Multi-class oscillating systems of interacting neurons

DEFF Research Database (Denmark)

Ditlevsen, Susanne; Löcherbach, Eva

2017-01-01

We consider multi-class systems of interacting nonlinear Hawkes processes modeling several large families of neurons and study their mean field limits. As the total number of neurons goes to infinity we prove that the evolution within each class can be described by a nonlinear limit differential...

Results on a Binding Neuron Model and Their Implications for Modified Hourglass Model for Neuronal Network

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Viswanathan Arunachalam

2013-01-01

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

Automatic fitting of spiking neuron models to electrophysiological recordings

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Cyrille Rossant

2010-03-01

Full Text Available Spiking models can accurately predict the spike trains produced by cortical neurons in response to somatically injected currents. Since the specific characteristics of the model depend on the neuron, a computational method is required to fit models to electrophysiological recordings. The fitting procedure can be very time consuming both in terms of computer simulations and in terms of code writing. We present algorithms to fit spiking models to electrophysiological data (time-varying input and spike trains that can run in parallel on graphics processing units (GPUs. The model fitting library is interfaced with Brian, a neural network simulator in Python. If a GPU is present it uses just-in-time compilation to translate model equations into optimized code. Arbitrary models can then be defined at script level and run on the graphics card. This tool can be used to obtain empirically validated spiking models of neurons in various systems. We demonstrate its use on public data from the INCF Quantitative Single-Neuron Modeling 2009 competition by comparing the performance of a number of neuron spiking models.

Model System for Live Imaging of Neuronal Responses to Injury and Repair

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Mathieu Gravel

2011-11-01

Full Text Available Although it has been well established that induction of growth-associated protein-43 (GAP-43 during development coincides with axonal outgrowth and early synapse formation, the existence of neuronal plasticity and neurite outgrowth in the adult central nervous system after injuries is more controversial. To visualize the processes of neuronal injury and repair in living animals, we generated reporter mice for bioluminescence and fluorescence imaging bearing the luc (luciferase and gfp (green fluorescent protein reporter genes under the control of the murine GAP-43 promoter. Reporter functionality was first observed during the development of transgenic embryos. Using in vivo bioluminescence and fluorescence imaging, we visualized induction of the GAP-43 signals from live embryos starting at E10.5, as well as neuronal responses to brain and peripheral nerve injuries (the signals peaked at 14 days postinjury. Moreover, three-dimensional analysis of the GAP-43 bioluminescent signal confirmed that it originated from brain structures affected by ischemic injury. The analysis of fluorescence signal at cellular level revealed colocalization between endogenous protein and the GAP-43-driven gfp transgene. Taken together, our results suggest that the GAP-43-luc/gfp reporter mouse represents a valid model system for real-time analysis of neurite outgrowth and the capacity of the adult nervous system to regenerate after injuries.

A distance constrained synaptic plasticity model of C. elegans neuronal network

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Badhwar, Rahul; Bagler, Ganesh

2017-03-01

Brain research has been driven by enquiry for principles of brain structure organization and its control mechanisms. The neuronal wiring map of C. elegans, the only complete connectome available till date, presents an incredible opportunity to learn basic governing principles that drive structure and function of its neuronal architecture. Despite its apparently simple nervous system, C. elegans is known to possess complex functions. The nervous system forms an important underlying framework which specifies phenotypic features associated to sensation, movement, conditioning and memory. In this study, with the help of graph theoretical models, we investigated the C. elegans neuronal network to identify network features that are critical for its control. The 'driver neurons' are associated with important biological functions such as reproduction, signalling processes and anatomical structural development. We created 1D and 2D network models of C. elegans neuronal system to probe the role of features that confer controllability and small world nature. The simple 1D ring model is critically poised for the number of feed forward motifs, neuronal clustering and characteristic path-length in response to synaptic rewiring, indicating optimal rewiring. Using empirically observed distance constraint in the neuronal network as a guiding principle, we created a distance constrained synaptic plasticity model that simultaneously explains small world nature, saturation of feed forward motifs as well as observed number of driver neurons. The distance constrained model suggests optimum long distance synaptic connections as a key feature specifying control of the network.

Modeling the Development of Goal-Specificity in Mirror Neurons.

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Thill, Serge; Svensson, Henrik; Ziemke, Tom

2011-12-01

Neurophysiological studies have shown that parietal mirror neurons encode not only actions but also the goal of these actions. Although some mirror neurons will fire whenever a certain action is perceived (goal-independently), most will only fire if the motion is perceived as part of an action with a specific goal. This result is important for the action-understanding hypothesis as it provides a potential neurological basis for such a cognitive ability. It is also relevant for the design of artificial cognitive systems, in particular robotic systems that rely on computational models of the mirror system in their interaction with other agents. Yet, to date, no computational model has explicitly addressed the mechanisms that give rise to both goal-specific and goal-independent parietal mirror neurons. In the present paper, we present a computational model based on a self-organizing map, which receives artificial inputs representing information about both the observed or executed actions and the context in which they were executed. We show that the map develops a biologically plausible organization in which goal-specific mirror neurons emerge. We further show that the fundamental cause for both the appearance and the number of goal-specific neurons can be found in geometric relationships between the different inputs to the map. The results are important to the action-understanding hypothesis as they provide a mechanism for the emergence of goal-specific parietal mirror neurons and lead to a number of predictions: (1) Learning of new goals may mostly reassign existing goal-specific neurons rather than recruit new ones; (2) input differences between executed and observed actions can explain observed corresponding differences in the number of goal-specific neurons; and (3) the percentage of goal-specific neurons may differ between motion primitives.

Conceptual Network Model From Sensory Neurons to Astrocytes of the Human Nervous System.

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Yang, Yiqun; Yeo, Chai Kiat

2015-07-01

From a single-cell animal like paramecium to vertebrates like ape, the nervous system plays an important role in responding to the variations of the environment. Compared to animals, the nervous system in the human body possesses more intricate organization and utility. The nervous system anatomy has been understood progressively, yet the explanation at the cell level regarding complete information transmission is still lacking. Along the signal pathway toward the brain, an external stimulus first activates action potentials in the sensing neuron and these electric pulses transmit along the spinal nerve or cranial nerve to the neurons in the brain. Second, calcium elevation is triggered in the branch of astrocyte at the tripartite synapse. Third, the local calcium wave expands to the entire territory of the astrocyte. Finally, the calcium wave propagates to the neighboring astrocyte via gap junction channel. In our study, we integrate the existing mathematical model and biological experiments in each step of the signal transduction to establish a conceptual network model for the human nervous system. The network is composed of four layers and the communication protocols of each layer could be adapted to entities with different characterizations. We verify our simulation results against the available biological experiments and mathematical models and provide a test case of the integrated network. As the production of conscious episode in the human nervous system is still under intense research, our model serves as a useful tool to facilitate, complement and verify current and future study in human cognition.

The mirror neuron system.

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Cattaneo, Luigi; Rizzolatti, Giacomo

2009-05-01

Mirror neurons are a class of neurons, originally discovered in the premotor cortex of monkeys, that discharge both when individuals perform a given motor act and when they observe others perform that same motor act. Ample evidence demonstrates the existence of a cortical network with the properties of mirror neurons (mirror system) in humans. The human mirror system is involved in understanding others' actions and their intentions behind them, and it underlies mechanisms of observational learning. Herein, we will discuss the clinical implications of the mirror system.

Bifurcation software in Matlab with applications in neuronal modeling.

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Govaerts, Willy; Sautois, Bart

2005-02-01

Many biological phenomena, notably in neuroscience, can be modeled by dynamical systems. We describe a recent improvement of a Matlab software package for dynamical systems with applications to modeling single neurons and all-to-all connected networks of neurons. The new software features consist of an object-oriented approach to bifurcation computations and the partial inclusion of C-code to speed up the computation. As an application, we study the origin of the spiking behaviour of neurons when the equilibrium state is destabilized by an incoming current. We show that Class II behaviour, i.e. firing with a finite frequency, is possible even if the destabilization occurs through a saddle-node bifurcation. Furthermore, we show that synchronization of an all-to-all connected network of such neurons with only excitatory connections is also possible in this case.

Digital hardware implementation of a stochastic two-dimensional neuron model.

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Grassia, F; Kohno, T; Levi, T

2016-11-01

This study explores the feasibility of stochastic neuron simulation in digital systems (FPGA), which realizes an implementation of a two-dimensional neuron model. The stochasticity is added by a source of current noise in the silicon neuron using an Ornstein-Uhlenbeck process. This approach uses digital computation to emulate individual neuron behavior using fixed point arithmetic operation. The neuron model's computations are performed in arithmetic pipelines. It was designed in VHDL language and simulated prior to mapping in the FPGA. The experimental results confirmed the validity of the developed stochastic FPGA implementation, which makes the implementation of the silicon neuron more biologically plausible for future hybrid experiments. Copyright © 2017 Elsevier Ltd. All rights reserved.

The mirror-neuron system.

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Rizzolatti, Giacomo; Craighero, Laila

2004-01-01

A category of stimuli of great importance for primates, humans in particular, is that formed by actions done by other individuals. If we want to survive, we must understand the actions of others. Furthermore, without action understanding, social organization is impossible. In the case of humans, there is another faculty that depends on the observation of others' actions: imitation learning. Unlike most species, we are able to learn by imitation, and this faculty is at the basis of human culture. In this review we present data on a neurophysiological mechanism--the mirror-neuron mechanism--that appears to play a fundamental role in both action understanding and imitation. We describe first the functional properties of mirror neurons in monkeys. We review next the characteristics of the mirror-neuron system in humans. We stress, in particular, those properties specific to the human mirror-neuron system that might explain the human capacity to learn by imitation. We conclude by discussing the relationship between the mirror-neuron system and language.

How neurons migrate: a dynamic in-silico model of neuronal migration in the developing cortex

LENUS (Irish Health Repository)

Setty, Yaki

2011-09-30

Abstract Background Neuronal migration, the process by which neurons migrate from their place of origin to their final position in the brain, is a central process for normal brain development and function. Advances in experimental techniques have revealed much about many of the molecular components involved in this process. Notwithstanding these advances, how the molecular machinery works together to govern the migration process has yet to be fully understood. Here we present a computational model of neuronal migration, in which four key molecular entities, Lis1, DCX, Reelin and GABA, form a molecular program that mediates the migration process. Results The model simulated the dynamic migration process, consistent with in-vivo observations of morphological, cellular and population-level phenomena. Specifically, the model reproduced migration phases, cellular dynamics and population distributions that concur with experimental observations in normal neuronal development. We tested the model under reduced activity of Lis1 and DCX and found an aberrant development similar to observations in Lis1 and DCX silencing expression experiments. Analysis of the model gave rise to unforeseen insights that could guide future experimental study. Specifically: (1) the model revealed the possibility that under conditions of Lis1 reduced expression, neurons experience an oscillatory neuron-glial association prior to the multipolar stage; and (2) we hypothesized that observed morphology variations in rats and mice may be explained by a single difference in the way that Lis1 and DCX stimulate bipolar motility. From this we make the following predictions: (1) under reduced Lis1 and enhanced DCX expression, we predict a reduced bipolar migration in rats, and (2) under enhanced DCX expression in mice we predict a normal or a higher bipolar migration. Conclusions We present here a system-wide computational model of neuronal migration that integrates theory and data within a precise

How neurons migrate: a dynamic in-silico model of neuronal migration in the developing cortex

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Skoblov Nikita

2011-09-01

Full Text Available Abstract Background Neuronal migration, the process by which neurons migrate from their place of origin to their final position in the brain, is a central process for normal brain development and function. Advances in experimental techniques have revealed much about many of the molecular components involved in this process. Notwithstanding these advances, how the molecular machinery works together to govern the migration process has yet to be fully understood. Here we present a computational model of neuronal migration, in which four key molecular entities, Lis1, DCX, Reelin and GABA, form a molecular program that mediates the migration process. Results The model simulated the dynamic migration process, consistent with in-vivo observations of morphological, cellular and population-level phenomena. Specifically, the model reproduced migration phases, cellular dynamics and population distributions that concur with experimental observations in normal neuronal development. We tested the model under reduced activity of Lis1 and DCX and found an aberrant development similar to observations in Lis1 and DCX silencing expression experiments. Analysis of the model gave rise to unforeseen insights that could guide future experimental study. Specifically: (1 the model revealed the possibility that under conditions of Lis1 reduced expression, neurons experience an oscillatory neuron-glial association prior to the multipolar stage; and (2 we hypothesized that observed morphology variations in rats and mice may be explained by a single difference in the way that Lis1 and DCX stimulate bipolar motility. From this we make the following predictions: (1 under reduced Lis1 and enhanced DCX expression, we predict a reduced bipolar migration in rats, and (2 under enhanced DCX expression in mice we predict a normal or a higher bipolar migration. Conclusions We present here a system-wide computational model of neuronal migration that integrates theory and data within a

Conditional ablation of orexin/hypocretin neurons: a new mouse model for the study of narcolepsy and orexin system function.

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Tabuchi, Sawako; Tsunematsu, Tomomi; Black, Sarah W; Tominaga, Makoto; Maruyama, Megumi; Takagi, Kazuyo; Minokoshi, Yasuhiko; Sakurai, Takeshi; Kilduff, Thomas S; Yamanaka, Akihiro

2014-05-07

The sleep disorder narcolepsy results from loss of hypothalamic orexin/hypocretin neurons. Although narcolepsy onset is usually postpubertal, current mouse models involve loss of either orexin peptides or orexin neurons from birth. To create a model of orexin/hypocretin deficiency with closer fidelity to human narcolepsy, diphtheria toxin A (DTA) was expressed in orexin neurons under control of the Tet-off system. Upon doxycycline removal from the diet of postpubertal orexin-tTA;TetO DTA mice, orexin neurodegeneration was rapid, with 80% cell loss within 7 d, and resulted in disrupted sleep architecture. Cataplexy, the pathognomic symptom of narcolepsy, occurred by 14 d when ∼5% of the orexin neurons remained. Cataplexy frequency increased for at least 11 weeks after doxycycline. Temporary doxycycline removal followed by reintroduction after several days enabled partial lesion of orexin neurons. DTA-induced orexin neurodegeneration caused a body weight increase without a change in food consumption, mimicking metabolic aspects of human narcolepsy. Because the orexin/hypocretin system has been implicated in the control of metabolism and addiction as well as sleep/wake regulation, orexin-tTA; TetO DTA mice are a novel model in which to study these functions, for pharmacological studies of cataplexy, and to study network reorganization as orexin input is lost.

Probing the dynamics of identified neurons with a data-driven modeling approach.

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Thomas Nowotny

2008-07-01

Full Text Available In controlling animal behavior the nervous system has to perform within the operational limits set by the requirements of each specific behavior. The implications for the corresponding range of suitable network, single neuron, and ion channel properties have remained elusive. In this article we approach the question of how well-constrained properties of neuronal systems may be on the neuronal level. We used large data sets of the activity of isolated invertebrate identified cells and built an accurate conductance-based model for this cell type using customized automated parameter estimation techniques. By direct inspection of the data we found that the variability of the neurons is larger when they are isolated from the circuit than when in the intact system. Furthermore, the responses of the neurons to perturbations appear to be more consistent than their autonomous behavior under stationary conditions. In the developed model, the constraints on different parameters that enforce appropriate model dynamics vary widely from some very tightly controlled parameters to others that are almost arbitrary. The model also allows predictions for the effect of blocking selected ionic currents and to prove that the origin of irregular dynamics in the neuron model is proper chaoticity and that this chaoticity is typical in an appropriate sense. Our results indicate that data driven models are useful tools for the in-depth analysis of neuronal dynamics. The better consistency of responses to perturbations, in the real neurons as well as in the model, suggests a paradigm shift away from measuring autonomous dynamics alone towards protocols of controlled perturbations. Our predictions for the impact of channel blockers on the neuronal dynamics and the proof of chaoticity underscore the wide scope of our approach.

Stochastic biomathematical models with applications to neuronal modeling

CERN Document Server

Batzel, Jerry; Ditlevsen, Susanne

2013-01-01

Stochastic biomathematical models are becoming increasingly important as new light is shed on the role of noise in living systems. In certain biological systems, stochastic effects may even enhance a signal, thus providing a biological motivation for the noise observed in living systems. Recent advances in stochastic analysis and increasing computing power facilitate the analysis of more biophysically realistic models, and this book provides researchers in computational neuroscience and stochastic systems with an overview of recent developments. Key concepts are developed in chapters written by experts in their respective fields. Topics include: one-dimensional homogeneous diffusions and their boundary behavior, large deviation theory and its application in stochastic neurobiological models, a review of mathematical methods for stochastic neuronal integrate-and-fire models, stochastic partial differential equation models in neurobiology, and stochastic modeling of spreading cortical depression.

Minimal time spiking in various ChR2-controlled neuron models.

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Renault, Vincent; Thieullen, Michèle; Trélat, Emmanuel

2018-02-01

We use conductance based neuron models, and the mathematical modeling of optogenetics to define controlled neuron models and we address the minimal time control of these affine systems for the first spike from equilibrium. We apply tools of geometric optimal control theory to study singular extremals, and we implement a direct method to compute optimal controls. When the system is too large to theoretically investigate the existence of singular optimal controls, we observe numerically the optimal bang-bang controls.

The synchronization of asymmetric-structured electric coupling neuronal system

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Wang, Guanping; Jin, Wuyin; Liu, Hao; Sun, Wei

2018-02-01

Based on the Hindmarsh-Rose (HR) model, the synchronization dynamics of asymmetric-structured electric coupling two neuronal system is investigated in this paper. It is discovered that when the time-delay scope and coupling strength for the synchronization are correlated positively under unequal time delay, the time-delay difference does not make a clear distinction between the two individual inter-spike intervals (ISI) bifurcation diagrams of the two coupled neurons. Therefore, the superficial difference of the system synchronization dynamics is not obvious for the unequal time-delay feedback. In the asymmetrical current incentives under asymmetric electric coupled system, the two neurons can only be almost completely synchronized in specific area of the interval which end-pointed with two discharge modes for a single neuron under different stimuli currents before coupling, but the intervention of time-delay feedback, together with the change of the coupling strength, can make the coupled system not only almost completely synchronized within anywhere in the front area, but also outside of it.

Energy Model of Neuron Activation.

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Romanyshyn, Yuriy; Smerdov, Andriy; Petrytska, Svitlana

2017-02-01

On the basis of the neurophysiological strength-duration (amplitude-duration) curve of neuron activation (which relates the threshold amplitude of a rectangular current pulse of neuron activation to the pulse duration), as well as with the use of activation energy constraint (the threshold curve corresponds to the energy threshold of neuron activation by a rectangular current pulse), an energy model of neuron activation by a single current pulse has been constructed. The constructed model of activation, which determines its spectral properties, is a bandpass filter. Under the condition of minimum-phase feature of the neuron activation model, on the basis of Hilbert transform, the possibilities of phase-frequency response calculation from its amplitude-frequency response have been considered. Approximation to the amplitude-frequency response by the response of the Butterworth filter of the first order, as well as obtaining the pulse response corresponding to this approximation, give us the possibility of analyzing the efficiency of activating current pulses of various shapes, including analysis in accordance with the energy constraint.

Complete functional characterization of sensory neurons by system identification.

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Wu, Michael C-K; David, Stephen V; Gallant, Jack L

2006-01-01

System identification is a growing approach to sensory neurophysiology that facilitates the development of quantitative functional models of sensory processing. This approach provides a clear set of guidelines for combining experimental data with other knowledge about sensory function to obtain a description that optimally predicts the way that neurons process sensory information. This prediction paradigm provides an objective method for evaluating and comparing computational models. In this chapter we review many of the system identification algorithms that have been used in sensory neurophysiology, and we show how they can be viewed as variants of a single statistical inference problem. We then review many of the practical issues that arise when applying these methods to neurophysiological experiments: stimulus selection, behavioral control, model visualization, and validation. Finally we discuss several problems to which system identification has been applied recently, including one important long-term goal of sensory neuroscience: developing models of sensory systems that accurately predict neuronal responses under completely natural conditions.

Complete Neuron-Astrocyte Interaction Model: Digital Multiplierless Design and Networking Mechanism.

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Haghiri, Saeed; Ahmadi, Arash; Saif, Mehrdad

2017-02-01

Glial cells, also known as neuroglia or glia, are non-neuronal cells providing support and protection for neurons in the central nervous system (CNS). They also act as supportive cells in the brain. Among a variety of glial cells, the star-shaped glial cells, i.e., astrocytes, are the largest cell population in the brain. The important role of astrocyte such as neuronal synchronization, synaptic information regulation, feedback to neural activity and extracellular regulation make the astrocytes play a vital role in brain disease. This paper presents a modified complete neuron-astrocyte interaction model that is more suitable for efficient and large scale biological neural network realization on digital platforms. Simulation results show that the modified complete interaction model can reproduce biological-like behavior of the original neuron-astrocyte mechanism. The modified interaction model is investigated in terms of digital realization feasibility and cost targeting a low cost hardware implementation. Networking behavior of this interaction is investigated and compared between two cases: i) the neuron spiking mechanism without astrocyte effects, and ii) the effect of astrocyte in regulating the neurons behavior and synaptic transmission via controlling the LTP and LTD processes. Hardware implementation on FPGA shows that the modified model mimics the main mechanism of neuron-astrocyte communication with higher performance and considerably lower hardware overhead cost compared with the original interaction model.

Interactions of the orexin/hypocretin neurones and the histaminergic system.

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Sundvik, M; Panula, P

2015-02-01

Histaminergic and orexin/hypocretin systems are components in the brain wake-promoting system. Both are affected in the sleep disorder narcolepsy, but the role of histamine in narcolepsy is unclear. The histaminergic neurones are activated by the orexin/hypocretin system in rodents, and the development of the orexin/hypocretin neurones is bidirectionally regulated by the histaminergic system in zebrafish. This review summarizes the current knowledge of the interactions of these two systems in normal and pathological conditions in humans and different animal models. © 2014 Scandinavian Physiological Society. Published by John Wiley & Sons Ltd.

Electrical Activity in a Time-Delay Four-Variable Neuron Model under Electromagnetic Induction

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Keming Tang

2017-11-01

Full Text Available To investigate the effect of electromagnetic induction on the electrical activity of neuron, the variable for magnetic flow is used to improve Hindmarsh–Rose neuron model. Simultaneously, due to the existence of time-delay when signals are propagated between neurons or even in one neuron, it is important to study the role of time-delay in regulating the electrical activity of the neuron. For this end, a four-variable neuron model is proposed to investigate the effects of electromagnetic induction and time-delay. Simulation results suggest that the proposed neuron model can show multiple modes of electrical activity, which is dependent on the time-delay and external forcing current. It means that suitable discharge mode can be obtained by selecting the time-delay or external forcing current, which could be helpful for further investigation of electromagnetic radiation on biological neuronal system.

Phase-flip bifurcation in a coupled Josephson junction neuron system

Energy Technology Data Exchange (ETDEWEB)

Segall, Kenneth, E-mail: ksegall@colgate.edu [Department of Physics and Astronomy, Colgate University, Hamilton, NY 13346 (United States); Guo, Siyang; Crotty, Patrick [Department of Physics and Astronomy, Colgate University, Hamilton, NY 13346 (United States); Schult, Dan [Department of Mathematics, Colgate University, Hamilton, NY 13346 (United States); Miller, Max [Department of Physics and Astronomy, Colgate University, Hamilton, NY 13346 (United States)

2014-12-15

Aiming to understand group behaviors and dynamics of neural networks, we have previously proposed the Josephson junction neuron (JJ neuron) as a fast analog model that mimics a biological neuron using superconducting Josephson junctions. In this study, we further analyze the dynamics of the JJ neuron numerically by coupling one JJ neuron to another. In this coupled system we observe a phase-flip bifurcation, where the neurons synchronize out-of-phase at weak coupling and in-phase at strong coupling. We verify this by simulation of the circuit equations and construct a bifurcation diagram for varying coupling strength using the phase response curve and spike phase difference map. The phase-flip bifurcation could be observed experimentally using standard digital superconducting circuitry.

Phase-flip bifurcation in a coupled Josephson junction neuron system

International Nuclear Information System (INIS)

Segall, Kenneth; Guo, Siyang; Crotty, Patrick; Schult, Dan; Miller, Max

2014-01-01

Aiming to understand group behaviors and dynamics of neural networks, we have previously proposed the Josephson junction neuron (JJ neuron) as a fast analog model that mimics a biological neuron using superconducting Josephson junctions. In this study, we further analyze the dynamics of the JJ neuron numerically by coupling one JJ neuron to another. In this coupled system we observe a phase-flip bifurcation, where the neurons synchronize out-of-phase at weak coupling and in-phase at strong coupling. We verify this by simulation of the circuit equations and construct a bifurcation diagram for varying coupling strength using the phase response curve and spike phase difference map. The phase-flip bifurcation could be observed experimentally using standard digital superconducting circuitry

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

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

2012-01-01

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

Neurons other than motor neurons in motor neuron disease.

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Ruffoli, Riccardo; Biagioni, Francesca; Busceti, Carla L; Gaglione, Anderson; Ryskalin, Larisa; Gambardella, Stefano; Frati, Alessandro; Fornai, Francesco

2017-11-01

Amyotrophic lateral sclerosis (ALS) is typically defined by a loss of motor neurons in the central nervous system. Accordingly, morphological analysis for decades considered motor neurons (in the cortex, brainstem and spinal cord) as the neuronal population selectively involved in ALS. Similarly, this was considered the pathological marker to score disease severity ex vivo both in patients and experimental models. However, the concept of non-autonomous motor neuron death was used recently to indicate the need for additional cell types to produce motor neuron death in ALS. This means that motor neuron loss occurs only when they are connected with other cell types. This concept originally emphasized the need for resident glia as well as non-resident inflammatory cells. Nowadays, the additional role of neurons other than motor neurons emerged in the scenario to induce non-autonomous motor neuron death. In fact, in ALS neurons diverse from motor neurons are involved. These cells play multiple roles in ALS: (i) they participate in the chain of events to produce motor neuron loss; (ii) they may even degenerate more than and before motor neurons. In the present manuscript evidence about multi-neuronal involvement in ALS patients and experimental models is discussed. Specific sub-classes of neurons in the whole spinal cord are reported either to degenerate or to trigger neuronal degeneration, thus portraying ALS as a whole spinal cord disorder rather than a disease affecting motor neurons solely. This is associated with a novel concept in motor neuron disease which recruits abnormal mechanisms of cell to cell communication.

Functionalized anatomical models for EM-neuron Interaction modeling

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

2016-06-01

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

Response of Electrical Activity in an Improved Neuron Model under Electromagnetic Radiation and Noise.

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Zhan, Feibiao; Liu, Shenquan

2017-01-01

Electrical activities are ubiquitous neuronal bioelectric phenomena, which have many different modes to encode the expression of biological information, and constitute the whole process of signal propagation between neurons. Therefore, we focus on the electrical activities of neurons, which is also causing widespread concern among neuroscientists. In this paper, we mainly investigate the electrical activities of the Morris-Lecar (M-L) model with electromagnetic radiation or Gaussian white noise, which can restore the authenticity of neurons in realistic neural network. First, we explore dynamical response of the whole system with electromagnetic induction (EMI) and Gaussian white noise. We find that there are slight differences in the discharge behaviors via comparing the response of original system with that of improved system, and electromagnetic induction can transform bursting or spiking state to quiescent state and vice versa. Furthermore, we research bursting transition mode and the corresponding periodic solution mechanism for the isolated neuron model with electromagnetic induction by using one-parameter and bi-parameters bifurcation analysis. Finally, we analyze the effects of Gaussian white noise on the original system and coupled system, which is conducive to understand the actual discharge properties of realistic neurons.

Response of Electrical Activity in an Improved Neuron Model under Electromagnetic Radiation and Noise

Directory of Open Access Journals (Sweden)

Feibiao Zhan

2017-11-01

Full Text Available Electrical activities are ubiquitous neuronal bioelectric phenomena, which have many different modes to encode the expression of biological information, and constitute the whole process of signal propagation between neurons. Therefore, we focus on the electrical activities of neurons, which is also causing widespread concern among neuroscientists. In this paper, we mainly investigate the electrical activities of the Morris-Lecar (M-L model with electromagnetic radiation or Gaussian white noise, which can restore the authenticity of neurons in realistic neural network. First, we explore dynamical response of the whole system with electromagnetic induction (EMI and Gaussian white noise. We find that there are slight differences in the discharge behaviors via comparing the response of original system with that of improved system, and electromagnetic induction can transform bursting or spiking state to quiescent state and vice versa. Furthermore, we research bursting transition mode and the corresponding periodic solution mechanism for the isolated neuron model with electromagnetic induction by using one-parameter and bi-parameters bifurcation analysis. Finally, we analyze the effects of Gaussian white noise on the original system and coupled system, which is conducive to understand the actual discharge properties of realistic neurons.

Chaos and its synchronization in two-neuron systems with discrete delays

International Nuclear Information System (INIS)

Zhou Shangbo; Liao Xiaofeng; Yu Juebang; Wong Kwokwo

2004-01-01

It is well known that complex dynamic behaviors exist in time-delayed neural systems. Infinite positive Lyapunov exponents can be found in time-delayed chaotic systems since the dimension of such systems is infinite. However, theoretical and experimental models studied thus far are low dimensional systems with only one positive Lyapunov exponent. Consequently, messages masked by such chaotic systems are shown to be easily extracted in some cases. Therefore, communication system with a higher security level can be design by means of the time-delayed neuron systems. In this paper, we firstly investigate the dynamical behaviors of two-neuron systems with discrete delays. Then, the chaos synchronization in time-delayed neuron system is studied based on the method of designing the coupled system and employing Krasovskii-Lyapunov theory to search the synchronization conditions. Numerical results illustrate the correctness of our theoretical analyses

Stability switches, Hopf bifurcation and chaos of a neuron model with delay-dependent parameters

International Nuclear Information System (INIS)

Xu, X.; Hu, H.Y.; Wang, H.L.

2006-01-01

It is very common that neural network systems usually involve time delays since the transmission of information between neurons is not instantaneous. Because memory intensity of the biological neuron usually depends on time history, some of the parameters may be delay dependent. Yet, little attention has been paid to the dynamics of such systems. In this Letter, a detailed analysis on the stability switches, Hopf bifurcation and chaos of a neuron model with delay-dependent parameters is given. Moreover, the direction and the stability of the bifurcating periodic solutions are obtained by the normal form theory and the center manifold theorem. It shows that the dynamics of the neuron model with delay-dependent parameters is quite different from that of systems with delay-independent parameters only

Transient Exposure to Ethanol during Zebrafish Embryogenesis Results in Defects in Neuronal Differentiation: An Alternative Model System to Study FASD

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Joya, Xavier; Garcia-Algar, Oscar; Vall, Oriol; Pujades, Cristina

2014-01-01

Background The exposure of the human embryo to ethanol results in a spectrum of disorders involving multiple organ systems, including the impairment of the development of the central nervous system (CNS). In spite of the importance for human health, the molecular basis of prenatal ethanol exposure remains poorly understood, mainly to the difficulty of sample collection. Zebrafish is now emerging as a powerful organism for the modeling and the study of human diseases. In this work, we have assessed the sensitivity of specific subsets of neurons to ethanol exposure during embryogenesis and we have visualized the sensitive embryonic developmental periods for specific neuronal groups by the use of different transgenic zebrafish lines. Methodology/Principal Findings In order to evaluate the teratogenic effects of acute ethanol exposure, we exposed zebrafish embryos to ethanol in a given time window and analyzed the effects in neurogenesis, neuronal differentiation and brain patterning. Zebrafish larvae exposed to ethanol displayed small eyes and/or a reduction of the body length, phenotypical features similar to the observed in children with prenatal exposure to ethanol. When neuronal populations were analyzed, we observed a clear reduction in the number of differentiated neurons in the spinal cord upon ethanol exposure. There was a decrease in the population of sensory neurons mainly due to a decrease in cell proliferation and subsequent apoptosis during neuronal differentiation, with no effect in motoneuron specification. Conclusion Our investigation highlights that transient exposure to ethanol during early embryonic development affects neuronal differentiation although does not result in defects in early neurogenesis. These results establish the use of zebrafish embryos as an alternative research model to elucidate the molecular mechanism(s) of ethanol-induced developmental toxicity at very early stages of embryonic development. PMID:25383948

Transient exposure to ethanol during zebrafish embryogenesis results in defects in neuronal differentiation: an alternative model system to study FASD.

