groups of pacemaker neurons display synchronized molecular clocks, yet each exhibits a distinct phase of daily Ca2+ activation. Light and neuropeptide PDF from morning cells (s-LNv) together delay the phase of the evening (LNd) group by ~12 h; PDF alone delays the phase of the DN3 group, by ~17 h. Neuropeptide sNPF, released from s-LNv and LNd pacemakers, produces latenight Ca2+ activation in the DN1 group. The circuit also features negative feedback by PDF to truncate the s-LNv Ca2+ wave and terminate PDF release. Both PDF and sNPF suppress basal Ca2+ levels in target pacemakers with long durations by cell autonomous actions. Thus, light and neuropeptides act dynamically at distinct hubs of the circuit to produce multiple suppressive events that create the proper tempo and sequence of circadian pacemaker neuronal activities. PMID:28552314

Reciprocal inhibition of inhibition: A circuit motif for flexible categorization in stimulus selection

PubMed Central

Knudsen, Eric I.

2011-01-01

As a precursor to the selection of a stimulus for gaze and attention, a midbrain network categorizes stimuli into “strongest” and “others.” The categorization tracks flexibly, in real-time, the absolute strength of the strongest stimulus. In this study, we take a first principles approach to computations that are essential for such categorization. We demonstrate that classical feedforward lateral inhibition cannot produce flexible categorization. However, circuits in which the strength of lateral inhibition varies with the relative strength of competing stimuli categorize successfully. One particular implementation - reciprocal inhibition of feedforward lateral inhibition – is structurally the simplest, and it outperforms others in flexibly categorizing rapidly and reliably. Strong predictions of this anatomically supported circuit model are validated by neural responses measured in the owl midbrain. The results demonstrate the extraordinary power of a remarkably simple, neurally grounded circuit motif in producing flexible categorization, a computation fundamental to attention, perception, and decision-making. PMID:22243757

Supervised Learning Using Spike-Timing-Dependent Plasticity of Memristive Synapses.

PubMed

Nishitani, Yu; Kaneko, Yukihiro; Ueda, Michihito

2015-12-01

We propose a supervised learning model that enables error backpropagation for spiking neural network hardware. The method is modeled by modifying an existing model to suit the hardware implementation. An example of a network circuit for the model is also presented. In this circuit, a three-terminal ferroelectric memristor (3T-FeMEM), which is a field-effect transistor with a gate insulator composed of ferroelectric materials, is used as an electric synapse device to store the analog synaptic weight. Our model can be implemented by reflecting the network error to the write voltage of the 3T-FeMEMs and introducing a spike-timing-dependent learning function to the device. An XOR problem was successfully demonstrated as a benchmark learning by numerical simulations using the circuit properties to estimate the learning performance. In principle, the learning time per step of this supervised learning model and the circuit is independent of the number of neurons in each layer, promising a high-speed and low-power calculation in large-scale neural networks.

Sleep Drive Is Encoded by Neural Plastic Changes in a Dedicated Circuit.

PubMed

Liu, Sha; Liu, Qili; Tabuchi, Masashi; Wu, Mark N

2016-06-02

Prolonged wakefulness leads to an increased pressure for sleep, but how this homeostatic drive is generated and subsequently persists is unclear. Here, from a neural circuit screen in Drosophila, we identify a subset of ellipsoid body (EB) neurons whose activation generates sleep drive. Patch-clamp analysis indicates these EB neurons are highly sensitive to sleep loss, switching from spiking to burst-firing modes. Functional imaging and translational profiling experiments reveal that elevated sleep need triggers reversible increases in cytosolic Ca(2+) levels, NMDA receptor expression, and structural markers of synaptic strength, suggesting these EB neurons undergo "sleep-need"-dependent plasticity. Strikingly, the synaptic plasticity of these EB neurons is both necessary and sufficient for generating sleep drive, indicating that sleep pressure is encoded by plastic changes within this circuit. These studies define an integrator circuit for sleep homeostasis and provide a mechanism explaining the generation and persistence of sleep drive. Copyright © 2016 Elsevier Inc. All rights reserved.

Python scripting in the nengo simulator.

PubMed

Stewart, Terrence C; Tripp, Bryan; Eliasmith, Chris

2009-01-01

Nengo (http://nengo.ca) is an open-source neural simulator that has been greatly enhanced by the recent addition of a Python script interface. Nengo provides a wide range of features that are useful for physiological simulations, including unique features that facilitate development of population-coding models using the neural engineering framework (NEF). This framework uses information theory, signal processing, and control theory to formalize the development of large-scale neural circuit models. Notably, it can also be used to determine the synaptic weights that underlie observed network dynamics and transformations of represented variables. Nengo provides rich NEF support, and includes customizable models of spike generation, muscle dynamics, synaptic plasticity, and synaptic integration, as well as an intuitive graphical user interface. All aspects of Nengo models are accessible via the Python interface, allowing for programmatic creation of models, inspection and modification of neural parameters, and automation of model evaluation. Since Nengo combines Python and Java, it can also be integrated with any existing Java or 100% Python code libraries. Current work includes connecting neural models in Nengo with existing symbolic cognitive models, creating hybrid systems that combine detailed neural models of specific brain regions with higher-level models of remaining brain areas. Such hybrid models can provide (1) more realistic boundary conditions for the neural components, and (2) more realistic sub-components for the larger cognitive models.

Python Scripting in the Nengo Simulator

PubMed Central

Stewart, Terrence C.; Tripp, Bryan; Eliasmith, Chris

2008-01-01

Nengo (http://nengo.ca) is an open-source neural simulator that has been greatly enhanced by the recent addition of a Python script interface. Nengo provides a wide range of features that are useful for physiological simulations, including unique features that facilitate development of population-coding models using the neural engineering framework (NEF). This framework uses information theory, signal processing, and control theory to formalize the development of large-scale neural circuit models. Notably, it can also be used to determine the synaptic weights that underlie observed network dynamics and transformations of represented variables. Nengo provides rich NEF support, and includes customizable models of spike generation, muscle dynamics, synaptic plasticity, and synaptic integration, as well as an intuitive graphical user interface. All aspects of Nengo models are accessible via the Python interface, allowing for programmatic creation of models, inspection and modification of neural parameters, and automation of model evaluation. Since Nengo combines Python and Java, it can also be integrated with any existing Java or 100% Python code libraries. Current work includes connecting neural models in Nengo with existing symbolic cognitive models, creating hybrid systems that combine detailed neural models of specific brain regions with higher-level models of remaining brain areas. Such hybrid models can provide (1) more realistic boundary conditions for the neural components, and (2) more realistic sub-components for the larger cognitive models. PMID:19352442

A structural and a functional aspect of stable information processing by the brain

PubMed Central

2007-01-01

Brain is an expert in producing the same output from a particular set of inputs, even from a very noisy environment. In this article a model of neural circuit in the brain has been proposed which is composed of cyclic sub-circuits. A big loop has been defined to be consisting of a feed forward path from the sensory neurons to the highest processing area of the brain and feed back paths from that region back up to close to the same sensory neurons. It has been mathematically shown how some smaller cycles can amplify signal. A big loop processes information by contrast and amplify principle. How a pair of presynaptic and postsynaptic neurons can be identified by an exact synchronization detection method has also been mentioned. It has been assumed that the spike train coming out of a firing neuron encodes all the information produced by it as output. It is possible to extract this information over a period of time by Fourier transforms. The Fourier coefficients arranged in a vector form will uniquely represent the neural spike train over a period of time. The information emanating out of all the neurons in a given neural circuit over a period of time can be represented by a collection of points in a multidimensional vector space. This cluster of points represents the functional or behavioral form of the neural circuit. It has been proposed that a particular cluster of vectors as the representation of a new behavior is chosen by the brain interactively with respect to the memory stored in that circuit and the amount of emotion involved. It has been proposed that in this situation a Coulomb force like expression governs the dynamics of functioning of the circuit and stability of the system is reached at the minimum of all the minima of a potential function derived from the force like expression. The calculations have been done with respect to a pseudometric defined in a multidimensional vector space. PMID:19003500

Adolescence and Reward: Making Sense of Neural and Behavioral Changes Amid the Chaos.

PubMed

Walker, Deena M; Bell, Margaret R; Flores, Cecilia; Gulley, Joshua M; Willing, Jari; Paul, Matthew J

2017-11-08

Adolescence is a time of significant neural and behavioral change with remarkable development in social, emotional, and cognitive skills. It is also a time of increased exploration and risk-taking (e.g., drug use). Many of these changes are thought to be the result of increased reward-value coupled with an underdeveloped inhibitory control, and thus a hypersensitivity to reward. Perturbations during adolescence can alter the developmental trajectory of the brain, resulting in long-term alterations in reward-associated behaviors. This review highlights recent developments in our understanding of how neural circuits, pubertal hormones, and environmental factors contribute to adolescent-typical reward-associated behaviors with a particular focus on sex differences, the medial prefrontal cortex, social reward, social isolation, and drug use. We then introduce a new approach that makes use of natural adaptations of seasonally breeding species to investigate the role of pubertal hormones in adolescent development. This research has only begun to parse out contributions of the many neural, endocrine, and environmental changes to the heightened reward sensitivity and increased vulnerability to mental health disorders that characterize this life stage. Copyright © 2017 the authors 0270-6474/17/3710855-12$15.00/0.

Neural predictors of purchases

PubMed Central

Knutson, Brian; Rick, Scott; Wimmer, G. Elliott; Prelec, Drazen; Loewenstein, George

2007-01-01

Microeconomic theory maintains that purchases are driven by a combination of consumer preference and price. Using event-related FMRI, we investigated how people weigh these factors to make purchasing decisions. Consistent with neuroimaging evidence suggesting that distinct circuits anticipate gain and loss, product preference activated the nucleus accumbens (NAcc), while excessive prices activated the insula and deactivated the mesial prefrontal cortex (MPFC) prior to the purchase decision. Activity from each of these regions independently predicted immediately subsequent purchases above and beyond self-report variables. These findings suggest that activation of distinct neural circuits related to anticipatory affect precedes and supports consumers’ purchasing decisions. PMID:17196537

Strange non-chaotic attractors in a state controlled-cellular neural network-based quasiperiodically forced MLC circuit

NASA Astrophysics Data System (ADS)

Ezhilarasu, P. Megavarna; Inbavalli, M.; Murali, K.; Thamilmaran, K.

2018-07-01

In this paper, we report the dynamical transitions to strange non-chaotic attractors in a quasiperiodically forced state controlled-cellular neural network (SC-CNN)-based MLC circuit via two different mechanisms, namely the Heagy-Hammel route and the gradual fractalisation route. These transitions were observed through numerical simulations and hardware experiments and confirmed using statistical tools, such as maximal Lyapunov exponent spectrum and its variance and singular continuous spectral analysis. We find that there is a remarkable agreement of the results from both numerical simulations as well as from hardware experiments.

Skilled movements require non-apoptotic Bax/Bak pathway-mediated corticospinal circuit reorganization

PubMed Central

Gu, Zirong; Serradj, Najet; Ueno, Masaki; Liang, Mishi; Li, Jie; Baccei, Mark L.; Martin, John H.; Yoshida, Yutaka

2017-01-01

Early postnatal mammals, including human babies, can perform only basic motor tasks. The acquisition of skilled behaviors occurs later, requiring anatomical changes in neural circuitry to support the development of coordinated activation or suppression of functionally related muscle groups. How this circuit reorganization occurs during postnatal development remains poorly understood. Here we explore the connectivity between corticospinal (CS) neurons in the motor cortex and muscles in mice. Using trans-synaptic viral and electrophysiological assays, we identify the early postnatal reorganization of CS circuitry for antagonistic muscle pairs. We further show that this synaptic rearrangement requires the activity-dependent, non-apoptotic Bax/Bak-caspase signaling cascade. Adult Bax/Bak mutant mice exhibit aberrant co-activation of antagonistic muscle pairs and skilled grasping deficits but normal reaching and retrieval behaviors. Our findings reveal key cellular and molecular mechanisms driving postnatal motor circuit reorganization and the resulting impacts on muscle activation patterns and the execution of skilled movements. PMID:28472660

Filopodial dynamics and growth cone stabilization in Drosophila visual circuit development

PubMed Central

Özel, Mehmet Neset; Langen, Marion; Hassan, Bassem A; Hiesinger, P Robin

2015-01-01

Filopodial dynamics are thought to control growth cone guidance, but the types and roles of growth cone dynamics underlying neural circuit assembly in a living brain are largely unknown. To address this issue, we have developed long-term, continuous, fast and high-resolution imaging of growth cone dynamics from axon growth to synapse formation in cultured Drosophila brains. Using R7 photoreceptor neurons as a model we show that >90% of the growth cone filopodia exhibit fast, stochastic dynamics that persist despite ongoing stepwise layer formation. Correspondingly, R7 growth cones stabilize early and change their final position by passive dislocation. N-Cadherin controls both fast filopodial dynamics and growth cone stabilization. Surprisingly, loss of N-Cadherin causes no primary targeting defects, but destabilizes R7 growth cones to jump between correct and incorrect layers. Hence, growth cone dynamics can influence wiring specificity without a direct role in target recognition and implement simple rules during circuit assembly. DOI: http://dx.doi.org/10.7554/eLife.10721.001 PMID:26512889

From wheels to wings with evolutionary spiking circuits.

PubMed

Floreano, Dario; Zufferey, Jean-Christophe; Nicoud, Jean-Daniel

2005-01-01

We give an overview of the EPFL indoor flying project, whose goal is to evolve neural controllers for autonomous, adaptive, indoor micro-flyers. Indoor flight is still a challenge because it requires miniaturization, energy efficiency, and control of nonlinear flight dynamics. This ongoing project consists of developing a flying, vision-based micro-robot, a bio-inspired controller composed of adaptive spiking neurons directly mapped into digital microcontrollers, and a method to evolve such a neural controller without human intervention. This article describes the motivation and methodology used to reach our goal as well as the results of a number of preliminary experiments on vision-based wheeled and flying robots.