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Xavier Joya

Full Text Available The exposure of the human embryo to ethanol results in a spectrum of disorders involving multiple organ systems, including the impairment of the development of the central nervous system (CNS. In spite of the importance for human health, the molecular basis of prenatal ethanol exposure remains poorly understood, mainly to the difficulty of sample collection. Zebrafish is now emerging as a powerful organism for the modeling and the study of human diseases. In this work, we have assessed the sensitivity of specific subsets of neurons to ethanol exposure during embryogenesis and we have visualized the sensitive embryonic developmental periods for specific neuronal groups by the use of different transgenic zebrafish lines.In order to evaluate the teratogenic effects of acute ethanol exposure, we exposed zebrafish embryos to ethanol in a given time window and analyzed the effects in neurogenesis, neuronal differentiation and brain patterning. Zebrafish larvae exposed to ethanol displayed small eyes and/or a reduction of the body length, phenotypical features similar to the observed in children with prenatal exposure to ethanol. When neuronal populations were analyzed, we observed a clear reduction in the number of differentiated neurons in the spinal cord upon ethanol exposure. There was a decrease in the population of sensory neurons mainly due to a decrease in cell proliferation and subsequent apoptosis during neuronal differentiation, with no effect in motoneuron specification.Our investigation highlights that transient exposure to ethanol during early embryonic development affects neuronal differentiation although does not result in defects in early neurogenesis. These results establish the use of zebrafish embryos as an alternative research model to elucidate the molecular mechanism(s of ethanol-induced developmental toxicity at very early stages of embryonic development.

A minimal model for a slow pacemaking neuron

International Nuclear Information System (INIS)

Zakharov, D.G.; Kuznetsov, A.

2012-01-01

Highlights: ► We have constructed a phenomenological model for slow pacemaking neurons. ► The model implements a nonlinearity introduced by an ion-dependent current. ► The new nonlinear dependence allows for differentiating responses to various stimuli. ► We discuss implications of our results for a broad class of neurons. - Abstract: We have constructed a phenomenological model for slow pacemaking neurons. These are neurons that generate very regular periodic oscillations of the membrane potential. Many of these neurons also differentially respond to various types of stimulation. The model is based on FitzHugh–Nagumo (FHN) oscillator and implements a nonlinearity introduced by a current that depends on an ion concentration. The comparison with the original FHN oscillator has shown that the new nonlinear dependence allows for differentiating responses to various stimuli. We discuss implications of our results for a broad class of neurons.

[Establishment of oxygen and glucose deprive model of in vitro cultured hippocampal neuron and effect of ligustrazine on intracellular Ca+ level in model neurons].

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Wan, Hai-tong; Wang, Yu; Yang, Jie-hong

2007-03-01

To establish the oxygen and glucose deprive (OGD) model in cultured hippocampal neuron and study the effect of ligustrazine on intracellular Ca2+ level in the model neurons. The OGD model was established in cultured hippocampal neuron, and the intracellular Ca2+ level in it was detected by laser scanning confocal microscope (LSCM). The OGD model was successfully established in cultured hippocampal neurons; the intracellular Ca2+ level in the OGD model group was significantly higher than that in the blank control group (P neuron, which could be antagonized by ligustrazine, indicating that ligustrazine has a protective effect on hippocampal neuron from hypoxic-ischemic injury.

A hidden Markov model approach to neuron firing patterns.

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Camproux, A C; Saunier, F; Chouvet, G; Thalabard, J C; Thomas, G

1996-11-01

Analysis and characterization of neuronal discharge patterns are of interest to neurophysiologists and neuropharmacologists. In this paper we present a hidden Markov model approach to modeling single neuron electrical activity. Basically the model assumes that each interspike interval corresponds to one of several possible states of the neuron. Fitting the model to experimental series of interspike intervals by maximum likelihood allows estimation of the number of possible underlying neuron states, the probability density functions of interspike intervals corresponding to each state, and the transition probabilities between states. We present an application to the analysis of recordings of a locus coeruleus neuron under three pharmacological conditions. The model distinguishes two states during halothane anesthesia and during recovery from halothane anesthesia, and four states after administration of clonidine. The transition probabilities yield additional insights into the mechanisms of neuron firing.

From spiking neuron models to linear-nonlinear models.

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Ostojic, Srdjan; Brunel, Nicolas

2011-01-20

Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates.

Hindbrain Catecholamine Neurons Activate Orexin Neurons During Systemic Glucoprivation in Male Rats.

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Li, Ai-Jun; Wang, Qing; Elsarelli, Megan M; Brown, R Lane; Ritter, Sue

2015-08-01

Hindbrain catecholamine neurons are required for elicitation of feeding responses to glucose deficit, but the forebrain circuitry required for these responses is incompletely understood. Here we examined interactions of catecholamine and orexin neurons in eliciting glucoprivic feeding. Orexin neurons, located in the perifornical lateral hypothalamus (PeFLH), are heavily innervated by hindbrain catecholamine neurons, stimulate food intake, and increase arousal and behavioral activation. Orexin neurons may therefore contribute importantly to appetitive responses, such as food seeking, during glucoprivation. Retrograde tracing results showed that nearly all innervation of the PeFLH from the hindbrain originated from catecholamine neurons and some raphe nuclei. Results also suggested that many catecholamine neurons project collaterally to the PeFLH and paraventricular hypothalamic nucleus. Systemic administration of the antiglycolytic agent, 2-deoxy-D-glucose, increased food intake and c-Fos expression in orexin neurons. Both responses were eliminated by a lesion of catecholamine neurons innervating orexin neurons using the retrogradely transported immunotoxin, anti-dopamine-β-hydroxylase saporin, which is specifically internalized by dopamine-β-hydroxylase-expressing catecholamine neurons. Using designer receptors exclusively activated by designer drugs in transgenic rats expressing Cre recombinase under the control of tyrosine hydroxylase promoter, catecholamine neurons in cell groups A1 and C1 of the ventrolateral medulla were activated selectively by peripheral injection of clozapine-N-oxide. Clozapine-N-oxide injection increased food intake and c-Fos expression in PeFLH orexin neurons as well as in paraventricular hypothalamic nucleus neurons. In summary, catecholamine neurons are required for the activation of orexin neurons during glucoprivation. Activation of orexin neurons may contribute to appetitive responses required for glucoprivic feeding.

Phospholipase A2 - nexus of aging, oxidative stress, neuronal excitability, and functional decline of the aging nervous system? Insights from a snail model system of neuronal aging and age-associated memory impairment.

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Hermann, Petra M; Watson, Shawn N; Wildering, Willem C

2014-01-01

The aging brain undergoes a range of changes varying from subtle structural and physiological changes causing only minor functional decline under healthy normal aging conditions, to severe cognitive or neurological impairment associated with extensive loss of neurons and circuits due to age-associated neurodegenerative disease conditions. Understanding how biological aging processes affect the brain and how they contribute to the onset and progress of age-associated neurodegenerative diseases is a core research goal in contemporary neuroscience. This review focuses on the idea that changes in intrinsic neuronal electrical excitability associated with (per)oxidation of membrane lipids and activation of phospholipase A2 (PLA2) enzymes are an important mechanism of learning and memory failure under normal aging conditions. Specifically, in the context of this special issue on the biology of cognitive aging we portray the opportunities offered by the identifiable neurons and behaviorally characterized neural circuits of the freshwater snail Lymnaea stagnalis in neuronal aging research and recapitulate recent insights indicating a key role of lipid peroxidation-induced PLA2 as instruments of aging, oxidative stress and inflammation in age-associated neuronal and memory impairment in this model system. The findings are discussed in view of accumulating evidence suggesting involvement of analogous mechanisms in the etiology of age-associated dysfunction and disease of the human and mammalian brain.

Phospholipase A2 - nexus of aging, oxidative stress, neuronal excitability and functional decline of the aging nervous system? Insights from a snail model system of neuronal aging and age-associated memory impairment.

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Petra Maria Hermann

2014-12-01

Full Text Available TThe aging brain can undergo a range of changes varying from subtle structural and physiological changes causing only minor functional decline under healthy normal aging conditions, to severe cognitive or neurological impairment associated with extensive loss of neurons and circuits due to age-associated neurodegenerative disease conditions. Understanding how biological aging processes affect the brain and how they contribute to the onset and progress of age-associated neurodegenerative diseases is a core research goal in contemporary neuroscience. This review focuses on the idea that changes in intrinsic neuronal electrical excitability associated with (peroxidation of membrane lipids and activation of phospholipase A2 (PLA2 enzymes are an important mechanism of learning and memory failure under normal aging conditions. Specifically, in the context of this special issue on the Biology of cognitive aging we (1 portray the opportunities offered by the identifiable neurons and behaviorally characterized neural circuits of the freshwater snail Lymnaea stagnalis in neuronal aging research and (2 recapitulate recent insights indicating a key role of lipid peroxidation-induced PLA2 as instruments of aging, oxidative stress and inflammation in age-associated neuronal and memory impairment in this model system. The findings are discussed in view of accumulating evidence suggesting involvement of analogous mechanisms in the etiology of age-associated dysfunction and disease of the human and mammalian brain.

Effects of neuronal loss in the dynamic model of neural networks

International Nuclear Information System (INIS)

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

2008-01-01

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

Models of the stochastic activity of neurones

CERN Document Server

Holden, Arun Vivian

1976-01-01

These notes have grown from a series of seminars given at Leeds between 1972 and 1975. They represent an attempt to gather together the different kinds of model which have been proposed to account for the stochastic activity of neurones, and to provide an introduction to this area of mathematical biology. A striking feature of the electrical activity of the nervous system is that it appears stochastic: this is apparent at all levels of recording, ranging from intracellular recordings to the electroencephalogram. The chapters start with fluctuations in membrane potential, proceed through single unit and synaptic activity and end with the behaviour of large aggregates of neurones: L have chgaen this seque~~e\\/~~';uggest that the interesting behaviourr~f :the nervous system - its individuality, variability and dynamic forms - may in part result from the stochastic behaviour of its components. I would like to thank Dr. Julio Rubio for reading and commenting on the drafts, Mrs. Doris Beighton for producing the fin...

Effect of Topology Structures on Synchronization Transition in Coupled Neuron Cells System

International Nuclear Information System (INIS)

Liang Li-Si; Zhang Ji-Qian; Xu Gui-Xia; Liu Le-Zhu; Huang Shou-Fang

2013-01-01

In this paper, by the help of evolutionary algorithm and using Hindmarsh—Rose (HR) neuron model, we investigate the effect of topology structures on synchronization transition between different states in coupled neuron cells system. First, we build different coupling structure with N cells, and found the effect of synchronized transition contact not only closely with the topology of the system, but also with whether there exist the ring structures in the system. In particular, both the size and the number of rings have greater effects on such transition behavior. Secondly, we introduce synchronization error to qualitative analyze the effect of the topology structure. Furthermore, by fitting the simulation results, we find that with the increment of the neurons number, there always exist the optimization structures which have the minimum number of connecting edges in the coupling systems. Above results show that the topology structures have a very crucial role on synchronization transition in coupled neuron system. Biological system may gradually acquire such efficient topology structures through the long-term evolution, thus the systems' information process may be optimized by this scheme. (interdisciplinary physics and related areas of science and technology)

A COMPUTATIONAL MODEL OF MOTOR NEURON DEGENERATION

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

2014-01-01

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

A computational model of motor neuron degeneration.

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

2014-08-20

To explore the link between bioenergetics and motor neuron degeneration, we used a computational model in which detailed morphology and ion conductance are paired with intracellular ATP production and consumption. We found that reduced ATP availability increases the metabolic cost of a single action potential and disrupts K+/Na+ homeostasis, resulting in a chronic depolarization. The magnitude of the ATP shortage at which this ionic instability occurs depends on the morphology and intrinsic conductance characteristic of the neuron. If ATP shortage is confined to the distal part of the axon, the ensuing local ionic instability eventually spreads to the whole neuron and involves fasciculation-like spiking events. A shortage of ATP also causes a rise in intracellular calcium. Our modeling work supports the notion that mitochondrial dysfunction can account for salient features of the paralytic disorder amyotrophic lateral sclerosis, including motor neuron hyperexcitability, fasciculation, and differential vulnerability of motor neuron subpopulations. Copyright © 2014 Elsevier Inc. All rights reserved.

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

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

2015-01-01

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

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

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Alessandra ePaffi

2015-05-01

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

The human mirror neuron system and embodied representations.

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Aziz-Zadeh, Lisa; Ivry, Richard B

2009-01-01

Mirror neurons are defined as neurons in the monkey cortex which respond to goal oriented actions, whether the behavior is self-generated or produced by another. Here we briefly review this literature and consider evidence from behavioral, neuropsychological, and brain imaging studies for a similar mirror neuron system in humans. Furthermore, we review functions of this system related to action comprehension and motor imagery, as well as evidence for speculations on the system's ties with conceptual knowledge and language.

Pharmacological activation/inhibition of the cannabinoid system affects alcohol withdrawal-induced neuronal hypersensitivity to excitotoxic insults.

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Marina Rubio

Full Text Available Cessation of chronic ethanol consumption can increase the sensitivity of the brain to excitotoxic damages. Cannabinoids have been proposed as neuroprotectants in different models of neuronal injury, but their effect have never been investigated in a context of excitotoxicity after alcohol cessation. Here we examined the effects of the pharmacological activation/inhibition of the endocannabinoid system in an in vitro model of chronic ethanol exposure and withdrawal followed by an excitotoxic challenge. Ethanol withdrawal increased N-methyl-D-aspartate (NMDA-evoked neuronal death, probably by altering the ratio between GluN2A and GluN2B NMDA receptor subunits. The stimulation of the endocannabinoid system with the cannabinoid agonist HU-210 decreased NMDA-induced neuronal death exclusively in ethanol-withdrawn neurons. This neuroprotection could be explained by a decrease in NMDA-stimulated calcium influx after the administration of HU-210, found exclusively in ethanol-withdrawn neurons. By contrast, the inhibition of the cannabinoid system with the CB1 receptor antagonist rimonabant (SR141716 during ethanol withdrawal increased death of ethanol-withdrawn neurons without any modification of NMDA-stimulated calcium influx. Moreover, chronic administration of rimonabant increased NMDA-stimulated toxicity not only in withdrawn neurons, but also in control neurons. In summary, we show for the first time that the stimulation of the endocannabinoid system is protective against the hyperexcitability developed during alcohol withdrawal. By contrast, the blockade of the endocannabinoid system is highly counterproductive during alcohol withdrawal.

Generalized rate-code model for neuron ensembles with finite populations

International Nuclear Information System (INIS)

Hasegawa, Hideo

2007-01-01

We have proposed a generalized Langevin-type rate-code model subjected to multiplicative noise, in order to study stationary and dynamical properties of an ensemble containing a finite number N of neurons. Calculations using the Fokker-Planck equation have shown that, owing to the multiplicative noise, our rate model yields various kinds of stationary non-Gaussian distributions such as Γ, inverse-Gaussian-like, and log-normal-like distributions, which have been experimentally observed. The dynamical properties of the rate model have been studied with the use of the augmented moment method (AMM), which was previously proposed by the author from a macroscopic point of view for finite-unit stochastic systems. In the AMM, the original N-dimensional stochastic differential equations (DEs) are transformed into three-dimensional deterministic DEs for the means and fluctuations of local and global variables. The dynamical responses of the neuron ensemble to pulse and sinusoidal inputs calculated by the AMM are in good agreement with those obtained by direct simulation. The synchronization in the neuronal ensemble is discussed. The variabilities of the firing rate and of the interspike interval are shown to increase with increasing magnitude of multiplicative noise, which may be a conceivable origin of the observed large variability in cortical neurons

Stability of discrete memory states to stochastic fluctuations in neuronal systems

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Miller, Paul; Wang, Xiao-Jing

2014-01-01

Noise can degrade memories by causing transitions from one memory state to another. For any biological memory system to be useful, the time scale of such noise-induced transitions must be much longer than the required duration for memory retention. Using biophysically-realistic modeling, we consider two types of memory in the brain: short-term memories maintained by reverberating neuronal activity for a few seconds, and long-term memories maintained by a molecular switch for years. Both systems require persistence of (neuronal or molecular) activity self-sustained by an autocatalytic process and, we argue, that both have limited memory lifetimes because of significant fluctuations. We will first discuss a strongly recurrent cortical network model endowed with feedback loops, for short-term memory. Fluctuations are due to highly irregular spike firing, a salient characteristic of cortical neurons. Then, we will analyze a model for long-term memory, based on an autophosphorylation mechanism of calcium/calmodulin-dependent protein kinase II (CaMKII) molecules. There, fluctuations arise from the fact that there are only a small number of CaMKII molecules at each postsynaptic density (putative synaptic memory unit). Our results are twofold. First, we demonstrate analytically and computationally the exponential dependence of stability on the number of neurons in a self-excitatory network, and on the number of CaMKII proteins in a molecular switch. Second, for each of the two systems, we implement graded memory consisting of a group of bistable switches. For the neuronal network we report interesting ramping temporal dynamics as a result of sequentially switching an increasing number of discrete, bistable, units. The general observation of an exponential increase in memory stability with the system size leads to a trade-off between the robustness of memories (which increases with the size of each bistable unit) and the total amount of information storage (which decreases

Neuromorphic Silicon Neuron Circuits

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Indiveri, Giacomo; Linares-Barranco, Bernabé; Hamilton, Tara Julia; van Schaik, André; Etienne-Cummings, Ralph; Delbruck, Tobi; Liu, Shih-Chii; Dudek, Piotr; Häfliger, Philipp; Renaud, Sylvie; Schemmel, Johannes; Cauwenberghs, Gert; Arthur, John; Hynna, Kai; Folowosele, Fopefolu; Saighi, Sylvain; Serrano-Gotarredona, Teresa; Wijekoon, Jayawan; Wang, Yingxue; Boahen, Kwabena

2011-01-01

Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain–machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin–Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips. PMID:21747754

Neuromorphic silicon neuron circuits

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Giacomo eIndiveri

2011-05-01

Full Text Available Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain-machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance based Hodgkin-Huxley models to bi-dimensional generalized adaptive Integrate and Fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips.

Mechanisms of magnetic stimulation of central nervous system neurons.

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Tamar Pashut

2011-03-01

Full Text Available Transcranial magnetic stimulation (TMS is a stimulation method in which a magnetic coil generates a magnetic field in an area of interest in the brain. This magnetic field induces an electric field that modulates neuronal activity. The spatial distribution of the induced electric field is determined by the geometry and location of the coil relative to the brain. Although TMS has been used for several decades, the biophysical basis underlying the stimulation of neurons in the central nervous system (CNS is still unknown. To address this problem we developed a numerical scheme enabling us to combine realistic magnetic stimulation (MS with compartmental modeling of neurons with arbitrary morphology. The induced electric field for each location in space was combined with standard compartmental modeling software to calculate the membrane current generated by the electromagnetic field for each segment of the neuron. In agreement with previous studies, the simulations suggested that peripheral axons were excited by the spatial gradients of the induced electric field. In both peripheral and central neurons, MS amplitude required for action potential generation was inversely proportional to the square of the diameter of the stimulated compartment. Due to the importance of the fiber's diameter, magnetic stimulation of CNS neurons depolarized the soma followed by initiation of an action potential in the initial segment of the axon. Passive dendrites affect this process primarily as current sinks, not sources. The simulations predict that neurons with low current threshold are more susceptible to magnetic stimulation. Moreover, they suggest that MS does not directly trigger dendritic regenerative mechanisms. These insights into the mechanism of MS may be relevant for the design of multi-intensity TMS protocols, may facilitate the construction of magnetic stimulators, and may aid the interpretation of results of TMS of the CNS.

Mechanisms of magnetic stimulation of central nervous system neurons.

Science.gov (United States)

Pashut, Tamar; Wolfus, Shuki; Friedman, Alex; Lavidor, Michal; Bar-Gad, Izhar; Yeshurun, Yosef; Korngreen, Alon

2011-03-01

Transcranial magnetic stimulation (TMS) is a stimulation method in which a magnetic coil generates a magnetic field in an area of interest in the brain. This magnetic field induces an electric field that modulates neuronal activity. The spatial distribution of the induced electric field is determined by the geometry and location of the coil relative to the brain. Although TMS has been used for several decades, the biophysical basis underlying the stimulation of neurons in the central nervous system (CNS) is still unknown. To address this problem we developed a numerical scheme enabling us to combine realistic magnetic stimulation (MS) with compartmental modeling of neurons with arbitrary morphology. The induced electric field for each location in space was combined with standard compartmental modeling software to calculate the membrane current generated by the electromagnetic field for each segment of the neuron. In agreement with previous studies, the simulations suggested that peripheral axons were excited by the spatial gradients of the induced electric field. In both peripheral and central neurons, MS amplitude required for action potential generation was inversely proportional to the square of the diameter of the stimulated compartment. Due to the importance of the fiber's diameter, magnetic stimulation of CNS neurons depolarized the soma followed by initiation of an action potential in the initial segment of the axon. Passive dendrites affect this process primarily as current sinks, not sources. The simulations predict that neurons with low current threshold are more susceptible to magnetic stimulation. Moreover, they suggest that MS does not directly trigger dendritic regenerative mechanisms. These insights into the mechanism of MS may be relevant for the design of multi-intensity TMS protocols, may facilitate the construction of magnetic stimulators, and may aid the interpretation of results of TMS of the CNS.

Multi-Scale Molecular Deconstruction of the Serotonin Neuron System.

Science.gov (United States)

Okaty, Benjamin W; Freret, Morgan E; Rood, Benjamin D; Brust, Rachael D; Hennessy, Morgan L; deBairos, Danielle; Kim, Jun Chul; Cook, Melloni N; Dymecki, Susan M

2015-11-18

Serotonergic (5HT) neurons modulate diverse behaviors and physiology and are implicated in distinct clinical disorders. Corresponding diversity in 5HT neuronal phenotypes is becoming apparent and is likely rooted in molecular differences, yet a comprehensive approach characterizing molecular variation across the 5HT system is lacking, as is concomitant linkage to cellular phenotypes. Here we combine intersectional fate mapping, neuron sorting, and genome-wide RNA-seq to deconstruct the mouse 5HT system at multiple levels of granularity-from anatomy, to genetic sublineages, to single neurons. Our unbiased analyses reveal principles underlying system organization, 5HT neuron subtypes, constellations of differentially expressed genes distinguishing subtypes, and predictions of subtype-specific functions. Using electrophysiology, subtype-specific neuron silencing, and conditional gene knockout, we show that these molecularly defined 5HT neuron subtypes are functionally distinct. Collectively, this resource classifies molecular diversity across the 5HT system and discovers sertonergic subtypes, markers, organizing principles, and subtype-specific functions with potential disease relevance. Copyright © 2015 Elsevier Inc. All rights reserved.

Dynamical analysis of Parkinsonian state emulated by hybrid Izhikevich neuron models

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

2015-11-01

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

Reorganization of neuronal circuits of the central olfactory system during postprandial sleep

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Masahiro eYamaguchi

2013-08-01

Full Text Available Plastic changes in neuronal circuits often occur in association with specific behavioral states. In this review, we focus on an emerging view that neuronal circuits in the olfactory system are reorganized along the wake-sleep cycle. Olfaction is crucial to sustaining the animals’ life, and odor-guided behaviors have to be newly acquired or updated to successfully cope with a changing odor world. It is therefore likely that neuronal circuits in the olfactory system are highly plastic and undergo repeated reorganization in daily life. A remarkably plastic feature of the olfactory system is that newly generated neurons are continually integrated into neuronal circuits of the olfactory bulb (OB throughout life. New neurons in the OB undergo an extensive selection process, during which many are eliminated by apoptosis for the fine tuning of neuronal circuits. The life and death decision of new neurons occurs extensively during a short time window of sleep after food consumption (postprandial sleep, a typical daily olfactory behavior. We review recent studies that explain how olfactory information is transferred between the OB and the olfactory cortex (OC along the course of the wake-sleep cycle. Olfactory sensory input is effectively transferred from the OB to the OC during waking, while synchronized top-down inputs from the OC to the OB are promoted during the slow-wave sleep. We discuss possible neuronal circuit mechanisms for the selection of new neurons in the OB, which involves the encoding of olfactory sensory inputs and memory trace formation during waking and internally generated activities in the OC and OB during subsequent sleep. The plastic changes in the OB and OC are well coordinated along the course of olfactory behavior during wakefulness and postbehavioral rest and sleep. We therefore propose that the olfactory system provides an excellent model in which to understand behavioral state-dependent plastic mechanisms of the neuronal

NeuronBank: a tool for cataloging neuronal circuitry

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Paul S Katz

2010-04-01

Full Text Available The basic unit of any nervous system is the neuron. Therefore, understanding the operation of nervous systems ultimately requires an inventory of their constituent neurons and synaptic connectivity, which form neural circuits. The presence of uniquely identifiable neurons or classes of neurons in many invertebrates has facilitated the construction of cellular-level connectivity diagrams that can be generalized across individuals within a species. Homologous neurons can also be recognized across species. Here we describe NeuronBank.org, a web-based tool that we are developing for cataloging, searching, and analyzing neuronal circuitry within and across species. Information from a single species is represented in an individual branch of NeuronBank. Users can search within a branch or perform queries across branches to look for similarities in neuronal circuits across species. The branches allow for an extensible ontology so that additional characteristics can be added as knowledge grows. Each entry in NeuronBank generates a unique accession ID, allowing it to be easily cited. There is also an automatic link to a Wiki page allowing an encyclopedic explanation of the entry. All of the 44 previously published neurons plus one previously unpublished neuron from the mollusc, Tritonia diomedea, have been entered into a branch of NeuronBank as have 4 previously published neurons from the mollusc, Melibe leonina. The ability to organize information about neuronal circuits will make this information more accessible, ultimately aiding research on these important models.

Direct Neuronal Reprogramming for Disease Modeling Studies Using Patient-Derived Neurons: What Have We Learned?

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Janelle Drouin-Ouellet

2017-09-01

Full Text Available Direct neuronal reprogramming, by which a neuron is formed via direct conversion from a somatic cell without going through a pluripotent intermediate stage, allows for the possibility of generating patient-derived neurons. A unique feature of these so-called induced neurons (iNs is the potential to maintain aging and epigenetic signatures of the donor, which is critical given that many diseases of the CNS are age related. Here, we review the published literature on the work that has been undertaken using iNs to model human brain disorders. Furthermore, as disease-modeling studies using this direct neuronal reprogramming approach are becoming more widely adopted, it is important to assess the criteria that are used to characterize the iNs, especially in relation to the extent to which they are mature adult neurons. In particular: i what constitutes an iN cell, ii which stages of conversion offer the earliest/optimal time to assess features that are specific to neurons and/or a disorder and iii whether generating subtype-specific iNs is critical to the disease-related features that iNs express. Finally, we discuss the range of potential biomedical applications that can be explored using patient-specific models of neurological disorders with iNs, and the challenges that will need to be overcome in order to realize these applications.

Optimizing NEURON Simulation Environment Using Remote Memory Access with Recursive Doubling on Distributed Memory Systems.

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Shehzad, Danish; Bozkuş, Zeki

2016-01-01

Increase in complexity of neuronal network models escalated the efforts to make NEURON simulation environment efficient. The computational neuroscientists divided the equations into subnets amongst multiple processors for achieving better hardware performance. On parallel machines for neuronal networks, interprocessor spikes exchange consumes large section of overall simulation time. In NEURON for communication between processors Message Passing Interface (MPI) is used. MPI_Allgather collective is exercised for spikes exchange after each interval across distributed memory systems. The increase in number of processors though results in achieving concurrency and better performance but it inversely affects MPI_Allgather which increases communication time between processors. This necessitates improving communication methodology to decrease the spikes exchange time over distributed memory systems. This work has improved MPI_Allgather method using Remote Memory Access (RMA) by moving two-sided communication to one-sided communication, and use of recursive doubling mechanism facilitates achieving efficient communication between the processors in precise steps. This approach enhanced communication concurrency and has improved overall runtime making NEURON more efficient for simulation of large neuronal network models.

Optimizing NEURON Simulation Environment Using Remote Memory Access with Recursive Doubling on Distributed Memory Systems

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Danish Shehzad

2016-01-01

Full Text Available Increase in complexity of neuronal network models escalated the efforts to make NEURON simulation environment efficient. The computational neuroscientists divided the equations into subnets amongst multiple processors for achieving better hardware performance. On parallel machines for neuronal networks, interprocessor spikes exchange consumes large section of overall simulation time. In NEURON for communication between processors Message Passing Interface (MPI is used. MPI_Allgather collective is exercised for spikes exchange after each interval across distributed memory systems. The increase in number of processors though results in achieving concurrency and better performance but it inversely affects MPI_Allgather which increases communication time between processors. This necessitates improving communication methodology to decrease the spikes exchange time over distributed memory systems. This work has improved MPI_Allgather method using Remote Memory Access (RMA by moving two-sided communication to one-sided communication, and use of recursive doubling mechanism facilitates achieving efficient communication between the processors in precise steps. This approach enhanced communication concurrency and has improved overall runtime making NEURON more efficient for simulation of large neuronal network models.

Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.

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

2011-11-01

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

Chimera-like states in a neuronal network model of the cat brain

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Santos, M. S.; Szezech, J. D.; Borges, F. S.; Iarosz, K. C.; Caldas, I. L.; Batista, A. M.; Viana, R. L.; Kurths, J.

2017-08-01

Neuronal systems have been modeled by complex networks in different description levels. Recently, it has been verified that networks can simultaneously exhibit one coherent and other incoherent domain, known as chimera states. In this work, we study the existence of chimera states in a network considering the connectivity matrix based on the cat cerebral cortex. The cerebral cortex of the cat can be separated in 65 cortical areas organised into the four cognitive regions: visual, auditory, somatosensory-motor and frontolimbic. We consider a network where the local dynamics is given by the Hindmarsh-Rose model. The Hindmarsh-Rose equations are a well known model of neuronal activity that has been considered to simulate membrane potential in neuron. Here, we analyse under which conditions chimera states are present, as well as the affects induced by intensity of coupling on them. We observe the existence of chimera states in that incoherent structure can be composed of desynchronised spikes or desynchronised bursts. Moreover, we find that chimera states with desynchronised bursts are more robust to neuronal noise than with desynchronised spikes.

An introduction to modeling neuronal dynamics

CERN Document Server

Börgers, Christoph

2017-01-01

This book is intended as a text for a one-semester course on Mathematical and Computational Neuroscience for upper-level undergraduate and beginning graduate students of mathematics, the natural sciences, engineering, or computer science. An undergraduate introduction to differential equations is more than enough mathematical background. Only a slim, high school-level background in physics is assumed, and none in biology. Topics include models of individual nerve cells and their dynamics, models of networks of neurons coupled by synapses and gap junctions, origins and functions of population rhythms in neuronal networks, and models of synaptic plasticity. An extensive online collection of Matlab programs generating the figures accompanies the book. .

Computational model of neuron-astrocyte interactions during focal seizure generation

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Davide eReato

2012-10-01

Full Text Available Empirical research in the last decade revealed that astrocytes can respond to neurotransmitters with Ca2+ elevations and generate feedback signals to neurons which modulate synaptic transmission and neuronal excitability. This discovery changed our basic understanding of brain function and provided new perspectives for how astrocytes can participate not only to information processing, but also to the genesis of brain disorders, such as epilepsy. Epilepsy is a neurological disorder characterized by recurrent seizures that can arise focally at restricted areas and propagate throughout the brain. Studies in brain slice models suggest that astrocytes contribute to epileptiform activity by increasing neuronal excitability through a Ca2+-dependent release of glutamate. The underlying mechanism remains, however, unclear. In this study, we implemented a parsimonious network model of neurons and astrocytes. The model consists of excitatory and inhibitory neurons described by Izhikevich's neuron dynamics. The experimentally observed Ca2+ change in astrocytes in response to neuronal activity was modeled with linear equations. We considered that glutamate is released from astrocytes above certain intracellular Ca2+ concentrations thus providing a non-linear positive feedback signal to neurons. Propagating seizure-like ictal discharges (IDs were reliably evoked in our computational model by repeatedly exciting a small area of the network, which replicates experimental results in a slice model of focal ID in entorhinal cortex. We found that the threshold of focal ID generation was lowered when an excitatory feedback-loop between astrocytes and neurons was included. Simulations show that astrocytes can contribute to ID generation by directly affecting the excitatory/inhibitory balance of the neuronal network. Our model can be used to obtain mechanistic insights into the distinct contributions of the different signaling pathways to the generation and

Multistability in a neuron model with extracellular potassium dynamics

Science.gov (United States)

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

2012-06-01

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

Optimal size of stochastic Hodgkin-Huxley neuronal systems for maximal energy efficiency in coding pulse signals

Science.gov (United States)

Yu, Lianchun; Liu, Liwei

2014-03-01

The generation and conduction of action potentials (APs) represents a fundamental means of communication in the nervous system and is a metabolically expensive process. In this paper, we investigate the energy efficiency of neural systems in transferring pulse signals with APs. By analytically solving a bistable neuron model that mimics the AP generation with a particle crossing the barrier of a double well, we find the optimal number of ion channels that maximizes the energy efficiency of a neuron. We also investigate the energy efficiency of a neuron population in which the input pulse signals are represented with synchronized spikes and read out with a downstream coincidence detector neuron. We find an optimal number of neurons in neuron population, as well as the number of ion channels in each neuron that maximizes the energy efficiency. The energy efficiency also depends on the characters of the input signals, e.g., the pulse strength and the interpulse intervals. These results are confirmed by computer simulation of the stochastic Hodgkin-Huxley model with a detailed description of the ion channel random gating. We argue that the tradeoff between signal transmission reliability and energy cost may influence the size of the neural systems when energy use is constrained.

Gravity and neuronal adaptation, in vitro and in vivo-from neuronal cells up to neuromuscular responses: a first model.

Science.gov (United States)

Kohn, Florian P M; Ritzmann, Ramona

2018-03-01

For decades it has been shown that acute changes in gravity have an effect on neuronal systems of human and animals on different levels, from the molecular level to the whole nervous system. The functional properties and gravity-dependent adaptations of these system levels have been investigated with no or barely any interconnection. This review summarizes the gravity-dependent adaptation processes in human and animal organisms from the in vitro cellular level with its biophysical properties to the in vivo motor responses and underlying sensorimotor functions of human subjects. Subsequently, a first model for short-term adaptation of neuronal transmission is presented and discussed for the first time, which integrates the responses of the different levels of organization to changes in gravity.

A Note on Some Numerical Approaches to Solve a θ˙ Neuron Networks Model

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Samir Kumar Bhowmik

2014-01-01

Full Text Available Space time integration plays an important role in analyzing scientific and engineering models. In this paper, we consider an integrodifferential equation that comes from modeling θ˙ neuron networks. Here, we investigate various schemes for time discretization of a theta-neuron model. We use collocation and midpoint quadrature formula for space integration and then apply various time integration schemes to get a full discrete system. We present some computational results to demonstrate the schemes.

Candidate glutamatergic neurons in the visual system of Drosophila.

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Shamprasad Varija Raghu

Full Text Available The visual system of Drosophila contains approximately 60,000 neurons that are organized in parallel, retinotopically arranged columns. A large number of these neurons have been characterized in great anatomical detail. However, studies providing direct evidence for synaptic signaling and the neurotransmitter used by individual neurons are relatively sparse. Here we present a first layout of neurons in the Drosophila visual system that likely release glutamate as their major neurotransmitter. We identified 33 different types of neurons of the lamina, medulla, lobula and lobula plate. Based on the previous Golgi-staining analysis, the identified neurons are further classified into 16 major subgroups representing lamina monopolar (L, transmedullary (Tm, transmedullary Y (TmY, Y, medulla intrinsic (Mi, Mt, Pm, Dm, Mi Am, bushy T (T, translobula plate (Tlp, lobula intrinsic (Lcn, Lt, Li, lobula plate tangential (LPTCs and lobula plate intrinsic (LPi cell types. In addition, we found 11 cell types that were not described by the previous Golgi analysis. This classification of candidate glutamatergic neurons fosters the future neurogenetic dissection of information processing in circuits of the fly visual system.

Dynamical System Approach for Edge Detection Using Coupled FitzHugh-Nagumo Neurons.

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Li, Shaobai; Dasmahapatra, Srinandan; Maharatna, Koushik

2015-12-01

The prospect of emulating the impressive computational capabilities of biological systems has led to considerable interest in the design of analog circuits that are potentially implementable in very large scale integration CMOS technology and are guided by biologically motivated models. For example, simple image processing tasks, such as the detection of edges in binary and grayscale images, have been performed by networks of FitzHugh-Nagumo-type neurons using the reaction-diffusion models. However, in these studies, the one-to-one mapping of image pixels to component neurons makes the size of the network a critical factor in any such implementation. In this paper, we develop a simplified version of the employed reaction-diffusion model in three steps. In the first step, we perform a detailed study to locate this threshold using continuous Lyapunov exponents from dynamical system theory. Furthermore, we render the diffusion in the system to be anisotropic, with the degree of anisotropy being set by the gradients of grayscale values in each image. The final step involves a simplification of the model that is achieved by eliminating the terms that couple the membrane potentials of adjacent neurons. We apply our technique to detect edges in data sets of artificially generated and real images, and we demonstrate that the performance is as good if not better than that of the previous methods without increasing the size of the network.

Mirror neurons and imitation: a computationally guided review.

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Oztop, Erhan; Kawato, Mitsuo; Arbib, Michael

2006-04-01

Neurophysiology reveals the properties of individual mirror neurons in the macaque while brain imaging reveals the presence of 'mirror systems' (not individual neurons) in the human. Current conceptual models attribute high level functions such as action understanding, imitation, and language to mirror neurons. However, only the first of these three functions is well-developed in monkeys. We thus distinguish current opinions (conceptual models) on mirror neuron function from more detailed computational models. We assess the strengths and weaknesses of current computational models in addressing the data and speculations on mirror neurons (macaque) and mirror systems (human). In particular, our mirror neuron system (MNS), mental state inference (MSI) and modular selection and identification for control (MOSAIC) models are analyzed in more detail. Conceptual models often overlook the computational requirements for posited functions, while too many computational models adopt the erroneous hypothesis that mirror neurons are interchangeable with imitation ability. Our meta-analysis underlines the gap between conceptual and computational models and points out the research effort required from both sides to reduce this gap.

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

Science.gov (United States)

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

2015-12-01

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

Theoretical analysis of transcranial magneto-acoustical stimulation with Hodgkin–Huxley neuron model

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Yi eYuan

2016-04-01

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

Towards reproducible descriptions of neuronal network models.

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Eilen Nordlie

2009-08-01

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

Simple cortical and thalamic neuron models for digital arithmetic circuit implementation

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Takuya eNanami

2016-05-01

Full Text Available Trade-off between reproducibility of neuronal activities and computational efficiency is one ofcrucial subjects in computational neuroscience and neuromorphic engineering. A wide variety ofneuronal models have been studied from different viewpoints. The digital spiking silicon neuron(DSSN model is a qualitative model that focuses on efficient implementation by digital arithmeticcircuits. We expanded the DSSN model and found appropriate parameter sets with which itreproduces the dynamical behaviors of the ionic-conductance models of four classes of corticaland thalamic neurons. We first developed a 4-variable model by reducing the number of variablesin the ionic-conductance models and elucidated its mathematical structures using bifurcationanalysis. Then, expanded DSSN models were constructed that reproduce these mathematicalstructures and capture the characteristic behavior of each neuron class. We confirmed thatstatistics of the neuronal spike sequences are similar in the DSSN and the ionic-conductancemodels. Computational cost of the DSSN model is larger than that of the recent sophisticatedIntegrate-and-Fire-based models, but smaller than the ionic-conductance models. This modelis intended to provide another meeting point for above trade-off that satisfies the demand forlarge-scale neuronal network simulation with closer-to-biology models.

One-dimensional map-based neuron model: A logistic modification

International Nuclear Information System (INIS)

Mesbah, Samineh; Moghtadaei, Motahareh; Hashemi Golpayegani, Mohammad Reza; Towhidkhah, Farzad

2014-01-01

A one-dimensional map is proposed for modeling some of the neuronal activities, including different spiking and bursting behaviors. The model is obtained by applying some modifications on the well-known Logistic map and is named the Modified and Confined Logistic (MCL) model. Map-based neuron models are known as phenomenological models and recently, they are widely applied in modeling tasks due to their computational efficacy. Most of discrete map-based models involve two variables representing the slow-fast prototype. There are also some one-dimensional maps, which can replicate some of the neuronal activities. However, the existence of four bifurcation parameters in the MCL model gives rise to reproduction of spiking behavior with control over the frequency of the spikes, and imitation of chaotic and regular bursting responses concurrently. It is also shown that the proposed model has the potential to reproduce more realistic bursting activity by adding a second variable. Moreover the MCL model is able to replicate considerable number of experimentally observed neuronal responses introduced in Izhikevich (2004) [23]. Some analytical and numerical analyses of the MCL model dynamics are presented to explain the emersion of complex dynamics from this one-dimensional map

Context-aware modeling of neuronal morphologies

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Benjamin eTorben-Nielsen

2014-09-01

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

Neuronal avalanches and learning

Energy Technology Data Exchange (ETDEWEB)

Arcangelis, Lucilla de, E-mail: dearcangelis@na.infn.it [Department of Information Engineering and CNISM, Second University of Naples, 81031 Aversa (Italy)

2011-05-01

Networks of living neurons represent one of the most fascinating systems of biology. If the physical and chemical mechanisms at the basis of the functioning of a single neuron are quite well understood, the collective behaviour of a system of many neurons is an extremely intriguing subject. Crucial ingredient of this complex behaviour is the plasticity property of the network, namely the capacity to adapt and evolve depending on the level of activity. This plastic ability is believed, nowadays, to be at the basis of learning and memory in real brains. Spontaneous neuronal activity has recently shown features in common to other complex systems. Experimental data have, in fact, shown that electrical information propagates in a cortex slice via an avalanche mode. These avalanches are characterized by a power law distribution for the size and duration, features found in other problems in the context of the physics of complex systems and successful models have been developed to describe their behaviour. In this contribution we discuss a statistical mechanical model for the complex activity in a neuronal network. The model implements the main physiological properties of living neurons and is able to reproduce recent experimental results. Then, we discuss the learning abilities of this neuronal network. Learning occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. The system is able to learn all the tested rules, in particular the exclusive OR (XOR) and a random rule with three inputs. The learning dynamics exhibits universal features as function of the strength of plastic adaptation. Any rule could be learned provided that the plastic adaptation is sufficiently slow.

Neuronal avalanches and learning

International Nuclear Information System (INIS)

Arcangelis, Lucilla de

2011-01-01

Networks of living neurons represent one of the most fascinating systems of biology. If the physical and chemical mechanisms at the basis of the functioning of a single neuron are quite well understood, the collective behaviour of a system of many neurons is an extremely intriguing subject. Crucial ingredient of this complex behaviour is the plasticity property of the network, namely the capacity to adapt and evolve depending on the level of activity. This plastic ability is believed, nowadays, to be at the basis of learning and memory in real brains. Spontaneous neuronal activity has recently shown features in common to other complex systems. Experimental data have, in fact, shown that electrical information propagates in a cortex slice via an avalanche mode. These avalanches are characterized by a power law distribution for the size and duration, features found in other problems in the context of the physics of complex systems and successful models have been developed to describe their behaviour. In this contribution we discuss a statistical mechanical model for the complex activity in a neuronal network. The model implements the main physiological properties of living neurons and is able to reproduce recent experimental results. Then, we discuss the learning abilities of this neuronal network. Learning occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. The system is able to learn all the tested rules, in particular the exclusive OR (XOR) and a random rule with three inputs. The learning dynamics exhibits universal features as function of the strength of plastic adaptation. Any rule could be learned provided that the plastic adaptation is sufficiently slow.

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

DEFF Research Database (Denmark)

Hagen, Espen; Dahmen, David; Stavrinou, Maria L

2016-01-01

on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network......With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical...... and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely...

Integrated neuron circuit for implementing neuromorphic system with synaptic device

Science.gov (United States)

Lee, Jeong-Jun; Park, Jungjin; Kwon, Min-Woo; Hwang, Sungmin; Kim, Hyungjin; Park, Byung-Gook

2018-02-01

In this paper, we propose and fabricate Integrate & Fire neuron circuit for implementing neuromorphic system. Overall operation of the circuit is verified by measuring discrete devices and the output characteristics of the circuit. Since the neuron circuit shows asymmetric output characteristic that can drive synaptic device with Spike-Timing-Dependent-Plasticity (STDP) characteristic, the autonomous weight update process is also verified by connecting the synaptic device and the neuron circuit. The timing difference of the pre-neuron and the post-neuron induce autonomous weight change of the synaptic device. Unlike 2-terminal devices, which is frequently used to implement neuromorphic system, proposed scheme of the system enables autonomous weight update and simple configuration by using 4-terminal synapse device and appropriate neuron circuit. Weight update process in the multi-layer neuron-synapse connection ensures implementation of the hardware-based artificial intelligence, based on Spiking-Neural- Network (SNN).

A Neuronal Network Model for Pitch Selectivity and Representation

OpenAIRE

Huang, Chengcheng; Rinzel, John

2016-01-01

Pitch is a perceptual correlate of periodicity. Sounds with distinct spectra can elicit the same pitch. Despite the importance of pitch perception, understanding the cellular mechanism of pitch perception is still a major challenge and a mechanistic model of pitch is lacking. A multi-stage neuronal network model is developed for pitch frequency estimation using biophysically-based, high-resolution coincidence detector neurons. The neuronal units respond only to highly coincident input among c...

Learning of spatial relationships between observed and imitated actions allows invariant inverse computation in the frontal mirror neuron system.

Science.gov (United States)

Oh, Hyuk; Gentili, Rodolphe J; Reggia, James A; Contreras-Vidal, José L

2011-01-01

It has been suggested that the human mirror neuron system can facilitate learning by imitation through coupling of observation and action execution. During imitation of observed actions, the functional relationship between and within the inferior frontal cortex, the posterior parietal cortex, and the superior temporal sulcus can be modeled within the internal model framework. The proposed biologically plausible mirror neuron system model extends currently available models by explicitly modeling the intraparietal sulcus and the superior parietal lobule in implementing the function of a frame of reference transformation during imitation. Moreover, the model posits the ventral premotor cortex as performing an inverse computation. The simulations reveal that: i) the transformation system can learn and represent the changes in extrinsic to intrinsic coordinates when an imitator observes a demonstrator; ii) the inverse model of the imitator's frontal mirror neuron system can be trained to provide the motor plans for the imitated actions.

An information theoretic model of information processing in the Drosophila olfactory system: the role of inhibitory neurons for system efficiency.

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Faghihi, Faramarz; Kolodziejski, Christoph; Fiala, André; Wörgötter, Florentin; Tetzlaff, Christian

2013-12-20

Fruit flies (Drosophila melanogaster) rely on their olfactory system to process environmental information. This information has to be transmitted without system-relevant loss by the olfactory system to deeper brain areas for learning. Here we study the role of several parameters of the fly's olfactory system and the environment and how they influence olfactory information transmission. We have designed an abstract model of the antennal lobe, the mushroom body and the inhibitory circuitry. Mutual information between the olfactory environment, simulated in terms of different odor concentrations, and a sub-population of intrinsic mushroom body neurons (Kenyon cells) was calculated to quantify the efficiency of information transmission. With this method we study, on the one hand, the effect of different connectivity rates between olfactory projection neurons and firing thresholds of Kenyon cells. On the other hand, we analyze the influence of inhibition on mutual information between environment and mushroom body. Our simulations show an expected linear relation between the connectivity rate between the antennal lobe and the mushroom body and firing threshold of the Kenyon cells to obtain maximum mutual information for both low and high odor concentrations. However, contradicting all-day experiences, high odor concentrations cause a drastic, and unrealistic, decrease in mutual information for all connectivity rates compared to low concentration. But when inhibition on the mushroom body is included, mutual information remains at high levels independent of other system parameters. This finding points to a pivotal role of inhibition in fly information processing without which the system efficiency will be substantially reduced.

Modeling task-specific neuronal ensembles improves decoding of grasp

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Smith, Ryan J.; Soares, Alcimar B.; Rouse, Adam G.; Schieber, Marc H.; Thakor, Nitish V.

2018-06-01

Objective. Dexterous movement involves the activation and coordination of networks of neuronal populations across multiple cortical regions. Attempts to model firing of individual neurons commonly treat the firing rate as directly modulating with motor behavior. However, motor behavior may additionally be associated with modulations in the activity and functional connectivity of neurons in a broader ensemble. Accounting for variations in neural ensemble connectivity may provide additional information about the behavior being performed. Approach. In this study, we examined neural ensemble activity in primary motor cortex (M1) and premotor cortex (PM) of two male rhesus monkeys during performance of a center-out reach, grasp and manipulate task. We constructed point process encoding models of neuronal firing that incorporated task-specific variations in the baseline firing rate as well as variations in functional connectivity with the neural ensemble. Models were evaluated both in terms of their encoding capabilities and their ability to properly classify the grasp being performed. Main results. Task-specific ensemble models correctly predicted the performed grasp with over 95% accuracy and were shown to outperform models of neuronal activity that assume only a variable baseline firing rate. Task-specific ensemble models exhibited superior decoding performance in 82% of units in both monkeys (p  <  0.01). Inclusion of ensemble activity also broadly improved the ability of models to describe observed spiking. Encoding performance of task-specific ensemble models, measured by spike timing predictability, improved upon baseline models in 62% of units. Significance. These results suggest that additional discriminative information about motor behavior found in the variations in functional connectivity of neuronal ensembles located in motor-related cortical regions is relevant to decode complex tasks such as grasping objects, and may serve the basis for more

Lemon Odor Reduces Stress-induced Neuronal Activation in the Emotion Expression System: An Animal Model Study

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Sanada, Kazue; Sugimoto, Koji; Shutoh, Fumihiro; Hisano, Setsuji

Perception of particular sensory stimuli from the surroundings can influence emotion in individuals. In an uncomfortable situation, humans protect themselves from some aversive stimulus by acutely evoking a stress response. Animal model studies have contributed to an understanding of neuronal mechanisms underlying the stress response in humans. To study a possible anti-stressful effect of lemon odor, an excitation of neurons secreting corticotropin-releasing hormone (CRH) as a primary factor of the hypothalamic-pituitary-adrenal axis (HPA) was analyzed in animal model experiments, in which rats are restrained in the presence or absence of the odor. The effect was evaluated by measuring expression of c-Fos (an excited neuron marker) in the hypothalamic paraventricular nucleus (PVN), a key structure of the HPA in the brain. We prepared 3 animal groups: Groups S, L and I. Groups S and L were restrained for 30 minutes while being blown by air and being exposed to the lemon odor, respectively. Group I was intact without any treatment. Two hours later of the onset of experiments, brains of all groups were sampled and processed for microscopic examination. Brain sections were processed for c-Fos immunostaining and/or in situ hybridization for CRH. In Group S but not in Group I, c-Fos expression was found in the PVN. A combined in situ hybridization-immunohistochemical dual labeling revealed that CRH mRNA-expressing neurons express c-Fos. In computer-assisted automatic counting, the incidence of c-Fos-expressing neurons in the entire PVN was statistically lower in Group L than in Group S. Detailed analysis of PVN subregions demonstrated that c-Fos-expressing neurons are fewer in Group L than in Group S in the dorsal part of the medial parvocellular subregion. These results may suggest that lemon odor attenuates the restraint stress-induced neuronal activation including CRH neurons, presumably mimicking an aspect of stress responses in humans.

A hierarchical model for structure learning based on the physiological characteristics of neurons

Institute of Scientific and Technical Information of China (English)

WEI Hui

2007-01-01

Almost all applications of Artificial Neural Networks (ANNs) depend mainly on their memory ability.The characteristics of typical ANN models are fixed connections,with evolved weights,globalized representations,and globalized optimizations,all based on a mathematical approach.This makes those models to be deficient in robustness,efficiency of learning,capacity,anti-jamming between training sets,and correlativity of samples,etc.In this paper,we attempt to address these problems by adopting the characteristics of biological neurons in morphology and signal processing.A hierarchical neural network was designed and realized to implement structure learning and representations based on connected structures.The basic characteristics of this model are localized and random connections,field limitations of neuron fan-in and fan-out,dynamic behavior of neurons,and samples represented through different sub-circuits of neurons specialized into different response patterns.At the end of this paper,some important aspects of error correction,capacity,learning efficiency,and soundness of structural representation are analyzed theoretically.This paper has demonstrated the feasibility and advantages of structure learning and representation.This model can serve as a fundamental element of cognitive systems such as perception and associative memory.Key-words structure learning,representation,associative memory,computational neuroscience

The mirror neuron system: new frontiers.

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Keysers, Christian; Fadiga, Luciano

2008-01-01

Since the discovery of mirror neurons, much effort has been invested into studying their location and properties in the human brain. Here we review these original findings and introduce the main topics of this special issue of Social Neuroscience. What does the mirror system code? How is the mirror system embedded into the mosaic of circuits that compose our brain? How does the mirror system contribute to communication, language and social interaction? Can the principle of mirror neurons be extended to emotions, sensations and thoughts? Papers using a wide range of methods, including single cell recordings, fMRI, TMS, EEG and psychophysics, collected in this special issue, start to give us some impressive answers.

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

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

2016-12-01

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

Cochlear spike synchronization and neuron coincidence detection model

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Bader, Rolf

2018-02-01

Coincidence detection of a spike pattern fed from the cochlea into a single neuron is investigated using a physical Finite-Difference model of the cochlea and a physiologically motivated neuron model. Previous studies have shown experimental evidence of increased spike synchronization in the nucleus cochlearis and the trapezoid body [Joris et al., J. Neurophysiol. 71(3), 1022-1036 and 1037-1051 (1994)] and models show tone partial phase synchronization at the transition from mechanical waves on the basilar membrane into spike patterns [Ch. F. Babbs, J. Biophys. 2011, 435135]. Still the traveling speed of waves on the basilar membrane cause a frequency-dependent time delay of simultaneously incoming sound wavefronts up to 10 ms. The present model shows nearly perfect synchronization of multiple spike inputs as neuron outputs with interspike intervals (ISI) at the periodicity of the incoming sound for frequencies from about 30 to 300 Hz for two different amounts of afferent nerve fiber neuron inputs. Coincidence detection serves here as a fusion of multiple inputs into one single event enhancing pitch periodicity detection for low frequencies, impulse detection, or increased sound or speech intelligibility due to dereverberation.

Learning of Spatial Relationships between Observed and Imitated Actions allows Invariant Inverse Computation in the Frontal Mirror Neuron System

Science.gov (United States)

Oh, Hyuk; Gentili, Rodolphe J.; Reggia, James A.; Contreras-Vidal, José L.

2014-01-01

It has been suggested that the human mirror neuron system can facilitate learning by imitation through coupling of observation and action execution. During imitation of observed actions, the functional relationship between and within the inferior frontal cortex, the posterior parietal cortex, and the superior temporal sulcus can be modeled within the internal model framework. The proposed biologically plausible mirror neuron system model extends currently available models by explicitly modeling the intraparietal sulcus and the superior parietal lobule in implementing the function of a frame of reference transformation during imitation. Moreover, the model posits the ventral premotor cortex as performing an inverse computation. The simulations reveal that: i) the transformation system can learn and represent the changes in extrinsic to intrinsic coordinates when an imitator observes a demonstrator; ii) the inverse model of the imitator’s frontal mirror neuron system can be trained to provide the motor plans for the imitated actions. PMID:22255261

On the number of preganglionic neurones driving human postganglionic sympathetic neurones: a comparison of modelling and empirical data

Directory of Open Access Journals (Sweden)

Vaughan G Macefield

2011-12-01

Full Text Available Postganglionic sympathetic axons in awake healthy human subjects, regardless of their identity as muscle vasoconstrictor, cutaneous vasoconstrictor or sudomotor neurones, discharge with a low firing probability (~30%, generate low firing rates (~0.5 Hz and typically fire only once per cardiac interval. The purpose of the present study was to use modelling of spike trains in an attempt to define the number of preganglionic neurones that drive an individual postganglionic neurone. Artificial spike trains were generated in 1-3 preganglionic neurones converging onto a single postganglionic neurone. Each preganglionic input fired with a mean interval distribution of either 1000, 1500, 2000, 2500 or 3000 ms and the standard deviation varied between 0.5, 1.0 and 2.0 x the mean interval; the discharge frequency of each preganglionic neurone exhibited positive skewness and kurtosis. Of the 45 patterns examined, the mean discharge properties of the postganglionic neurone could only be explained by it being driven by, on average, two preganglionic neurones firing with a mean interspike interval of 2500 ms and SD of 5000 ms. The mean firing rate resulting from this pattern was 0.22 Hz, comparable to that of spontaneously active muscle vasoconstrictor neurones in healthy subjects (0.40 Hz. Likewise, the distribution of the number of spikes per cardiac interval was similar between the modelled and actual data: 0 spikes (69.5 vs 66.6 %, 1 spike (25.6 vs 21.2 %, 2 spikes (4.3 vs 6.4 %, 3 spikes (0.5 vs 1.7 % and 4 spikes (0.1 vs 0.7 %. Although some features of the firing patterns could be explained by the postganglionic neurone being driven by a single preganglionic neurone, none of the emulated firing patterns generated by the firing of three preganglionic neurones matched the discharge of the real neurones. These modelling data indicate that, on average, human postganglionic sympathetic neurones are driven by two preganglionic inputs.

Six types of multistability in a neuronal model based on slow calcium current.

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Tatiana Malashchenko

Full Text Available BACKGROUND: Multistability of oscillatory and silent regimes is a ubiquitous phenomenon exhibited by excitable systems such as neurons and cardiac cells. Multistability can play functional roles in short-term memory and maintaining posture. It seems to pose an evolutionary advantage for neurons which are part of multifunctional Central Pattern Generators to possess multistability. The mechanisms supporting multistability of bursting regimes are not well understood or classified. METHODOLOGY/PRINCIPAL FINDINGS: Our study is focused on determining the bio-physical mechanisms underlying different types of co-existence of the oscillatory and silent regimes observed in a neuronal model. We develop a low-dimensional model typifying the dynamics of a single leech heart interneuron. We carry out a bifurcation analysis of the model and show that it possesses six different types of multistability of dynamical regimes. These types are the co-existence of 1 bursting and silence, 2 tonic spiking and silence, 3 tonic spiking and subthreshold oscillations, 4 bursting and subthreshold oscillations, 5 bursting, subthreshold oscillations and silence, and 6 bursting and tonic spiking. These first five types of multistability occur due to the presence of a separating regime that is either a saddle periodic orbit or a saddle equilibrium. We found that the parameter range wherein multistability is observed is limited by the parameter values at which the separating regimes emerge and terminate. CONCLUSIONS: We developed a neuronal model which exhibits a rich variety of different types of multistability. We described a novel mechanism supporting the bistability of bursting and silence. This neuronal model provides a unique opportunity to study the dynamics of networks with neurons possessing different types of multistability.

Biophysics Model of Heavy-Ion Degradation of Neuron Morphology in Mouse Hippocampal Granular Cell Layer Neurons.

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Alp, Murat; Cucinotta, Francis A

2018-03-01

Exposure to heavy-ion radiation during cancer treatment or space travel may cause cognitive detriments that have been associated with changes in neuron morphology and plasticity. Observations in mice of reduced neuronal dendritic complexity have revealed a dependence on radiation quality and absorbed dose, suggesting that microscopic energy deposition plays an important role. In this work we used morphological data for mouse dentate granular cell layer (GCL) neurons and a stochastic model of particle track structure and microscopic energy deposition (ED) to develop a predictive model of high-charge and energy (HZE) particle-induced morphological changes to the complex structures of dendritic arbors. We represented dendrites as cylindrical segments of varying diameter with unit aspect ratios, and developed a fast sampling method to consider the stochastic distribution of ED by δ rays (secondary electrons) around the path of heavy ions, to reduce computational times. We introduce probabilistic models with a small number of parameters to describe the induction of precursor lesions that precede dendritic snipping, denoted as snip sites. Predictions for oxygen ( 16 O, 600 MeV/n) and titanium ( 48 Ti, 600 MeV/n) particles with LET of 16.3 and 129 keV/μm, respectively, are considered. Morphometric parameters to quantify changes in neuron morphology are described, including reduction in total dendritic length, number of branch points and branch numbers. Sholl analysis is applied for single neurons to elucidate dose-dependent reductions in dendritic complexity. We predict important differences in measurements from imaging of tissues from brain slices with single neuron cell observations due to the role of neuron death through both soma apoptosis and excessive dendritic length reduction. To further elucidate the role of track structure, random segment excision (snips) models are introduced and a sensitivity study of the effects of the modes of neuron death in predictions

Novel animal model defines genetic contributions for neuron-to-neuron transfer of α-synuclein.

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Tyson, Trevor; Senchuk, Megan; Cooper, Jason F; George, Sonia; Van Raamsdonk, Jeremy M; Brundin, Patrik

2017-08-08

Cell-to-cell spreading of misfolded α-synuclein (α-syn) is suggested to contribute to the progression of neuropathology in Parkinson's disease (PD). Compelling evidence supports the hypothesis that misfolded α-syn transmits from neuron-to-neuron and seeds aggregation of the protein in the recipient cells. Furthermore, α-syn frequently appears to propagate in the brains of PD patients following a stereotypic pattern consistent with progressive spreading along anatomical pathways. We have generated a C. elegans model that mirrors this progression and allows us to monitor α-syn neuron-to-neuron transmission in a live animal over its lifespan. We found that modulation of autophagy or exo/endocytosis, affects α-syn transfer. Furthermore, we demonstrate that silencing C. elegans orthologs of PD-related genes also increases the accumulation of α-syn. This novel worm model is ideal for screening molecules and genes to identify those that modulate prion-like spreading of α-syn in order to target novel strategies for disease modification in PD and other synucleinopathies.

View-Invariant Visuomotor Processing in Computational Mirror Neuron System for Humanoid

Science.gov (United States)

Dawood, Farhan; Loo, Chu Kiong

2016-01-01

Mirror neurons are visuo-motor neurons found in primates and thought to be significant for imitation learning. The proposition that mirror neurons result from associative learning while the neonate observes his own actions has received noteworthy empirical support. Self-exploration is regarded as a procedure by which infants become perceptually observant to their own body and engage in a perceptual communication with themselves. We assume that crude sense of self is the prerequisite for social interaction. However, the contribution of mirror neurons in encoding the perspective from which the motor acts of others are seen have not been addressed in relation to humanoid robots. In this paper we present a computational model for development of mirror neuron system for humanoid based on the hypothesis that infants acquire MNS by sensorimotor associative learning through self-exploration capable of sustaining early imitation skills. The purpose of our proposed model is to take into account the view-dependency of neurons as a probable outcome of the associative connectivity between motor and visual information. In our experiment, a humanoid robot stands in front of a mirror (represented through self-image using camera) in order to obtain the associative relationship between his own motor generated actions and his own visual body-image. In the learning process the network first forms mapping from each motor representation onto visual representation from the self-exploratory perspective. Afterwards, the representation of the motor commands is learned to be associated with all possible visual perspectives. The complete architecture was evaluated by simulation experiments performed on DARwIn-OP humanoid robot. PMID:26998923

View-Invariant Visuomotor Processing in Computational Mirror Neuron System for Humanoid.

Science.gov (United States)

Dawood, Farhan; Loo, Chu Kiong

2016-01-01

Mirror neurons are visuo-motor neurons found in primates and thought to be significant for imitation learning. The proposition that mirror neurons result from associative learning while the neonate observes his own actions has received noteworthy empirical support. Self-exploration is regarded as a procedure by which infants become perceptually observant to their own body and engage in a perceptual communication with themselves. We assume that crude sense of self is the prerequisite for social interaction. However, the contribution of mirror neurons in encoding the perspective from which the motor acts of others are seen have not been addressed in relation to humanoid robots. In this paper we present a computational model for development of mirror neuron system for humanoid based on the hypothesis that infants acquire MNS by sensorimotor associative learning through self-exploration capable of sustaining early imitation skills. The purpose of our proposed model is to take into account the view-dependency of neurons as a probable outcome of the associative connectivity between motor and visual information. In our experiment, a humanoid robot stands in front of a mirror (represented through self-image using camera) in order to obtain the associative relationship between his own motor generated actions and his own visual body-image. In the learning process the network first forms mapping from each motor representation onto visual representation from the self-exploratory perspective. Afterwards, the representation of the motor commands is learned to be associated with all possible visual perspectives. The complete architecture was evaluated by simulation experiments performed on DARwIn-OP humanoid robot.

View-Invariant Visuomotor Processing in Computational Mirror Neuron System for Humanoid.