Electronic neural networks for global optimization

NASA Technical Reports Server (NTRS)

Thakoor, A. P.; Moopenn, A. W.; Eberhardt, S.

1990-01-01

An electronic neural network with feedback architecture, implemented in analog custom VLSI is described. Its application to problems of global optimization for dynamic assignment is discussed. The convergence properties of the neural network hardware are compared with computer simulation results. The neural network's ability to provide optimal or near optimal solutions within only a few neuron time constants, a speed enhancement of several orders of magnitude over conventional search methods, is demonstrated. The effect of noise on the circuit dynamics and the convergence behavior of the neural network hardware is also examined.

Scalable, Lightweight, Integrated and Quick-to-Assemble (SLIQ) Hyperdrives for Functional Circuit Dissection.

PubMed

Liang, Li; Oline, Stefan N; Kirk, Justin C; Schmitt, Lukas Ian; Komorowski, Robert W; Remondes, Miguel; Halassa, Michael M

2017-01-01

Independently adjustable multielectrode arrays are routinely used to interrogate neuronal circuit function, enabling chronic in vivo monitoring of neuronal ensembles in freely behaving animals at a single-cell, single spike resolution. Despite the importance of this approach, its widespread use is limited by highly specialized design and fabrication methods. To address this, we have developed a Scalable, Lightweight, Integrated and Quick-to-assemble multielectrode array platform. This platform additionally integrates optical fibers with independently adjustable electrodes to allow simultaneous single unit recordings and circuit-specific optogenetic targeting and/or manipulation. In current designs, the fully assembled platforms are scalable from 2 to 32 microdrives, and yet range 1-3 g, light enough for small animals. Here, we describe the design process starting from intent in computer-aided design, parameter testing through finite element analysis and experimental means, and implementation of various applications across mice and rats. Combined, our methods may expand the utility of multielectrode recordings and their continued integration with other tools enabling functional dissection of intact neural circuits.

Dendritic nonlinearities are tuned for efficient spike-based computations in cortical circuits

PubMed Central

Ujfalussy, Balázs B; Makara, Judit K; Branco, Tiago; Lengyel, Máté

2015-01-01

Cortical neurons integrate thousands of synaptic inputs in their dendrites in highly nonlinear ways. It is unknown how these dendritic nonlinearities in individual cells contribute to computations at the level of neural circuits. Here, we show that dendritic nonlinearities are critical for the efficient integration of synaptic inputs in circuits performing analog computations with spiking neurons. We developed a theory that formalizes how a neuron's dendritic nonlinearity that is optimal for integrating synaptic inputs depends on the statistics of its presynaptic activity patterns. Based on their in vivo preynaptic population statistics (firing rates, membrane potential fluctuations, and correlations due to ensemble dynamics), our theory accurately predicted the responses of two different types of cortical pyramidal cells to patterned stimulation by two-photon glutamate uncaging. These results reveal a new computational principle underlying dendritic integration in cortical neurons by suggesting a functional link between cellular and systems--level properties of cortical circuits. DOI: http://dx.doi.org/10.7554/eLife.10056.001 PMID:26705334

Ephrin-B3 coordinates timed axon targeting and amygdala spinogenesis for innate fear behaviour

PubMed Central

Zhu, Xiao-Na; Liu, Xian-Dong; Sun, Suya; Zhuang, Hanyi; Yang, Jing-Yu; Henkemeyer, Mark; Xu, Nan-Jie

2016-01-01

Innate emotion response to environmental stimuli is a fundamental brain function that is controlled by specific neural circuits. Dysfunction of early emotional circuits may lead to neurodevelopmental disorders such as autism and schizophrenia. However, how the functional circuits are formed to prime initial emotional behaviours remain elusive. We reveal here using gene-targeted mutations an essential role for ephrin-B3 ligand-like activity in the development of innate fear in the neonatal brain. We further demonstrate that ephrin-B3 controls axon targeting and coordinates spinogenesis and neuronal activity within the amygdala. The morphological and behavioural abnormalities in ephrin-B3 mutant mice are rescued by conditional knock-in of wild-type ephrin-B3 during the critical period when axon targeting and fear responses are initiated. Our results thus define a key axonal molecule that participates in the wiring of amygdala circuits and helps bring about fear emotion during the important adolescence period. PMID:27008987

Ephrin-B3 coordinates timed axon targeting and amygdala spinogenesis for innate fear behaviour.

PubMed

Zhu, Xiao-Na; Liu, Xian-Dong; Sun, Suya; Zhuang, Hanyi; Yang, Jing-Yu; Henkemeyer, Mark; Xu, Nan-Jie

2016-03-24

Innate emotion response to environmental stimuli is a fundamental brain function that is controlled by specific neural circuits. Dysfunction of early emotional circuits may lead to neurodevelopmental disorders such as autism and schizophrenia. However, how the functional circuits are formed to prime initial emotional behaviours remain elusive. We reveal here using gene-targeted mutations an essential role for ephrin-B3 ligand-like activity in the development of innate fear in the neonatal brain. We further demonstrate that ephrin-B3 controls axon targeting and coordinates spinogenesis and neuronal activity within the amygdala. The morphological and behavioural abnormalities in ephrin-B3 mutant mice are rescued by conditional knock-in of wild-type ephrin-B3 during the critical period when axon targeting and fear responses are initiated. Our results thus define a key axonal molecule that participates in the wiring of amygdala circuits and helps bring about fear emotion during the important adolescence period.

Emotion and the motivational brain

PubMed Central

Lang, Peter J.; Bradley, Margaret M.

2013-01-01

Psychophysiological and neuroscience studies of emotional processing undertaken by investigators at the University of Florida Laboratory of the Center for the Study of Emotion and Attention (CSEA) are reviewed, with a focus on reflex reactions, neural structures and functional circuits that mediate emotional expression. The theoretical view shared among the investigators is that expressed emotions are founded on motivational circuits in the brain that developed early in evolutionary history to ensure the survival of individuals and their progeny. These circuits react to appetitive and aversive environmental and memorial cues, mediating appetitive and defensive reflexes that tune sensory systems and mobilize the organism for action and underly negative and positive affects. The research reviewed here assesses the reflex physiology of emotion, both autonomic and somatic, studying affects evoked in picture perception, memory imagery, and in the context of tangible reward and punishment, and using the electroencephalograph (EEG) and functional magnetic resonance imaging (fMRI), explores the brain’s motivational circuits that determine human emotion. PMID:19879918

Rules and mechanisms for efficient two-stage learning in neural circuits.

PubMed

Teşileanu, Tiberiu; Ölveczky, Bence; Balasubramanian, Vijay

2017-04-04

Trial-and-error learning requires evaluating variable actions and reinforcing successful variants. In songbirds, vocal exploration is induced by LMAN, the output of a basal ganglia-related circuit that also contributes a corrective bias to the vocal output. This bias is gradually consolidated in RA, a motor cortex analogue downstream of LMAN. We develop a new model of such two-stage learning. Using stochastic gradient descent, we derive how the activity in 'tutor' circuits ( e.g., LMAN) should match plasticity mechanisms in 'student' circuits ( e.g., RA) to achieve efficient learning. We further describe a reinforcement learning framework through which the tutor can build its teaching signal. We show that mismatches between the tutor signal and the plasticity mechanism can impair learning. Applied to birdsong, our results predict the temporal structure of the corrective bias from LMAN given a plasticity rule in RA. Our framework can be applied predictively to other paired brain areas showing two-stage learning.

DISSECTING OCD CIRCUITS: FROM ANIMAL MODELS TO TARGETED TREATMENTS.

PubMed

Ahmari, Susanne E; Dougherty, Darin D

2015-08-01

Obsessive-compulsive disorder (OCD) is a chronic, severe mental illness with up to 2-3% prevalence worldwide. In fact, OCD has been classified as one of the world's 10 leading causes of illness-related disability according to the World Health Organization, largely because of the chronic nature of disabling symptoms.([1]) Despite the severity and high prevalence of this chronic and disabling disorder, there is still relatively limited understanding of its pathophysiology. However, this is now rapidly changing due to development of powerful technologies that can be used to dissect the neural circuits underlying pathologic behaviors. In this article, we describe recent technical advances that have allowed neuroscientists to start identifying the circuits underlying complex repetitive behaviors using animal model systems. In addition, we review current surgical and stimulation-based treatments for OCD that target circuit dysfunction. Finally, we discuss how findings from animal models may be applied in the clinical arena to help inform and refine targeted brain stimulation-based treatment approaches. © 2015 Wiley Periodicals, Inc.

Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework

PubMed Central

Wang, Xiao-Jing

2016-01-01

The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs) that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, “trained” networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them arbitrarily, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features such as the distinction between excitatory and inhibitory units (Dale’s principle), which are essential if RNNs are to provide insights into the operation of biological circuits. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here, we describe a framework for gradient descent-based training of excitatory-inhibitory RNNs that can incorporate a variety of biological knowledge. We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions. We validate this framework by applying it to well-known experimental paradigms such as perceptual decision-making, context-dependent integration, multisensory integration, parametric working memory, and motor sequence generation. Our results demonstrate the wide range of neural activity patterns and behavior that can be modeled, and suggest a unified setting in which diverse cognitive computations and mechanisms can be studied. PMID:26928718

Entorhinal-Hippocampal Neuronal Circuits Bridge Temporally Discontiguous Events

ERIC Educational Resources Information Center

Kitamura, Takashi; Macdonald, Christopher J.; Tonegawa, Susumu

2015-01-01

The entorhinal cortex (EC)-hippocampal (HPC) network plays an essential role for episodic memory, which preserves spatial and temporal information about the occurrence of past events. Although there has been significant progress toward understanding the neural circuits underlying the spatial dimension of episodic memory, the relevant circuits…

Neural mechanisms of mother-infant bonding and pair bonding: Similarities, differences, and broader implications.

PubMed

Numan, Michael; Young, Larry J

2016-01-01

This article is part of a Special Issue "Parental Care". Mother-infant bonding is a characteristic of virtually all mammals. The maternal neural system may have provided the scaffold upon which other types of social bonds in mammals have been built. For example, most mammals exhibit a polygamous mating system, but monogamy and pair bonding between mating partners occur in ~5% of mammalian species. In mammals, it is plausible that the neural mechanisms that promote mother-infant bonding have been modified by natural selection to establish the capacity to develop a selective bond with a mate during the evolution of monogamous mating strategies. Here we compare the details of the neural mechanisms that promote mother-infant bonding in rats and other mammals with those that underpin pair bond formation in the monogamous prairie vole. Although details remain to be resolved, remarkable similarities and a few differences between the mechanisms underlying these two types of bond formation are revealed. For example, amygdala and nucleus accumbens-ventral pallidum (NA-VP) circuits are involved in both types of bond formation, and dopamine and oxytocin actions within NA appear to promote the synaptic plasticity that allows either infant or mating partner stimuli to persistently activate NA-VP attraction circuits, leading to an enduring social attraction and bonding. Further, although the medial preoptic area is essential for maternal behavior, its role in pair bonding remains to be determined. Our review concludes by examining the broader implications of this comparative analysis, and evidence is provided that the maternal care system may have also provided the basic neural foundation for other types of strong social relationships, beyond pair bonding, in mammals, including humans. Copyright © 2015 Elsevier Inc. All rights reserved.

Neural mechanisms of mother-infant bonding and pair bonding: Similarities, differences, and broader implications

PubMed Central

Numan, Michael; Young, Larry J.

2015-01-01

Mother-infant bonding is a characteristic of virtually all mammals. The maternal neural system may have provided the scaffold upon which other types of social bonds in mammals have been built. For example, most mammals exhibit a polygamous mating system, but monogamy and pair bonding between mating partners occurs in ∼5% of mammalian species. In mammals, it is plausible that the neural mechanisms that promote mother-infant bonding have been modified by natural selection to establish the capacity to develop a selective bond with a mate during the evolution of monogamous mating strategies. Here we compare the details of the neural mechanisms that promote mother-infant bonding in rats and other mammals with those that underpin pair bond formation in the monogamous prairie vole. Although details remain to be resolved, remarkable similarities and a few differences between the mechanisms underlying these two types of bond formation are revealed. For example, amygdala and nucleus accumbens-ventral pallidum (NA-VP) circuits are involved in both types of bond formation, and dopamine and oxytocin action within NA appears to promote the synaptic plasticity that allows either infant or mating partner stimuli to persistently activate NA-VP attraction circuits, leading to an enduring social attraction and bonding. Further, although the medial preoptic area is essential for maternal behavior, its role in pair bonding remains to be determined. Our review concludes by examining the broader implications of this comparative analysis, and evidence is provided that the maternal care system may have also provided the basic neural foundation for other types of strong social relationships, beyond pair bonding, in mammals, including humans. PMID:26062432

MRI/DTI of the Brain Stem Reveals Reversible and Irreversible Disruption of the Baroreflex Neural Circuits: Clinical Implications

PubMed Central

Su, Chia-Hao; Tsai, Ching-Yi; Chang, Alice Y.W.; Chan, Julie Y.H.; Chan, Samuel H.H.