Directory of Open Access Journals (Sweden)

Farhan Dawood

Full Text Available Mirror neurons are visuo-motor neurons found in primates and thought to be significant for imitation learning. The proposition that mirror neurons result from associative learning while the neonate observes his own actions has received noteworthy empirical support. Self-exploration is regarded as a procedure by which infants become perceptually observant to their own body and engage in a perceptual communication with themselves. We assume that crude sense of self is the prerequisite for social interaction. However, the contribution of mirror neurons in encoding the perspective from which the motor acts of others are seen have not been addressed in relation to humanoid robots. In this paper we present a computational model for development of mirror neuron system for humanoid based on the hypothesis that infants acquire MNS by sensorimotor associative learning through self-exploration capable of sustaining early imitation skills. The purpose of our proposed model is to take into account the view-dependency of neurons as a probable outcome of the associative connectivity between motor and visual information. In our experiment, a humanoid robot stands in front of a mirror (represented through self-image using camera in order to obtain the associative relationship between his own motor generated actions and his own visual body-image. In the learning process the network first forms mapping from each motor representation onto visual representation from the self-exploratory perspective. Afterwards, the representation of the motor commands is learned to be associated with all possible visual perspectives. The complete architecture was evaluated by simulation experiments performed on DARwIn-OP humanoid robot.

Neural Plasticity: Single Neuron Models for Discrimination and Generalization and AN Experimental Ensemble Approach.

Science.gov (United States)

Munro, Paul Wesley

A special form for modification of neuronal response properties is described in which the change in the synaptic state vector is parallel to the vector of afferent activity. This process is termed "parallel modification" and its theoretical and experimental implications are examined. A theoretical framework has been devised to describe the complementary functions of generalization and discrimination by single neurons. This constitutes a basis for three models each describing processes for the development of maximum selectivity (discrimination) and minimum selectivity (generalization) by neurons. Strengthening and weakening of synapses is expressed as a product of the presynaptic activity and a nonlinear modulatory function of two postsynaptic variables--namely a measure of the spatially integrated activity of the cell and a temporal integration (time-average) of that activity. Some theorems are given for low-dimensional systems and computer simulation results from more complex systems are discussed. Model neurons that achieve high selectivity mimic the development of cat visual cortex neurons in a wide variety of rearing conditions. A role for low-selectivity neurons is proposed in which they provide inhibitory input to neurons of the opposite type, thereby suppressing the common component of a pattern class and enhancing their selective properties. Such contrast-enhancing circuits are analyzed and supported by computer simulation. To enable maximum selectivity, the net inhibition to a cell must become strong enough to offset whatever excitation is produced by the non-preferred patterns. Ramifications of parallel models for certain experimental paradigms are analyzed. A methodology is outlined for testing synaptic modification hypotheses in the laboratory. A plastic projection from one neuronal population to another will attain stable equilibrium under periodic electrical stimulation of constant intensity. The perturbative effect of shifting this intensity level

Computational modeling of seizure dynamics using coupled neuronal networks: factors shaping epileptiform activity.

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Sebastien Naze

2015-05-01

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

Thalamic neuron models encode stimulus information by burst-size modulation

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Daniel Henry Elijah

2015-09-01

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

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

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

2015-01-01

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

Modeling Functional Neuroanatomy for an Anatomy Information System

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Niggemann, Jörg M.; Gebert, Andreas; Schulz, Stefan

2008-01-01

Objective Existing neuroanatomical ontologies, databases and information systems, such as the Foundational Model of Anatomy (FMA), represent outgoing connections from brain structures, but cannot represent the “internal wiring” of structures and as such, cannot distinguish between different independent connections from the same structure. Thus, a fundamental aspect of Neuroanatomy, the functional pathways and functional systems of the brain such as the pupillary light reflex system, is not adequately represented. This article identifies underlying anatomical objects which are the source of independent connections (collections of neurons) and uses these as basic building blocks to construct a model of functional neuroanatomy and its functional pathways. Design The basic representational elements of the model are unnamed groups of neurons or groups of neuron segments. These groups, their relations to each other, and the relations to the objects of macroscopic anatomy are defined. The resulting model can be incorporated into the FMA. Measurements The capabilities of the presented model are compared to the FMA and the Brain Architecture Management System (BAMS). Results Internal wiring as well as functional pathways can correctly be represented and tracked. Conclusion This model bridges the gap between representations of single neurons and their parts on the one hand and representations of spatial brain structures and areas on the other hand. It is capable of drawing correct inferences on pathways in a nervous system. The object and relation definitions are related to the Open Biomedical Ontology effort and its relation ontology, so that this model can be further developed into an ontology of neuronal functional systems. PMID:18579841

The infant mirror neuron system studied with high density EEG.

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Nyström, Pär

2008-01-01

The mirror neuron system has been suggested to play a role in many social capabilities such as action understanding, imitation, language and empathy. These are all capabilities that develop during infancy and childhood, but the human mirror neuron system has been poorly studied using neurophysiological measures. This study measured the brain activity of 6-month-old infants and adults using a high-density EEG net with the aim of identifying mirror neuron activity. The subjects viewed both goal-directed movements and non-goal-directed movements. An independent component analysis was used to extract the sources of cognitive processes. The desynchronization of the mu rhythm in adults has been shown to be a marker for activation of the mirror neuron system and was used as a criterion to categorize independent components between subjects. The results showed significant mu desynchronization in the adult group and significantly higher ERP activation in both adults and 6-month-olds for the goal-directed action observation condition. This study demonstrate that infants as young as 6 months display mirror neuron activity and is the first to present a direct ERP measure of the mirror neuron system in infants.

From in silico astrocyte cell models to neuron-astrocyte network models: A review.

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Oschmann, Franziska; Berry, Hugues; Obermayer, Klaus; Lenk, Kerstin

2018-01-01

The idea that astrocytes may be active partners in synaptic information processing has recently emerged from abundant experimental reports. Because of their spatial proximity to neurons and their bidirectional communication with them, astrocytes are now considered as an important third element of the synapse. Astrocytes integrate and process synaptic information and by doing so generate cytosolic calcium signals that are believed to reflect neuronal transmitter release. Moreover, they regulate neuronal information transmission by releasing gliotransmitters into the synaptic cleft affecting both pre- and postsynaptic receptors. Concurrent with the first experimental reports of the astrocytic impact on neural network dynamics, computational models describing astrocytic functions have been developed. In this review, we give an overview over the published computational models of astrocytic functions, from single-cell dynamics to the tripartite synapse level and network models of astrocytes and neurons. Copyright © 2017 Elsevier Inc. All rights reserved.

Recurrently connected and localized neuronal communities initiate coordinated spontaneous activity in neuronal networks

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Amin, Hayder; Maccione, Alessandro; Nieus, Thierry

2017-01-01

Developing neuronal systems intrinsically generate coordinated spontaneous activity that propagates by involving a large number of synchronously firing neurons. In vivo, waves of spikes transiently characterize the activity of developing brain circuits and are fundamental for activity-dependent circuit formation. In vitro, coordinated spontaneous spiking activity, or network bursts (NBs), interleaved within periods of asynchronous spikes emerge during the development of 2D and 3D neuronal cultures. Several studies have investigated this type of activity and its dynamics, but how a neuronal system generates these coordinated events remains unclear. Here, we investigate at a cellular level the generation of network bursts in spontaneously active neuronal cultures by exploiting high-resolution multielectrode array recordings and computational network modelling. Our analysis reveals that NBs are generated in specialized regions of the network (functional neuronal communities) that feature neuronal links with high cross-correlation peak values, sub-millisecond lags and that share very similar structural connectivity motifs providing recurrent interactions. We show that the particular properties of these local structures enable locally amplifying spontaneous asynchronous spikes and that this mechanism can lead to the initiation of NBs. Through the analysis of simulated and experimental data, we also show that AMPA currents drive the coordinated activity, while NMDA and GABA currents are only involved in shaping the dynamics of NBs. Overall, our results suggest that the presence of functional neuronal communities with recurrent local connections allows a neuronal system to generate spontaneous coordinated spiking activity events. As suggested by the rules used for implementing our computational model, such functional communities might naturally emerge during network development by following simple constraints on distance-based connectivity. PMID:28749937

Recurrently connected and localized neuronal communities initiate coordinated spontaneous activity in neuronal networks.

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Davide Lonardoni

2017-07-01

Full Text Available Developing neuronal systems intrinsically generate coordinated spontaneous activity that propagates by involving a large number of synchronously firing neurons. In vivo, waves of spikes transiently characterize the activity of developing brain circuits and are fundamental for activity-dependent circuit formation. In vitro, coordinated spontaneous spiking activity, or network bursts (NBs, interleaved within periods of asynchronous spikes emerge during the development of 2D and 3D neuronal cultures. Several studies have investigated this type of activity and its dynamics, but how a neuronal system generates these coordinated events remains unclear. Here, we investigate at a cellular level the generation of network bursts in spontaneously active neuronal cultures by exploiting high-resolution multielectrode array recordings and computational network modelling. Our analysis reveals that NBs are generated in specialized regions of the network (functional neuronal communities that feature neuronal links with high cross-correlation peak values, sub-millisecond lags and that share very similar structural connectivity motifs providing recurrent interactions. We show that the particular properties of these local structures enable locally amplifying spontaneous asynchronous spikes and that this mechanism can lead to the initiation of NBs. Through the analysis of simulated and experimental data, we also show that AMPA currents drive the coordinated activity, while NMDA and GABA currents are only involved in shaping the dynamics of NBs. Overall, our results suggest that the presence of functional neuronal communities with recurrent local connections allows a neuronal system to generate spontaneous coordinated spiking activity events. As suggested by the rules used for implementing our computational model, such functional communities might naturally emerge during network development by following simple constraints on distance-based connectivity.

Neuronal medium that supports basic synaptic functions and activity of human neurons in vitro.

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Bardy, Cedric; van den Hurk, Mark; Eames, Tameji; Marchand, Cynthia; Hernandez, Ruben V; Kellogg, Mariko; Gorris, Mark; Galet, Ben; Palomares, Vanessa; Brown, Joshua; Bang, Anne G; Mertens, Jerome; Böhnke, Lena; Boyer, Leah; Simon, Suzanne; Gage, Fred H

2015-05-19

Human cell reprogramming technologies offer access to live human neurons from patients and provide a new alternative for modeling neurological disorders in vitro. Neural electrical activity is the essence of nervous system function in vivo. Therefore, we examined neuronal activity in media widely used to culture neurons. We found that classic basal media, as well as serum, impair action potential generation and synaptic communication. To overcome this problem, we designed a new neuronal medium (BrainPhys basal + serum-free supplements) in which we adjusted the concentrations of inorganic salts, neuroactive amino acids, and energetic substrates. We then tested that this medium adequately supports neuronal activity and survival of human neurons in culture. Long-term exposure to this physiological medium also improved the proportion of neurons that were synaptically active. The medium was designed to culture human neurons but also proved adequate for rodent neurons. The improvement in BrainPhys basal medium to support neurophysiological activity is an important step toward reducing the gap between brain physiological conditions in vivo and neuronal models in vitro.

The mirror neuron system and the consequences of its dysfunction.

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Iacoboni, Marco; Dapretto, Mirella

2006-12-01

The discovery of premotor and parietal cells known as mirror neurons in the macaque brain that fire not only when the animal is in action, but also when it observes others carrying out the same actions provides a plausible neurophysiological mechanism for a variety of important social behaviours, from imitation to empathy. Recent data also show that dysfunction of the mirror neuron system in humans might be a core deficit in autism, a socially isolating condition. Here, we review the neurophysiology of the mirror neuron system and its role in social cognition and discuss the clinical implications of mirror neuron dysfunction.

A neuronal network model with simplified tonotopicity for tinnitus generation and its relief by sound therapy.

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Nagashino, Hirofumi; Kinouchi, Yohsuke; Danesh, Ali A; Pandya, Abhijit S

2013-01-01

Tinnitus is the perception of sound in the ears or in the head where no external source is present. Sound therapy is one of the most effective techniques for tinnitus treatment that have been proposed. In order to investigate mechanisms of tinnitus generation and the clinical effects of sound therapy, we have proposed conceptual and computational models with plasticity using a neural oscillator or a neuronal network model. In the present paper, we propose a neuronal network model with simplified tonotopicity of the auditory system as more detailed structure. In this model an integrate-and-fire neuron model is employed and homeostatic plasticity is incorporated. The computer simulation results show that the present model can show the generation of oscillation and its cessation by external input. It suggests that the present framework is promising as a modeling for the tinnitus generation and the effects of sound therapy.

Lateral Information Processing by Spiking Neurons: A Theoretical Model of the Neural Correlate of Consciousness

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Marc Ebner

2011-01-01

Full Text Available Cognitive brain functions, for example, sensory perception, motor control and learning, are understood as computation by axonal-dendritic chemical synapses in networks of integrate-and-fire neurons. Cognitive brain functions may occur either consciously or nonconsciously (on “autopilot”. Conscious cognition is marked by gamma synchrony EEG, mediated largely by dendritic-dendritic gap junctions, sideways connections in input/integration layers. Gap-junction-connected neurons define a sub-network within a larger neural network. A theoretical model (the “conscious pilot” suggests that as gap junctions open and close, a gamma-synchronized subnetwork, or zone moves through the brain as an executive agent, converting nonconscious “auto-pilot” cognition to consciousness, and enhancing computation by coherent processing and collective integration. In this study we implemented sideways “gap junctions” in a single-layer artificial neural network to perform figure/ground separation. The set of neurons connected through gap junctions form a reconfigurable resistive grid or sub-network zone. In the model, outgoing spikes are temporally integrated and spatially averaged using the fixed resistive grid set up by neurons of similar function which are connected through gap-junctions. This spatial average, essentially a feedback signal from the neuron's output, determines whether particular gap junctions between neurons will open or close. Neurons connected through open gap junctions synchronize their output spikes. We have tested our gap-junction-defined sub-network in a one-layer neural network on artificial retinal inputs using real-world images. Our system is able to perform figure/ground separation where the laterally connected sub-network of neurons represents a perceived object. Even though we only show results for visual stimuli, our approach should generalize to other modalities. The system demonstrates a moving sub-network zone of

Memory effects on a resonate-and-fire neuron model subjected to Ornstein-Uhlenbeck noise

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Paekivi, S.; Mankin, R.; Rekker, A.

2017-10-01

We consider a generalized Langevin equation with an exponentially decaying memory kernel as a model for the firing process of a resonate-and-fire neuron. The effect of temporally correlated random neuronal input is modeled as Ornstein-Uhlenbeck noise. In the noise-induced spiking regime of the neuron, we derive exact analytical formulas for the dependence of some statistical characteristics of the output spike train, such as the probability distribution of the interspike intervals (ISIs) and the survival probability, on the parameters of the input stimulus. Particularly, on the basis of these exact expressions, we have established sufficient conditions for the occurrence of memory-time-induced transitions between unimodal and multimodal structures of the ISI density and a critical damping coefficient which marks a dynamical transition in the behavior of the system.

Sensory neurons do not induce motor neuron loss in a human stem cell model of spinal muscular atrophy.

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Schwab, Andrew J; Ebert, Allison D

2014-01-01

Spinal muscular atrophy (SMA) is an autosomal recessive disorder leading to paralysis and early death due to reduced SMN protein. It is unclear why there is such a profound motor neuron loss, but recent evidence from fly and mouse studies indicate that cells comprising the whole sensory-motor circuit may contribute to motor neuron dysfunction and loss. Here, we used induced pluripotent stem cells derived from SMA patients to test whether sensory neurons directly contribute to motor neuron loss. We generated sensory neurons from SMA induced pluripotent stem cells and found no difference in neuron generation or survival, although there was a reduced calcium response to depolarizing stimuli. Using co-culture of SMA induced pluripotent stem cell derived sensory neurons with control induced pluripotent stem cell derived motor neurons, we found no significant reduction in motor neuron number or glutamate transporter boutons on motor neuron cell bodies or neurites. We conclude that SMA sensory neurons do not overtly contribute to motor neuron loss in this human stem cell system.

Neuronal degeneration in autonomic nervous system of Dystonia musculorum mice

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Liu Kang-Jen

2011-01-01

Full Text Available Abstract Background Dystonia musculorum (dt is an autosomal recessive hereditary neuropathy with a characteristic uncoordinated movement and is caused by a defect in the bullous pemphigoid antigen 1 (BPAG1 gene. The neural isoform of BPAG1 is expressed in various neurons, including those in the central and peripheral nerve systems of mice. However, most previous studies on neuronal degeneration in BPAG1-deficient mice focused on peripheral sensory neurons and only limited investigation of the autonomic system has been conducted. Methods In this study, patterns of nerve innervation in cutaneous and iridial tissues were examined using general neuronal marker protein gene product 9.5 via immunohistochemistry. To perform quantitative analysis of the autonomic neuronal number, neurons within the lumbar sympathetic and parasympathetic ciliary ganglia were calculated. In addition, autonomic neurons were cultured from embryonic dt/dt mutants to elucidate degenerative patterns in vitro. Distribution patterns of neuronal intermediate filaments in cultured autonomic neurons were thoroughly studied under immunocytochemistry and conventional electron microscopy. Results Our immunohistochemistry results indicate that peripheral sensory nerves and autonomic innervation of sweat glands and irises dominated degeneration in dt/dt mice. Quantitative results confirmed that the number of neurons was significantly decreased in the lumbar sympathetic ganglia as well as in the parasympathetic ciliary ganglia of dt/dt mice compared with those of wild-type mice. We also observed that the neuronal intermediate filaments were aggregated abnormally in cultured autonomic neurons from dt/dt embryos. Conclusions These results suggest that a deficiency in the cytoskeletal linker BPAG1 is responsible for dominant sensory nerve degeneration and severe autonomic degeneration in dt/dt mice. Additionally, abnormally aggregated neuronal intermediate filaments may participate in

A Neuron-Based Model of Sleep-Wake Cycles

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Postnova, Svetlana; Peters, Achim; Braun, Hans

2008-03-01

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

Beyond Critical Exponents in Neuronal Avalanches

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Friedman, Nir; Butler, Tom; Deville, Robert; Beggs, John; Dahmen, Karin

2011-03-01

Neurons form a complex network in the brain, where they interact with one another by firing electrical signals. Neurons firing can trigger other neurons to fire, potentially causing avalanches of activity in the network. In many cases these avalanches have been found to be scale independent, similar to critical phenomena in diverse systems such as magnets and earthquakes. We discuss models for neuronal activity that allow for the extraction of testable, statistical predictions. We compare these models to experimental results, and go beyond critical exponents.

Dynamic neuronal ensembles: Issues in representing structure change in object-oriented, biologically-based brain models

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Vahie, S.; Zeigler, B.P.; Cho, H. [Univ. of Arizona, Tucson, AZ (United States)

1996-12-31

This paper describes the structure of dynamic neuronal ensembles (DNEs). DNEs represent a new paradigm for learning, based on biological neural networks that use variable structures. We present a computational neural element that demonstrates biological neuron functionality such as neurotransmitter feedback absolute refractory period and multiple output potentials. More specifically, we will develop a network of neural elements that have the ability to dynamically strengthen, weaken, add and remove interconnections. We demonstrate that the DNE is capable of performing dynamic modifications to neuron connections and exhibiting biological neuron functionality. In addition to its applications for learning, DNEs provide an excellent environment for testing and analysis of biological neural systems. An example of habituation and hyper-sensitization in biological systems, using a neural circuit from a snail is presented and discussed. This paper provides an insight into the DNE paradigm using models developed and simulated in DEVS.

An image-free opto-mechanical system for creating virtual environments and imaging neuronal activity in freely moving Caenorhabditis elegans.

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Serge Faumont

Full Text Available Non-invasive recording in untethered animals is arguably the ultimate step in the analysis of neuronal function, but such recordings remain elusive. To address this problem, we devised a system that tracks neuron-sized fluorescent targets in real time. The system can be used to create virtual environments by optogenetic activation of sensory neurons, or to image activity in identified neurons at high magnification. By recording activity in neurons of freely moving C. elegans, we tested the long-standing hypothesis that forward and reverse locomotion are generated by distinct neuronal circuits. Surprisingly, we found motor neurons that are active during both types of locomotion, suggesting a new model of locomotion control in C. elegans. These results emphasize the importance of recording neuronal activity in freely moving animals and significantly expand the potential of imaging techniques by providing a mean to stabilize fluorescent targets.

A Neuronal Culture System to Detect Prion Synaptotoxicity.

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Cheng Fang

2016-05-01

Full Text Available Synaptic pathology is an early feature of prion as well as other neurodegenerative diseases. Although the self-templating process by which prions propagate is well established, the mechanisms by which prions cause synaptotoxicity are poorly understood, due largely to the absence of experimentally tractable cell culture models. Here, we report that exposure of cultured hippocampal neurons to PrPSc, the infectious isoform of the prion protein, results in rapid retraction of dendritic spines. This effect is entirely dependent on expression of the cellular prion protein, PrPC, by target neurons, and on the presence of a nine-amino acid, polybasic region at the N-terminus of the PrPC molecule. Both protease-resistant and protease-sensitive forms of PrPSc cause dendritic loss. This system provides new insights into the mechanisms responsible for prion neurotoxicity, and it provides a platform for characterizing different pathogenic forms of PrPSc and testing potential therapeutic agents.

Novel human neuronal tau model exhibiting neurofibrillary tangles and transcellular propagation.

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Reilly, Patrick; Winston, Charisse N; Baron, Kelsey R; Trejo, Margarita; Rockenstein, Edward M; Akers, Johnny C; Kfoury, Najla; Diamond, Marc; Masliah, Eliezer; Rissman, Robert A; Yuan, Shauna H

2017-10-01

Tauopathies are a class of neurodegenerative diseases, including Alzheimer's disease, frontotemporal dementia and progressive supranuclear palsy, which are associated with the pathological aggregation of tau protein into neurofibrillary tangles (NFT). Studies have characterized tau as a "prion-like" protein given its ability to form distinct, stable amyloid conformations capable of transcellular and multigenerational propagation in clonal fashion. It has been proposed that progression of tauopathy could be due to the prion-like propagation of tau, suggesting the possibility that end-stage pathologies, like NFT formation, may require an instigating event such as tau seeding. To investigate this, we applied a novel human induced pluripotent stem cell (hiPSC) system we have developed to serve as a human neuronal model. We introduced the tau repeat domain (tau-RD) with P301L and V337M (tau-RD-LM) mutations into hiPSC-derived neurons and observed expression of tau-RD at levels similar to total tau in postmortem AD brains. Tau aggregation occurred without the addition of recombinant tau fibrils. The conditioned media from tau-RD cultures contained tau-RD seeds, which were capable of inducing aggregate formation in homotypic mode in non-transduced recipient neuronal cultures. The resultant NFTs were thioflavin-positive, silver stain-positive, and assumed fibrillary appearance on transmission electron microscopy (TEM) with immunogold, which revealed paired helical filament 1 (PHF1)-positive NFTs, representing possible recruitment of endogenous tau in the aggregates. Functionally, expression of tau-RD caused neurotoxicity that manifested as axon retraction, synaptic density reduction, and enlargement of lysosomes. The results of our hiPSC study were reinforced by the observation that Tau-RD-LM is excreted in exosomes, which mediated the transfer of human tau to wild-type mouse neurons in vivo. Our hiPSC human neuronal system provides a model for further studies of tau

Modelling of pathologies of the nervous system by the example of computational and electronic models of elementary nervous systems

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Shumilov, V. N., E-mail: vnshumilov@rambler.ru; Syryamkin, V. I., E-mail: maximus70sir@gmail.com; Syryamkin, M. V., E-mail: maximus70sir@gmail.com [National Research Tomsk State University, 634050, Tomsk, Lenin Avenue, 36 (Russian Federation)

2015-11-17

The paper puts forward principles of action of devices operating similarly to the nervous system and the brain of biological systems. We propose an alternative method of studying diseases of the nervous system, which may significantly influence prevention, medical treatment, or at least retardation of development of these diseases. This alternative is to use computational and electronic models of the nervous system. Within this approach, we represent the brain in the form of a huge electrical circuit composed of active units, namely, neuron-like units and connections between them. As a result, we created computational and electronic models of elementary nervous systems, which are based on the principles of functioning of biological nervous systems that we have put forward. Our models demonstrate reactions to external stimuli and their change similarly to the behavior of simplest biological organisms. The models possess the ability of self-training and retraining in real time without human intervention and switching operation/training modes. In our models, training and memorization take place constantly under the influence of stimuli on the organism. Training is without any interruption and switching operation modes. Training and formation of new reflexes occur by means of formation of new connections between excited neurons, between which formation of connections is physically possible. Connections are formed without external influence. They are formed under the influence of local causes. Connections are formed between outputs and inputs of two neurons, when the difference between output and input potentials of excited neurons exceeds a value sufficient to form a new connection. On these grounds, we suggest that the proposed principles truly reflect mechanisms of functioning of biological nervous systems and the brain. In order to confirm the correspondence of the proposed principles to biological nature, we carry out experiments for the study of processes of

Modelling of pathologies of the nervous system by the example of computational and electronic models of elementary nervous systems

International Nuclear Information System (INIS)

Shumilov, V. N.; Syryamkin, V. I.; Syryamkin, M. V.

2015-01-01

The paper puts forward principles of action of devices operating similarly to the nervous system and the brain of biological systems. We propose an alternative method of studying diseases of the nervous system, which may significantly influence prevention, medical treatment, or at least retardation of development of these diseases. This alternative is to use computational and electronic models of the nervous system. Within this approach, we represent the brain in the form of a huge electrical circuit composed of active units, namely, neuron-like units and connections between them. As a result, we created computational and electronic models of elementary nervous systems, which are based on the principles of functioning of biological nervous systems that we have put forward. Our models demonstrate reactions to external stimuli and their change similarly to the behavior of simplest biological organisms. The models possess the ability of self-training and retraining in real time without human intervention and switching operation/training modes. In our models, training and memorization take place constantly under the influence of stimuli on the organism. Training is without any interruption and switching operation modes. Training and formation of new reflexes occur by means of formation of new connections between excited neurons, between which formation of connections is physically possible. Connections are formed without external influence. They are formed under the influence of local causes. Connections are formed between outputs and inputs of two neurons, when the difference between output and input potentials of excited neurons exceeds a value sufficient to form a new connection. On these grounds, we suggest that the proposed principles truly reflect mechanisms of functioning of biological nervous systems and the brain. In order to confirm the correspondence of the proposed principles to biological nature, we carry out experiments for the study of processes of

Complex Behavior in a Selective Aging Neuron Model Based on Small World Networks

International Nuclear Information System (INIS)

Zhang Guiqing; Chen Tianlun

2008-01-01

Complex behavior in a selective aging simple neuron model based on small world networks is investigated. The basic elements of the model are endowed with the main features of a neuron function. The structure of the selective aging neuron model is discussed. We also give some properties of the new network and find that the neuron model displays a power-law behavior. If the brain network is small world-like network, the mean avalanche size is almost the same unless the aging parameter is big enough.

Neuronal medium that supports basic synaptic functions and activity of human neurons in vitro

NARCIS (Netherlands)

Bardy, C.; Hurk, M. van den; Eames, T.; Marchand, C.; Hernandez, R.V.; Kellogg, M.; Gorris, M.A.J.; Galet, B.; Palomares, V.; Brown, J.; Bang, A.G.; Mertens, J.; Bohnke, L.; Boyer, L.; Simon, S.; Gage, F.H.

2015-01-01

Human cell reprogramming technologies offer access to live human neurons from patients and provide a new alternative for modeling neurological disorders in vitro. Neural electrical activity is the essence of nervous system function in vivo. Therefore, we examined neuronal activity in media widely

Stochastic resonance in models of neuronal ensembles

International Nuclear Information System (INIS)

Chialvo, D.R.; Longtin, A.; Mueller-Gerkin, J.

1997-01-01

Two recently suggested mechanisms for the neuronal encoding of sensory information involving the effect of stochastic resonance with aperiodic time-varying inputs are considered. It is shown, using theoretical arguments and numerical simulations, that the nonmonotonic behavior with increasing noise of the correlation measures used for the so-called aperiodic stochastic resonance (ASR) scenario does not rely on the cooperative effect typical of stochastic resonance in bistable and excitable systems. Rather, ASR with slowly varying signals is more properly interpreted as linearization by noise. Consequently, the broadening of the open-quotes resonance curveclose quotes in the multineuron stochastic resonance without tuning scenario can also be explained by this linearization. Computation of the input-output correlation as a function of both signal frequency and noise for the model system further reveals conditions where noise-induced firing with aperiodic inputs will benefit from stochastic resonance rather than linearization by noise. Thus, our study clarifies the tuning requirements for the optimal transduction of subthreshold aperiodic signals. It also shows that a single deterministic neuron can perform as well as a network when biased into a suprathreshold regime. Finally, we show that the inclusion of a refractory period in the spike-detection scheme produces a better correlation between instantaneous firing rate and input signal. copyright 1997 The American Physical Society

Efficient induction of dopaminergic neuron differentiation from induced pluripotent stem cells reveals impaired mitophagy in PARK2 neurons.

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Suzuki, Sadafumi; Akamatsu, Wado; Kisa, Fumihiko; Sone, Takefumi; Ishikawa, Kei-Ichi; Kuzumaki, Naoko; Katayama, Hiroyuki; Miyawaki, Atsushi; Hattori, Nobutaka; Okano, Hideyuki

2017-01-29

Patient-specific induced pluripotent stem cells (iPSCs) show promise for use as tools for in vitro modeling of Parkinson's disease. We sought to improve the efficiency of dopaminergic (DA) neuron induction from iPSCs by the using surface markers expressed in DA progenitors to increase the significance of the phenotypic analysis. By sorting for a CD184 high /CD44 - fraction during neural differentiation, we obtained a population of cells that were enriched in DA neuron precursor cells and achieved higher differentiation efficiencies than those obtained through the same protocol without sorting. This high efficiency method of DA neuronal induction enabled reliable detection of reactive oxygen species (ROS) accumulation and vulnerable phenotypes in PARK2 iPSCs-derived DA neurons. We additionally established a quantitative system using the mt-mKeima reporter system to monitor mitophagy in which mitochondria fuse with lysosomes and, by combining this system with the method of DA neuronal induction described above, determined that mitophagy is impaired in PARK2 neurons. These findings suggest that the efficiency of DA neuron induction is important for the precise detection of cellular phenotypes in modeling Parkinson's disease. Copyright © 2016. Published by Elsevier Inc.

Controlling the Regional Identity of hPSC-Derived Neurons to Uncover Neuronal Subtype Specificity of Neurological Disease Phenotypes

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Kent Imaizumi

2015-12-01

Full Text Available The CNS contains many diverse neuronal subtypes, and most neurological diseases target specific subtypes. However, the mechanism of neuronal subtype specificity of disease phenotypes remains elusive. Although in vitro disease models employing human pluripotent stem cells (PSCs have great potential to clarify the association of neuronal subtypes with disease, it is currently difficult to compare various PSC-derived subtypes. This is due to the limited number of subtypes whose induction is established, and different cultivation protocols for each subtype. Here, we report a culture system to control the regional identity of PSC-derived neurons along the anteroposterior (A-P and dorsoventral (D-V axes. This system was successfully used to obtain various neuronal subtypes based on the same protocol. Furthermore, we reproduced subtype-specific phenotypes of amyotrophic lateral sclerosis (ALS and Alzheimer’s disease (AD by comparing the obtained subtypes. Therefore, our culture system provides new opportunities for modeling neurological diseases with PSCs.

Wnt1 from cochlear schwann cells enhances neuronal differentiation of transplanted neural stem cells in a rat spiral ganglion neuron degeneration model.

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He, Ya; Zhang, Peng-Zhi; Sun, Dong; Mi, Wen-Juan; Zhang, Xin-Yi; Cui, Yong; Jiang, Xing-Wang; Mao, Xiao-Bo; Qiu, Jian-Hua

2014-04-01

Although neural stem cell (NSC) transplantation is widely expected to become a therapy for nervous system degenerative diseases and injuries, the low neuronal differentiation rate of NSCs transplanted into the inner ear is a major obstacle for the successful treatment of spiral ganglion neuron (SGN) degeneration. In this study, we validated whether the local microenvironment influences the neuronal differentiation of transplanted NSCs in the inner ear. Using a rat SGN degeneration model, we demonstrated that transplanted NSCs were more likely to differentiate into microtubule-associated protein 2 (MAP2)-positive neurons in SGN-degenerated cochleae than in control cochleae. Using real-time quantitative PCR and an immunofluorescence assay, we also proved that the expression of Wnt1 (a ligand of Wnt signaling) increases significantly in Schwann cells in the SGN-degenerated cochlea. We further verified that NSC cultures express receptors and signaling components for Wnts. Based on these expression patterns, we hypothesized that Schwann cell-derived Wnt1 and Wnt signaling might be involved in the regulation of the neuronal differentiation of transplanted NSCs. We verified our hypothesis in vitro using a coculture system. We transduced a lentiviral vector expressing Wnt1 into cochlear Schwann cell cultures and cocultured them with NSC cultures. The coculture with Wnt1-expressing Schwann cells resulted in a significant increase in the percentage of NSCs that differentiated into MAP2-positive neurons, whereas this differentiation-enhancing effect was prevented by Dkk1 (an inhibitor of the Wnt signaling pathway). These results suggested that Wnt1 derived from cochlear Schwann cells enhanced the neuronal differentiation of transplanted NSCs through Wnt signaling pathway activation. Alterations of the microenvironment deserve detailed investigation because they may help us to conceive effective strategies to overcome the barrier of the low differentiation rate of transplanted

Neuronal human BACE1 knockin induces systemic diabetes in mice.