2016-01-01

Baroreflex is the physiological mechanism for the maintenance of blood pressure and heart rate. Impairment of baroreflex is not a disease per se. However, depending on severity, the eventuality of baroreflex dysfunction varies from inconvenience in daily existence to curtailment of mobility to death. Despite universal acceptance, neuronal traffic within the contemporary neural circuits during the execution of baroreflex has never been visualized. By enhancing signal detection and fine-tuning the scanning parameters, we have successfully implemented tractographic analysis of the medulla oblongata in mice that allowed for visualization of connectivity between key brain stem nuclei in the baroreflex circuits. When viewed in conjunction with radiotelemetric analysis of the baroreflex, we found that under pathophysiological conditions when the disrupted connectivity between key nuclei in the baroreflex circuits was reversible, the associated disease condition (e.g. neurogenic hypertension) was amenable to remedial measures. Nevertheless, fatality ensues under pathological conditions (e.g. hepatic encephalopathy) when the connectivity between key substrates in the baroreflex circuits was irreversibly severed. MRI/DTI also prompted partial re-wiring of the contemporary circuit for baroreflex-mediated sympathetic vasomotor tone, and unearthed an explanation for the time lapse between brain death and the inevitable asystole signifying cardiac death that follows. PMID:27162554

MRI/DTI of the Brain Stem Reveals Reversible and Irreversible Disruption of the Baroreflex Neural Circuits: Clinical Implications.

PubMed

Su, Chia-Hao; Tsai, Ching-Yi; Chang, Alice Y W; Chan, Julie Y H; Chan, Samuel H H

2016-01-01

Baroreflex is the physiological mechanism for the maintenance of blood pressure and heart rate. Impairment of baroreflex is not a disease per se. However, depending on severity, the eventuality of baroreflex dysfunction varies from inconvenience in daily existence to curtailment of mobility to death. Despite universal acceptance, neuronal traffic within the contemporary neural circuits during the execution of baroreflex has never been visualized. By enhancing signal detection and fine-tuning the scanning parameters, we have successfully implemented tractographic analysis of the medulla oblongata in mice that allowed for visualization of connectivity between key brain stem nuclei in the baroreflex circuits. When viewed in conjunction with radiotelemetric analysis of the baroreflex, we found that under pathophysiological conditions when the disrupted connectivity between key nuclei in the baroreflex circuits was reversible, the associated disease condition (e.g. neurogenic hypertension) was amenable to remedial measures. Nevertheless, fatality ensues under pathological conditions (e.g. hepatic encephalopathy) when the connectivity between key substrates in the baroreflex circuits was irreversibly severed. MRI/DTI also prompted partial re-wiring of the contemporary circuit for baroreflex-mediated sympathetic vasomotor tone, and unearthed an explanation for the time lapse between brain death and the inevitable asystole signifying cardiac death that follows.

Decoding a neural circuit controlling global animal state in C. elegans

PubMed Central

Laurent, Patrick; Soltesz, Zoltan; Nelson, Geoffrey M; Chen, Changchun; Arellano-Carbajal, Fausto; Levy, Emmanuel; de Bono, Mario

2015-01-01

Brains organize behavior and physiology to optimize the response to threats or opportunities. We dissect how 21% O2, an indicator of surface exposure, reprograms C. elegans' global state, inducing sustained locomotory arousal and altering expression of neuropeptides, metabolic enzymes, and other non-neural genes. The URX O2-sensing neurons drive arousal at 21% O2 by tonically activating the RMG interneurons. Stimulating RMG is sufficient to switch behavioral state. Ablating the ASH, ADL, or ASK sensory neurons connected to RMG by gap junctions does not disrupt arousal. However, disrupting cation currents in these neurons curtails RMG neurosecretion and arousal. RMG signals high O2 by peptidergic secretion. Neuropeptide reporters reveal neural circuit state, as neurosecretion stimulates neuropeptide expression. Neural imaging in unrestrained animals shows that URX and RMG encode O2 concentration rather than behavior, while the activity of downstream interneurons such as AVB and AIY reflect both O2 levels and the behavior being executed. DOI: http://dx.doi.org/10.7554/eLife.04241.001 PMID:25760081

Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis.

PubMed

Williams, Alex H; Kim, Tony Hyun; Wang, Forea; Vyas, Saurabh; Ryu, Stephen I; Shenoy, Krishna V; Schnitzer, Mark; Kolda, Tamara G; Ganguli, Surya

2018-06-27

Perceptions, thoughts, and actions unfold over millisecond timescales, while learned behaviors can require many days to mature. While recent experimental advances enable large-scale and long-term neural recordings with high temporal fidelity, it remains a formidable challenge to extract unbiased and interpretable descriptions of how rapid single-trial circuit dynamics change slowly over many trials to mediate learning. We demonstrate a simple tensor component analysis (TCA) can meet this challenge by extracting three interconnected, low-dimensional descriptions of neural data: neuron factors, reflecting cell assemblies; temporal factors, reflecting rapid circuit dynamics mediating perceptions, thoughts, and actions within each trial; and trial factors, describing both long-term learning and trial-to-trial changes in cognitive state. We demonstrate the broad applicability of TCA by revealing insights into diverse datasets derived from artificial neural networks, large-scale calcium imaging of rodent prefrontal cortex during maze navigation, and multielectrode recordings of macaque motor cortex during brain machine interface learning. Copyright © 2018 Elsevier Inc. All rights reserved.

Representing Sex in the Brain, One Module at a Time

PubMed Central

Yang, Cindy F.; Shah, Nirao M.

2014-01-01

Summary Sexually dimorphic behaviors, qualitative or quantitative differences in behaviors between the sexes, result from the activity of a sexually differentiated nervous system. Sensory cues and sex hormones control the entire repertoire of sexually dimorphic behaviors, including those commonly thought to be charged with emotion such as courtship and aggression. Recent studies show that these over-arching control mechanisms regulate distinct genes and neurons that in turn specify the display of such behaviors in a modular manner. How such modular control is transformed into cohesive internal states that correspond to sexually dimorphic behavior is poorly understood. We summarize current understanding of the neural circuit control of sexually dimorphic behaviors from several perspectives, including how neural circuits in general, and sexually dimorphic neurons in particular, can generate sex differences in behavior, and how molecular mechanisms and evolutionary constraints shape these behaviors. We propose that emergent themes such as the modular genetic and neural control of dimorphic behavior are broadly applicable to the neural control of other behaviors. PMID:24742456

Shared neural circuits for mentalizing about the self and others.

PubMed

Lombardo, Michael V; Chakrabarti, Bhismadev; Bullmore, Edward T; Wheelwright, Sally J; Sadek, Susan A; Suckling, John; Baron-Cohen, Simon

2010-07-01

Although many examples exist for shared neural representations of self and other, it is unknown how such shared representations interact with the rest of the brain. Furthermore, do high-level inference-based shared mentalizing representations interact with lower level embodied/simulation-based shared representations? We used functional neuroimaging (fMRI) and a functional connectivity approach to assess these questions during high-level inference-based mentalizing. Shared mentalizing representations in ventromedial prefrontal cortex, posterior cingulate/precuneus, and temporo-parietal junction (TPJ) all exhibited identical functional connectivity patterns during mentalizing of both self and other. Connectivity patterns were distributed across low-level embodied neural systems such as the frontal operculum/ventral premotor cortex, the anterior insula, the primary sensorimotor cortex, and the presupplementary motor area. These results demonstrate that identical neural circuits are implementing processes involved in mentalizing of both self and other and that the nature of such processes may be the integration of low-level embodied processes within higher level inference-based mentalizing.

A mixed-signal implementation of a polychronous spiking neural network with delay adaptation

PubMed Central

Wang, Runchun M.; Hamilton, Tara J.; Tapson, Jonathan C.; van Schaik, André

2014-01-01

We present a mixed-signal implementation of a re-configurable polychronous spiking neural network capable of storing and recalling spatio-temporal patterns. The proposed neural network contains one neuron array and one axon array. Spike Timing Dependent Delay Plasticity is used to fine-tune delays and add dynamics to the network. In our mixed-signal implementation, the neurons and axons have been implemented as both analog and digital circuits. The system thus consists of one FPGA, containing the digital neuron array and the digital axon array, and one analog IC containing the analog neuron array and the analog axon array. The system can be easily configured to use different combinations of each. We present and discuss the experimental results of all combinations of the analog and digital axon arrays and the analog and digital neuron arrays. The test results show that the proposed neural network is capable of successfully recalling more than 85% of stored patterns using both analog and digital circuits. PMID:24672422

A mixed-signal implementation of a polychronous spiking neural network with delay adaptation.

PubMed

Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan C; van Schaik, André

2014-01-01

We present a mixed-signal implementation of a re-configurable polychronous spiking neural network capable of storing and recalling spatio-temporal patterns. The proposed neural network contains one neuron array and one axon array. Spike Timing Dependent Delay Plasticity is used to fine-tune delays and add dynamics to the network. In our mixed-signal implementation, the neurons and axons have been implemented as both analog and digital circuits. The system thus consists of one FPGA, containing the digital neuron array and the digital axon array, and one analog IC containing the analog neuron array and the analog axon array. The system can be easily configured to use different combinations of each. We present and discuss the experimental results of all combinations of the analog and digital axon arrays and the analog and digital neuron arrays. The test results show that the proposed neural network is capable of successfully recalling more than 85% of stored patterns using both analog and digital circuits.

Central neural pathways for thermoregulation.

PubMed

Morrison, Shaun F; Nakamura, Kazuhiro

2011-01-01

Central neural circuits orchestrate a homeostatic repertoire to maintain body temperature during environmental temperature challenges and to alter body temperature during the inflammatory response. This review summarizes the functional organization of the neural pathways through which cutaneous thermal receptors alter thermoregulatory effectors: the cutaneous circulation for heat loss, the brown adipose tissue, skeletal muscle and heart for thermogenesis and species-dependent mechanisms (sweating, panting and saliva spreading) for evaporative heat loss. These effectors are regulated by parallel but distinct, effector-specific neural pathways that share a common peripheral thermal sensory input. The thermal afferent circuits include cutaneous thermal receptors, spinal dorsal horn neurons and lateral parabrachial nucleus neurons projecting to the preoptic area to influence warm-sensitive, inhibitory output neurons which control thermogenesis-promoting neurons in the dorsomedial hypothalamus that project to premotor neurons in the rostral ventromedial medulla, including the raphe pallidus, that descend to provide the excitation necessary to drive thermogenic thermal effectors. A distinct population of warm-sensitive preoptic neurons controls heat loss through an inhibitory input to raphe pallidus neurons controlling cutaneous vasoconstriction.

Central control of body temperature

PubMed Central

Morrison, Shaun F.

2016-01-01

Central neural circuits orchestrate the behavioral and autonomic repertoire that maintains body temperature during environmental temperature challenges and alters body temperature during the inflammatory response and behavioral states and in response to declining energy homeostasis. This review summarizes the central nervous system circuit mechanisms controlling the principal thermoeffectors for body temperature regulation: cutaneous vasoconstriction regulating heat loss and shivering and brown adipose tissue for thermogenesis. The activation of these thermoeffectors is regulated by parallel but distinct efferent pathways within the central nervous system that share a common peripheral thermal sensory input. The model for the neural circuit mechanism underlying central thermoregulatory control provides a useful platform for further understanding of the functional organization of central thermoregulation, for elucidating the hypothalamic circuitry and neurotransmitters involved in body temperature regulation, and for the discovery of novel therapeutic approaches to modulating body temperature and energy homeostasis. PMID:27239289

Central control of body temperature.

PubMed

Morrison, Shaun F

2016-01-01

Central neural circuits orchestrate the behavioral and autonomic repertoire that maintains body temperature during environmental temperature challenges and alters body temperature during the inflammatory response and behavioral states and in response to declining energy homeostasis. This review summarizes the central nervous system circuit mechanisms controlling the principal thermoeffectors for body temperature regulation: cutaneous vasoconstriction regulating heat loss and shivering and brown adipose tissue for thermogenesis. The activation of these thermoeffectors is regulated by parallel but distinct efferent pathways within the central nervous system that share a common peripheral thermal sensory input. The model for the neural circuit mechanism underlying central thermoregulatory control provides a useful platform for further understanding of the functional organization of central thermoregulation, for elucidating the hypothalamic circuitry and neurotransmitters involved in body temperature regulation, and for the discovery of novel therapeutic approaches to modulating body temperature and energy homeostasis.

Dopamine modulation of emotional processing in cortical and subcortical neural circuits: evidence for a final common pathway in schizophrenia?

PubMed

Laviolette, Steven R

2007-07-01

The neural regulation of emotional perception, learning, and memory is essential for normal behavioral and cognitive functioning. Many of the symptoms displayed by individuals with schizophrenia may arise from fundamental disturbances in the ability to accurately process emotionally salient sensory information. The neurotransmitter dopamine (DA) and its ability to modulate neural regions involved in emotional learning, perception, and memory formation has received considerable research attention as a potential final common pathway to account for the aberrant emotional regulation and psychosis present in the schizophrenic syndrome. Evidence from both human neuroimaging studies and animal-based research using neurodevelopmental, behavioral, and electrophysiological techniques have implicated the mesocorticolimbic DA circuit as a crucial system for the encoding and expression of emotionally salient learning and memory formation. While many theories have examined the cortical-subcortical interactions between prefrontal cortical regions and subcortical DA substrates, many questions remain as to how DA may control emotional perception and learning and how disturbances linked to DA abnormalities may underlie the disturbed emotional processing in schizophrenia. Beyond the mesolimbic DA system, increasing evidence points to the amygdala-prefrontal cortical circuit as an important processor of emotionally salient information and how neurodevelopmental perturbances within this circuitry may lead to dysregulation of DAergic modulation of emotional processing and learning along this cortical-subcortical emotional processing circuit.

Dynamical systems, attractors, and neural circuits.

PubMed

Miller, Paul

2016-01-01

Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular field of systems neuroscience, neural circuits are rife with nonlinearities at all levels of description, rendering simple methodologies and our own intuition unreliable. Therefore, our ideas are likely to be wrong unless informed by good models. These models should be based on the mathematical theories of dynamical systems since functioning neurons are dynamic-they change their membrane potential and firing rates with time. Thus, selecting the appropriate type of dynamical system upon which to base a model is an important first step in the modeling process. This step all too easily goes awry, in part because there are many frameworks to choose from, in part because the sparsely sampled data can be consistent with a variety of dynamical processes, and in part because each modeler has a preferred modeling approach that is difficult to move away from. This brief review summarizes some of the main dynamical paradigms that can arise in neural circuits, with comments on what they can achieve computationally and what signatures might reveal their presence within empirical data. I provide examples of different dynamical systems using simple circuits of two or three cells, emphasizing that any one connectivity pattern is compatible with multiple, diverse functions.

Glutamate Receptors in the Central Nucleus of the Amygdala Mediate Cisplatin-Induced Malaise and Energy Balance Dysregulation through Direct Hindbrain Projections

PubMed Central

Alhadeff, Amber L.; Holland, Ruby A.; Nelson, Alexandra; Grill, Harvey J.

2015-01-01

Cisplatin chemotherapy is used commonly to treat a variety of cancers despite severe side effects such as nausea, vomiting, and anorexia that compromise quality of life and limit treatment adherence. The neural mechanisms mediating these side effects remain elusive despite decades of clinical use. Recent data highlight the dorsal vagal complex (DVC), lateral parabrachial nucleus (lPBN), and central nucleus of the amygdala (CeA) as potential sites of action in mediating the side effects of cisplatin. Here, results from immunohistochemical studies in rats identified a population of cisplatin-activated DVC neurons that project to the lPBN and a population of cisplatin-activated lPBN calcitonin gene-related peptide (CGRP, a marker for glutamatergic neurons in the lPBN) neurons that project to the CeA, outlining a neuroanatomical circuit that is activated by cisplatin. CeA gene expressions of AMPA and NMDA glutamate receptor subunits were markedly increased after cisplatin treatment, suggesting that CeA glutamate receptor signaling plays a role in mediating cisplatin side effects. Consistent with gene expression results, behavioral/pharmacological data showed that CeA AMPA/kainate receptor blockade attenuates cisplatin-induced pica (a proxy for nausea/behavioral malaise in nonvomiting laboratory rodents) and that CeA NMDA receptor blockade attenuates cisplatin-induced anorexia and body weight loss in addition to pica, demonstrating that glutamate receptor signaling in the CeA is critical for the energy balance dysregulation caused by cisplatin treatment. Together, these data highlight a novel circuit and CGRP/glutamatergic mechanism through which cisplatin-induced malaise and energy balance dysregulation are mediated. SIGNIFICANCE STATEMENT To treat cancer effectively, patients must follow prescribed chemotherapy treatments without interruption, yet most cancer treatments produce side effects that devastate quality of life (e.g., nausea, vomiting, anorexia, weight loss). Although hundreds of thousands of patients undergo chemotherapies each year, the neural mechanisms mediating their side effects are unknown. The current data outline a neural circuit activated by cisplatin chemotherapy and demonstrate that glutamate signaling in the amygdala, arising from hindbrain projections, is required for the full expression of cisplatin-induced malaise, anorexia, and body weight loss. Together, these data help to characterize the neural circuits and neurotransmitters mediating chemotherapy-induced energy balance dysregulation, which will ultimately provide an opportunity for the development of well tolerated cancer and anti-emetic treatments. PMID:26245970

512-Channel and 13-Region Simultaneous Recordings Coupled with Optogenetic Manipulation in Freely Behaving Mice

PubMed Central

Xie, Kun; Fox, Grace E.; Liu, Jun; Tsien, Joe Z.

2016-01-01

The development of technologies capable of recording both single-unit activity and local field potentials (LFPs) over a wide range of brain circuits in freely behaving animals is the key to constructing brain activity maps. Although mice are the most popular mammalian genetic model, in vivo neural recording has been traditionally limited to smaller channel count and fewer brain structures because of the mouse’s small size and thin skull. Here, we describe a 512-channel tetrode system that allows us to record simultaneously over a dozen cortical and subcortical structures in behaving mice. This new technique offers two major advantages – namely, the ultra-low cost and the do-it-yourself flexibility for targeting any combination of many brain areas. We show the successful recordings of both single units and LFPs from 13 distinct neural circuits of the mouse brain, including subregions of the anterior cingulate cortices, retrosplenial cortices, somatosensory cortices, secondary auditory cortex, hippocampal CA1, dentate gyrus, subiculum, lateral entorhinal cortex, perirhinal cortex, and prelimbic cortex. This 512-channel system can also be combined with Cre-lox neurogenetics and optogenetics to further examine interactions between genes, cell types, and circuit dynamics across a wide range of brain structures. Finally, we demonstrate that complex stimuli – such as an earthquake and fear-inducing foot-shock – trigger firing changes in all of the 13 brain regions recorded, supporting the notion that neural code is highly distributed. In addition, we show that localized optogenetic manipulation in any given brain region could disrupt network oscillations and caused changes in single-unit firing patterns in a brain-wide manner, thereby raising the cautionary note of the interpretation of optogenetically manipulated behaviors. PMID:27378865

Olfactory systems and neural circuits that modulate predator odor fear

PubMed Central

Takahashi, Lorey K.

2014-01-01

When prey animals detect the odor of a predator a constellation of fear-related autonomic, endocrine, and behavioral responses rapidly occur to facilitate survival. How olfactory sensory systems process predator odor and channel that information to specific brain circuits is a fundamental issue that is not clearly understood. However, research in the last 15 years has begun to identify some of the essential features of the sensory detection systems and brain structures that underlie predator odor fear. For instance, the main (MOS) and accessory olfactory systems (AOS) detect predator odors and different types of predator odors are sensed by specific receptors located in either the MOS or AOS. However, complex predator chemosignals may be processed by both the MOS and AOS, which complicate our understanding of the specific neural circuits connected directly and indirectly from the MOS and AOS to activate the physiological and behavioral components of unconditioned and conditioned fear. Studies indicate that brain structures including the dorsal periaqueductal gray (DPAG), paraventricular nucleus (PVN) of the hypothalamus, and the medial amygdala (MeA) appear to be broadly involved in predator odor induced autonomic activity and hypothalamic-pituitary-adrenal (HPA) stress hormone secretion. The MeA also plays a key role in predator odor unconditioned fear behavior and retrieval of contextual fear memory associated with prior predator odor experiences. Other neural structures including the bed nucleus of the stria terminalis and the ventral hippocampus (VHC) appear prominently involved in predator odor fear behavior. The basolateral amygdala (BLA), medial hypothalamic nuclei, and medial prefrontal cortex (mPFC) are also activated by some but not all predator odors. Future research that characterizes how distinct predator odors are uniquely processed in olfactory systems and neural circuits will provide significant insights into the differences of how diverse predator odors activate fear. PMID:24653685

Olfactory systems and neural circuits that modulate predator odor fear.

PubMed

Takahashi, Lorey K

2014-01-01

When prey animals detect the odor of a predator a constellation of fear-related autonomic, endocrine, and behavioral responses rapidly occur to facilitate survival. How olfactory sensory systems process predator odor and channel that information to specific brain circuits is a fundamental issue that is not clearly understood. However, research in the last 15 years has begun to identify some of the essential features of the sensory detection systems and brain structures that underlie predator odor fear. For instance, the main (MOS) and accessory olfactory systems (AOS) detect predator odors and different types of predator odors are sensed by specific receptors located in either the MOS or AOS. However, complex predator chemosignals may be processed by both the MOS and AOS, which complicate our understanding of the specific neural circuits connected directly and indirectly from the MOS and AOS to activate the physiological and behavioral components of unconditioned and conditioned fear. Studies indicate that brain structures including the dorsal periaqueductal gray (DPAG), paraventricular nucleus (PVN) of the hypothalamus, and the medial amygdala (MeA) appear to be broadly involved in predator odor induced autonomic activity and hypothalamic-pituitary-adrenal (HPA) stress hormone secretion. The MeA also plays a key role in predator odor unconditioned fear behavior and retrieval of contextual fear memory associated with prior predator odor experiences. Other neural structures including the bed nucleus of the stria terminalis and the ventral hippocampus (VHC) appear prominently involved in predator odor fear behavior. The basolateral amygdala (BLA), medial hypothalamic nuclei, and medial prefrontal cortex (mPFC) are also activated by some but not all predator odors. Future research that characterizes how distinct predator odors are uniquely processed in olfactory systems and neural circuits will provide significant insights into the differences of how diverse predator odors activate fear.

Temporal Information of Directed Causal Connectivity in Multi-Trial ERP Data using Partial Granger Causality.

PubMed

Youssofzadeh, Vahab; Prasad, Girijesh; Naeem, Muhammad; Wong-Lin, KongFatt

2016-01-01

Partial Granger causality (PGC) has been applied to analyse causal functional neural connectivity after effectively mitigating confounding influences caused by endogenous latent variables and exogenous environmental inputs. However, it is not known how this connectivity obtained from PGC evolves over time. Furthermore, PGC has yet to be tested on realistic nonlinear neural circuit models and multi-trial event-related potentials (ERPs) data. In this work, we first applied a time-domain PGC technique to evaluate simulated neural circuit models, and demonstrated that the PGC measure is more accurate and robust in detecting connectivity patterns as compared to conditional Granger causality and partial directed coherence, especially when the circuit is intrinsically nonlinear. Moreover, the connectivity in PGC settles faster into a stable and correct configuration over time. After method verification, we applied PGC to reveal the causal connections of ERP trials of a mismatch negativity auditory oddball paradigm. The PGC analysis revealed a significant bilateral but asymmetrical localised activity in the temporal lobe close to the auditory cortex, and causal influences in the frontal, parietal and cingulate cortical areas, consistent with previous studies. Interestingly, the time to reach a stable connectivity configuration (~250–300 ms) coincides with the deviation of ensemble ERPs of oddball from standard tones. Finally, using a sliding time window, we showed higher resolution dynamics of causal connectivity within an ERP trial. In summary, time-domain PGC is promising in deciphering directed functional connectivity in nonlinear and ERP trials accurately, and at a sufficiently early stage. This data-driven approach can reduce computational time, and determine the key architecture for neural circuit modeling.

Wireless Neural Recording With Single Low-Power Integrated Circuit

PubMed Central

Harrison, Reid R.; Kier, Ryan J.; Chestek, Cynthia A.; Gilja, Vikash; Nuyujukian, Paul; Ryu, Stephen; Greger, Bradley; Solzbacher, Florian; Shenoy, Krishna V.

2010-01-01

We present benchtop and in vivo experimental results from an integrated circuit designed for wireless implantable neural recording applications. The chip, which was fabricated in a commercially available 0.6-μm 2P3M BiCMOS process, contains 100 amplifiers, a 10-bit analog-to-digital converter (ADC), 100 threshold-based spike detectors, and a 902–928 MHz frequency-shift-keying (FSK) transmitter. Neural signals from a selected amplifier are sampled by the ADC at 15.7 kSps and telemetered over the FSK wireless data link. Power, clock, and command signals are sent to the chip wirelessly over a 2.765-MHz inductive (coil-to-coil) link. The chip is capable of operating with only two off-chip components: a power/command receiving coil and a 100-nF capacitor. PMID:19497825

A canonical neural mechanism for behavioral variability

PubMed Central

Darshan, Ran; Wood, William E.; Peters, Susan; Leblois, Arthur; Hansel, David

2017-01-01

The ability to generate variable movements is essential for learning and adjusting complex behaviours. This variability has been linked to the temporal irregularity of neuronal activity in the central nervous system. However, how neuronal irregularity actually translates into behavioural variability is unclear. Here we combine modelling, electrophysiological and behavioural studies to address this issue. We demonstrate that a model circuit comprising topographically organized and strongly recurrent neural networks can autonomously generate irregular motor behaviours. Simultaneous recordings of neurons in singing finches reveal that neural correlations increase across the circuit driving song variability, in agreement with the model predictions. Analysing behavioural data, we find remarkable similarities in the babbling statistics of 5–6-month-old human infants and juveniles from three songbird species and show that our model naturally accounts for these ‘universal' statistics. PMID:28530225

A proposed solution to integrating cognitive-affective neuroscience and neuropsychiatry in psychiatry residency training: The time is now.

PubMed

Torous, John; Stern, Adam P; Padmanabhan, Jaya L; Keshavan, Matcheri S; Perez, David L

2015-10-01

Despite increasing recognition of the importance of a strong neuroscience and neuropsychiatry education in the training of psychiatry residents, achieving this competency has proven challenging. In this perspective article, we selectively discuss the current state of these educational efforts and outline how using brain-symptom relationships from a systems-level neural circuit approach in clinical formulations may help residents value, understand, and apply cognitive-affective neuroscience based principles towards the care of psychiatric patients. To demonstrate the utility of this model, we present a case of major depressive disorder and discuss suspected abnormal neural circuits and therapeutic implications. A clinical neural systems-level, symptom-based approach to conceptualize mental illness can complement and expand residents' existing psychiatric knowledge. Copyright © 2015 Elsevier B.V. All rights reserved.