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Plucińska, Kaja; Dekeryte, Ruta; Koss, David; Shearer, Kirsty; Mody, Nimesh; Whitfield, Phillip D; Doherty, Mary K; Mingarelli, Marco; Welch, Andy; Riedel, Gernot; Delibegovic, Mirela; Platt, Bettina

2016-07-01

β-Secretase 1 (BACE1) is a key enzyme in Alzheimer's disease pathogenesis that catalyses the amyloidogenic cleavage of amyloid precursor protein (APP). Recently, global Bace1 deletion was shown to protect against diet-induced obesity and diabetes, suggesting that BACE1 is a potential regulator of glucose homeostasis. Here, we investigated whether increased neuronal BACE1 is sufficient to alter systemic glucose metabolism, using a neuron-specific human BACE1 knockin mouse model (PLB4). Glucose homeostasis and adiposity were determined by glucose tolerance tests and EchoMRI, lipid species were measured by quantitative lipidomics, and biochemical and molecular alterations were assessed by western blotting, quantitative PCR and ELISAs. Glucose uptake in the brain and upper body was measured via (18)FDG-PET imaging. Physiological and molecular analyses demonstrated that centrally expressed human BACE1 induced systemic glucose intolerance in mice from 4 months of age onward, alongside a fatty liver phenotype and impaired hepatic glycogen storage. This diabetic phenotype was associated with hypothalamic pathology, i.e. deregulation of the melanocortin system, and advanced endoplasmic reticulum (ER) stress indicated by elevated central C/EBP homologous protein (CHOP) signalling and hyperphosphorylation of its regulator eukaryotic translation initiation factor 2α (eIF2α). In vivo (18)FDG-PET imaging further confirmed brain glucose hypometabolism in these mice; this corresponded with altered neuronal insulin-related signalling, enhanced protein tyrosine phosphatase 1B (PTP1B) and retinol-binding protein 4 (RBP4) levels, along with upregulation of the ribosomal protein and lipid translation machinery. Increased forebrain and plasma lipid accumulation (i.e. ceramides, triacylglycerols, phospholipids) was identified via lipidomics analysis. Our data reveal that neuronal BACE1 is a key regulator of metabolic homeostasis and provide a potential mechanism for the high

Measuring neuronal avalanches in disordered systems with absorbing states

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Girardi-Schappo, M.; Tragtenberg, M. H. R.

2018-04-01

Power-law-shaped avalanche-size distributions are widely used to probe for critical behavior in many different systems, particularly in neural networks. The definition of avalanche is ambiguous. Usually, theoretical avalanches are defined as the activity between a stimulus and the relaxation to an inactive absorbing state. On the other hand, experimental neuronal avalanches are defined by the activity between consecutive silent states. We claim that the latter definition may be extended to some theoretical models to characterize their power-law avalanches and critical behavior. We study a system in which the separation of driving and relaxation time scales emerges from its structure. We apply both definitions of avalanche to our model. Both yield power-law-distributed avalanches that scale with system size in the critical point as expected. Nevertheless, we find restricted power-law-distributed avalanches outside of the critical region within the experimental procedure, which is not expected by the standard theoretical definition. We remark that these results are dependent on the model details.

An experimental electronic model for a neuronal cell

International Nuclear Information System (INIS)

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

2014-01-01

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

A self-organizing state-space-model approach for parameter estimation in hodgkin-huxley-type models of single neurons.

Directory of Open Access Journals (Sweden)

Dimitrios V Vavoulis

Full Text Available Traditional approaches to the problem of parameter estimation in biophysical models of neurons and neural networks usually adopt a global search algorithm (for example, an evolutionary algorithm, often in combination with a local search method (such as gradient descent in order to minimize the value of a cost function, which measures the discrepancy between various features of the available experimental data and model output. In this study, we approach the problem of parameter estimation in conductance-based models of single neurons from a different perspective. By adopting a hidden-dynamical-systems formalism, we expressed parameter estimation as an inference problem in these systems, which can then be tackled using a range of well-established statistical inference methods. The particular method we used was Kitagawa's self-organizing state-space model, which was applied on a number of Hodgkin-Huxley-type models using simulated or actual electrophysiological data. We showed that the algorithm can be used to estimate a large number of parameters, including maximal conductances, reversal potentials, kinetics of ionic currents, measurement and intrinsic noise, based on low-dimensional experimental data and sufficiently informative priors in the form of pre-defined constraints imposed on model parameters. The algorithm remained operational even when very noisy experimental data were used. Importantly, by combining the self-organizing state-space model with an adaptive sampling algorithm akin to the Covariance Matrix Adaptation Evolution Strategy, we achieved a significant reduction in the variance of parameter estimates. The algorithm did not require the explicit formulation of a cost function and it was straightforward to apply on compartmental models and multiple data sets. Overall, the proposed methodology is particularly suitable for resolving high-dimensional inference problems based on noisy electrophysiological data and, therefore, a

Noise-Induced Transition in a Voltage-Controlled Oscillator Neuron Model

International Nuclear Information System (INIS)

Xie Huizhang; Liu Xuemei; Li Zhibing; Ai Baoquan; Liu Lianggang

2008-01-01

In the presence of Gaussian white noise, we study the properties of voltage-controlled oscillator neuron model and discuss the effects of the additive and multiplicative noise. It is found that the additive noise can accelerate and counterwork the firing of neuron, which depends on the value of central frequency of neuron itself, while multiplicative noise can induce the continuous change or mutation of membrane potential

A Neuronal Network Model for Pitch Selectivity and Representation.

Science.gov (United States)

Huang, Chengcheng; Rinzel, John

2016-01-01

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

A metabolomic comparison of mouse models of the Neuronal Ceroid Lipofuscinoses

Energy Technology Data Exchange (ETDEWEB)

Salek, Reza M.; Pears, Michael R. [University of Cambridge, Department of Biochemistry and Cambridge Systems Biology Centre (United Kingdom); Cooper, Jonathan D. [King' s College London, Pediatric Storage Disorders Laboratory, Department of Neuroscience, Institute of Psychiatry (United Kingdom); Mitchison, Hannah M. [Royal Free and University College Medical School, Department of Paediatrics and Child Health (United Kingdom); Pearce, David A. [Sanford School of Medicine of the University of South Dakota, Department of Pediatrics (United States); Mortishire-Smith, Russell J. [Johnson and Johnson PR and D (Belgium); Griffin, Julian L., E-mail: jlg40@mole.bio.cam.ac.uk [University of Cambridge, Department of Biochemistry and the Cambridge Systems Biology Centre (United Kingdom)

2011-04-15

The Neuronal Ceroid Lipofuscinoses (NCL) are a group of fatal inherited neurodegenerative diseases in humans distinguished by a common clinical pathology, characterized by the accumulation of storage body material in cells and gross brain atrophy. In this study, metabolic changes in three NCL mouse models were examined looking for pathways correlated with neurodegeneration. Two mouse models; motor neuron degeneration (mnd) mouse and a variant model of late infantile NCL, termed the neuronal ceroid lipofuscinosis (nclf) mouse were investigated experimentally. Both models exhibit a characteristic accumulation of autofluorescent lipopigment in neuronal and non neuronal cells. The NMR profiles derived from extracts of the cortex and cerebellum from mnd and nclf mice were distinguished according to disease/wildtype status. In particular, a perturbation in glutamine and glutamate metabolism, and a decrease in {gamma}-amino butyric acid (GABA) in the cerebellum and cortices of mnd (adolescent mice) and nclf mice relative to wildtype at all ages were detected. Our results were compared to the Cln3 mouse model of NCL. The metabolism of mnd mice resembled older (6 month) Cln3 mice, where the disease is relatively advanced, while the metabolism of nclf mice was more akin to younger (1-2 months) Cln3 mice, where the disease is in its early stages of progression. Overall, our results allowed the identification of metabolic traits common to all NCL subtypes for the three animal models.

A metabolomic comparison of mouse models of the Neuronal Ceroid Lipofuscinoses

International Nuclear Information System (INIS)

Salek, Reza M.; Pears, Michael R.; Cooper, Jonathan D.; Mitchison, Hannah M.; Pearce, David A.; Mortishire-Smith, Russell J.; Griffin, Julian L.

2011-01-01

The Neuronal Ceroid Lipofuscinoses (NCL) are a group of fatal inherited neurodegenerative diseases in humans distinguished by a common clinical pathology, characterized by the accumulation of storage body material in cells and gross brain atrophy. In this study, metabolic changes in three NCL mouse models were examined looking for pathways correlated with neurodegeneration. Two mouse models; motor neuron degeneration (mnd) mouse and a variant model of late infantile NCL, termed the neuronal ceroid lipofuscinosis (nclf) mouse were investigated experimentally. Both models exhibit a characteristic accumulation of autofluorescent lipopigment in neuronal and non neuronal cells. The NMR profiles derived from extracts of the cortex and cerebellum from mnd and nclf mice were distinguished according to disease/wildtype status. In particular, a perturbation in glutamine and glutamate metabolism, and a decrease in γ-amino butyric acid (GABA) in the cerebellum and cortices of mnd (adolescent mice) and nclf mice relative to wildtype at all ages were detected. Our results were compared to the Cln3 mouse model of NCL. The metabolism of mnd mice resembled older (6 month) Cln3 mice, where the disease is relatively advanced, while the metabolism of nclf mice was more akin to younger (1-2 months) Cln3 mice, where the disease is in its early stages of progression. Overall, our results allowed the identification of metabolic traits common to all NCL subtypes for the three animal models.

Neurosemantics, neurons and system theory.

Science.gov (United States)

Breidbach, Olaf

2007-08-01

Following the concept of internal representations, signal processing in a neuronal system has to be evaluated exclusively based on internal system characteristics. Thus, this approach omits the external observer as a control function for sensory integration. Instead, the configuration of the system and its computational performance are the effects of endogenous factors. Such self-referential operation is due to a strictly local computation in a network and, thereby, computations follow a set of rules that constitute the emergent behaviour of the system. These rules can be shown to correspond to a "logic" that is intrinsic to the system, an idea which provides the basis for neurosemantics.

The transfer function of neuron spike.

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Palmieri, Igor; Monteiro, Luiz H A; Miranda, Maria D

2015-08-01

The mathematical modeling of neuronal signals is a relevant problem in neuroscience. The complexity of the neuron behavior, however, makes this problem a particularly difficult task. Here, we propose a discrete-time linear time-invariant (LTI) model with a rational function in order to represent the neuronal spike detected by an electrode located in the surroundings of the nerve cell. The model is presented as a cascade association of two subsystems: one that generates an action potential from an input stimulus, and one that represents the medium between the cell and the electrode. The suggested approach employs system identification and signal processing concepts, and is dissociated from any considerations about the biophysical processes of the neuronal cell, providing a low-complexity alternative to model the neuronal spike. The model is validated by using in vivo experimental readings of intracellular and extracellular signals. A computational simulation of the model is presented in order to assess its proximity to the neuronal signal and to observe the variability of the estimated parameters. The implications of the results are discussed in the context of spike sorting. Copyright © 2015 Elsevier Ltd. All rights reserved.

Influence of Selective Edge Removal and Refractory Period in a Self-Organized Critical Neuron Model

International Nuclear Information System (INIS)

Lin Min; Gang, Zhao; Chen Tianlun

2009-01-01

A simple model for a set of integrate-and-fire neurons based on the weighted network is introduced. By considering the neurobiological phenomenon in brain development and the difference of the synaptic strength, we construct weighted networks develop with link additions and followed by selective edge removal. The network exhibits the small-world and scale-free properties with high network efficiency. The model displays an avalanche activity on a power-law distribution. We investigate the effect of selective edge removal and the neuron refractory period on the self-organized criticality of the system. (condensed matter: structural, mechanical, and thermal properties)

Biophysically realistic minimal model of dopamine neuron

Science.gov (United States)

Oprisan, Sorinel

2008-03-01

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

Arnold tongues and the Devil's Staircase in a discrete-time Hindmarsh–Rose neuron model

International Nuclear Information System (INIS)

Felicio, Carolini C.; Rech, Paulo C.

2015-01-01

We investigate a three-dimensional discrete-time dynamical system, described by a three-dimensional map derived from a continuous-time Hindmarsh–Rose neuron model by the forward Euler method. For a fixed integration step size, we report a two-dimensional parameter-space for this system, where periodic structures, the so-called Arnold tongues, can be seen with periods organized in a Farey tree sequence. We also report possible modifications in this parameter-space, as a function of the integration step size. - Highlights: • We investigate the parameter-space of a particular 3D map. • Periodic structures, namely Arnold tongues, can be seen there. • They are organized in a Farey tree sequence. • The map was derived from a continuous-time Hindmarsh–Rose neuron model. • The forward Euler method was used for such purpose.

Human Brain Networks: Spiking Neuron Models, Multistability, Synchronization, Thermodynamics, Maximum Entropy Production, and Anesthetic Cascade Mechanisms

Directory of Open Access Journals (Sweden)

Wassim M. Haddad

2014-07-01

Full Text Available Advances in neuroscience have been closely linked to mathematical modeling beginning with the integrate-and-fire model of Lapicque and proceeding through the modeling of the action potential by Hodgkin and Huxley to the current era. The fundamental building block of the central nervous system, the neuron, may be thought of as a dynamic element that is “excitable”, and can generate a pulse or spike whenever the electrochemical potential across the cell membrane of the neuron exceeds a threshold. A key application of nonlinear dynamical systems theory to the neurosciences is to study phenomena of the central nervous system that exhibit nearly discontinuous transitions between macroscopic states. A very challenging and clinically important problem exhibiting this phenomenon is the induction of general anesthesia. In any specific patient, the transition from consciousness to unconsciousness as the concentration of anesthetic drugs increases is very sharp, resembling a thermodynamic phase transition. This paper focuses on multistability theory for continuous and discontinuous dynamical systems having a set of multiple isolated equilibria and/or a continuum of equilibria. Multistability is the property whereby the solutions of a dynamical system can alternate between two or more mutually exclusive Lyapunov stable and convergent equilibrium states under asymptotically slowly changing inputs or system parameters. In this paper, we extend the theory of multistability to continuous, discontinuous, and stochastic nonlinear dynamical systems. In particular, Lyapunov-based tests for multistability and synchronization of dynamical systems with continuously differentiable and absolutely continuous flows are established. The results are then applied to excitatory and inhibitory biological neuronal networks to explain the underlying mechanism of action for anesthesia and consciousness from a multistable dynamical system perspective, thereby providing a

Sign language processing and the mirror neuron system.

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Corina, David P; Knapp, Heather

2006-05-01

In this paper we review evidence for frontal and parietal lobe involvement in sign language comprehension and production, and evaluate the extent to which these data can be interpreted within the context of a mirror neuron system for human action observation and execution. We present data from three literatures--aphasia, cortical stimulation, and functional neuroimaging. Generally, we find support for the idea that sign language comprehension and production can be viewed in the context of a broadly-construed frontal-parietal human action observation/execution system. However, sign language data cannot be fully accounted for under a strict interpretation of the mirror neuron system. Additionally, we raise a number of issues concerning the lack of specificity in current accounts of the human action observation/execution system.

Successive neuron loss in the thalamus and cortex in a mouse model of infantile neuronal ceroid lipofuscinosis.

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Kielar, Catherine; Maddox, Lucy; Bible, Ellen; Pontikis, Charlie C; Macauley, Shannon L; Griffey, Megan A; Wong, Michael; Sands, Mark S; Cooper, Jonathan D

2007-01-01

Infantile neuronal ceroid lipofuscinosis (INCL) is caused by deficiency of the lysosomal enzyme, palmitoyl protein thioesterase 1 (PPT1). We have investigated the onset and progression of pathological changes in Ppt1 deficient mice (Ppt1-/-) and the development of their seizure phenotype. Surprisingly, cortical atrophy and neuron loss occurred only late in disease progression but were preceded by localized astrocytosis within individual thalamic nuclei and the progressive loss of thalamic neurons that relay different sensory modalities to the cortex. This thalamic neuron loss occurred first within the visual system and only subsequently in auditory and somatosensory relay nuclei or the inhibitory reticular thalamic nucleus. The loss of granule neurons and GABAergic interneurons followed in each corresponding cortical region, before the onset of seizure activity. These findings provide novel evidence for successive neuron loss within the thalamus and cortex in Ppt1-/- mice, revealing the thalamus as an important early focus of INCL pathogenesis.

Comparison of Langevin and Markov channel noise models for neuronal signal generation.

Science.gov (United States)

Sengupta, B; Laughlin, S B; Niven, J E

2010-01-01

The stochastic opening and closing of voltage-gated ion channels produce noise in neurons. The effect of this noise on the neuronal performance has been modeled using either an approximate or Langevin model based on stochastic differential equations or an exact model based on a Markov process model of channel gating. Yet whether the Langevin model accurately reproduces the channel noise produced by the Markov model remains unclear. Here we present a comparison between Langevin and Markov models of channel noise in neurons using single compartment Hodgkin-Huxley models containing either Na+ and K+, or only K+ voltage-gated ion channels. The performance of the Langevin and Markov models was quantified over a range of stimulus statistics, membrane areas, and channel numbers. We find that in comparison to the Markov model, the Langevin model underestimates the noise contributed by voltage-gated ion channels, overestimating information rates for both spiking and nonspiking membranes. Even with increasing numbers of channels, the difference between the two models persists. This suggests that the Langevin model may not be suitable for accurately simulating channel noise in neurons, even in simulations with large numbers of ion channels.

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

Science.gov (United States)

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

2014-01-01

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

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

Directory of Open Access Journals (Sweden)

Péter eFriedrich

2014-07-01

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

GABA-ergic neurons in the leach central nervous system

International Nuclear Information System (INIS)

Cline, H.T.

1985-01-01

GABA is a candidate for an inhibitory neurotransmitter in the leech central nervous system because of the well-documented inhibitory action of GABA in other invertebrates. To demonstrate that GABA meets the criteria used to identify a substance as a neurotransmitter, the author examined GABA metabolism and synaptic interactions of inhibitory motor neurons in two leech species, Hirudo medicinalis and Haementeria ghilianii. Segmental ganglia of the leech ventral nerve cord and identified inhibitors have the capacity to synthesize GABA when incubated in the presence of the precursor glutamate. Application of GABA to cell bodies of excitatory motor neurons or muscle fibers innervated by the inhibitors hyperpolarizes the membrane potential of the target cell and activates a chloride ion conductance channel, similar to the inhibitory membrane response following intracellular stimulation of the inhibitor. Bicuculline methiodide (5 x 10 -5 M), GABA receptor antagonist, blocks reversibly the response to applied GABA and the inhibitory synaptic inputs onto the postsynaptic neurons or muscle fibers without interfering with their excitatory inputs. Furthermore, the inhibitors are included among approximately 25 neurons per segmental ganglion that take up GABA by a high affinity uptake system, as revealed by 3 H-GABA-autoradiography. The development of the capacities to synthesize and to take up GABA were examined in leech embryos. The embryos are able to synthesize GABA at early stages of the development of the nervous system, before any neurons have extended neutrites

Memristors Empower Spiking Neurons With Stochasticity

KAUST Repository

Al-Shedivat, Maruan

2015-06-01

Recent theoretical studies have shown that probabilistic spiking can be interpreted as learning and inference in cortical microcircuits. This interpretation creates new opportunities for building neuromorphic systems driven by probabilistic learning algorithms. However, such systems must have two crucial features: 1) the neurons should follow a specific behavioral model, and 2) stochastic spiking should be implemented efficiently for it to be scalable. This paper proposes a memristor-based stochastically spiking neuron that fulfills these requirements. First, the analytical model of the memristor is enhanced so it can capture the behavioral stochasticity consistent with experimentally observed phenomena. The switching behavior of the memristor model is demonstrated to be akin to the firing of the stochastic spike response neuron model, the primary building block for probabilistic algorithms in spiking neural networks. Furthermore, the paper proposes a neural soma circuit that utilizes the intrinsic nondeterminism of memristive switching for efficient spike generation. The simulations and analysis of the behavior of a single stochastic neuron and a winner-take-all network built of such neurons and trained on handwritten digits confirm that the circuit can be used for building probabilistic sampling and pattern adaptation machinery in spiking networks. The findings constitute an important step towards scalable and efficient probabilistic neuromorphic platforms. © 2011 IEEE.

Multi-timescale Modeling of Activity-Dependent Metabolic Coupling in the Neuron-Glia-Vasculature Ensemble

KAUST Repository

Jolivet, Renaud

2015-02-26

Glucose is the main energy substrate in the adult brain under normal conditions. Accumulating evidence, however, indicates that lactate produced in astrocytes (a type of glial cell) can also fuel neuronal activity. The quantitative aspects of this so-called astrocyte-neuron lactate shuttle (ANLS) are still debated. To address this question, we developed a detailed biophysical model of the brain’s metabolic interactions. Our model integrates three modeling approaches, the Buxton-Wang model of vascular dynamics, the Hodgkin-Huxley formulation of neuronal membrane excitability and a biophysical model of metabolic pathways. This approach provides a template for large-scale simulations of the neuron-glia-vasculature (NGV) ensemble, and for the first time integrates the respective timescales at which energy metabolism and neuronal excitability occur. The model is constrained by relative neuronal and astrocytic oxygen and glucose utilization, by the concentration of metabolites at rest and by the temporal dynamics of NADH upon activation. These constraints produced four observations. First, a transfer of lactate from astrocytes to neurons emerged in response to activity. Second, constrained by activity-dependent NADH transients, neuronal oxidative metabolism increased first upon activation with a subsequent delayed astrocytic glycolysis increase. Third, the model correctly predicted the dynamics of extracellular lactate and oxygen as observed in vivo in rats. Fourth, the model correctly predicted the temporal dynamics of tissue lactate, of tissue glucose and oxygen consumption, and of the BOLD signal as reported in human studies. These findings not only support the ANLS hypothesis but also provide a quantitative mathematical description of the metabolic activation in neurons and glial cells, as well as of the macroscopic measurements obtained during brain imaging.

Multi-timescale Modeling of Activity-Dependent Metabolic Coupling in the Neuron-Glia-Vasculature Ensemble

Science.gov (United States)

Jolivet, Renaud; Coggan, Jay S.; Allaman, Igor; Magistretti, Pierre J.

2015-01-01

Glucose is the main energy substrate in the adult brain under normal conditions. Accumulating evidence, however, indicates that lactate produced in astrocytes (a type of glial cell) can also fuel neuronal activity. The quantitative aspects of this so-called astrocyte-neuron lactate shuttle (ANLS) are still debated. To address this question, we developed a detailed biophysical model of the brain’s metabolic interactions. Our model integrates three modeling approaches, the Buxton-Wang model of vascular dynamics, the Hodgkin-Huxley formulation of neuronal membrane excitability and a biophysical model of metabolic pathways. This approach provides a template for large-scale simulations of the neuron-glia-vasculature (NGV) ensemble, and for the first time integrates the respective timescales at which energy metabolism and neuronal excitability occur. The model is constrained by relative neuronal and astrocytic oxygen and glucose utilization, by the concentration of metabolites at rest and by the temporal dynamics of NADH upon activation. These constraints produced four observations. First, a transfer of lactate from astrocytes to neurons emerged in response to activity. Second, constrained by activity-dependent NADH transients, neuronal oxidative metabolism increased first upon activation with a subsequent delayed astrocytic glycolysis increase. Third, the model correctly predicted the dynamics of extracellular lactate and oxygen as observed in vivo in rats. Fourth, the model correctly predicted the temporal dynamics of tissue lactate, of tissue glucose and oxygen consumption, and of the BOLD signal as reported in human studies. These findings not only support the ANLS hypothesis but also provide a quantitative mathematical description of the metabolic activation in neurons and glial cells, as well as of the macroscopic measurements obtained during brain imaging. PMID:25719367

Multi-timescale modeling of activity-dependent metabolic coupling in the neuron-glia-vasculature ensemble.

Directory of Open Access Journals (Sweden)

Renaud Jolivet

2015-02-01

Full Text Available Glucose is the main energy substrate in the adult brain under normal conditions. Accumulating evidence, however, indicates that lactate produced in astrocytes (a type of glial cell can also fuel neuronal activity. The quantitative aspects of this so-called astrocyte-neuron lactate shuttle (ANLS are still debated. To address this question, we developed a detailed biophysical model of the brain's metabolic interactions. Our model integrates three modeling approaches, the Buxton-Wang model of vascular dynamics, the Hodgkin-Huxley formulation of neuronal membrane excitability and a biophysical model of metabolic pathways. This approach provides a template for large-scale simulations of the neuron-glia-vasculature (NGV ensemble, and for the first time integrates the respective timescales at which energy metabolism and neuronal excitability occur. The model is constrained by relative neuronal and astrocytic oxygen and glucose utilization, by the concentration of metabolites at rest and by the temporal dynamics of NADH upon activation. These constraints produced four observations. First, a transfer of lactate from astrocytes to neurons emerged in response to activity. Second, constrained by activity-dependent NADH transients, neuronal oxidative metabolism increased first upon activation with a subsequent delayed astrocytic glycolysis increase. Third, the model correctly predicted the dynamics of extracellular lactate and oxygen as observed in vivo in rats. Fourth, the model correctly predicted the temporal dynamics of tissue lactate, of tissue glucose and oxygen consumption, and of the BOLD signal as reported in human studies. These findings not only support the ANLS hypothesis but also provide a quantitative mathematical description of the metabolic activation in neurons and glial cells, as well as of the macroscopic measurements obtained during brain imaging.

A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models

Directory of Open Access Journals (Sweden)

Alexander eHanuschkin

2013-06-01

Full Text Available Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop a correlation-based theory of interactions between a sensory and a motor area. We show that a simple eligibility-weighted Hebbian learning rule, operating within a sensorimotor loop during motor explorations and stabilized by heterosynaptic competition, naturally gives rise to mirror neurons as well as control theoretic inverse models encoded in the synaptic weights from sensory to motor neurons. Crucially, we find that the correlational structure or stereotypy of the neural code underlying motor explorations determines the nature of the learned inverse model: Random motor codes lead to causal inverses that map sensory activity patterns to their motor causes; such inverses are maximally useful, they allow for imitating arbitrary sensory target sequences. By contrast, stereotyped motor codes lead to less useful predictive inverses that map sensory activity to future motor actions.Our theory generalizes previous work on inverse models by showing that such models can be learned in a simple Hebbian framework without the need for error signals or backpropagation, and it makes new conceptual connections between the causal nature of inverse models, the statistical structure of motor variability, and the time-lag between sensory and motor responses of mirror neurons. Applied to bird song learning, our theory can account for puzzling aspects of the song system, including necessity of sensorimotor gating and selectivity of auditory responses to bird’s own song

A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models.

Science.gov (United States)

Hanuschkin, A; Ganguli, S; Hahnloser, R H R

2013-01-01

Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map-desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop a correlation-based theory of interactions between a sensory and a motor area. We show that a simple eligibility-weighted Hebbian learning rule, operating within a sensorimotor loop during motor explorations and stabilized by heterosynaptic competition, naturally gives rise to mirror neurons as well as control theoretic inverse models encoded in the synaptic weights from sensory to motor neurons. Crucially, we find that the correlational structure or stereotypy of the neural code underlying motor explorations determines the nature of the learned inverse model: random motor codes lead to causal inverses that map sensory activity patterns to their motor causes; such inverses are maximally useful, by allowing the imitation of arbitrary sensory target sequences. By contrast, stereotyped motor codes lead to less useful predictive inverses that map sensory activity to future motor actions. Our theory generalizes previous work on inverse models by showing that such models can be learned in a simple Hebbian framework without the need for error signals or backpropagation, and it makes new conceptual connections between the causal nature of inverse models, the statistical structure of motor variability, and the time-lag between sensory and motor responses of mirror neurons. Applied to bird song learning, our theory can account for puzzling aspects of the song system, including necessity of sensorimotor gating and selectivity of auditory responses to bird's own song (BOS) stimuli.

Effects of inhibitory neurons on the quorum percolation model and dynamical extension with the Brette-Gerstner model

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Fardet, Tanguy; Bottani, Samuel; Métens, Stéphane; Monceau, Pascal

2018-06-01

The Quorum Percolation model (QP) has been designed in the context of neurobiology to describe the initiation of activity bursts occurring in neuronal cultures from the point of view of statistical physics rather than from a dynamical synchronization approach. This paper aims at investigating an extension of the original QP model by taking into account the presence of inhibitory neurons in the cultures (IQP model). The first part of this paper is focused on an equivalence between the presence of inhibitory neurons and a reduction of the network connectivity. By relying on a simple topological argument, we show that the mean activation behavior of networks containing a fraction η of inhibitory neurons can be mapped onto purely excitatory networks with an appropriately modified wiring, provided that η remains in the range usually observed in neuronal cultures, namely η ⪅ 20%. As a striking result, we show that such a mapping enables to predict the evolution of the critical point of the IQP model with the fraction of inhibitory neurons. In a second part, we bridge the gap between the description of bursts in the framework of percolation and the temporal description of neural networks activity by showing how dynamical simulations of bursts with an adaptive exponential integrate-and-fire model lead to a mean description of bursts activation which is captured by Quorum Percolation.

[The mirror neuron system in motor and sensory rehabilitation].

Science.gov (United States)

Oouchida, Yutaka; Izumi, Shinichi

2014-06-01

The discovery of the mirror neuron system has dramatically changed the study of motor control in neuroscience. The mirror neuron system provides a conceptual framework covering the aspects of motor as well as sensory functions in motor control. Previous studies of motor control can be classified as studies of motor or sensory functions, and these two classes of studies appear to have advanced independently. In rehabilitation requiring motor learning, such as relearning movement after limb paresis, however, sensory information of feedback for motor output as well as motor command are essential. During rehabilitation from chronic pain, motor exercise is one of the most effective treatments for pain caused by dysfunction in the sensory system. In rehabilitation where total intervention unifying the motor and sensory aspects of motor control is important, learning through imitation, which is associated with the mirror neuron system can be effective and suitable. In this paper, we introduce the clinical applications of imitated movement in rehabilitation from motor impairment after brain damage and phantom limb pain after limb amputation.

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

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Francesca eCamera

2015-04-01

Full Text Available Several experimental results published in the literature showed that weak pulsed magnetic fields affected the response of the central nervous system. However, the specific biological mechanisms that regulate the observed behaviors are still unclear and further scientific investigation is required. In this work we performed simulations on a neuronal network model exposed to a specific pulsed magnetic field signal that seems to be very effective in modulating the brain activity: the Complex Neuroelectromagnetic Pulse (CNP. Results show that CNP can silence the neurons of a feed-forward network for signal intensities that depend on the strength of the bias current, the endogenous noise level and the specific waveforms of the pulses.

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

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

2009-12-01

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

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

International Nuclear Information System (INIS)

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

2009-01-01

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

Implementation of an integrated op-amp based chaotic neuron model and observation of its chaotic dynamics

International Nuclear Information System (INIS)

Jung, Jinwoo; Lee, Jewon; Song, Hanjung

2011-01-01

This paper presents a fully integrated circuit implementation of an operational amplifier (op-amp) based chaotic neuron model with a bipolar output function, experimental measurements, and analyses of its chaotic behavior. The proposed chaotic neuron model integrated circuit consists of several op-amps, sample and hold circuits, a nonlinear function block for chaotic signal generation, a clock generator, a nonlinear output function, etc. Based on the HSPICE (circuit program) simulation results, approximated empirical equations for analyses were formulated. Then, the chaotic dynamical responses such as bifurcation diagrams, time series, and Lyapunov exponent were calculated using these empirical equations. In addition, we performed simulations about two chaotic neuron systems with four synapses to confirm neural network connections and got normal behavior of the chaotic neuron such as internal state bifurcation diagram according to the synaptic weight variation. The proposed circuit was fabricated using a 0.8-μm single poly complementary metal-oxide semiconductor technology. Measurements of the fabricated single chaotic neuron with ±2.5 V power supplies and a 10 kHz sampling clock frequency were carried out and compared with the simulated results.

Morphological and Physiological Interactions Between GnRH3 and Hypocretin/Orexin Neuronal Systems in Zebrafish (Danio rerio).