Simultaneous two-photon imaging and two-photon optogenetics of cortical circuits in three dimensions

PubMed Central

Carrillo-Reid, Luis; Bando, Yuki; Peterka, Darcy S

2018-01-01

The simultaneous imaging and manipulating of neural activity could enable the functional dissection of neural circuits. Here we have combined two-photon optogenetics with simultaneous volumetric two-photon calcium imaging to measure and manipulate neural activity in mouse neocortex in vivo in three-dimensions (3D) with cellular resolution. Using a hybrid holographic approach, we simultaneously photostimulate more than 80 neurons over 150 μm in depth in layer 2/3 of the mouse visual cortex, while simultaneously imaging the activity of the surrounding neurons. We validate the usefulness of the method by photoactivating in 3D selected groups of interneurons, suppressing the response of nearby pyramidal neurons to visual stimuli in awake animals. Our all-optical approach could be used as a general platform to read and write neuronal activity. PMID:29412138

The Roles of Sequencing and Verbal Working Memory in Sentence Comprehension Deficits in Parkinson's Disease

ERIC Educational Resources Information Center

Hochstadt, Jesse; Nakano, Hiroko; Lieberman, Philip; Friedman, Joseph

2006-01-01

Studies of sentence comprehension deficits in Parkinson's disease (PD) patients suggest that language processing involves circuits connecting subcortical and cortical regions. Anatomically segregated neural circuits appear to support different cognitive and motor functions. To investigate which functions are implicated in PD comprehension…

Stabilization of memory States by stochastic facilitating synapses.

PubMed

Miller, Paul

2013-12-06

Bistability within a small neural circuit can arise through an appropriate strength of excitatory recurrent feedback. The stability of a state of neural activity, measured by the mean dwelling time before a noise-induced transition to another state, depends on the neural firing-rate curves, the net strength of excitatory feedback, the statistics of spike times, and increases exponentially with the number of equivalent neurons in the circuit. Here, we show that such stability is greatly enhanced by synaptic facilitation and reduced by synaptic depression. We take into account the alteration in times of synaptic vesicle release, by calculating distributions of inter-release intervals of a synapse, which differ from the distribution of its incoming interspike intervals when the synapse is dynamic. In particular, release intervals produced by a Poisson spike train have a coefficient of variation greater than one when synapses are probabilistic and facilitating, whereas the coefficient of variation is less than one when synapses are depressing. However, in spite of the increased variability in postsynaptic input produced by facilitating synapses, their dominant effect is reduced synaptic efficacy at low input rates compared to high rates, which increases the curvature of neural input-output functions, leading to wider regions of bistability in parameter space and enhanced lifetimes of memory states. Our results are based on analytic methods with approximate formulae and bolstered by simulations of both Poisson processes and of circuits of noisy spiking model neurons.

Robust information propagation through noisy neural circuits

PubMed Central

Pouget, Alexandre

2017-01-01

Sensory neurons give highly variable responses to stimulation, which can limit the amount of stimulus information available to downstream circuits. Much work has investigated the factors that affect the amount of information encoded in these population responses, leading to insights about the role of covariability among neurons, tuning curve shape, etc. However, the informativeness of neural responses is not the only relevant feature of population codes; of potentially equal importance is how robustly that information propagates to downstream structures. For instance, to quantify the retina’s performance, one must consider not only the informativeness of the optic nerve responses, but also the amount of information that survives the spike-generating nonlinearity and noise corruption in the next stage of processing, the lateral geniculate nucleus. Our study identifies the set of covariance structures for the upstream cells that optimize the ability of information to propagate through noisy, nonlinear circuits. Within this optimal family are covariances with “differential correlations”, which are known to reduce the information encoded in neural population activities. Thus, covariance structures that maximize information in neural population codes, and those that maximize the ability of this information to propagate, can be very different. Moreover, redundancy is neither necessary nor sufficient to make population codes robust against corruption by noise: redundant codes can be very fragile, and synergistic codes can—in some cases—optimize robustness against noise. PMID:28419098

Investigating habits: strategies, technologies and models

PubMed Central

Smith, Kyle S.; Graybiel, Ann M.

2014-01-01

Understanding habits at a biological level requires a combination of behavioral observations and measures of ongoing neural activity. Theoretical frameworks as well as definitions of habitual behaviors emerging from classic behavioral research have been enriched by new approaches taking account of the identification of brain regions and circuits related to habitual behavior. Together, this combination of experimental and theoretical work has provided key insights into how brain circuits underlying action-learning and action-selection are organized, and how a balance between behavioral flexibility and fixity is achieved. New methods to monitor and manipulate neural activity in real time are allowing us to have a first look “under the hood” of a habit as it is formed and expressed. Here we discuss ideas emerging from such approaches. We pay special attention to the unexpected findings that have arisen from our own experiments suggesting that habitual behaviors likely require the simultaneous activity of multiple distinct components, or operators, seen as responsible for the contrasting dynamics of neural activity in both cortico-limbic and sensorimotor circuits recorded concurrently during different stages of habit learning. The neural dynamics identified thus far do not fully meet expectations derived from traditional models of the structure of habits, and the behavioral measures of habits that we have made also are not fully aligned with these models. We explore these new clues as opportunities to refine an understanding of habits. PMID:24574988

Serotonin homeostasis and serotonin receptors as actors of cortical construction: special attention to the 5-HT3A and 5-HT6 receptor subtypes

PubMed Central

Vitalis, Tania; Ansorge, Mark S.; Dayer, Alexandre G.

2013-01-01

Cortical circuits control higher-order cognitive processes and their function is highly dependent on their structure that emerges during development. The construction of cortical circuits involves the coordinated interplay between different types of cellular processes such as proliferation, migration, and differentiation of neural and glial cell subtypes. Among the multiple factors that regulate the assembly of cortical circuits, 5-HT is an important developmental signal that impacts on a broad diversity of cellular processes. 5-HT is detected at the onset of embryonic telencephalic formation and a variety of serotonergic receptors are dynamically expressed in the embryonic developing cortex in a region and cell-type specific manner. Among these receptors, the ionotropic 5-HT3A receptor and the metabotropic 5-HT6 receptor have recently been identified as novel serotonergic targets regulating different aspects of cortical construction including neuronal migration and dendritic differentiation. In this review, we focus on the developmental impact of serotonergic systems on the construction of cortical circuits and discuss their potential role in programming risk for human psychiatric disorders. PMID:23801939

What the laboratory rat has taught us about social play behavior: role in behavioral development and neural mechanisms.

PubMed

Vanderschuren, Louk J M J; Trezza, Viviana

2014-01-01

Social play behavior is the most vigorous and characteristic form of social interaction displayed by developing mammals. The laboratory rat is an ideal species to study this behavior, since it shows ample social play that can be easily recognized and quantified. In this chapter, we will first briefly describe the structure of social play behavior in rats. Next, we will discuss studies that used social isolation rearing during the period in life when social play is most abundant to investigate the developmental functions of social play behavior in rats, focusing on the consequences of play deprivation on social, cognitive, emotional, and sensorimotor development. Last, we will discuss the neural substrates of social play behavior in rats, with emphasis on the limbic corticostriatal circuits that underlie emotions and their influence on behavior.

Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems

NASA Astrophysics Data System (ADS)

Broccard, Frédéric D.; Joshi, Siddharth; Wang, Jun; Cauwenberghs, Gert

2017-08-01

Objective. Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. Approach. This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. Main results. Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. Significance. Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a computational tool for investigating fundamental questions related to neural dynamics, the sophistication of current neuromorphic systems now allows direct interfaces with large neuronal networks and circuits, resulting in potentially interesting clinical applications for neuroengineering systems, neuroprosthetics and neurorehabilitation.

An analog neural hardware implementation using charge-injection multipliers and neutron-specific gain control.

PubMed

Massengill, L W; Mundie, D B

1992-01-01

A neural network IC based on a dynamic charge injection is described. The hardware design is space and power efficient, and achieves massive parallelism of analog inner products via charge-based multipliers and spatially distributed summing buses. Basic synaptic cells are constructed of exponential pulse-decay modulation (EPDM) dynamic injection multipliers operating sequentially on propagating signal vectors and locally stored analog weights. Individually adjustable gain controls on each neutron reduce the effects of limited weight dynamic range. A hardware simulator/trainer has been developed which incorporates the physical (nonideal) characteristics of actual circuit components into the training process, thus absorbing nonlinearities and parametric deviations into the macroscopic performance of the network. Results show that charge-based techniques may achieve a high degree of neural density and throughput using standard CMOS processes.

Non-invasive neural stimulation

NASA Astrophysics Data System (ADS)

Tyler, William J.; Sanguinetti, Joseph L.; Fini, Maria; Hool, Nicholas

2017-05-01

Neurotechnologies for non-invasively interfacing with neural circuits have been evolving from those capable of sensing neural activity to those capable of restoring and enhancing human brain function. Generally referred to as non-invasive neural stimulation (NINS) methods, these neuromodulation approaches rely on electrical, magnetic, photonic, and acoustic or ultrasonic energy to influence nervous system activity, brain function, and behavior. Evidence that has been surmounting for decades shows that advanced neural engineering of NINS technologies will indeed transform the way humans treat diseases, interact with information, communicate, and learn. The physics underlying the ability of various NINS methods to modulate nervous system activity can be quite different from one another depending on the energy modality used as we briefly discuss. For members of commercial and defense industry sectors that have not traditionally engaged in neuroscience research and development, the science, engineering and technology required to advance NINS methods beyond the state-of-the-art presents tremendous opportunities. Within the past few years alone there have been large increases in global investments made by federal agencies, foundations, private investors and multinational corporations to develop advanced applications of NINS technologies. Driven by these efforts NINS methods and devices have recently been introduced to mass markets via the consumer electronics industry. Further, NINS continues to be explored in a growing number of defense applications focused on enhancing human dimensions. The present paper provides a brief introduction to the field of non-invasive neural stimulation by highlighting some of the more common methods in use or under current development today.

Decision making in the ageing brain: changes in affective and motivational circuits.

PubMed

Samanez-Larkin, Gregory R; Knutson, Brian

2015-05-01

As the global population ages, older decision makers will be required to take greater responsibility for their own physical, psychological and financial well-being. With this in mind, researchers have begun to examine the effects of ageing on decision making and associated neural circuits. A new 'affect-integration-motivation' (AIM) framework may help to clarify how affective and motivational circuits support decision making. Recent research has shed light on whether and how ageing influences these circuits, providing an interdisciplinary account of how ageing can alter decision making.

Decision making in the ageing brain: Changes in affective and motivational circuits

PubMed Central

Samanez-Larkin, Gregory R.; Knutson, Brian

2017-01-01

As the global population ages, older decision makers will be required to take greater responsibility for their own physical, psychological and financial well-being. With this in mind, researchers have begun to examine the effects of ageing on decision making and associated neural circuits. A new “affect, integration, motivation” (or AIM) framework may help clarify how affective and motivational circuits support decision making. Recent research has shed light on whether and how ageing influences these circuits, providing an interdisciplinary account of how ageing can alter decision making. PMID:25873038

Biological and bionic hands: natural neural coding and artificial perception.

PubMed

Bensmaia, Sliman J

2015-09-19

The first decade and a half of the twenty-first century brought about two major innovations in neuroprosthetics: the development of anthropomorphic robotic limbs that replicate much of the function of a native human arm and the refinement of algorithms that decode intended movements from brain activity. However, skilled manipulation of objects requires somatosensory feedback, for which vision is a poor substitute. For upper-limb neuroprostheses to be clinically viable, they must therefore provide for the restoration of touch and proprioception. In this review, I discuss efforts to elicit meaningful tactile sensations through stimulation of neurons in somatosensory cortex. I focus on biomimetic approaches to sensory restoration, which leverage our current understanding about how information about grasped objects is encoded in the brain of intact individuals. I argue that not only can sensory neuroscience inform the development of sensory neuroprostheses, but also that the converse is true: stimulating the brain offers an exceptional opportunity to causally interrogate neural circuits and test hypotheses about natural neural coding.

On the Role of Glutamate in Presynaptic Development: Possible Contributions of Presynaptic NMDA Receptors.

PubMed

Fedder, Karlie N; Sabo, Shasta L

2015-12-14

Proper formation and maturation of synapses during development is a crucial step in building the functional neural circuits that underlie perception and behavior. It is well established that experience modifies circuit development. Therefore, understanding how synapse formation is controlled by synaptic activity is a key question in neuroscience. In this review, we focus on the regulation of excitatory presynaptic terminal development by glutamate, the predominant excitatory neurotransmitter in the brain. We discuss the evidence that NMDA receptor activation mediates these effects of glutamate and present the hypothesis that local activation of presynaptic NMDA receptors (preNMDARs) contributes to glutamate-dependent control of presynaptic development. Abnormal glutamate signaling and aberrant synapse development are both thought to contribute to the pathogenesis of a variety of neurodevelopmental disorders, including autism spectrum disorders, intellectual disability, epilepsy, anxiety, depression, and schizophrenia. Therefore, understanding how glutamate signaling and synapse development are linked is important for understanding the etiology of these diseases.

Fractional Hopfield Neural Networks: Fractional Dynamic Associative Recurrent Neural Networks.

PubMed

Pu, Yi-Fei; Yi, Zhang; Zhou, Ji-Liu

2017-10-01

This paper mainly discusses a novel conceptual framework: fractional Hopfield neural networks (FHNN). As is commonly known, fractional calculus has been incorporated into artificial neural networks, mainly because of its long-term memory and nonlocality. Some researchers have made interesting attempts at fractional neural networks and gained competitive advantages over integer-order neural networks. Therefore, it is naturally makes one ponder how to generalize the first-order Hopfield neural networks to the fractional-order ones, and how to implement FHNN by means of fractional calculus. We propose to introduce a novel mathematical method: fractional calculus to implement FHNN. First, we implement fractor in the form of an analog circuit. Second, we implement FHNN by utilizing fractor and the fractional steepest descent approach, construct its Lyapunov function, and further analyze its attractors. Third, we perform experiments to analyze the stability and convergence of FHNN, and further discuss its applications to the defense against chip cloning attacks for anticounterfeiting. The main contribution of our work is to propose FHNN in the form of an analog circuit by utilizing a fractor and the fractional steepest descent approach, construct its Lyapunov function, prove its Lyapunov stability, analyze its attractors, and apply FHNN to the defense against chip cloning attacks for anticounterfeiting. A significant advantage of FHNN is that its attractors essentially relate to the neuron's fractional order. FHNN possesses the fractional-order-stability and fractional-order-sensitivity characteristics.