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Zhao, Yali; Singh, Chanpreet; Prober, David A; Wayne, Nancy L

2016-10-01

GnRH neurons integrate internal and external cues to control sexual maturation and fertility. Homeostasis of energy balance and food intake correlates strongly with the status of reproduction. Neuropeptides secreted by the hypothalamus involved in modulating energy balance and feeding may play additional roles in the regulation of reproduction. Hypocretin (Hcrt) (also known as orexin) is one such peptide, primarily controlling sleep/wakefulness, food intake, and reward processing. There is a growing body of evidence indicating that Hcrt/orexin (Hcrt) modulates reproduction through interacting with the hypothalamo-pituitary-gonadal axis in mammals. To explore potential morphological and functional interactions between the GnRH and Hcrt neuronal systems, we employed a variety of experimental approaches including confocal imaging, immunohistochemistry, and electrophysiology in transgenic zebrafish, in which fluorescent proteins are genetically expressed in GnRH3 and Hcrt neurons. Our imaging data revealed close apposition and direct connection between GnRH3 and Hcrt neuronal systems in the hypothalamus during larval development through adulthood. Furthermore, the Hcrt receptor (HcrtR) is expressed in GnRH3 neurons. Electrophysiological data revealed a reversible inhibitory effect of Hcrt on GnRH3 neuron electrical activity, which was blocked by the HcrtR antagonist almorexant. In addition, Hcrt had no effect on the electrical activity of GnRH3 neurons in the HcrtR null mutant zebrafish (HcrtR -/- ). Our findings demonstrate a close anatomical and functional relationship between Hcrt and GnRH neuronal systems in zebrafish. It is the first demonstration of a link between neuronal circuits controlling sleeping/arousal/feeding and reproduction in zebrafish, an important animal model for investigating the molecular genetics of development.

Model-Based Design of Stimulus Trains for Selective Microstimulation of Targeted Neuronal Populations

National Research Council Canada - National Science Library

McIntyre, Cameron

2001-01-01

... that accurately reproduced the dynamic firing properties of mammalian neurons, The neuron models were coupled to a three-dimensional finite element model of the spinal cord that solved for the potentials...

The mirror neuron system : New frontiers

NARCIS (Netherlands)

Keysers, Christian; Fadiga, Luciano

2008-01-01

Since the discovery of mirror neurons, much effort has been invested into Studying their location and properties in the human brain. Here we review these original findings and introduce the Main topics of this special issue of Social Neuroscience. What does the mirror system code? How is the mirror

Tissue-specific models of spinal muscular atrophy confirm a critical role of SMN in motor neurons from embryonic to adult stages.

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Laird, Angela S; Mackovski, Nikolce; Rinkwitz, Silke; Becker, Thomas S; Giacomotto, Jean

2016-05-01

Spinal muscular atrophy (SMA) is an autosomal recessive disease linked to survival motor neuron (SMN) protein deficiency. While SMN protein is expressed ubiquitously, its deficiency triggers tissue-specific hallmarks, including motor neuron death and muscle atrophy, leading to impaired motor functions and premature death. Here, using stable miR-mediated knockdown technology in zebrafish, we developed the first vertebrate system allowing transgenic spatio-temporal control of the smn1 gene. Using this new model it is now possible to investigate normal and pathogenic SMN function(s) in specific cell types, independently or in synergy with other cell populations. We took advantage of this new system to first test the effect of motor neuron or muscle-specific smn1 silencing. Anti-smn1 miRNA expression in motor neurons, but not in muscles, reproduced SMA hallmarks, including abnormal motor neuron development, poor motor function and premature death. Interestingly, smn1 knockdown in motor neurons also induced severe late-onset phenotypes including scoliosis-like body deformities, weight loss, muscle atrophy and, seen for the first time in zebrafish, reduction in the number of motor neurons, indicating motor neuron degeneration. Taken together, we have developed a new transgenic system allowing spatio-temporal control of smn1 expression in zebrafish, and using this model, we have demonstrated that smn1 silencing in motor neurons alone is sufficient to reproduce SMA hallmarks in zebrafish. It is noteworthy that this research is going beyond SMA as this versatile gene-silencing transgenic system can be used to knockdown any genes of interest, filling the gap in the zebrafish genetic toolbox and opening new avenues to study gene functions in this organism. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

The digital bee brain: integrating and managing neurons in a common 3D reference system

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Jürgen Rybak

2010-07-01

Full Text Available The honeybee standard brain (HSB serves as an interactive tool for relating morphologies of bee brain neurons and provides a reference system for functional and bibliographical properties (http://www.neurobiologie.fu-berlin.de/beebrain/. The ultimate goal is to document not only the morphological network properties of neurons collected from separate brains, but also to establish a graphical user interface for a neuron-related data base. Here, we review the current methods and protocols used to incorporate neuronal reconstructions into the HSB. Our registration protocol consists of two separate steps applied to imaging data from two-channel confocal microscopy scans: (1 The reconstruction of the neuron, facilitated by an automatic extraction of the neuron’s skeleton based on threshold segmentation, and (2 the semi-automatic 3D segmentation of the neuropils and their registration with the HSB. The integration of neurons in the HSB is performed by applying the transformation computed in step (2 to the reconstructed neurons of step (1. The most critical issue of this protocol in terms of user interaction time – the segmentation process – is drastically improved by the use of a model-based segmentation process. Furthermore, the underlying statistical shape models (SSM allow the visualization and analysis of characteristic variations in large sets of bee brain data. The anatomy of neural networks composed of multiple neurons that are registered into the HSB are visualized by depicting the 3D reconstructions together with semantic information with the objective to integrate data from multiple sources (electrophysiology, imaging, immunocytochemistry, molecular biology. Ultimately, this will allow the user to specify cell types and retrieve their morphologies along with physiological characterizations.

Population density models of integrate-and-fire neurons with jumps: well-posedness.

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Dumont, Grégory; Henry, Jacques

2013-09-01

In this paper we study the well-posedness of different models of population of leaky integrate-and-fire neurons with a population density approach. The synaptic interaction between neurons is modeled by a potential jump at the reception of a spike. We study populations that are self excitatory or self inhibitory. We distinguish the cases where this interaction is instantaneous from the one where there is a repartition of conduction delays. In the case of a bounded density of delays both excitatory and inhibitory population models are shown to be well-posed. But without conduction delay the solution of the model of self excitatory neurons may blow up. We analyze the different behaviours of the model with jumps compared to its diffusion approximation.

Mirror Neurons Modeled Through Spike-Timing-Dependent Plasticity are Affected by Channelopathies Associated with Autism Spectrum Disorder.

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Antunes, Gabriela; Faria da Silva, Samuel F; Simoes de Souza, Fabio M

2018-06-01

Mirror neurons fire action potentials both when the agent performs a certain behavior and watches someone performing a similar action. Here, we present an original mirror neuron model based on the spike-timing-dependent plasticity (STDP) between two morpho-electrical models of neocortical pyramidal neurons. Both neurons fired spontaneously with basal firing rate that follows a Poisson distribution, and the STDP between them was modeled by the triplet algorithm. Our simulation results demonstrated that STDP is sufficient for the rise of mirror neuron function between the pairs of neocortical neurons. This is a proof of concept that pairs of neocortical neurons associating sensory inputs to motor outputs could operate like mirror neurons. In addition, we used the mirror neuron model to investigate whether channelopathies associated with autism spectrum disorder could impair the modeled mirror function. Our simulation results showed that impaired hyperpolarization-activated cationic currents (Ih) affected the mirror function between the pairs of neocortical neurons coupled by STDP.

A Dynamic Bayesian Model for Characterizing Cross-Neuronal Interactions During Decision-Making.

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Zhou, Bo; Moorman, David E; Behseta, Sam; Ombao, Hernando; Shahbaba, Babak

2016-01-01

The goal of this paper is to develop a novel statistical model for studying cross-neuronal spike train interactions during decision making. For an individual to successfully complete the task of decision-making, a number of temporally-organized events must occur: stimuli must be detected, potential outcomes must be evaluated, behaviors must be executed or inhibited, and outcomes (such as reward or no-reward) must be experienced. Due to the complexity of this process, it is likely the case that decision-making is encoded by the temporally-precise interactions between large populations of neurons. Most existing statistical models, however, are inadequate for analyzing such a phenomenon because they provide only an aggregated measure of interactions over time. To address this considerable limitation, we propose a dynamic Bayesian model which captures the time-varying nature of neuronal activity (such as the time-varying strength of the interactions between neurons). The proposed method yielded results that reveal new insight into the dynamic nature of population coding in the prefrontal cortex during decision making. In our analysis, we note that while some neurons in the prefrontal cortex do not synchronize their firing activity until the presence of a reward, a different set of neurons synchronize their activity shortly after stimulus onset. These differentially synchronizing sub-populations of neurons suggests a continuum of population representation of the reward-seeking task. Secondly, our analyses also suggest that the degree of synchronization differs between the rewarded and non-rewarded conditions. Moreover, the proposed model is scalable to handle data on many simultaneously-recorded neurons and is applicable to analyzing other types of multivariate time series data with latent structure. Supplementary materials (including computer codes) for our paper are available online.

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

Science.gov (United States)

Brookings, Ted; Goeritz, Marie L; Marder, Eve

2014-11-01

We describe a new technique to fit conductance-based neuron models to intracellular voltage traces from isolated biological neurons. The biological neurons are recorded in current-clamp with pink (1/f) noise injected to perturb the activity of the neuron. The new algorithm finds a set of parameters that allows a multicompartmental model neuron to match the recorded voltage trace. Attempting to match a recorded voltage trace directly has a well-known problem: mismatch in the timing of action potentials between biological and model neuron is inevitable and results in poor phenomenological match between the model and data. Our approach avoids this by applying a weak control adjustment to the model to promote alignment during the fitting procedure. This approach is closely related to the control theoretic concept of a Luenberger observer. We tested this approach on synthetic data and on data recorded from an anterior gastric receptor neuron from the stomatogastric ganglion of the crab Cancer borealis. To test the flexibility of this approach, the synthetic data were constructed with conductance models that were different from the ones used in the fitting model. For both synthetic and biological data, the resultant models had good spike-timing accuracy. Copyright © 2014 the American Physiological Society.

A study of single multiplicative neuron model with nonlinear filters for hourly wind speed prediction

International Nuclear Information System (INIS)

Wu, Xuedong; Zhu, Zhiyu; Su, Xunliang; Fan, Shaosheng; Du, Zhaoping; Chang, Yanchao; Zeng, Qingjun

2015-01-01

Wind speed prediction is one important methods to guarantee the wind energy integrated into the whole power system smoothly. However, wind power has a non–schedulable nature due to the strong stochastic nature and dynamic uncertainty nature of wind speed. Therefore, wind speed prediction is an indispensable requirement for power system operators. Two new approaches for hourly wind speed prediction are developed in this study by integrating the single multiplicative neuron model and the iterated nonlinear filters for updating the wind speed sequence accurately. In the presented methods, a nonlinear state–space model is first formed based on the single multiplicative neuron model and then the iterated nonlinear filters are employed to perform dynamic state estimation on wind speed sequence with stochastic uncertainty. The suggested approaches are demonstrated using three cases wind speed data and are compared with autoregressive moving average, artificial neural network, kernel ridge regression based residual active learning and single multiplicative neuron model methods. Three types of prediction errors, mean absolute error improvement ratio and running time are employed for different models’ performance comparison. Comparison results from Tables 1–3 indicate that the presented strategies have much better performance for hourly wind speed prediction than other technologies. - Highlights: • Developed two novel hybrid modeling methods for hourly wind speed prediction. • Uncertainty and fluctuations of wind speed can be better explained by novel methods. • Proposed strategies have online adaptive learning ability. • Proposed approaches have shown better performance compared with existed approaches. • Comparison and analysis of two proposed novel models for three cases are provided

Altering neuronal excitability to preserve network connectivity in a computational model of Alzheimer's disease.

Directory of Open Access Journals (Sweden)

Willem de Haan

2017-09-01

Full Text Available Neuronal hyperactivity and hyperexcitability of the cerebral cortex and hippocampal region is an increasingly observed phenomenon in preclinical Alzheimer's disease (AD. In later stages, oscillatory slowing and loss of functional connectivity are ubiquitous. Recent evidence suggests that neuronal dynamics have a prominent role in AD pathophysiology, making it a potentially interesting therapeutic target. However, although neuronal activity can be manipulated by various (non-pharmacological means, intervening in a highly integrated system that depends on complex dynamics can produce counterintuitive and adverse effects. Computational dynamic network modeling may serve as a virtual test ground for developing effective interventions. To explore this approach, a previously introduced large-scale neural mass network with human brain topology was used to simulate the temporal evolution of AD-like, activity-dependent network degeneration. In addition, six defense strategies that either enhanced or diminished neuronal excitability were tested against the degeneration process, targeting excitatory and inhibitory neurons combined or separately. Outcome measures described oscillatory, connectivity and topological features of the damaged networks. Over time, the various interventions produced diverse large-scale network effects. Contrary to our hypothesis, the most successful strategy was a selective stimulation of all excitatory neurons in the network; it substantially prolonged the preservation of network integrity. The results of this study imply that functional network damage due to pathological neuronal activity can be opposed by targeted adjustment of neuronal excitability levels. The present approach may help to explore therapeutic effects aimed at preserving or restoring neuronal network integrity and contribute to better-informed intervention choices in future clinical trials in AD.

Modeling the phenotype of spinal muscular atrophy by the direct conversion of human fibroblasts to motor neurons.

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Zhang, Qi-Jie; Li, Jin-Jing; Lin, Xiang; Lu, Ying-Qian; Guo, Xin-Xin; Dong, En-Lin; Zhao, Miao; He, Jin; Wang, Ning; Chen, Wan-Jin

2017-02-14

Spinal muscular atrophy (SMA) is a lethal autosomal recessive neurological disease characterized by selective degeneration of motor neurons in the spinal cord. In recent years, the development of cellular reprogramming technology has provided an alternative and effective method for obtaining patient-specific neurons in vitro. In the present study, we applied this technology to the field of SMA to acquire patient-specific induced motor neurons that were directly converted from fibroblasts via the forced expression of 8 defined transcription factors. The infected fibroblasts began to grow in a dipolar manner, and the nuclei gradually enlarged. Typical Tuj1-positive neurons were generated at day 23. After day 35, induced neurons with multiple neurites were observed, and these neurons also expressed the hallmarks of Tuj1, HB9, ISL1 and CHAT. The conversion efficiencies were approximately 5.8% and 5.5% in the SMA and control groups, respectively. Additionally, the SMA-induced neurons exhibited a significantly reduced neurite outgrowth rate compared with the control neurons. After day 60, the SMA-induced neurons also exhibited a liability of neuronal degeneration and remarkable fracturing of the neurites was observed. By directly reprogramming fibroblasts, we established a feeder-free conversion system to acquire SMA patient-specific induced motor neurons that partially modeled the phenotype of SMA in vitro.

Systemic Glucoregulation by Glucose-Sensing Neurons in the Ventromedial Hypothalamic Nucleus (VMH).

Science.gov (United States)

Shimazu, Takashi; Minokoshi, Yasuhiko

2017-05-01

The ventromedial hypothalamic nucleus (VMH) regulates glucose production in the liver as well as glucose uptake and utilization in peripheral tissues, including skeletal muscle and brown adipose tissue, via efferent sympathetic innervation and neuroendocrine mechanisms. The action of leptin on VMH neurons also increases glucose uptake in specific peripheral tissues through the sympathetic nervous system, with improved insulin sensitivity. On the other hand, subsets of VMH neurons, such as those that express steroidogenic factor 1 (SF1), sense changes in the ambient glucose concentration and are characterized as glucose-excited (GE) and glucose-inhibited (GI) neurons whose action potential frequency increases and decreases, respectively, as glucose levels rise. However, how these glucose-sensing (GE and GI) neurons in the VMH contribute to systemic glucoregulation remains poorly understood. In this review, we provide historical background and discuss recent advances related to glucoregulation by VMH neurons. In particular, the article describes the role of GE neurons in the control of peripheral glucose utilization and insulin sensitivity, which depend on mitochondrial uncoupling protein 2 of the neurons, as well as that of GI neurons in the control of hepatic glucose production through hypoglycemia-induced counterregulatory mechanisms.

Self-repair in a Bidirectionally Coupled Astrocyte-Neuron (AN System based on Retrograde Signaling

Directory of Open Access Journals (Sweden)

John eWade

2012-09-01

Full Text Available In this paper we demonstrate that retrograde signaling via astrocytes may underpin self-repair in the brain. Faults manifest themselves in silent or near silent neurons caused by low transmission probability synapses; the enhancement of the transmission probability of a healthy neighbouring synapse by retrograde signaling can enhance the transmission probability of the faulty synapse (repair. Our model of self-repair is based on recent research showing that retrograde signaling via astrocytes can increase the probability of neurotransmitter release at damaged or low transmission probability synapses. The model demonstrates that astrocytes are capable of bidirectional communication with neurons which leads to modulation of synaptic activity, and that indirect signaling through retrograde messengers such as endocannabinoids leads to modulation of synaptic transmission probability. Although our model operates at the level of cells, it provides a new research direction on brain-like self-repair which can be extended to networks of astrocytes and neurons. It also provides a biologically inspired basis for developing highly adaptive, distributed computing systems that can, at fine levels of granularity, fault detect, diagnose and self-repair autonomously, without the traditional constraint of a central fault detect/repair unit.

Composite mathematical modeling of calcium signaling behind neuronal cell death in Alzheimer's disease.

Science.gov (United States)

Ranjan, Bobby; Chong, Ket Hing; Zheng, Jie

2018-04-11

Alzheimer's disease (AD) is a progressive neurological disorder, recognized as the most common cause of dementia affecting people aged 65 and above. AD is characterized by an increase in amyloid metabolism, and by the misfolding and deposition of β-amyloid oligomers in and around neurons in the brain. These processes remodel the calcium signaling mechanism in neurons, leading to cell death via apoptosis. Despite accumulating knowledge about the biological processes underlying AD, mathematical models to date are restricted to depicting only a small portion of the pathology. Here, we integrated multiple mathematical models to analyze and understand the relationship among amyloid depositions, calcium signaling and mitochondrial permeability transition pore (PTP) related cell apoptosis in AD. The model was used to simulate calcium dynamics in the absence and presence of AD. In the absence of AD, i.e. without β-amyloid deposition, mitochondrial and cytosolic calcium level remains in the low resting concentration. However, our in silico simulation of the presence of AD with the β-amyloid deposition, shows an increase in the entry of calcium ions into the cell and dysregulation of Ca 2+ channel receptors on the Endoplasmic Reticulum. This composite model enabled us to make simulation that is not possible to measure experimentally. Our mathematical model depicting the mechanisms affecting calcium signaling in neurons can help understand AD at the systems level and has potential for diagnostic and therapeutic applications.

Colored noise and memory effects on formal spiking neuron models

Science.gov (United States)

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

2015-06-01

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

Codimension-two bifurcation analysis on firing activities in Chay neuron model

International Nuclear Information System (INIS)

Duan Lixia; Lu Qishao

2006-01-01

Using codimension-two bifurcation analysis in the Chay neuron model, the relationship between the electric activities and the parameters of neurons is revealed. The whole parameter space is divided into two parts, that is, the firing and silence regions of neurons. It is found that the transition sets between firing and silence regions are composed of the Hopf bifurcation curves of equilibrium states and the saddle-node bifurcation curves of limit cycles, with some codimension-two bifurcation points. The transitions from silence to firing in neurons are due to the Hopf bifurcation or the fold limit cycle bifurcation, but the codimension-two singularities lead to complexity in dynamical behaviour of neuronal firing

Codimension-two bifurcation analysis on firing activities in Chay neuron model

Energy Technology Data Exchange (ETDEWEB)

Duan Lixia [School of Science, Beijing University of Aeronautics and Astronautics, Beijing 100083 (China); Lu Qishao [School of Science, Beijing University of Aeronautics and Astronautics, Beijing 100083 (China)]. E-mail: qishaolu@hotmail.com

2006-12-15

Using codimension-two bifurcation analysis in the Chay neuron model, the relationship between the electric activities and the parameters of neurons is revealed. The whole parameter space is divided into two parts, that is, the firing and silence regions of neurons. It is found that the transition sets between firing and silence regions are composed of the Hopf bifurcation curves of equilibrium states and the saddle-node bifurcation curves of limit cycles, with some codimension-two bifurcation points. The transitions from silence to firing in neurons are due to the Hopf bifurcation or the fold limit cycle bifurcation, but the codimension-two singularities lead to complexity in dynamical behaviour of neuronal firing.

Noise and Synchronization Analysis of the Cold-Receptor Neuronal Network Model

Directory of Open Access Journals (Sweden)

Ying Du

2014-01-01

Full Text Available This paper analyzes the dynamics of the cold receptor neural network model. First, it examines noise effects on neuronal stimulus in the model. From ISI plots, it is shown that there are considerable differences between purely deterministic simulations and noisy ones. The ISI-distance is used to measure the noise effects on spike trains quantitatively. It is found that spike trains observed in neural models can be more strongly affected by noise for different temperatures in some aspects; meanwhile, spike train has greater variability with the noise intensity increasing. The synchronization of neuronal network with different connectivity patterns is also studied. It is shown that chaotic and high period patterns are more difficult to get complete synchronization than the situation in single spike and low period patterns. The neuronal network will exhibit various patterns of firing synchronization by varying some key parameters such as the coupling strength. Different types of firing synchronization are diagnosed by a correlation coefficient and the ISI-distance method. The simulations show that the synchronization status of neurons is related to the network connectivity patterns.

Spider Silk as Guiding Biomaterial for Human Model Neurons

Directory of Open Access Journals (Sweden)

Frank Roloff

2014-01-01

Full Text Available Over the last years, a number of therapeutic strategies have emerged to promote axonal regeneration. An attractive strategy is the implantation of biodegradable and nonimmunogenic artificial scaffolds into injured peripheral nerves. In previous studies, transplantation of decellularized veins filled with spider silk for bridging critical size nerve defects resulted in axonal regeneration and remyelination by invading endogenous Schwann cells. Detailed interaction of elongating neurons and the spider silk as guidance material is unknown. To visualize direct cellular interactions between spider silk and neurons in vitro, we developed an in vitro crossed silk fiber array. Here, we describe in detail for the first time that human (NT2 model neurons attach to silk scaffolds. Extending neurites can bridge gaps between single silk fibers and elongate afterwards on the neighboring fiber. Culturing human neurons on the silk arrays led to an increasing migration and adhesion of neuronal cell bodies to the spider silk fibers. Within three to four weeks, clustered somata and extending neurites formed ganglion-like cell structures. Microscopic imaging of human neurons on the crossed fiber arrays in vitro will allow for a more efficient development of methods to maximize cell adhesion and neurite growth on spider silk prior to transplantation studies.

Gender differences in the mu rhythm of the human mirror-neuron system.

Science.gov (United States)

Cheng, Yawei; Lee, Po-Lei; Yang, Chia-Yen; Lin, Ching-Po; Hung, Daisy; Decety, Jean

2008-05-07

Psychologically, females are usually thought to be superior in interpersonal sensitivity than males. The human mirror-neuron system is considered to provide the basic mechanism for social cognition. However, whether the human mirror-neuron system exhibits gender differences is not yet clear. We measured the electroencephalographic mu rhythm, as a reliable indicator of the human mirror-neuron system activity, when female (N = 20) and male (N = 20) participants watched either hand actions or a moving dot. The display of the hand actions included androgynous, male, and female characteristics. The results demonstrate that females displayed significantly stronger mu suppression than males when watching hand actions. Instead, mu suppression was similar across genders when participants observed the moving dot and between the perceived sex differences (same-sex vs. opposite-sex). In addition, the mu suppressions during the observation of hand actions positively correlated with the personal distress subscale of the interpersonal reactivity index and negatively correlated with the systemizing quotient. The present findings indirectly lend support to the extreme male brain theory put forward by Baron-Cohen (2005), and may cast some light on the mirror-neuron dysfunction in autism spectrum disorders. The mu rhythm in the human mirror-neuron system can be a potential biomarker of empathic mimicry.

Diffusion approximation of neuronal models revisited

Czech Academy of Sciences Publication Activity Database

Čupera, Jakub

2014-01-01

Roč. 11, č. 1 (2014), s. 11-25 ISSN 1547-1063. [International Workshop on Neural Coding (NC) /10./. Praha, 02.09.2012-07.09.2012] R&D Projects: GA ČR(CZ) GAP103/11/0282 Institutional support: RVO:67985823 Keywords : stochastic model * neuronal activity * first-passage time Subject RIV: JD - Computer Applications, Robotics Impact factor: 0.840, year: 2014

Cholinergic drugs as therapeutic tools in inflammatory diseases: participation of neuronal and non-neuronal cholinergic systems.

Science.gov (United States)

Sales, María Elena

2013-01-01

Acetylcholine (ACh) is synthesized by choline acetyltransferase (ChAT) from acetylcoenzime A and choline. This reaction occurs not only in pre-ganglionic fibers of the autonomic nervous system and post-ganglionic parasympathetic nervous fibers but also in non neuronal cells. This knowledge led to expand the role of ACh as a neurotransmitter and to consider it as a "cytotransmitter" and also to evaluate the existence of a non-neuronal cholinergic system comprising ACh, ChAT, acetylcholinesterase, and the nicotinic and muscarinic ACh receptors, outside the nervous system. This review analyzes the participation of cholinergic system in inflammation and discusses the role of different muscarinic and nicotinic drugs that are being used to treat skin inflammatory disorders, asthma, and chronic obstructive pulmonary disease as well as, intestinal inflammation and systemic inflammatory diseases, among others, to assess the potential application of these compounds as therapeutic tools.

Auditory information coding by modeled cochlear nucleus neurons.

Science.gov (United States)

Wang, Huan; Isik, Michael; Borst, Alexander; Hemmert, Werner

2011-06-01

In this paper we use information theory to quantify the information in the output spike trains of modeled cochlear nucleus globular bushy cells (GBCs). GBCs are part of the sound localization pathway. They are known for their precise temporal processing, and they code amplitude modulations with high fidelity. Here we investigated the information transmission for a natural sound, a recorded vowel. We conclude that the maximum information transmission rate for a single neuron was close to 1,050 bits/s, which corresponds to a value of approximately 5.8 bits per spike. For quasi-periodic signals like voiced speech, the transmitted information saturated as word duration increased. In general, approximately 80% of the available information from the spike trains was transmitted within about 20 ms. Transmitted information for speech signals concentrated around formant frequency regions. The efficiency of neural coding was above 60% up to the highest temporal resolution we investigated (20 μs). The increase in transmitted information to that precision indicates that these neurons are able to code information with extremely high fidelity, which is required for sound localization. On the other hand, only 20% of the information was captured when the temporal resolution was reduced to 4 ms. As the temporal resolution of most speech recognition systems is limited to less than 10 ms, this massive information loss might be one of the reasons which are responsible for the lack of noise robustness of these systems.

A simple white noise analysis of neuronal light responses.

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Chichilnisky, E J

2001-05-01

A white noise technique is presented for estimating the response properties of spiking visual system neurons. The technique is simple, robust, efficient and well suited to simultaneous recordings from multiple neurons. It provides a complete and easily interpretable model of light responses even for neurons that display a common form of response nonlinearity that precludes classical linear systems analysis. A theoretical justification of the technique is presented that relies only on elementary linear algebra and statistics. Implementation is described with examples. The technique and the underlying model of neural responses are validated using recordings from retinal ganglion cells, and in principle are applicable to other neurons. Advantages and disadvantages of the technique relative to classical approaches are discussed.

Impulsive control and synchronization of chaotic Hindmarsh-Rose models for neuronal activity

International Nuclear Information System (INIS)

Wu Quanjun; Zhou Jin; Xiang Lan; Liu Zengrong

2009-01-01

The issues of impulsive control and synchronization of chaotic Hindmarsh-Rose model are investigated in this paper. Based on impulsive control theory of dynamical systems, some simple yet less conservative criteria ensuring impulsive stabilization and synchronization of the Hindmarsh-Rose models are derived analytically. Furthermore, two numerical results are presented to demonstrate the effectiveness of the proposed control techniques. It is shown that the obtained results should be helpful to understand dynamical mechanism of signal encoding and transduction from information processing of real neuronal activity.

Growth of cortical neuronal network in vitro: Modeling and analysis

International Nuclear Information System (INIS)

Lai, P.-Y.; Jia, L. C.; Chan, C. K.

2006-01-01

We present a detailed analysis and theoretical growth models to account for recent experimental data on the growth of cortical neuronal networks in vitro [Phys. Rev. Lett. 93, 088101 (2004)]. The experimentally observed synchronized firing frequency of a well-connected neuronal network is shown to be proportional to the mean network connectivity. The growth of the network is consistent with the model of an early enhanced growth of connection, but followed by a retarded growth once the synchronized cluster is formed. Microscopic models with dominant excluded volume interactions are consistent with the observed exponential decay of the mean connection probability as a function of the mean network connectivity. The biological implications of the growth model are also discussed

Non-Cell Autonomous Influence of the Astrocyte System xc − on Hypoglycaemic Neuronal Cell Death

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Nicole A Jackman

2012-01-01

Full Text Available Despite longstanding evidence that hypoglycaemic neuronal injury is mediated by glutamate excitotoxicity, the cellular and molecular mechanisms involved remain incompletely defined. Here, we demonstrate that the excitotoxic neuronal death that follows GD (glucose deprivation is initiated by glutamate extruded from astrocytes via system xc −– – an amino acid transporter that imports L-cystine and exports L-glutamate. Specifically, we find that depriving mixed cortical cell cultures of glucose for up to 8 h injures neurons, but not astrocytes. Neuronal death is prevented by ionotropic glutamate receptor antagonism and is partially sensitive to tetanus toxin. Removal of amino acids during the deprivation period prevents – whereas addition of L-cystine restores – GD-induced neuronal death, implicating the cystine/glutamate antiporter, system xc−–. Indeed, drugs known to inhibit system xc −– ameliorate GD-induced neuronal death. Further, a dramatic reduction in neuronal death is observed in chimaeric cultures consisting of neurons derived from WT (wild-type mice plated on top of astrocytes derived from sut mice, which harbour a naturally occurring null mutation in the gene (Slc7a11 that encodes the substrate-specific light chain of system xc −– (xCT. Finally, enhancement of astrocytic system xc −– expression and function via IL-1β (interleukin-1β exposure potentiates hypoglycaemic neuronal death, the process of which is prevented by removal of L-cystine and/or addition of system xc −– inhibitors. Thus, under the conditions of GD, our studies demonstrate that astrocytes, via system xc −–, have a direct, non-cell autonomous effect on cortical neuron survival.

Demonstration of neuron-glia transfer of precursors for GABA biosynthesis in a co-culture system of dissociated mouse cerebral cortex.

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Leke, Renata; Bak, Lasse K; Schousboe, Arne; Waagepetersen, Helle S

2008-12-01

Co-cultures of neurons and astrocytes were prepared from dissociated embryonic mouse cerebral cortex and cultured for 7 days. To investigate if these cultures may serve as a functional model system to study neuron-glia interaction with regard to GABA biosynthesis, the cells were incubated either in media containing [U-(13)C]glutamine (0.1, 0.3 and 0.5 mM) or 1 mM acetate plus 2.5 mM glucose plus 1 mM lactate. In the latter case one of the 3 substrates was uniformly (13)C labeled. Cellular contents and (13)C labeling of glutamate, GABA, aspartate and glutamine were determined in the cells after an incubation period of 2.5 h. The GABA biosynthetic machinery exhibited the expected complexity with regard to metabolic compartmentation and involvement of TCA cycle activity as seen in other culture systems containing GABAergic neurons. Metabolism of acetate clearly demonstrated glial synthesis of glutamine and its transfer to the neuronal compartment. It is concluded that this co-culture system serves as a reliable model in which functional and pharmacological aspects of GABA biosynthesis can be investigated.

Building functional networks of spiking model neurons.

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Abbott, L F; DePasquale, Brian; Memmesheimer, Raoul-Martin

2016-03-01

Most of the networks used by computer scientists and many of those studied by modelers in neuroscience represent unit activities as continuous variables. Neurons, however, communicate primarily through discontinuous spiking. We review methods for transferring our ability to construct interesting networks that perform relevant tasks from the artificial continuous domain to more realistic spiking network models. These methods raise a number of issues that warrant further theoretical and experimental study.