Peptide neuromodulation in invertebrate model systems

PubMed Central

Taghert, Paul H.; Nitabach, Michael N.

2012-01-01

Neuropeptides modulate neural circuits controlling adaptive animal behaviors and physiological processes, such as feeding/metabolism, reproductive behaviors, circadian rhythms, central pattern generation, and sensorimotor integration. Invertebrate model systems have enabled detailed experimental analysis using combined genetic, behavioral, and physiological approaches. Here we review selected examples of neuropeptide modulation in crustaceans, mollusks, insects, and nematodes, with a particular emphasis on the genetic model organisms Drosophila melanogaster and Caenorhabditis elegans, where remarkable progress has been made. On the basis of this survey, we provide several integrating conceptual principles for understanding how neuropeptides modulate circuit function, and also propose that continued progress in this area requires increased emphasis on the development of richer, more sophisticated behavioral paradigms. PMID:23040808

Modulation of Perineuronal Nets and Parvalbumin with Developmental Song Learning

PubMed Central

Balmer, Timothy S.; Carels, Vanessa M.; Frisch, Jillian L.; Nick, Teresa A.

2009-01-01

Neural circuits and behavior are shaped during developmental phases of maximal plasticity known as sensitive or critical periods. Neural correlates of sensory critical periods have been identified, but their roles remain unclear. Factors that define critical periods in sensorimotor circuits and behavior are not known. Birdsong learning in the zebra finch occurs during a sensitive period similar to that for human speech. We now show that perineuronal nets, which correlate with sensory critical periods, surround parvalbumin-positive neurons in brain areas that are dedicated to singing. The percentage of both total and parvalbumin-positive neurons with perineuronal nets increased with development. In HVC (this acronym is the proper name), a song area important for sensorimotor integration, the percentage of parvalbumin neurons with perineuronal nets correlated with song maturity. Shifting the vocal critical period with tutor song deprivation decreased the percentage of neurons that were parvalbumin positive and the relative staining intensity of both parvalbumin and a component of perineuronal nets. Developmental song learning shares key characteristics with sensory critical periods, suggesting shared underlying mechanisms. PMID:19828802

An Asynchronous Recurrent Network of Cellular Automaton-Based Neurons and Its Reproduction of Spiking Neural Network Activities.

PubMed

Matsubara, Takashi; Torikai, Hiroyuki

2016-04-01

Modeling and implementation approaches for the reproduction of input-output relationships in biological nervous tissues contribute to the development of engineering and clinical applications. However, because of high nonlinearity, the traditional modeling and implementation approaches encounter difficulties in terms of generalization ability (i.e., performance when reproducing an unknown data set) and computational resources (i.e., computation time and circuit elements). To overcome these difficulties, asynchronous cellular automaton-based neuron (ACAN) models, which are described as special kinds of cellular automata that can be implemented as small asynchronous sequential logic circuits have been proposed. This paper presents a novel type of such ACAN and a theoretical analysis of its excitability. This paper also presents a novel network of such neurons, which can mimic input-output relationships of biological and nonlinear ordinary differential equation model neural networks. Numerical analyses confirm that the presented network has a higher generalization ability than other major modeling and implementation approaches. In addition, Field-Programmable Gate Array-implementations confirm that the presented network requires lower computational resources.

Progress towards biocompatible intracortical microelectrodes for neural interfacing applications

NASA Astrophysics Data System (ADS)

Jorfi, Mehdi; Skousen, John L.; Weder, Christoph; Capadona, Jeffrey R.

2015-02-01

To ensure long-term consistent neural recordings, next-generation intracortical microelectrodes are being developed with an increased emphasis on reducing the neuro-inflammatory response. The increased emphasis stems from the improved understanding of the multifaceted role that inflammation may play in disrupting both biologic and abiologic components of the overall neural interface circuit. To combat neuro-inflammation and improve recording quality, the field is actively progressing from traditional inorganic materials towards approaches that either minimizes the microelectrode footprint or that incorporate compliant materials, bioactive molecules, conducting polymers or nanomaterials. However, the immune-privileged cortical tissue introduces an added complexity compared to other biomedical applications that remains to be fully understood. This review provides a comprehensive reflection on the current understanding of the key failure modes that may impact intracortical microelectrode performance. In addition, a detailed overview of the current status of various materials-based approaches that have gained interest for neural interfacing applications is presented, and key challenges that remain to be overcome are discussed. Finally, we present our vision on the future directions of materials-based treatments to improve intracortical microelectrodes for neural interfacing.

Progress Towards Biocompatible Intracortical Microelectrodes for Neural Interfacing Applications

PubMed Central

Jorfi, Mehdi; Skousen, John L.; Weder, Christoph; Capadona, Jeffrey R.

2015-01-01

To ensure long-term consistent neural recordings, next-generation intracortical microelectrodes are being developed with an increased emphasis on reducing the neuro-inflammatory response. The increased emphasis stems from the improved understanding of the multifaceted role that inflammation may play in disrupting both biologic and abiologic components of the overall neural interface circuit. To combat neuro-inflammation and improve recording quality, the field is actively progressing from traditional inorganic materials towards approaches that either minimizes the microelectrode footprint or that incorporate compliant materials, bioactive molecules, conducting polymers or nanomaterials. However, the immune-privileged cortical tissue introduces an added complexity compared to other biomedical applications that remains to be fully understood. This review provides a comprehensive reflection on the current understanding of the key failure modes that may impact intracortical microelectrode performance. In addition, a detailed overview of the current status of various materials-based approaches that have gained interest for neural interfacing applications is presented, and key challenges that remain to be overcome are discussed. Finally, we present our vision on the future directions of materials-based treatments to improve intracortical microelectrodes for neural interfacing. PMID:25460808

Development of cognitive and affective control networks and decision making.

PubMed

Kar, Bhoomika R; Vijay, Nivita; Mishra, Shreyasi

2013-01-01

Cognitive control and decision making are two important research areas in the realm of higher-order cognition. Control processes such as interference control and monitoring in cognitive and affective contexts have been found to influence the process of decision making. Development of control processes follows a gradual growth pattern associated with the prolonged maturation of underlying neural circuits including the lateral prefrontal cortex, anterior cingulate, and the medial prefrontal cortex. These circuits are also involved in the control of processes that influences decision making, particularly with respect to choice behavior. Developmental studies on affective control have shown distinct patterns of brain activity with adolescents showing greater activation of amygdala whereas adults showing greater activity in ventral prefrontal cortex. Conflict detection, monitoring, and adaptation involve anticipation and subsequent performance adjustments which are also critical to complex decision making. We discuss the gradual developmental patterns observed in two of our studies on conflict monitoring and adaptation in affective and nonaffective contexts. Findings of these studies indicate the need to look at the differences in the effects of the development of cognitive and affective control on decision making in children and particularly adolescents. Neuroimaging studies have shown the involvement of separable neural networks for cognitive (medial prefrontal cortex and anterior cingulate) and affective control (amygdala, ventral medial prefrontal cortex) shows that one system can affect the other also at the neural level. Hence, an understanding of the interaction and balance between the cognitive and affective brain networks may be crucial for self-regulation and decision making during the developmental period, particularly late childhood and adolescence. The chapter highlights the need for empirical investigation on the interaction between the different aspects of cognitive control and decision making from a developmental perspective. Copyright © 2013 Elsevier B.V. All rights reserved.

Optogenetic Activation of Zebrafish Somatosensory Neurons using ChEF-tdTomato

PubMed Central

Palanca, Ana Marie S.; Sagasti, Alvaro

2013-01-01

Larval zebrafish are emerging as a model for describing the development and function of simple neural circuits. Due to their external fertilization, rapid development, and translucency, zebrafish are particularly well suited for optogenetic approaches to investigate neural circuit function. In this approach, light-sensitive ion channels are expressed in specific neurons, enabling the experimenter to activate or inhibit them at will and thus assess their contribution to specific behaviors. Applying these methods in larval zebrafish is conceptually simple but requires the optimization of technical details. Here we demonstrate a procedure for expressing a channelrhodopsin variant in larval zebrafish somatosensory neurons, photo-activating single cells, and recording the resulting behaviors. By introducing a few modifications to previously established methods, this approach could be used to elicit behavioral responses from single neurons activated up to at least 4 days post-fertilization (dpf). Specifically, we created a transgene using a somatosensory neuron enhancer, CREST3, to drive the expression of the tagged channelrhodopsin variant, ChEF-tdTomato. Injecting this transgene into 1-cell stage embryos results in mosaic expression in somatosensory neurons, which can be imaged with confocal microscopy. Illuminating identified cells in these animals with light from a 473 nm DPSS laser, guided through a fiber optic cable, elicits behaviors that can be recorded with a high-speed video camera and analyzed quantitatively. This technique could be adapted to study behaviors elicited by activating any zebrafish neuron. Combining this approach with genetic or pharmacological perturbations will be a powerful way to investigate circuit formation and function. PMID:23407374

Neural circuits underlying mother’s voice perception predict social communication abilities in children

PubMed Central

Abrams, Daniel A.; Chen, Tianwen; Odriozola, Paola; Cheng, Katherine M.; Baker, Amanda E.; Padmanabhan, Aarthi; Ryali, Srikanth; Kochalka, John; Feinstein, Carl; Menon, Vinod

2016-01-01

The human voice is a critical social cue, and listeners are extremely sensitive to the voices in their environment. One of the most salient voices in a child’s life is mother's voice: Infants discriminate their mother’s voice from the first days of life, and this stimulus is associated with guiding emotional and social function during development. Little is known regarding the functional circuits that are selectively engaged in children by biologically salient voices such as mother’s voice or whether this brain activity is related to children’s social communication abilities. We used functional MRI to measure brain activity in 24 healthy children (mean age, 10.2 y) while they attended to brief (<1 s) nonsense words produced by their biological mother and two female control voices and explored relationships between speech-evoked neural activity and social function. Compared to female control voices, mother’s voice elicited greater activity in primary auditory regions in the midbrain and cortex; voice-selective superior temporal sulcus (STS); the amygdala, which is crucial for processing of affect; nucleus accumbens and orbitofrontal cortex of the reward circuit; anterior insula and cingulate of the salience network; and a subregion of fusiform gyrus associated with face perception. The strength of brain connectivity between voice-selective STS and reward, affective, salience, memory, and face-processing regions during mother’s voice perception predicted social communication skills. Our findings provide a novel neurobiological template for investigation of typical social development as well as clinical disorders, such as autism, in which perception of biologically and socially salient voices may be impaired. PMID:27185915

Neural circuits underlying mother's voice perception predict social communication abilities in children.

PubMed

Abrams, Daniel A; Chen, Tianwen; Odriozola, Paola; Cheng, Katherine M; Baker, Amanda E; Padmanabhan, Aarthi; Ryali, Srikanth; Kochalka, John; Feinstein, Carl; Menon, Vinod

2016-05-31

The human voice is a critical social cue, and listeners are extremely sensitive to the voices in their environment. One of the most salient voices in a child's life is mother's voice: Infants discriminate their mother's voice from the first days of life, and this stimulus is associated with guiding emotional and social function during development. Little is known regarding the functional circuits that are selectively engaged in children by biologically salient voices such as mother's voice or whether this brain activity is related to children's social communication abilities. We used functional MRI to measure brain activity in 24 healthy children (mean age, 10.2 y) while they attended to brief (<1 s) nonsense words produced by their biological mother and two female control voices and explored relationships between speech-evoked neural activity and social function. Compared to female control voices, mother's voice elicited greater activity in primary auditory regions in the midbrain and cortex; voice-selective superior temporal sulcus (STS); the amygdala, which is crucial for processing of affect; nucleus accumbens and orbitofrontal cortex of the reward circuit; anterior insula and cingulate of the salience network; and a subregion of fusiform gyrus associated with face perception. The strength of brain connectivity between voice-selective STS and reward, affective, salience, memory, and face-processing regions during mother's voice perception predicted social communication skills. Our findings provide a novel neurobiological template for investigation of typical social development as well as clinical disorders, such as autism, in which perception of biologically and socially salient voices may be impaired.

A hardware experimental platform for neural circuits in the auditory cortex

NASA Astrophysics Data System (ADS)

Rodellar-Biarge, Victoria; García-Dominguez, Pablo; Ruiz-Rizaldos, Yago; Gómez-Vilda, Pedro

2011-05-01

Speech processing in the human brain is a very complex process far from being fully understood although much progress has been done recently. Neuromorphic Speech Processing is a new research orientation in bio-inspired systems approach to find solutions to automatic treatment of specific problems (recognition, synthesis, segmentation, diarization, etc) which can not be adequately solved using classical algorithms. In this paper a neuromorphic speech processing architecture is presented. The systematic bottom-up synthesis of layered structures reproduce the dynamic feature detection of speech related to plausible neural circuits which work as interpretation centres located in the Auditory Cortex. The elementary model is based on Hebbian neuron-like units. For the computation of the architecture a flexible framework is proposed in the environment of Matlab®/Simulink®/HDL, which allows building models in different description styles, complexity and implementation levels. It provides a flexible platform for experimenting on the influence of the number of neurons and interconnections, in the precision of the results and in performance evaluation. The experimentation with different architecture configurations may help both in better understanding how neural circuits may work in the brain as well as in how speech processing can benefit from this understanding.

Integrative approaches for modeling regulation and function of the respiratory system.