Language comprehension warps the mirror neuron system

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Noah eZarr

2013-12-01

Full Text Available Is the mirror neuron system (MNS used in language understanding? According to embodied accounts of language comprehension, understanding sentences describing actions makes use of neural mechanisms of action control, including the MNS. Consequently, repeatedly comprehending sentences describing similar actions should induce adaptation of the MNS thereby warping its use in other cognitive processes such as action recognition and prediction. To test this prediction, participants read blocks of multiple sentences where each sentence in the block described transfer of objects in a direction away or toward the reader. Following each block, adaptation was measured by having participants predict the end-point of videotaped actions. The adapting sentences disrupted prediction of actions in the same direction, but a only for videos of biological motion, and b only when the effector implied by the language (e.g., the hand matched the videos. These findings are signatures of the mirror neuron system.

Stochastic resonance induced by novel random transitions of motion of FitzHugh-Nagumo neuron model

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Zhang Guangjun; Xu Jianxue

2005-01-01

In contrast to the previous studies which have dealt with stochastic resonance induced by random transitions of system motion between two coexisting limit cycle attractors in the FitzHugh-Nagumo (FHN) neuron model after Hopf bifurcation and which have dealt with the phenomenon of stochastic resonance induced by external noise when the model with periodic input has only one attractor before Hopf bifurcation, in this paper we have focused our attention on stochastic resonance (SR) induced by a novel transition behavior, the transitions of motion of the model among one attractor on the left side of bifurcation point and two attractors on the right side of bifurcation point under the perturbation of noise. The results of research show: since one bifurcation of transition from one to two limit cycle attractors and the other bifurcation of transition from two to one limit cycle attractors occur in turn besides Hopf bifurcation, the novel transitions of motion of the model occur when bifurcation parameter is perturbed by weak internal noise; the bifurcation point of the model may stochastically slightly shift to the left or right when FHN neuron model is perturbed by external Gaussian distributed white noise, and then the novel transitions of system motion also occur under the perturbation of external noise; the novel transitions could induce SR alone, and when the novel transitions of motion of the model and the traditional transitions between two coexisting limit cycle attractors after bifurcation occur in the same process the SR also may occur with complicated behaviors types; the mechanism of SR induced by external noise when FHN neuron model with periodic input has only one attractor before Hopf bifurcation is related to this kind of novel transition mentioned above

Modeling chemotherapeutic neurotoxicity with human induced pluripotent stem cell-derived neuronal cells.

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Heather E Wheeler

Full Text Available There are no effective agents to prevent or treat chemotherapy-induced peripheral neuropathy (CIPN, the most common non-hematologic toxicity of chemotherapy. Therefore, we sought to evaluate the utility of human neuron-like cells derived from induced pluripotent stem cells (iPSCs as a means to study CIPN. We used high content imaging measurements of neurite outgrowth phenotypes to compare the changes that occur to iPSC-derived neuronal cells among drugs and among individuals in response to several classes of chemotherapeutics. Upon treatment of these neuronal cells with the neurotoxic drug paclitaxel, vincristine or cisplatin, we identified significant differences in five morphological phenotypes among drugs, including total outgrowth, mean/median/maximum process length, and mean outgrowth intensity (P < 0.05. The differences in damage among drugs reflect differences in their mechanisms of action and clinical CIPN manifestations. We show the potential of the model for gene perturbation studies by demonstrating decreased expression of TUBB2A results in significantly increased sensitivity of neurons to paclitaxel (0.23 ± 0.06 decrease in total neurite outgrowth, P = 0.011. The variance in several neurite outgrowth and apoptotic phenotypes upon treatment with one of the neurotoxic drugs is significantly greater between than within neurons derived from four different individuals (P < 0.05, demonstrating the potential of iPSC-derived neurons as a genetically diverse model for CIPN. The human neuron model will allow both for mechanistic studies of specific genes and genetic variants discovered in clinical studies and for screening of new drugs to prevent or treat CIPN.

Arnold tongues and the Devil's Staircase in a discrete-time Hindmarsh–Rose neuron model

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Felicio, Carolini C., E-mail: carolini.cf@gmail.com; Rech, Paulo C., E-mail: paulo.rech@udesc.br

2015-11-06

We investigate a three-dimensional discrete-time dynamical system, described by a three-dimensional map derived from a continuous-time Hindmarsh–Rose neuron model by the forward Euler method. For a fixed integration step size, we report a two-dimensional parameter-space for this system, where periodic structures, the so-called Arnold tongues, can be seen with periods organized in a Farey tree sequence. We also report possible modifications in this parameter-space, as a function of the integration step size. - Highlights: • We investigate the parameter-space of a particular 3D map. • Periodic structures, namely Arnold tongues, can be seen there. • They are organized in a Farey tree sequence. • The map was derived from a continuous-time Hindmarsh–Rose neuron model. • The forward Euler method was used for such purpose.

Sleep-Active Neurons: Conserved Motors of Sleep

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Bringmann, Henrik

2018-01-01

Sleep is crucial for survival and well-being. This behavioral and physiological state has been studied in all major genetically accessible model animals, including rodents, fish, flies, and worms. Genetic and optogenetic studies have identified several neurons that control sleep, making it now possible to compare circuit mechanisms across species. The “motor” of sleep across animal species is formed by neurons that depolarize at the onset of sleep to actively induce this state by directly inhibiting wakefulness. These sleep-inducing neurons are themselves controlled by inhibitory or activating upstream pathways, which act as the “drivers” of the sleep motor: arousal inhibits “sleep-active” neurons whereas various sleep-promoting “tiredness” pathways converge onto sleep-active neurons to depolarize them. This review provides the first overview of sleep-active neurons across the major model animals. The occurrence of sleep-active neurons and their regulation by upstream pathways in both vertebrate and invertebrate species suggests that these neurons are general and ancient components that evolved early in the history of nervous systems. PMID:29618588

Spiking Neurons for Analysis of Patterns

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Huntsberger, Terrance

2008-01-01

Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern-analysis and pattern-recognition computational systems. These neurons are represented by a mathematical model denoted the state-variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets. Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a tree-like interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers. The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological

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

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Moral, A. del, E-mail: delmoral@unizar.es [Laboratorio de Magnetismo, Departamento de Física de Materia Condensada and Instituto de Ciencia de Materiales, Universidad de Zaragoza and Consejo Superior de Investigaciones Científicas, 50009 Zaragoza (Spain); Laboratorio de Magnetobiología, Departamento de Anatomía e Histología, Facultad de Medicina, Universidad de Zaragoza, 50009 Zaragoza (Spain); Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Madrid (Spain); Azanza, María J., E-mail: mjazanza@unizar.es [Laboratorio de Magnetobiología, Departamento de Anatomía e Histología, Facultad de Medicina, Universidad de Zaragoza, 50009 Zaragoza (Spain); Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Madrid (Spain)

2015-03-01

A biomagnetic-electrical model is presented that explains rather well the experimentally observed synchronization of the bioelectric potential firing rate (“frequency”), f, of single unit neurons of Helix aspersa mollusc under the application of extremely low frequency (ELF) weak alternating (AC) magnetic fields (MF). The proposed model incorporates to our widely experimentally tested model of superdiamagnetism (SD) and Ca{sup 2+} Coulomb explosion (CE) from lipid (LP) bilayer membrane (SD–CE model), the electrical quadrupolar long range interaction between the bilayer LP membranes of synchronized neuron pairs, not considered before. The quadrupolar interaction is capable of explaining well the observed synchronization. Actual extension of our SD–CE-model shows that the neuron firing frequency field, B, dependence becomes not modified, but the bioelectric frequency is decreased and its spontaneous temperature, T, dependence is modified. A comparison of the model with synchronization experimental results of pair of neurons under weak (B{sub 0}≅0.2–15 mT) AC-MF of frequency f{sub M}=50 Hz is reported. From the deduced size of synchronized LP clusters under B, is suggested the formation of small neuron networks via the membrane lipid correlation. - Highlights: • Neuron pair synchronization under low frequency alternating (AC) magnetic field (MF). • Superdiamagnetism and Ca{sup 2+} Coulomb explosion for AC MF effect in synchronized frequency. • Membrane lipid electrical quadrupolar pair interaction as synchronization mechamism. • Good agreement of model with electrophysiological experiments on mollusc Helix neurons.

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

International Nuclear Information System (INIS)

Moral, A. del; Azanza, María J.

2015-01-01

A biomagnetic-electrical model is presented that explains rather well the experimentally observed synchronization of the bioelectric potential firing rate (“frequency”), f, of single unit neurons of Helix aspersa mollusc under the application of extremely low frequency (ELF) weak alternating (AC) magnetic fields (MF). The proposed model incorporates to our widely experimentally tested model of superdiamagnetism (SD) and Ca 2+ Coulomb explosion (CE) from lipid (LP) bilayer membrane (SD–CE model), the electrical quadrupolar long range interaction between the bilayer LP membranes of synchronized neuron pairs, not considered before. The quadrupolar interaction is capable of explaining well the observed synchronization. Actual extension of our SD–CE-model shows that the neuron firing frequency field, B, dependence becomes not modified, but the bioelectric frequency is decreased and its spontaneous temperature, T, dependence is modified. A comparison of the model with synchronization experimental results of pair of neurons under weak (B 0 ≅0.2–15 mT) AC-MF of frequency f M =50 Hz is reported. From the deduced size of synchronized LP clusters under B, is suggested the formation of small neuron networks via the membrane lipid correlation. - Highlights: • Neuron pair synchronization under low frequency alternating (AC) magnetic field (MF). • Superdiamagnetism and Ca 2+ Coulomb explosion for AC MF effect in synchronized frequency. • Membrane lipid electrical quadrupolar pair interaction as synchronization mechamism. • Good agreement of model with electrophysiological experiments on mollusc Helix neurons

Bistability induces episodic spike communication by inhibitory neurons in neuronal networks.

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Kazantsev, V B; Asatryan, S Yu

2011-09-01

Bistability is one of the important features of nonlinear dynamical systems. In neurodynamics, bistability has been found in basic Hodgkin-Huxley equations describing the cell membrane dynamics. When the neuron is clamped near its threshold, the stable rest potential may coexist with the stable limit cycle describing periodic spiking. However, this effect is often neglected in network computations where the neurons are typically reduced to threshold firing units (e.g., integrate-and-fire models). We found that the bistability may induce spike communication by inhibitory coupled neurons in the spiking network. The communication is realized in the form of episodic discharges with synchronous (correlated) spikes during the episodes. A spiking phase map is constructed to describe the synchronization and to estimate basic spike phase locking modes.

Hopf bifurcation of an (n + 1) -neuron bidirectional associative memory neural network model with delays.

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Xiao, Min; Zheng, Wei Xing; Cao, Jinde

2013-01-01

Recent studies on Hopf bifurcations of neural networks with delays are confined to simplified neural network models consisting of only two, three, four, five, or six neurons. It is well known that neural networks are complex and large-scale nonlinear dynamical systems, so the dynamics of the delayed neural networks are very rich and complicated. Although discussing the dynamics of networks with a few neurons may help us to understand large-scale networks, there are inevitably some complicated problems that may be overlooked if simplified networks are carried over to large-scale networks. In this paper, a general delayed bidirectional associative memory neural network model with n + 1 neurons is considered. By analyzing the associated characteristic equation, the local stability of the trivial steady state is examined, and then the existence of the Hopf bifurcation at the trivial steady state is established. By applying the normal form theory and the center manifold reduction, explicit formulae are derived to determine the direction and stability of the bifurcating periodic solution. Furthermore, the paper highlights situations where the Hopf bifurcations are particularly critical, in the sense that the amplitude and the period of oscillations are very sensitive to errors due to tolerances in the implementation of neuron interconnections. It is shown that the sensitivity is crucially dependent on the delay and also significantly influenced by the feature of the number of neurons. Numerical simulations are carried out to illustrate the main results.

Silencing neuronal mutant androgen receptor in a mouse model of spinal and bulbar muscular atrophy.

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Sahashi, Kentaro; Katsuno, Masahisa; Hung, Gene; Adachi, Hiroaki; Kondo, Naohide; Nakatsuji, Hideaki; Tohnai, Genki; Iida, Madoka; Bennett, C Frank; Sobue, Gen

2015-11-01

Spinal and bulbar muscular atrophy (SBMA), an adult-onset neurodegenerative disease that affects males, results from a CAG triplet repeat/polyglutamine expansions in the androgen receptor (AR) gene. Patients develop progressive muscular weakness and atrophy, and no effective therapy is currently available. The tissue-specific pathogenesis, especially relative pathological contributions between degenerative motor neurons and muscles, remains inconclusive. Though peripheral pathology in skeletal muscle caused by toxic AR protein has been recently reported to play a pivotal role in the pathogenesis of SBMA using mouse models, the role of motor neuron degeneration in SBMA has not been rigorously investigated. Here, we exploited synthetic antisense oligonucleotides to inhibit the RNA levels of mutant AR in the central nervous system (CNS) and explore its therapeutic effects in our SBMA mouse model that harbors a mutant AR gene with 97 CAG expansions and characteristic SBMA-like neurogenic phenotypes. A single intracerebroventricular administration of the antisense oligonucleotides in the presymptomatic phase efficiently suppressed the mutant gene expression in the CNS, and delayed the onset and progression of motor dysfunction, improved body weight gain and survival with the amelioration of neuronal histopathology in motor units such as spinal motor neurons, neuromuscular junctions and skeletal muscle. These findings highlight the importance of the neurotoxicity of mutant AR protein in motor neurons as a therapeutic target. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Bidirectional coupling between astrocytes and neurons mediates learning and dynamic coordination in the brain: a multiple modeling approach.

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John J Wade

Full Text Available In recent years research suggests that astrocyte networks, in addition to nutrient and waste processing functions, regulate both structural and synaptic plasticity. To understand the biological mechanisms that underpin such plasticity requires the development of cell level models that capture the mutual interaction between astrocytes and neurons. This paper presents a detailed model of bidirectional signaling between astrocytes and neurons (the astrocyte-neuron model or AN model which yields new insights into the computational role of astrocyte-neuronal coupling. From a set of modeling studies we demonstrate two significant findings. Firstly, that spatial signaling via astrocytes can relay a "learning signal" to remote synaptic sites. Results show that slow inward currents cause synchronized postsynaptic activity in remote neurons and subsequently allow Spike-Timing-Dependent Plasticity based learning to occur at the associated synapses. Secondly, that bidirectional communication between neurons and astrocytes underpins dynamic coordination between neuron clusters. Although our composite AN model is presently applied to simplified neural structures and limited to coordination between localized neurons, the principle (which embodies structural, functional and dynamic complexity, and the modeling strategy may be extended to coordination among remote neuron clusters.

Neuron array with plastic synapses and programmable dendrites.

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Ramakrishnan, Shubha; Wunderlich, Richard; Hasler, Jennifer; George, Suma

2013-10-01

We describe a novel neuromorphic chip architecture that models neurons for efficient computation. Traditional architectures of neuron array chips consist of large scale systems that are interfaced with AER for implementing intra- or inter-chip connectivity. We present a chip that uses AER for inter-chip communication but uses fast, reconfigurable FPGA-style routing with local memory for intra-chip connectivity. We model neurons with biologically realistic channel models, synapses and dendrites. This chip is suitable for small-scale network simulations and can also be used for sequence detection, utilizing directional selectivity properties of dendrites, ultimately for use in word recognition.

Restoring neuronal progranulin reverses deficits in a mouse model of frontotemporal dementia.

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Arrant, Andrew E; Filiano, Anthony J; Unger, Daniel E; Young, Allen H; Roberson, Erik D

2017-05-01

Loss-of-function mutations in progranulin (GRN), a secreted glycoprotein expressed by neurons and microglia, are a common autosomal dominant cause of frontotemporal dementia, a neurodegenerative disease commonly characterized by disrupted social and emotional behaviour. GRN mutations are thought to cause frontotemporal dementia through progranulin haploinsufficiency, therefore, boosting progranulin expression from the intact allele is a rational treatment strategy. However, this approach has not been tested in an animal model of frontotemporal dementia and it is unclear if boosting progranulin could correct pre-existing deficits. Here, we show that adeno-associated virus-driven expression of progranulin in the medial prefrontal cortex reverses social dominance deficits in Grn+/- mice, an animal model of frontotemporal dementia due to GRN mutations. Adeno-associated virus-progranulin also corrected lysosomal abnormalities in Grn+/- mice. The adeno-associated virus-progranulin vector only transduced neurons, suggesting that restoring neuronal progranulin is sufficient to correct deficits in Grn+/- mice. To further test the role of neuronal progranulin in the development of frontotemporal dementia-related deficits, we generated two neuronal progranulin-deficient mouse lines using CaMKII-Cre and Nestin-Cre. Measuring progranulin levels in these lines indicated that most brain progranulin is derived from neurons. Both neuronal progranulin-deficient lines developed social dominance deficits similar to those in global Grn+/- mice, showing that neuronal progranulin deficiency is sufficient to disrupt social behaviour. These data support the concept of progranulin-boosting therapies for frontotemporal dementia and highlight an important role for neuron-derived progranulin in maintaining normal social function. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Establishment of mouse neuron and microglial cell co-cultured models and its action mechanism.

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Zhang, Bo; Yang, Yunfeng; Tang, Jun; Tao, Yihao; Jiang, Bing; Chen, Zhi; Feng, Hua; Yang, Liming; Zhu, Gang

2017-06-27

The objective of this study is to establish a co-culture model of mouse neurons and microglial cells, and to analyze the mechanism of action of oxygen glucose deprivation (OGD) and transient oxygen glucose deprivation (tOGD) preconditioning cell models. Mouse primary neurons and BV2 microglial cells were successfully cultured, and the OGD and tOGD models were also established. In the co-culture of mouse primary neurons and microglial cells, the cell number of tOGD mouse neurons and microglial cells was larger than the OGD cell number, observed by a microscope. CCK-8 assay result showed that at 1h after treatment, the OD value in the control group is lower compared to all the other three groups (P control group compared to other three groups (P neurons cells were cultured. In the meantime mouse BV2 microglia cells were cultured. Two types of cells were co-cultured, and OGD and tOGD cell models were established. There were four groups in the experiment: control group (OGD), treatment group (tOGD+OGD), placebo group (tOGD+OGD+saline) and minocycline intervention group (tOGD+OGD+minocycline). CCK-8 kit was used to detect cell viability and flow cytometry was used to detect apoptosis. In this study, mouse primary neurons and microglial cells were co-cultured. The OGD and tOGD models were established successfully. tOGD was able to effectively protect neurons and microglial cells from damage, and inhibit the apoptosis caused by oxygen glucose deprivation.

Modeling of Auditory Neuron Response Thresholds with Cochlear Implants

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Frederic Venail

2015-01-01

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

Synaptic potentiation onto habenula neurons in learned helplessness model of depression

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

2010-01-01

The cellular basis of depressive disorders is poorly understood1. Recent studies in monkeys indicate that neurons in the lateral habenula (LHb), a nucleus that mediates communication between forebrain and midbrain structures, can increase their activity when an animal fails to receive an expected positive reward or receives a stimulus that predicts aversive conditions (i.e. disappointment or anticipation of a negative outcome)2, 3, 4. LHb neurons project to and modulate dopamine-rich regions such as the ventral-tegmental area (VTA)2, 5 that control reward-seeking behavior6 and participate in depressive disorders7. Here we show in two learned helplessness models of depression that excitatory synapses onto LHb neurons projecting to the VTA are potentiated. Synaptic potentiation correlates with an animal’s helplessness behavior and is due to an enhanced presynaptic release probability. Depleting transmitter release by repeated electrical stimulation of LHb afferents, using a protocol that can be effective on depressed patients8, 9, dramatically suppresses synaptic drive onto VTA-projecting LHb neurons in brain slices and can significantly reduce learned helplessness behavior in rats. Our results indicate that increased presynaptic action onto LHb neurons contributes to the rodent learned helplessness model of depression. PMID:21350486

Recapitulation of spinal motor neuron-specific disease phenotypes in a human cell model of spinal muscular atrophy

Institute of Scientific and Technical Information of China (English)

Zhi-Bo Wang; Xiaoqing Zhang; Xue-Jun Li

2013-01-01

Establishing human cell models of spinal muscular atrophy (SMA) to mimic motor neuron-specific phenotypes holds the key to understanding the pathogenesis of this devastating disease.Here,we developed a closely representative cell model of SMA by knocking down the disease-determining gene,survival motor neuron (SMN),in human embryonic stem cells (hESCs).Our study with this cell model demonstrated that knocking down of SMN does not interfere with neural induction or the initial specification of spinal motor neurons.Notably,the axonal outgrowth of spinal motor neurons was significantly impaired and these disease-mimicking neurons subsequently degenerated.Furthermore,these disease phenotypes were caused by SMN-full length (SMN-FL) but not SMN-A7 (lacking exon 7)knockdown,and were specific to spinal motor neurons.Restoring the expression of SMN-FL completely ameliorated all of the disease phenotypes,including specific axonal defects and motor neuron loss.Finally,knockdown of SMNFL led to excessive mitochondrial oxidative stress in human motor neuron progenitors.The involvement of oxidative stress in the degeneration of spinal motor neurons in the SMA cell model was further confirmed by the administration of N-acetylcysteine,a potent antioxidant,which prevented disease-related apoptosis and subsequent motor neuron death.Thus,we report here the successful establishment of an hESC-based SMA model,which exhibits disease gene isoform specificity,cell type specificity,and phenotype reversibility.Our model provides a unique paradigm for studying how motor neurons specifically degenerate and highlights the potential importance of antioxidants for the treatment of SMA.

Serotonin Neuron Abnormalities in the BTBR Mouse Model of Autism

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Guo, Yue-Ping; Commons, Kathryn G.

2017-01-01

The inbred mouse strain BTBR T+ Itpr3tf/J (BTBR) i studied as a model of idiopathic autism because they are less social and more resistant to change than other strains. Forebrain serotonin receptors and the response to serotonin drugs are altered in BTBR mice, yet it remains unknown if serotonin neurons themselves are abnormal. In this study, we found that serotonin tissue content and the density of serotonin axons is reduced in the hippocampus of BTBR mice in comparison to C57BL/6J (C57) mice. This was accompanied by possible compensatory changes in serotonin neurons that were most pronounced in regions known to provide innervation to the hippocampus: the caudal dorsal raphe (B6) and the median raphe. These changes included increased numbers of serotonin neurons and hyperactivation of Fos expression. Metrics of serotonin neurons in the rostral 2/3 of the dorsal raphe and serotonin content of the prefrontal cortex were less impacted. Thus, serotonin neurons exhibit region-dependent abnormalities in the BTBR mouse that may contribute to their altered behavioral profile. PMID:27478061

[Development of intellect, emotion, and intentions, and their neuronal systems].

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Segawa, Masaya

2008-09-01

Intellect, emotion and intentions, the major components of the human mentality, are neurologically correlated to memory and sensorimotor integration, the neuronal system consisting of the amygdale and hypothalamus, and motivation and learning, respectively. Development of these neuronal processes was evaluated by correlating the pathophysiologies of idiopathic developmental neuropsychiatric disorders and developmental courses of sleep parameters, sleep-wake rhythm (SWR), and locomotion. The memory system and sensory pathways develop by the 9th gestational months. Habituation or dorsal bundle extinction (DBE) develop after the 34th gestational week. In the first 4 months after birth, DBE is consolidated and fine tuning of the primary sensory cortex and its neuronal connection to the unimodal sensory association area along with functional lateralization of the cortex are accomplished. After 4 months, restriction of atonia in the REM stage enables the integrative function of the brain and induces synaptogenesis of the cortex around 6 months and locomotion in late infancy by activating the dopaminergic (DA) neurons induces synaptogenesis of the frontal cortex. Locomotion in early infancy involves functional specialization of the cortex and in childhood with development of biphasic SWR activation of the areas of the prefrontal cortex. Development of emotions reflects in the development of personal communication and the arousal function of the hypothalamus. The former is shown in the mother-child relationship in the first 4 months, in communication with adults and playmates in late infancy to early childhood, and in development of social relationships with sympathy by the early school age with functional maturation of the orbitofrontal cortex. The latter is demonstrated in the secretion of melatonin during night time by 4 months, in the circadian rhythm of body temperature by 8 months, and in the secretion of the growth hormone by 4-5 years with synchronization to the

Self-Organized Criticality in a Simple Neuron Model Based on Scale-Free Networks

International Nuclear Information System (INIS)

Lin Min; Wang Gang; Chen Tianlun

2006-01-01

A simple model for a set of interacting idealized neurons in scale-free networks is introduced. The basic elements of the model are endowed with the main features of a neuron function. We find that our model displays power-law behavior of avalanche sizes and generates long-range temporal correlation. More importantly, we find different dynamical behavior for nodes with different connectivity in the scale-free networks.

Dynamical patterns of calcium signaling in a functional model of neuron-astrocyte networks

DEFF Research Database (Denmark)

Postnov, D.E.; Koreshkov, R.N.; Brazhe, N.A.

2009-01-01

We propose a functional mathematical model for neuron-astrocyte networks. The model incorporates elements of the tripartite synapse and the spatial branching structure of coupled astrocytes. We consider glutamate-induced calcium signaling as a specific mode of excitability and transmission...... in astrocytic-neuronal networks. We reproduce local and global dynamical patterns observed experimentally....

A Communication Theoretical Modeling of Axonal Propagation in Hippocampal Pyramidal Neurons.

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Ramezani, Hamideh; Akan, Ozgur B

2017-06-01

Understanding the fundamentals of communication among neurons, known as neuro-spike communication, leads to reach bio-inspired nanoscale communication paradigms. In this paper, we focus on a part of neuro-spike communication, known as axonal transmission, and propose a realistic model for it. The shape of the spike during axonal transmission varies according to previously applied stimulations to the neuron, and these variations affect the amount of information communicated between neurons. Hence, to reach an accurate model for neuro-spike communication, the memory of axon and its effect on the axonal transmission should be considered, which are not studied in the existing literature. In this paper, we extract the important factors on the memory of axon and define memory states based on these factors. We also describe the transition among these states and the properties of axonal transmission in each of them. Finally, we demonstrate that the proposed model can follow changes in the axonal functionality properly by simulating the proposed model and reporting the root mean square error between simulation results and experimental data.

Predictive features of persistent activity emergence in regular spiking and intrinsic bursting model neurons.

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Kyriaki Sidiropoulou

Full Text Available Proper functioning of working memory involves the expression of stimulus-selective persistent activity in pyramidal neurons of the prefrontal cortex (PFC, which refers to neural activity that persists for seconds beyond the end of the stimulus. The mechanisms which PFC pyramidal neurons use to discriminate between preferred vs. neutral inputs at the cellular level are largely unknown. Moreover, the presence of pyramidal cell subtypes with different firing patterns, such as regular spiking and intrinsic bursting, raises the question as to what their distinct role might be in persistent firing in the PFC. Here, we use a compartmental modeling approach to search for discriminatory features in the properties of incoming stimuli to a PFC pyramidal neuron and/or its response that signal which of these stimuli will result in persistent activity emergence. Furthermore, we use our modeling approach to study cell-type specific differences in persistent activity properties, via implementing a regular spiking (RS and an intrinsic bursting (IB model neuron. We identify synaptic location within the basal dendrites as a feature of stimulus selectivity. Specifically, persistent activity-inducing stimuli consist of activated synapses that are located more distally from the soma compared to non-inducing stimuli, in both model cells. In addition, the action potential (AP latency and the first few inter-spike-intervals of the neuronal response can be used to reliably detect inducing vs. non-inducing inputs, suggesting a potential mechanism by which downstream neurons can rapidly decode the upcoming emergence of persistent activity. While the two model neurons did not differ in the coding features of persistent activity emergence, the properties of persistent activity, such as the firing pattern and the duration of temporally-restricted persistent activity were distinct. Collectively, our results pinpoint to specific features of the neuronal response to a given

Model-based iterative learning control of Parkinsonian state in thalamic relay neuron

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Liu, Chen; Wang, Jiang; Li, Huiyan; Xue, Zhiqin; Deng, Bin; Wei, Xile

2014-09-01

Although the beneficial effects of chronic deep brain stimulation on Parkinson's disease motor symptoms are now largely confirmed, the underlying mechanisms behind deep brain stimulation remain unclear and under debate. Hence, the selection of stimulation parameters is full of challenges. Additionally, due to the complexity of neural system, together with omnipresent noises, the accurate model of thalamic relay neuron is unknown. Thus, the iterative learning control of the thalamic relay neuron's Parkinsonian state based on various variables is presented. Combining the iterative learning control with typical proportional-integral control algorithm, a novel and efficient control strategy is proposed, which does not require any particular knowledge on the detailed physiological characteristics of cortico-basal ganglia-thalamocortical loop and can automatically adjust the stimulation parameters. Simulation results demonstrate the feasibility of the proposed control strategy to restore the fidelity of thalamic relay in the Parkinsonian condition. Furthermore, through changing the important parameter—the maximum ionic conductance densities of low-threshold calcium current, the dominant characteristic of the proposed method which is independent of the accurate model can be further verified.

Connecting mirror neurons and forward models.

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Miall, R C

2003-12-02

Two recent developments in motor neuroscience are promising the extension of theoretical concepts from motor control towards cognitive processes, including human social interactions and understanding the intentions of others. The first of these is the discovery of what are now called mirror neurons, which code for both observed and executed actions. The second is the concept of internal models, and in particular recent proposals that forward and inverse models operate in paired modules. These two ideas will be briefly introduced, and a recent suggestion linking between the two processes of mirroring and modelling will be described which may underlie our abilities for imitating actions, for cooperation between two actors, and possibly for communication via gesture and language.

Emergent properties of interacting populations of spiking neurons.

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Cardanobile, Stefano; Rotter, Stefan

2011-01-01

Dynamic neuronal networks are a key paradigm of increasing importance in brain research, concerned with the functional analysis of biological neuronal networks and, at the same time, with the synthesis of artificial brain-like systems. In this context, neuronal network models serve as mathematical tools to understand the function of brains, but they might as well develop into future tools for enhancing certain functions of our nervous system. Here, we present and discuss our recent achievements in developing multiplicative point processes into a viable mathematical framework for spiking network modeling. The perspective is that the dynamic behavior of these neuronal networks is faithfully reflected by a set of non-linear rate equations, describing all interactions on the population level. These equations are similar in structure to Lotka-Volterra equations, well known by their use in modeling predator-prey relations in population biology, but abundant applications to economic theory have also been described. We present a number of biologically relevant examples for spiking network function, which can be studied with the help of the aforementioned correspondence between spike trains and specific systems of non-linear coupled ordinary differential equations. We claim that, enabled by the use of multiplicative point processes, we can make essential contributions to a more thorough understanding of the dynamical properties of interacting neuronal populations.

Chimera states in a Hodgkin-Huxley model of thermally sensitive neurons

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Glaze, Tera A.; Lewis, Scott; Bahar, Sonya

2016-08-01

Chimera states occur when identically coupled groups of nonlinear oscillators exhibit radically different dynamics, with one group exhibiting synchronized oscillations and the other desynchronized behavior. This dynamical phenomenon has recently been studied in computational models and demonstrated experimentally in mechanical, optical, and chemical systems. The theoretical basis of these states is currently under active investigation. Chimera behavior is of particular relevance in the context of neural synchronization, given the phenomenon of unihemispheric sleep and the recent observation of asymmetric sleep in human patients with sleep apnea. The similarity of neural chimera states to neural "bump" states, which have been suggested as a model for working memory and visual orientation tuning in the cortex, adds to their interest as objects of study. Chimera states have been demonstrated in the FitzHugh-Nagumo model of excitable cells and in the Hindmarsh-Rose neural model. Here, we demonstrate chimera states and chimera-like behaviors in a Hodgkin-Huxley-type model of thermally sensitive neurons both in a system with Abrams-Strogatz (mean field) coupling and in a system with Kuramoto (distance-dependent) coupling. We map the regions of parameter space for which chimera behavior occurs in each of the two coupling schemes.