PubMed

Ben-Tal, Alona; Tawhai, Merryn H

2013-01-01

Mathematical models have been central to understanding the interaction between neural control and breathing. Models of the entire respiratory system-which comprises the lungs and the neural circuitry that controls their ventilation-have been derived using simplifying assumptions to compartmentalize each component of the system and to define the interactions between components. These full system models often rely-through necessity-on empirically derived relationships or parameters, in addition to physiological values. In parallel with the development of whole respiratory system models are mathematical models that focus on furthering a detailed understanding of the neural control network, or of the several functions that contribute to gas exchange within the lung. These models are biophysically based, and rely on physiological parameters. They include single-unit models for a breathing lung or neural circuit, through to spatially distributed models of ventilation and perfusion, or multicircuit models for neural control. The challenge is to bring together these more recent advances in models of neural control with models of lung function, into a full simulation for the respiratory system that builds upon the more detailed models but remains computationally tractable. This requires first understanding the mathematical models that have been developed for the respiratory system at different levels, and which could be used to study how physiological levels of O2 and CO2 in the blood are maintained. Copyright © 2013 Wiley Periodicals, Inc.

Cortical control of intraspinal microstimulation: Toward a new approach for restoration of function after spinal cord injury.

PubMed

Shahdoost, Shahab; Frost, Shawn; Dunham, Caleb; DeJong, Stacey; Barbay, Scott; Nudo, Randolph; Mohseni, Pedram

2015-08-01

Approximately 6 million people in the United States are currently living with paralysis in which 23% of the cases are related to spinal cord injury (SCI). Miniaturized closed-loop neural interfaces have the potential for restoring function and mobility lost to debilitating neural injuries such as SCI by leveraging recent advancements in bioelectronics and a better understanding of the processes that underlie functional and anatomical reorganization in an injured nervous system. This paper describes our current progress toward developing a miniaturized brain-machine-spinal cord interface (BMSI) that converts in real time the neural command signals recorded from the cortical motor regions to electrical stimuli delivered to the spinal cord below the injury level. Using a combination of custom integrated circuit (IC) technology for corticospinal interfacing and field-programmable gate array (FPGA)-based technology for embedded signal processing, we demonstrate proof-of-concept of distinct muscle pattern activation via intraspinal microstimulation (ISMS) controlled in real time by intracortical neural spikes in an anesthetized laboratory rat.

All-optical bidirectional neural interfacing using hybrid multiphoton holographic optogenetic stimulation.

PubMed

Paluch-Siegler, Shir; Mayblum, Tom; Dana, Hod; Brosh, Inbar; Gefen, Inna; Shoham, Shy

2015-07-01

Our understanding of neural information processing could potentially be advanced by combining flexible three-dimensional (3-D) neuroimaging and stimulation. Recent developments in optogenetics suggest that neurophotonic approaches are in principle highly suited for noncontact stimulation of network activity patterns. In particular, two-photon holographic optical neural stimulation (2P-HONS) has emerged as a leading approach for multisite 3-D excitation, and combining it with temporal focusing (TF) further enables axially confined yet spatially extended light patterns. Here, we study key steps toward bidirectional cell-targeted 3-D interfacing by introducing and testing a hybrid new 2P-TF-HONS stimulation path for accurate parallel optogenetic excitation into a recently developed hybrid multiphoton 3-D imaging system. The system is shown to allow targeted all-optical probing of in vitro cortical networks expressing channelrhodopsin-2 using a regeneratively amplified femtosecond laser source tuned to 905 nm. These developments further advance a prospective new tool for studying and achieving distributed control over 3-D neuronal circuits both in vitro and in vivo.

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.

PubMed

Zenke, Friedemann; Ganguli, Surya

2018-06-01

A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.

Extracellular space preservation aids the connectomic analysis of neural circuits.

PubMed

Pallotto, Marta; Watkins, Paul V; Fubara, Boma; Singer, Joshua H; Briggman, Kevin L

2015-12-09

Dense connectomic mapping of neuronal circuits is limited by the time and effort required to analyze 3D electron microscopy (EM) datasets. Algorithms designed to automate image segmentation suffer from substantial error rates and require significant manual error correction. Any improvement in segmentation error rates would therefore directly reduce the time required to analyze 3D EM data. We explored preserving extracellular space (ECS) during chemical tissue fixation to improve the ability to segment neurites and to identify synaptic contacts. ECS preserved tissue is easier to segment using machine learning algorithms, leading to significantly reduced error rates. In addition, we observed that electrical synapses are readily identified in ECS preserved tissue. Finally, we determined that antibodies penetrate deep into ECS preserved tissue with only minimal permeabilization, thereby enabling correlated light microscopy (LM) and EM studies. We conclude that preservation of ECS benefits multiple aspects of the connectomic analysis of neural circuits.

Temporally precise single-cell resolution optogenetics

PubMed Central

Shemesh, Or A.; Tanese, Dimitrii; Zampini, Valeria; Linghu, Changyang; Piatkevich, Kiryl; Ronzitti, Emiliano; Papagiakoumou, Eirini; Boyden, Edward S.; Emiliani, Valentina

2017-01-01

Optogenetic control of individual neurons with high temporal precision, within intact mammalian brain circuitry, would enable powerful explorations of how neural circuits operate. Two-photon computer generated holography enables precise sculpting of light, and could in principle enable simultaneous illumination of many neurons in a network, with the requisite temporal precision to simulate accurate neural codes. We designed a high efficacy soma-targeted opsin, finding that fusing the N-terminal 150 residues of kainate receptor subunit 2 (KA2) to the recently discovered high-photocurrent channelrhodopsin CoChR restricted expression of this opsin primarily to the cell body of mammalian cortical neurons. In combination with two-photon holographic stimulation, we found that this somatic CoChR (soCoChR) enabled photostimulation of individual cells in intact cortical circuits with single cell resolution and <1 millisecond temporal precision, and use soCoChR to perform connectivity mapping on intact cortical circuits. PMID:29184208

Emergence of binocular functional properties in a monocular neural circuit

PubMed Central

Ramdya, Pavan; Engert, Florian

2010-01-01

Sensory circuits frequently integrate converging inputs while maintaining precise functional relationships between them. For example, in mammals with stereopsis, neurons at the first stages of binocular visual processing show a close alignment of receptive-field properties for each eye. Still, basic questions about the global wiring mechanisms that enable this functional alignment remain unanswered, including whether the addition of a second retinal input to an otherwise monocular neural circuit is sufficient for the emergence of these binocular properties. We addressed this question by inducing a de novo binocular retinal projection to the larval zebrafish optic tectum and examining recipient neuronal populations using in vivo two-photon calcium imaging. Notably, neurons in rewired tecta were predominantly binocular and showed matching direction selectivity for each eye. We found that a model based on local inhibitory circuitry that computes direction selectivity using the topographic structure of both retinal inputs can account for the emergence of this binocular feature. PMID:19160507

A common neural circuit mechanism for internally guided and externally reinforced forms of motor learning.

PubMed

Hisey, Erin; Kearney, Matthew Gene; Mooney, Richard

2018-04-01

The complex skills underlying verbal and musical expression can be learned without external punishment or reward, indicating their learning is internally guided. The neural mechanisms that mediate internally guided learning are poorly understood, but a circuit comprising dopamine-releasing neurons in the midbrain ventral tegmental area (VTA) and their targets in the basal ganglia are important to externally reinforced learning. Juvenile zebra finches copy a tutor song in a process that is internally guided and, in adulthood, can learn to modify the fundamental frequency (pitch) of a target syllable in response to external reinforcement with white noise. Here we combined intersectional genetic ablation of VTA neurons, reversible blockade of dopamine receptors in the basal ganglia, and singing-triggered optogenetic stimulation of VTA terminals to establish that a common VTA-basal ganglia circuit enables internally guided song copying and externally reinforced syllable pitch learning.

Hypothalamic Survival Circuits: Blueprints for Purposive Behaviors

PubMed Central

Sternson, Scott M.

2015-01-01

Neural processes that direct an animal’s actions toward environmental goals are critical elements for understanding behavior. The hypothalamus is closely associated with motivated behaviors required for survival and reproduction. Intense feeding, drinking, aggressive, and sexual behaviors can be produced by a simple neuronal stimulus applied to discrete hypothalamic regions. What can these “evoked behaviors” teach us about the neural processes that determine behavioral intent and intensity? Small populations of neurons sufficient to evoke a complex motivated behavior may be used as entry points to identify circuits that energize and direct behavior to specific goals. Here, I review recent applications of molecular genetic, optogenetic, and pharmacogenetic approaches that overcome previous limitations for analyzing anatomically complex hypothalamic circuits and their interactions with the rest of the brain. These new tools have the potential to bridge the gaps between neurobiological and psychological thinking about the mechanisms of complex motivated behavior. PMID:23473313

Hypothalamic survival circuits: blueprints for purposive behaviors.

PubMed

Sternson, Scott M

2013-03-06

Neural processes that direct an animal's actions toward environmental goals are critical elements for understanding behavior. The hypothalamus is closely associated with motivated behaviors required for survival and reproduction. Intense feeding, drinking, aggressive, and sexual behaviors can be produced by a simple neuronal stimulus applied to discrete hypothalamic regions. What can these "evoked behaviors" teach us about the neural processes that determine behavioral intent and intensity? Small populations of neurons sufficient to evoke a complex motivated behavior may be used as entry points to identify circuits that energize and direct behavior to specific goals. Here, I review recent applications of molecular genetic, optogenetic, and pharmacogenetic approaches that overcome previous limitations for analyzing anatomically complex hypothalamic circuits and their interactions with the rest of the brain. These new tools have the potential to bridge the gaps between neurobiological and psychological thinking about the mechanisms of complex motivated behavior. Copyright © 2013 Elsevier Inc. All rights reserved.

A Wirelessly Powered and Controlled Device for Optical Neural Control of Freely-Behaving Animals

PubMed Central

Wentz, Christian T.; Bernstein, Jacob G.; Monahan, Patrick; Guerra, Alexander; Rodriguez, Alex; Boyden, Edward S.

2011-01-01

Optogenetics, the ability to use light to activate and silence specific neuron types within neural networks in vivo and in vitro, is revolutionizing neuroscientists’ capacity to understand how defined neural circuit elements contribute to normal and pathological brain functions. Typically awake behaving experiments are conducted by inserting an optical fiber into the brain, tethered to a remote laser, or by utilizing an implanted LED, tethered to a remote power source. A fully wireless system would enable chronic or longitudinal experiments where long duration tethering is impractical, and would also support high-throughput experimentation. However, the high power requirements of light sources (LEDs, lasers), especially in the context of the high-frequency pulse trains often desired in experiments, precludes battery-powered approaches from being widely applicable. We have developed a headborne device weighing 2 grams capable of wirelessly receiving power using a resonant RF power link and storing the energy in an adaptive supercapacitor circuit, which can algorithmically control one or more headborne LEDs via a microcontroller. The device can deliver approximately 2W of power to the LEDs in steady state, and 4.3W in bursts. We also present an optional radio transceiver module (1 gram) which, when added to the base headborne device, enables real-time updating of light delivery protocols; dozens of devices can be simultaneously controlled from one computer. We demonstrate use of the technology to wirelessly drive cortical control of movement in mice. These devices may serve as prototypes for clinical ultra-precise neural prosthetics that use light as the modality of biological control. PMID:21701058

Central control of interlimb coordination and speed‐dependent gait expression in quadrupeds

PubMed Central

Danner, Simon M.; Wilshin, Simon D.; Shevtsova, Natalia A.

2016-01-01

Key points Quadrupeds express different gaits depending on speed of locomotion.Central pattern generators (one per limb) within the spinal cord generate locomotor oscillations and control limb movements. Neural interactions between these generators define interlimb coordination and gait.We present a computational model of spinal circuits representing four rhythm generators with left–right excitatory and inhibitory commissural and fore–hind inhibitory interactions within the cord.Increasing brainstem drive to all rhythm generators and excitatory commissural interneurons induces an increasing frequency of locomotor oscillations accompanied by speed‐dependent gait changes from walk to trot and to gallop and bound.The model closely reproduces and suggests explanations for multiple experimental data, including speed‐dependent gait transitions in intact mice and changes in gait expression in mutants lacking certain types of commissural interneurons. The model suggests the possible circuit organization in the spinal cord and proposes predictions that can be tested experimentally. Abstract As speed of locomotion is increasing, most quadrupeds, including mice, demonstrate sequential gait transitions from walk to trot and to gallop and bound. The neural mechanisms underlying these transitions are poorly understood. We propose that the speed‐dependent expression of different gaits results from speed‐dependent changes in the interactions between spinal circuits controlling different limbs and interlimb coordination. As a result, the expression of each gait depends on (1) left–right interactions within the spinal cord mediated by different commissural interneurons (CINs), (2) fore–hind interactions on each side of the spinal cord and (3) brainstem drives to rhythm‐generating circuits and CIN pathways. We developed a computational model of spinal circuits consisting of four rhythm generators (RGs) with bilateral left–right interactions mediated by V0 CINs (V0D and V0V sub‐types) providing left–right alternation, and conditional V3 CINs promoting left–right synchronization. Fore and hind RGs mutually inhibited each other. We demonstrate that linearly increasing excitatory drives to the RGs and V3 CINs can produce a progressive increase in the locomotor speed accompanied by sequential changes of gaits from walk to trot and to gallop and bound. The model closely reproduces and suggests explanations for the speed‐dependent gait expression observed in vivo in intact mice and in mutants lacking V0V or all V0 CINs. Specifically, trot is not expressed after removal of V0V CINs, and only bound is expressed after removal of all V0 CINs. The model provides important insights into the organization of spinal circuits and neural control of locomotion. PMID:27633893

Optimization of interneuron function by direct coupling of cell migration and axonal targeting.

PubMed

Lim, Lynette; Pakan, Janelle M P; Selten, Martijn M; Marques-Smith, André; Llorca, Alfredo; Bae, Sung Eun; Rochefort, Nathalie L; Marín, Oscar

2018-06-18

Neural circuit assembly relies on the precise synchronization of developmental processes, such as cell migration and axon targeting, but the cell-autonomous mechanisms coordinating these events remain largely unknown. Here we found that different classes of interneurons use distinct routes of migration to reach the embryonic cerebral cortex. Somatostatin-expressing interneurons that migrate through the marginal zone develop into Martinotti cells, one of the most distinctive classes of cortical interneurons. For these cells, migration through the marginal zone is linked to the development of their characteristic layer 1 axonal arborization. Altering the normal migratory route of Martinotti cells by conditional deletion of Mafb-a gene that is preferentially expressed by these cells-cell-autonomously disrupts axonal development and impairs the function of these cells in vivo. Our results suggest that migration and axon targeting programs are coupled to optimize the assembly of inhibitory circuits in the cerebral cortex.

Mechanisms Underlying Development of Visual Maps and Receptive Fields

PubMed Central

Huberman, Andrew D.; Feller, Marla B.; Chapman, Barbara

2008-01-01

Patterns of synaptic connections in the visual system are remarkably precise. These connections dictate the receptive field properties of individual visual neurons and ultimately determine the quality of visual perception. Spontaneous neural activity is necessary for the development of various receptive field properties and visual feature maps. In recent years, attention has shifted to understanding the mechanisms by which spontaneous activity in the developing retina, lateral geniculate nucleus, and visual cortex instruct the axonal and dendritic refinements that give rise to orderly connections in the visual system. Axon guidance cues and a growing list of other molecules, including immune system factors, have also recently been implicated in visual circuit wiring. A major goal now is to determine how these molecules cooperate with spontaneous and visually evoked activity to give rise to the circuits underlying precise receptive field tuning and orderly visual maps. PMID:18558864

Combinations of stroke neurorehabilitation to facilitate motor recovery: perspectives on Hebbian plasticity and homeostatic metaplasticity

PubMed Central

Takeuchi, Naoyuki; Izumi, Shin-Ichi

2015-01-01

Motor recovery after stroke involves developing new neural connections, acquiring new functions, and compensating for impairments. These processes are related to neural plasticity. Various novel stroke rehabilitation techniques based on basic science and clinical studies of neural plasticity have been developed to aid motor recovery. Current research aims to determine whether using combinations of these techniques can synergistically improve motor recovery. When different stroke neurorehabilitation therapies are combined, the timing of each therapeutic program must be considered to enable optimal neural plasticity. Synchronizing stroke rehabilitation with voluntary neural and/or muscle activity can lead to motor recovery by targeting Hebbian plasticity. This reinforces the neural connections between paretic muscles and the residual motor area. Homeostatic metaplasticity, which stabilizes the activity of neurons and neural circuits, can either augment or reduce the synergic effect depending on the timing of combination therapy and types of neurorehabilitation that are used. Moreover, the possibility that the threshold and degree of induced plasticity can be altered after stroke should be noted. This review focuses on the mechanisms underlying combinations of neurorehabilitation approaches and their future clinical applications. We suggest therapeutic approaches for cortical reorganization and maximal functional gain in patients with stroke, based on the processes of Hebbian plasticity and homeostatic metaplasticity. Few of the possible combinations of stroke neurorehabilitation have been tested experimentally; therefore, further studies are required to determine the appropriate combination for motor recovery. PMID:26157374

Goal-Directed Decision Making with Spiking Neurons.

PubMed

Friedrich, Johannes; Lengyel, Máté

2016-02-03

Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules such that its dynamics devise a near-optimal plan of action. By systematically comparing our model results to experimental data, we show that it reproduces behavioral decision times and choice probabilities as well as neural responses in a rich set of tasks. Our results thus offer the first biologically realistic account for complex goal-directed decision making at a computational, algorithmic, and implementational level. Copyright © 2016 the authors 0270-6474/16/361529-18$15.00/0.

Goal-Directed Decision Making with Spiking Neurons

PubMed Central

Lengyel, Máté

2016-01-01

Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. SIGNIFICANCE STATEMENT Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules such that its dynamics devise a near-optimal plan of action. By systematically comparing our model results to experimental data, we show that it reproduces behavioral decision times and choice probabilities as well as neural responses in a rich set of tasks. Our results thus offer the first biologically realistic account for complex goal-directed decision making at a computational, algorithmic, and implementational level. PMID:26843636

Pavlovian Conditioning of "Hermissenda": Current Cellular, Molecular, and Circuit Perspectives

ERIC Educational Resources Information Center

Crow, Terry

2004-01-01

The less-complex central nervous system of many invertebrates make them attractive for not only the molecular analysis of the associative learning and memory, but also in determining how neural circuits are modified by learning to generate changes in behavior. The nudibranch mollusk "Hermissenda crassicornis" is a preparation that has contributed…

Beyond the Bolus: Transgenic Tools for Investigating the Neurophysiology of Learning and Memory

ERIC Educational Resources Information Center

Lykken, Christine; Kentros, Clifford G.

2014-01-01

Understanding the neural mechanisms underlying learning and memory in the entorhinal-hippocampal circuit is a central challenge of systems neuroscience. For more than 40 years, electrophysiological recordings in awake, behaving animals have been used to relate the receptive fields of neurons in this circuit to learning and memory. However, the…

Optogenetic insights on the relationship between anxiety-related behaviors and social deficits

PubMed Central

Allsop, Stephen A.; Vander Weele, Caitlin M.; Wichmann, Romy; Tye, Kay M.

2014-01-01

Many psychiatric illnesses are characterized by deficits in the social domain. For example, there is a high rate of co-morbidity between autism spectrum disorders and anxiety disorders. However, the common neural circuit mechanisms by which social deficits and other psychiatric disease states, such as anxiety, are co-expressed remains unclear. Here, we review optogenetic investigations of neural circuits in animal models of anxiety-related behaviors and social behaviors and discuss the important role of the amygdala in mediating aspects of these behaviors. In particular, we focus on recent evidence that projections from the basolateral amygdala (BLA) to the ventral hippocampus (vHPC) modulate anxiety-related behaviors and also alter social interaction. Understanding how this circuit influences both social behavior and anxiety may provide a mechanistic explanation for the pathogenesis of social anxiety disorder, as well as the prevalence of patients co-diagnosed with autism spectrum disorders and anxiety disorders. Furthermore, elucidating how circuits that modulate social behavior also mediate other complex emotional states will lead to a better understanding of the underlying mechanisms by which social deficits are expressed in psychiatric disease. PMID:25076878

The neural circuits of innate fear: detection, integration, action, and memorization

PubMed Central

Silva, Bianca A.; Gross, Cornelius T.

2016-01-01

How fear is represented in the brain has generated a lot of research attention, not only because fear increases the chances for survival when appropriately expressed but also because it can lead to anxiety and stress-related disorders when inadequately processed. In this review, we summarize recent progress in the understanding of the neural circuits processing innate fear in rodents. We propose that these circuits are contained within three main functional units in the brain: a detection unit, responsible for gathering sensory information signaling the presence of a threat; an integration unit, responsible for incorporating the various sensory information and recruiting downstream effectors; and an output unit, in charge of initiating appropriate bodily and behavioral responses to the threatful stimulus. In parallel, the experience of innate fear also instructs a learning process leading to the memorization of the fearful event. Interestingly, while the detection, integration, and output units processing acute fear responses to different threats tend to be harbored in distinct brain circuits, memory encoding of these threats seems to rely on a shared learning system. PMID:27634145

Top-Down Modulation on Perceptual Decision with Balanced Inhibition through Feedforward and Feedback Inhibitory Neurons

PubMed Central

Wang, Cheng-Te; Lee, Chung-Ting; Wang, Xiao-Jing; Lo, Chung-Chuan

2013-01-01

Recent physiological studies have shown that neurons in various regions of the central nervous systems continuously receive noisy excitatory and inhibitory synaptic inputs in a balanced and covaried fashion. While this balanced synaptic input (BSI) is typically described in terms of maintaining the stability of neural circuits, a number of experimental and theoretical studies have suggested that BSI plays a proactive role in brain functions such as top-down modulation for executive control. Two issues have remained unclear in this picture. First, given the noisy nature of neuronal activities in neural circuits, how do the modulatory effects change if the top-down control implements BSI with different ratios between inhibition and excitation? Second, how is a top-down BSI realized via only excitatory long-range projections in the neocortex? To address the first issue, we systematically tested how the inhibition/excitation ratio affects the accuracy and reaction times of a spiking neural circuit model of perceptual decision. We defined an energy function to characterize the network dynamics, and found that different ratios modulate the energy function of the circuit differently and form two distinct functional modes. To address the second issue, we tested BSI with long-distance projection to inhibitory neurons that are either feedforward or feedback, depending on whether these inhibitory neurons do or do not receive inputs from local excitatory cells, respectively. We found that BSI occurs in both cases. Furthermore, when relying on feedback inhibitory neurons, through the recurrent interactions inside the circuit, BSI dynamically and automatically speeds up the decision by gradually reducing its inhibitory component in the course of a trial when a decision process takes too long. PMID:23626812

Top-down modulation on perceptual decision with balanced inhibition through feedforward and feedback inhibitory neurons.

PubMed

Wang, Cheng-Te; Lee, Chung-Ting; Wang, Xiao-Jing; Lo, Chung-Chuan

2013-01-01

Recent physiological studies have shown that neurons in various regions of the central nervous systems continuously receive noisy excitatory and inhibitory synaptic inputs in a balanced and covaried fashion. While this balanced synaptic input (BSI) is typically described in terms of maintaining the stability of neural circuits, a number of experimental and theoretical studies have suggested that BSI plays a proactive role in brain functions such as top-down modulation for executive control. Two issues have remained unclear in this picture. First, given the noisy nature of neuronal activities in neural circuits, how do the modulatory effects change if the top-down control implements BSI with different ratios between inhibition and excitation? Second, how is a top-down BSI realized via only excitatory long-range projections in the neocortex? To address the first issue, we systematically tested how the inhibition/excitation ratio affects the accuracy and reaction times of a spiking neural circuit model of perceptual decision. We defined an energy function to characterize the network dynamics, and found that different ratios modulate the energy function of the circuit differently and form two distinct functional modes. To address the second issue, we tested BSI with long-distance projection to inhibitory neurons that are either feedforward or feedback, depending on whether these inhibitory neurons do or do not receive inputs from local excitatory cells, respectively. We found that BSI occurs in both cases. Furthermore, when relying on feedback inhibitory neurons, through the recurrent interactions inside the circuit, BSI dynamically and automatically speeds up the decision by gradually reducing its inhibitory component in the course of a trial when a decision process takes too long.

Motor control in a Drosophila taste circuit

PubMed Central

Gordon, Michael D.; Scott, Kristin

2009-01-01

Tastes elicit innate behaviors critical for directing animals to ingest nutritious substances and reject toxic compounds, but the neural basis of these behaviors is not understood. Here, we use a neural silencing screen to identify neurons required for a simple Drosophila taste behavior, and characterize a neural population that controls a specific subprogram of this behavior. By silencing and activating subsets of the defined cell population, we identify the neurons involved in the taste behavior as a pair of motor neurons located in the subesophageal ganglion (SOG). The motor neurons are activated by sugar stimulation of gustatory neurons and inhibited by bitter compounds; however, experiments utilizing split-GFP detect no direct connections between the motor neurons and primary sensory neurons, indicating that further study will be necessary to elucidate the circuitry bridging these populations. Combined, these results provide a general strategy and a valuable starting point for future taste circuit analysis. PMID:19217375

Psychological Processing in Chronic Pain: A Neural Systems Approach

PubMed Central

Simons, Laura; Elman, Igor; Borsook, David

2014-01-01

Our understanding of chronic pain involves complex brain circuits that include sensory, emotional, cognitive and interoceptive processing. The feed-forward interactions between physical (e.g., trauma) and emotional pain and the consequences of altered psychological status on the expression of pain have made the evaluation and treatment of chronic pain a challenge in the clinic. By understanding the neural circuits involved in psychological processes, a mechanistic approach to the implementation of psychology-based treatments may be better understood. In this review we evaluate some of the principle processes that may be altered as a consequence of chronic pain in the context of localized and integrated neural networks. These changes are ongoing, vary in their magnitude, and their hierarchical manifestations, and may be temporally and sequentially altered by treatments, and all contribute to an overall pain phenotype. Furthermore, we link altered psychological processes to specific evidence-based treatments to put forth a model of pain neuroscience psychology. PMID:24374383

Altered topology of neural circuits in congenital prosopagnosia.

PubMed

Rosenthal, Gideon; Tanzer, Michal; Simony, Erez; Hasson, Uri; Behrmann, Marlene; Avidan, Galia

2017-08-21

Using a novel, fMRI-based inter-subject functional correlation (ISFC) approach, which isolates stimulus-locked inter-regional correlation patterns, we compared the cortical topology of the neural circuit for face processing in participants with an impairment in face recognition, congenital prosopagnosia (CP), and matched controls. Whereas the anterior temporal lobe served as the major network hub for face processing in controls, this was not the case for the CPs. Instead, this group evinced hyper-connectivity in posterior regions of the visual cortex, mostly associated with the lateral occipital and the inferior temporal cortices. Moreover, the extent of this hyper-connectivity was correlated with the face recognition deficit. These results offer new insights into the perturbed cortical topology in CP, which may serve as the underlying neural basis of the behavioral deficits typical of this disorder. The approach adopted here has the potential to uncover altered topologies in other neurodevelopmental disorders, as well.