Finite element modeling of the neuron-electrode interface: stimulus transfer and geometry

NARCIS (Netherlands)

Buitenweg, Jan R.; Rutten, Wim; Marani, Enrico

1999-01-01

The relation between stimulus transfer and the geometry of the neuron-electrode interface can not be determined properly using electrical equivalent circuits, since current that flows from the sealing gap through the neuronal membrane is difficult to model in these circuits. Therefore, finite

Chronic Hypergravity Induces Changes in the Dopaminergic Neuronal System in Drosophila Melanogaster

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Pelos, Andrew; Hosamani, Ravikumar; Bhattacharya, Sharmila

2017-01-01

Upon atmospheric exitre-entry and during training, astronauts are subjected to temporary periods of hypergravity, which has been implicated in the activation of oxidative stress pathways contributing to mitochondrial dysfunction and neuronal degeneration. The pathogenesis of Parkinsons disease and other neurodegenerative disorders is associated with oxidative damage to neurons involved in dopamine systems of the brain. Our study aims to examine the effects of a hypergravitational developmental environment on the degeneration of dopaminergic systems in Drosophila melanogaster. Male and female flies (Gal4-UAS transgenic line) were hatched and raised to adulthood in centrifugal hypergravity (97rpm, 3g). The nuclear expression of the reporter, Green Fluorescent Protein (GFP) is driven by the dopaminergic enzyme tyrosine hydroxylase (TH) promoter, allowing for the targeted visualization of dopamine producing neurons. After being raised to adulthood and kept in hypergravity until 18 days of age, flies were dissected and the expression of TH was measured by fluorescence confocal microscopy. TH expression in the fly brains was used to obtain counts of healthy dopaminergic neurons for flies raised in chronic hypergravity and control groups. Dopaminergic neuron expression data were compared with those of previous studies that limited hypergravity exposure to late life in order to determine the flies adaptability to the gravitational environment when raised from hatching through adulthood. Overall, we observed a significant effect of chronic hypergravity exposure contributing to deficits in dopaminergic neuron expression (p 0.003). Flies raised in 3g had on average lower dopaminergic neuron counts (mean 97.7) when compared with flies raised in 1g (mean 122.8). We suspect these lower levels of TH expression are a result of oxidative dopaminergic cell loss in flies raised in hypergravity. In future studies, we hope to further elucidate the mechanism by which hypergravity

Effects of Chronic Hypergravity on the Dopaminergic Neuronal System in Drosophila Melanogaster

Science.gov (United States)

Pelos, Andrew; Hosamani, Ravikumar; Bhattacharya, Sharmila

2017-01-01

Upon atmospheric exitre-entry and during training, astronauts are subjected to temporary periods of hypergravity, which has been implicated in the activation of oxidative stress pathways contributing to mitochondrial dysfunction and neuronal degeneration. The pathogenesis of Parkinsons disease and other neurodegenerative disorders is associated with oxidative damage to neurons involved in dopamine systems of the brain. Our study aims to examine the effects of a hypergravitational developmental environment on the degeneration of dopaminergic systems in Drosophila melanogaster. Male and female flies (Gal4-UAS transgenic line) were hatched and raised to adulthood in centrifugal hypergravity (97rpm, 3g). The nuclear expression of the reporter, Green Fluorescent Protein (GFP) is driven by the dopaminergic enzyme tyrosine hydroxylase (TH) promoter, allowing for the targeted visualization of dopamine producing neurons. After being raised to adulthood and kept in hypergravity until 18 days of age, flies were dissected and the expression of TH was measured by fluorescence confocal microscopy. TH expression in the fly brains was used to obtain counts of healthy dopaminergic neurons for flies raised in chronic hypergravity and control groups. Dopaminergic neuron expression data were compared with those of previous studies that limited hypergravity exposure to late life in order to determine the flies adaptability to the gravitational environment when raised from hatching through adulthood. Overall, we observed a significant effect of chronic hypergravity exposure contributing to deficits in dopaminergic neuron expression (p 0.003). Flies raised in 3g had on average lower dopaminergic neuron counts (mean 97.7) when compared with flies raised in 1g (mean 122.8). We suspect these lower levels of TH expression are a result of oxidative dopaminergic cell loss in flies raised in hypergravity. In future studies, we hope to further elucidate the mechanism by which hypergravity

A Neuronal Model of Classical Conditioning.

Science.gov (United States)

1987-10-01

suggests that nervous system activity can be understood in terms of two- 20. DISTRIBUTION/ AVAILABILIT Y OF ABSTRACT 21 ABSTRACT SECURITY CLASSIF ICATION...unconditionea stimuli having an external source (food and water are examples). Acquired neuronal drives, likewise, are expected to have internal...Moore, B. R. (19731. The form of the autushdpeo response with food or water reitArcers. Journal of the Experitiental Analykis uf Behavior, 20, 1b3-18

Observing complex action sequences: The role of the fronto-parietal mirror neuron system.

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Molnar-Szakacs, Istvan; Kaplan, Jonas; Greenfield, Patricia M; Iacoboni, Marco

2006-11-15

A fronto-parietal mirror neuron network in the human brain supports the ability to represent and understand observed actions allowing us to successfully interact with others and our environment. Using functional magnetic resonance imaging (fMRI), we wanted to investigate the response of this network in adults during observation of hierarchically organized action sequences of varying complexity that emerge at different developmental stages. We hypothesized that fronto-parietal systems may play a role in coding the hierarchical structure of object-directed actions. The observation of all action sequences recruited a common bilateral network including the fronto-parietal mirror neuron system and occipito-temporal visual motion areas. Activity in mirror neuron areas varied according to the motoric complexity of the observed actions, but not according to the developmental sequence of action structures, possibly due to the fact that our subjects were all adults. These results suggest that the mirror neuron system provides a fairly accurate simulation process of observed actions, mimicking internally the level of motoric complexity. We also discuss the results in terms of the links between mirror neurons, language development and evolution.

Bayesian nonparametric modeling for comparison of single-neuron firing intensities.

Science.gov (United States)

Kottas, Athanasios; Behseta, Sam

2010-03-01

We propose a fully inferential model-based approach to the problem of comparing the firing patterns of a neuron recorded under two distinct experimental conditions. The methodology is based on nonhomogeneous Poisson process models for the firing times of each condition with flexible nonparametric mixture prior models for the corresponding intensity functions. We demonstrate posterior inferences from a global analysis, which may be used to compare the two conditions over the entire experimental time window, as well as from a pointwise analysis at selected time points to detect local deviations of firing patterns from one condition to another. We apply our method on two neurons recorded from the primary motor cortex area of a monkey's brain while performing a sequence of reaching tasks.

VTA GABA neurons modulate specific learning behaviours through the control of dopamine and cholinergic systems

Directory of Open Access Journals (Sweden)

Meaghan C Creed

2014-01-01

Full Text Available The mesolimbic reward system is primarily comprised of the ventral tegmental area (VTA and the nucleus accumbens (NAc as well as their afferent and efferent connections. This circuitry is essential for learning about stimuli associated with motivationally-relevant outcomes. Moreover, addictive drugs affect and remodel this system, which may underlie their addictive properties. In addition to DA neurons, the VTA also contains approximately 30% ɣ-aminobutyric acid (GABA neurons. The task of signalling both rewarding and aversive events from the VTA to the NAc has mostly been ascribed to DA neurons and the role of GABA neurons has been largely neglected until recently. GABA neurons provide local inhibition of DA neurons and also long-range inhibition of projection regions, including the NAc. Here we review studies using a combination of in vivo and ex vivo electrophysiology, pharmacogenetic and optogenetic manipulations that have characterized the functional neuroanatomy of inhibitory circuits in the mesolimbic system, and describe how GABA neurons of the VTA regulate reward and aversion-related learning. We also discuss pharmacogenetic manipulation of this system with benzodiazepines (BDZs, a class of addictive drugs, which act directly on GABAA receptors located on GABA neurons of the VTA. The results gathered with each of these approaches suggest that VTA GABA neurons bi-directionally modulate activity of local DA neurons, underlying reward or aversion at the behavioural level. Conversely, long-range GABA projections from the VTA to the NAc selectively target cholinergic interneurons (CINs to pause their firing and temporarily reduce cholinergic tone in the NAc, which modulates associative learning. Further characterization of inhibitory circuit function within and beyond the VTA is needed in order to fully understand the function of the mesolimbic system under normal and pathological conditions.

Altered expression of IGF-I system in neurons of the inflamed spinal cord during acute experimental autoimmune encephalomyelitis.

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Parvaneh Tafreshi, Azita; Talebi, Farideh; Ghorbani, Samira; Bernard, Claude; Noorbakhsh, Farshid

2017-10-01

There is growing evidence that the impaired IGF-I system contributes to neurodegeneration. In this study, we examined the spinal cords of the EAE, the animal model of multiple sclerosis, to see if the expression of the IGF-I system is altered. To induce EAE, C57/BL6 mice were immunized with the Hooke lab MOG kit, sacrificed at the peak of the disease and their spinal cords were examined for the immunoreactivities (ir) of the IGF-I, IGF binding protein-1 (IGFBP-1) and glycogen synthase kinase 3β (GSK3β), as one major downstream molecule in the IGF-I signaling. Although neurons in the non EAE spinal cords did not show the IGF-I immunoreactivity, they were numerously positive for the IGFBP-1. In the inflamed EAE spinal cord however, the patterns of expressions were reversed, that is, a significant increased number of IGF-I expressing neurons versus a reduced number of IGFBP-1 positive neurons. Moreover, while nearly all IGF-I-ir neurons expressed GSK3β, some expressed it more intensely. Considering our previous finding where we showed a significant reduced number of the inactive (phosphorylated) but not that of the total GSK3β expressing neurons in the EAE spinal cord, it is conceivable that the intense total GSK3β expression in the IGF-I-ir neurons belongs to the active form of GSK3β known to exert neuroinflammatory effects. We therefore suggest that the altered expression of the IGF-I system including GSK3β in spinal cord neurons might involve in pathophysiological events during the EAE. © 2017 Wiley Periodicals, Inc.

Mode-locking behavior of Izhikevich neurons under periodic external forcing

Science.gov (United States)

Farokhniaee, AmirAli; Large, Edward W.

2017-06-01

Many neurons in the auditory system of the brain must encode periodic signals. These neurons under periodic stimulation display rich dynamical states including mode locking and chaotic responses. Periodic stimuli such as sinusoidal waves and amplitude modulated sounds can lead to various forms of n :m mode-locked states, in which a neuron fires n action potentials per m cycles of the stimulus. Here, we study mode-locking in the Izhikevich neurons, a reduced model of the Hodgkin-Huxley neurons. The Izhikevich model is much simpler in terms of the dimension of the coupled nonlinear differential equations compared with other existing models, but excellent for generating the complex spiking patterns observed in real neurons. We obtained the regions of existence of the various mode-locked states on the frequency-amplitude plane, called Arnold tongues, for the Izhikevich neurons. Arnold tongue analysis provides useful insight into the organization of mode-locking behavior of neurons under periodic forcing. We find these tongues for both class-1 and class-2 excitable neurons in both deterministic and noisy regimes.

Parallel Stochastic discrete event simulation of calcium dynamics in neuron.

Science.gov (United States)

Ishlam Patoary, Mohammad Nazrul; Tropper, Carl; McDougal, Robert A; Zhongwei, Lin; Lytton, William W

2017-09-26

The intra-cellular calcium signaling pathways of a neuron depends on both biochemical reactions and diffusions. Some quasi-isolated compartments (e.g. spines) are so small and calcium concentrations are so low that one extra molecule diffusing in by chance can make a nontrivial difference in its concentration (percentage-wise). These rare events can affect dynamics discretely in such way that they cannot be evaluated by a deterministic simulation. Stochastic models of such a system provide a more detailed understanding of these systems than existing deterministic models because they capture their behavior at a molecular level. Our research focuses on the development of a high performance parallel discrete event simulation environment, Neuron Time Warp (NTW), which is intended for use in the parallel simulation of stochastic reaction-diffusion systems such as intra-calcium signaling. NTW is integrated with NEURON, a simulator which is widely used within the neuroscience community. We simulate two models, a calcium buffer and a calcium wave model. The calcium buffer model is employed in order to verify the correctness and performance of NTW by comparing it to a serial deterministic simulation in NEURON. We also derived a discrete event calcium wave model from a deterministic model using the stochastic IP3R structure.

Spatio-temporal dynamics of the mirror neuron system during social intentions.

Science.gov (United States)

Cacioppo, Stephanie; Bolmont, Mylene; Monteleone, George

2017-10-27

Previous research has shown that specific goals and intentions influence a person's allocation of social attention. From a neural viewpoint, a growing body of evidence suggests that the inferior fronto-parietal network, including the mirror neuron system, plays a role in the planning and the understanding of motor intentions. However, it is unclear whether and when the mirror neuron system plays a role in social intentions. Combining a behavioral task with electrical neuroimaging in 22 healthy male participants, the current study investigates whether the temporal brain dynamic of the mirror neuron system differs during two types of social intentions i.e., lust vs. romantic intentions. Our results showed that 62% of the stimuli evoking lustful intentions also evoked romantic intentions, and both intentions were sustained by similar activations of the inferior frontal gyrus and the inferior parietal lobule/angular gyrus for the first 432 ms after stimulus onset. Intentions to not love or not lust, on the other hand, were characterized by earlier differential activations of the inferior fronto-parietal network i.e., as early as 244 ms after stimulus onset. These results suggest that the mirror neuron system may not only code for the motor correlates of intentions, but also for the social meaning of intentions and its valence at both early/automatic and later/more elaborative stages of information processing.

Changes in Enteric Neurons of Small Intestine in a Rat Model of Irritable Bowel Syndrome with Diarrhea.

Science.gov (United States)

Li, Shan; Fei, Guijun; Fang, Xiucai; Yang, Xilin; Sun, Xiaohong; Qian, Jiaming; Wood, Jackie D; Ke, Meiyun

2016-04-30

Physical and/or emotional stresses are important factors in the exacerbation of symptoms in irritable bowel syndrome (IBS). Several lines of evidence support that a major impact of stress on the gastrointestinal tract occurs via the enteric nervous system. We aimed to evaluate histological changes in the submucosal plexus (SMP) and myenteric plexus (MP) of the distal ileum in concert with the intestinal motor function in a rat model of IBS with diarrhea. The rat model was induced by heterotypic chronic and acute stress (CAS). The intestinal transit was measured by administering powdered carbon by gastric gavage. Double immunohistochemical fluorescence staining with whole-mount preparations of SMP and MP of enteric nervous system was used to assess changes in expression of choline acetyltransferase, vasoactive intestinal peptide, or nitric oxide synthase in relation to the pan neuronal marker, anti-Hu. The intestinal transit ratio increased significantly from control values of 50.8% to 60.6% in the CAS group. The numbers of enteric ganglia and neurons in the SMP were increased in the CAS group. The proportions of choline acetyltransferase- and vasoactive intestinal peptide-immunoreactive neurons in the SMP were increased (82.1 ± 4.3% vs. 76.0 ± 5.0%, P = 0.021; 40.5 ± 5.9% vs 28.9 ± 3.7%, P = 0.001), while nitric oxide synthase-immunoreactive neurons in the MP were decreased compared with controls (23.3 ± 4.5% vs 32.4 ± 4.5%, P = 0.002). These morphological changes in enteric neurons to CAS might contribute to the dysfunction in motility and secretion in IBS with diarrhea.

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

Science.gov (United States)

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

2011-02-24

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

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

International Nuclear Information System (INIS)

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

2011-01-01

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

Leaders of neuronal cultures in a quorum percolation model

Directory of Open Access Journals (Sweden)

Jean-Pierre Eckmann

2010-09-01

Full Text Available We present a theoretical framework using quorum-percolation for describing the initiation of activity in a neural culture. The cultures are modeled as random graphs, whose nodes are neurons with $kin$ inputs and $kout$ outputs, and whose input degrees $kin=k$ obey given distribution functions $p_k$. We examine the firing activity of the population of neurons according to their input degree ($k$ classes and calculate for each class its firing probability $Phi_k(t$ as a function of $t$. The probability of a node to fire is found to be determined by its in-degree $k$, and the first-to-fire neurons are those that have a high $k$. A small minority of high-$k$ classes may be called ``Leaders,'' as they form an inter-connected subnetwork that consistently fires much before the rest of the culture. Once initiated, the activity spreads from the Leaders to the less connected majority of the culture. We then use the distribution of in-degree of the Leaders to study the growth rate of the number of neurons active in a burst, which was experimentally measured to be initially exponential. We find that this kind of growth rate is best described by a population that has an in-degree distribution that is a Gaussian centered around $k=75$ with width $sigma=31$ for the majority of the neurons, but also has a power law tail with exponent $-2$ for ten percent of the population. Neurons in the tail may have as many as $k=4,700$ inputs. We explore and discuss the correspondence between the degree distribution and a dynamic neuronal threshold, showing that from the functional point of view, structure and elementary dynamics are interchangeable. We discuss possible geometric origins of this distribution, and comment on the importance of size, or of having a large number of neurons, in the culture.

Presynaptic learning and memory with a persistent firing neuron and a habituating synapse: a model of short term persistent habituation.

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Ramanathan, Kiruthika; Ning, Ning; Dhanasekar, Dhiviya; Li, Guoqi; Shi, Luping; Vadakkepat, Prahlad

2012-08-01

Our paper explores the interaction of persistent firing axonal and presynaptic processes in the generation of short term memory for habituation. We first propose a model of a sensory neuron whose axon is able to switch between passive conduction and persistent firing states, thereby triggering short term retention to the stimulus. Then we propose a model of a habituating synapse and explore all nine of the behavioral characteristics of short term habituation in a two neuron circuit. We couple the persistent firing neuron to the habituation synapse and investigate the behavior of short term retention of habituating response. Simulations show that, depending on the amount of synaptic resources, persistent firing either results in continued habituation or maintains the response, both leading to longer recovery times. The effectiveness of the model as an element in a bio-inspired memory system is discussed.

Neuronal Entropy-Rate Feature of Entopeduncular Nucleus in Rat Model of Parkinson's Disease.

Science.gov (United States)

Darbin, Olivier; Jin, Xingxing; Von Wrangel, Christof; Schwabe, Kerstin; Nambu, Atsushi; Naritoku, Dean K; Krauss, Joachim K; Alam, Mesbah

2016-03-01

The function of the nigro-striatal pathway on neuronal entropy in the basal ganglia (BG) output nucleus, i.e. the entopeduncular nucleus (EPN) was investigated in the unilaterally 6-hyroxydopamine (6-OHDA)-lesioned rat model of Parkinson's disease (PD). In both control subjects and subjects with 6-OHDA lesion of dopamine (DA) the nigro-striatal pathway, a histological hallmark for parkinsonism, neuronal entropy in EPN was maximal in neurons with firing rates ranging between 15 and 25 Hz. In 6-OHDA lesioned rats, neuronal entropy in the EPN was specifically higher in neurons with firing rates above 25 Hz. Our data establishes that the nigro-striatal pathway controls neuronal entropy in motor circuitry and that the parkinsonian condition is associated with abnormal relationship between firing rate and neuronal entropy in BG output nuclei. The neuronal firing rates and entropy relationship provide putative relevant electrophysiological information to investigate the sensory-motor processing in normal condition and conditions such as movement disorders.

A Computational Model to Investigate Astrocytic Glutamate Uptake Influence on Synaptic Transmission and Neuronal Spiking

Directory of Open Access Journals (Sweden)

Sushmita Lakshmi Allam

2012-10-01

Full Text Available Over the past decades, our view of astrocytes has switched from passive support cells to active processing elements in the brain. The current view is that astrocytes shape neuronal communication and also play an important role in many neurodegenerative diseases. Despite the growing awareness of the importance of astrocytes, the exact mechanisms underlying neuron-astrocyte communication and the physiological consequences of astrocytic-neuronal interactions remain largely unclear. In this work, we define a modeling framework that will permit to address unanswered questions regarding the role of astrocytes. Our computational model of a detailed glutamatergic synapse facilitates the analysis of neural system responses to various stimuli and conditions that are otherwise difficult to obtain experimentally, in particular the readouts at the sub-cellular level. In this paper, we extend a detailed glutamatergic synaptic model, to include astrocytic glutamate transporters. We demonstrate how these glial transporters, responsible for the majority of glutamate uptake, modulate synaptic transmission mediated by ionotropic AMPA and NMDA receptors at glutamatergic synapses. Furthermore, we investigate how these local signaling effects at the synaptic level are translated into varying spatio-temporal patterns of neuron firing. Paired pulse stimulation results reveal that the effect of astrocytic glutamate uptake is more apparent when the input inter-spike interval is sufficiently long to allow the receptors to recover from desensitization. These results suggest an important functional role of astrocytes in spike timing dependent processes and demand further investigation of the molecular basis of certain neurological diseases specifically related to alterations in astrocytic glutamate uptake, such as epilepsy.

The Effects of Guanfacine and Phenylephrine on a Spiking Neuron Model of Working Memory.

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Duggins, Peter; Stewart, Terrence C; Choo, Xuan; Eliasmith, Chris

2017-01-01

We use a spiking neural network model of working memory (WM) capable of performing the spatial delayed response task (DRT) to investigate two drugs that affect WM: guanfacine (GFC) and phenylephrine (PHE). In this model, the loss of information over time results from changes in the spiking neural activity through recurrent connections. We reproduce the standard forgetting curve and then show that this curve changes in the presence of GFC and PHE, whose application is simulated by manipulating functional, neural, and biophysical properties of the model. In particular, applying GFC causes increased activity in neurons that are sensitive to the information currently being remembered, while applying PHE leads to decreased activity in these same neurons. Interestingly, these differential effects emerge from network-level interactions because GFC and PHE affect all neurons equally. We compare our model to both electrophysiological data from neurons in monkey dorsolateral prefrontal cortex and to behavioral evidence from monkeys performing the DRT. Copyright © 2016 Cognitive Science Society, Inc.

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

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Magyar, Andrew; Collins, John

2015-07-01

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

Comparative mapping of GABA-immunoreactive neurons in the central nervous systems of nudibranch molluscs.

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Gunaratne, Charuni A; Sakurai, Akira; Katz, Paul S

2014-03-01

The relative simplicity of certain invertebrate nervous systems, such as those of gastropod molluscs, allows behaviors to be dissected at the level of small neural circuits composed of individually identifiable neurons. Elucidating the neurotransmitter phenotype of neurons in neural circuits is important for understanding how those neural circuits function. In this study, we examined the distribution of γ-aminobutyric-acid;-immunoreactive (GABA-ir) neurons in four species of sea slugs (Mollusca, Gastropoda, Opisthobranchia, Nudibranchia): Tritonia diomedea, Melibe leonina, Dendronotus iris, and Hermissenda crassicornis. We found consistent patterns of GABA immunoreactivity in the pedal and cerebral-pleural ganglia across species. In particular, there were bilateral clusters in the lateral and medial regions of the dorsal surface of the cerebral ganglia as well as a cluster on the ventral surface of the pedal ganglia. There were also individual GABA-ir neurons that were recognizable across species. The invariant presence of these individual neurons and clusters suggests that they are homologous, although there were interspecies differences in the numbers of neurons in the clusters. The GABAergic system was largely restricted to the central nervous system, with the majority of axons confined to ganglionic connectives and commissures, suggesting a central, integrative role for GABA. GABA was a candidate inhibitory neurotransmitter for neurons in central pattern generator (CPG) circuits underlying swimming behaviors in these species, however none of the known swim CPG neurons were GABA-ir. Although the functions of these GABA-ir neurons are not known, it is clear that their presence has been strongly conserved across nudibranchs. Copyright © 2013 Wiley Periodicals, Inc.

Using a hybrid neuron in physiologically inspired models of the basal ganglia

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Corey Michael Thibeault

2013-07-01

Full Text Available Our current understanding of the basal ganglia has facilitated the creation of computational models that have contributed novel theories, explored new functional anatomy and demonstrated results complementing physiological experiments. However, the utility of these models extends beyond these applications. Particularly in neuromorphic engineering, where the basal ganglia's role in computation is important for applications such as power efficient autonomous agents and model-based control strategies. The neurons used in existing computational models of the basal ganglia however, are not amenable for many low-power hardware implementations. Motivated by a need for more hardware accessible networks, we replicate four published models of the basal ganglia, spanning single neuron and small networks, replacing the more computationally expensive neuron models with an Izhikevich hybrid neuron. This begins with a network modeling action-selection, where the basal activity levels and the ability to appropriately select the most salient input is reproduced. A Parkinson's disease model is then explored under normal conditions, Parkinsonian conditions and during subthalamic nucleus deep brain stimulation. The resulting network is capable of replicating the loss of thalamic relay capabilities in the Parkinsonian state and its return under deep brain stimulation. This is also demonstrated using a network capable of action-selection. Finally, a study of correlation transfer under different patterns of Parkinsonian activity is presented. These networks successfully captured the significant results of the originals studies. This not only creates a foundation for neuromorphic hardware implementations but may also support the development of large-scale biophysical models. The former potentially providing a way of improving the efficacy of deep brain stimulation and the latter allowing for the efficient simulation of larger more comprehensive networks.

Sexually dimorphic distribution of Prokr2 neurons revealed by the Prokr2-Cre mouse model.

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Mohsen, Zaid; Sim, Hosung; Garcia-Galiano, David; Han, Xingfa; Bellefontaine, Nicole; Saunders, Thomas L; Elias, Carol F

2017-12-01

Prokineticin receptor 2 (PROKR2) is predominantly expressed in the mammalian central nervous system. Loss-of-function mutations of PROKR2 in humans are associated with Kallmann syndrome due to the disruption of gonadotropin releasing hormone neuronal migration and deficient olfactory bulb morphogenesis. PROKR2 has been also implicated in the neuroendocrine control of GnRH neurons post-migration and other physiological systems. However, the brain circuitry and mechanisms associated with these actions have been difficult to investigate mainly due to the widespread distribution of Prokr2-expressing cells, and the lack of animal models and molecular tools. Here, we describe the generation, validation and characterization of a new mouse model that expresses Cre recombinase driven by the Prokr2 promoter, using CRISPR-Cas9 technology. Cre expression was visualized using reporter genes, tdTomato and GFP, in males and females. Expression of Cre-induced reporter genes was found in brain sites previously described to express Prokr2, e.g., the paraventricular and the suprachiasmatic nuclei, and the area postrema. The Prokr2-Cre mouse model was further validated by colocalization of Cre-induced GFP and Prokr2 mRNA. No disruption of Prokr2 expression, GnRH neuronal migration or fertility was observed. Comparative analysis of Prokr2-Cre expression in male and female brains revealed a sexually dimorphic distribution confirmed by in situ hybridization. In females, higher Cre activity was found in the medial preoptic area, ventromedial nucleus of the hypothalamus, arcuate nucleus, medial amygdala and lateral parabrachial nucleus. In males, Cre was higher in the amygdalo-hippocampal area. The sexually dimorphic pattern of Prokr2 expression indicates differential roles in reproductive function and, potentially, in other physiological systems.

Aberrant neuronal activity-induced signaling and gene expression in a mouse model of RASopathy.

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Franziska Altmüller

2017-03-01

Full Text Available Noonan syndrome (NS is characterized by reduced growth, craniofacial abnormalities, congenital heart defects, and variable cognitive deficits. NS belongs to the RASopathies, genetic conditions linked to mutations in components and regulators of the Ras signaling pathway. Approximately 50% of NS cases are caused by mutations in PTPN11. However, the molecular mechanisms underlying cognitive impairments in NS patients are still poorly understood. Here, we report the generation and characterization of a new conditional mouse strain that expresses the overactive Ptpn11D61Y allele only in the forebrain. Unlike mice with a global expression of this mutation, this strain is viable and without severe systemic phenotype, but shows lower exploratory activity and reduced memory specificity, which is in line with a causal role of disturbed neuronal Ptpn11 signaling in the development of NS-linked cognitive deficits. To explore the underlying mechanisms we investigated the neuronal activity-regulated Ras signaling in brains and neuronal cultures derived from this model. We observed an altered surface expression and trafficking of synaptic glutamate receptors, which are crucial for hippocampal neuronal plasticity. Furthermore, we show that the neuronal activity-induced ERK signaling, as well as the consecutive regulation of gene expression are strongly perturbed. Microarray-based hippocampal gene expression profiling revealed profound differences in the basal state and upon stimulation of neuronal activity. The neuronal activity-dependent gene regulation was strongly attenuated in Ptpn11D61Y neurons. In silico analysis of functional networks revealed changes in the cellular signaling beyond the dysregulation of Ras/MAPK signaling that is nearly exclusively discussed in the context of NS at present. Importantly, changes in PI3K/AKT/mTOR and JAK/STAT signaling were experimentally confirmed. In summary, this study uncovers aberrant neuronal activity

Unsteady exergy destruction of the neuron under dynamic stress conditions

International Nuclear Information System (INIS)

Genc, S.; Sorguven, E.; Ozilgen, M.; Aksan Kurnaz, I.

2013-01-01

Just like all physical systems, biological systems also obey laws of thermodynamics, and as such the useful work potential of a biological system is its exergy. In some studies, exergy of living systems is considered with respect to work performance of humans in offices or buildings; however the exergy analysis of biochemical reactions in a cell as a closed system goes largely untouched. In this study, exergy analysis was applied to glucose metabolism of a model neuron, and dynamic exergy destructions were calculated for four different conditions, namely normoxia, hypoxia, glucose starvation and excess glucose. Our results showed that neuronal metabolism achieved a new steady state under each condition within 5 min. This dynamic model predicts that, both exergy destruction and work potential rates increase with increasing blood glucose concentration. The ratio of exergy destruction rate to work potential rate increases logarithmically with increasing blood glucose concentration. The neuronal metabolism is thus found to function in an efficient way and switches to lower exergy destruction under stress conditions such as glucose starvation. This behavior seen in this exergy analysis study confirms the assumption of minimum entropy production in living systems. - Highlights: • Unsteady exergy analysis of glucose metabolism of a model neuron is performed. • Dynamic exergy losses were calculated for four different conditions: normoxia, hypoxia, glucose starvation and excess glucose. • Neuronal metabolism achieved a new steady state under each condition within 5 min. • Both exergy loss and work potential rates increase with increasing blood glucose concentration. • Neuronal metabolism functions in an efficient way and switches to lower exergy loss under stress conditions

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

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Kristoffer Myczek

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

Neuronal synchrony: peculiarity and generality.

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Nowotny, Thomas; Huerta, Ramon; Rabinovich, Mikhail I

2008-09-01

Synchronization in neuronal systems is a new and intriguing application of dynamical systems theory. Why are neuronal systems different as a subject for synchronization? (1) Neurons in themselves are multidimensional nonlinear systems that are able to exhibit a wide variety of different activity patterns. Their "dynamical repertoire" includes regular or chaotic spiking, regular or chaotic bursting, multistability, and complex transient regimes. (2) Usually, neuronal oscillations are the result of the cooperative activity of many synaptically connected neurons (a neuronal circuit). Thus, it is necessary to consider synchronization between different neuronal circuits as well. (3) The synapses that implement the coupling between neurons are also dynamical elements and their intrinsic dynamics influences the process of synchronization or entrainment significantly. In this review we will focus on four new problems: (i) the synchronization in minimal neuronal networks with plastic synapses (synchronization with activity dependent coupling), (ii) synchronization of bursts that are generated by a group of nonsymmetrically coupled inhibitory neurons (heteroclinic synchronization), (iii) the coordination of activities of two coupled neuronal networks (partial synchronization of small composite structures), and (iv) coarse grained synchronization in larger systems (synchronization on a mesoscopic scale). (c) 2008 American Institute of Physics.

Statistics of a neuron model driven by asymmetric colored noise.

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

2015-02-01

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

Spatio-Temporal Modeling of Neuron Fields

DEFF Research Database (Denmark)

Lund, Adam

The starting point and focal point for this thesis was stochastic dynamical modelling of neuronal imaging data with the declared objective of drawing inference, within this model framework, in a large-scale (high-dimensional) data setting. Implicitly this objective entails carrying out three...... be achieved if the scale of the data is taken into consideration throughout i) - iii). The strategy in this project was, relying on a space and time continuous stochastic modelling approach, to obtain a stochastic functional differential equation on a Hilbert space. By decomposing the drift operator...... of this SFDE such that each component is essentially represented by a smooth function of time and space and expanding these component functions in a tensor product basis we implicitly reduce the number of model parameters. In addition, the component-wise tensor representation induce a corresponding component...

Network dynamics in nociceptive pathways assessed by the neuronal avalanche model

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Wu José

2012-04-01

Full Text Available Abstract Background Traditional electroencephalography provides a critical assessment of pain responses. The perception of pain, however, may involve a series of signal transmission pathways in higher cortical function. Recent studies have shown that a mathematical method, the neuronal avalanche model, may be applied to evaluate higher-order network dynamics. The neuronal avalanche is a cascade of neuronal activity, the size distribution of which can be approximated by a power law relationship manifested by the slope of a straight line (i.e., the α value. We investigated whether the neuronal avalanche could be a useful index for nociceptive assessment. Findings Neuronal activity was recorded with a 4 × 8 multichannel electrode array in the primary somatosensory cortex (S1 and anterior cingulate cortex (ACC. Under light anesthesia, peripheral pinch stimulation increased the slope of the α value in both the ACC and S1, whereas brush stimulation increased the α value only in the S1. The increase in α values was blocked in both regions under deep anesthesia. The increase in α values in the ACC induced by peripheral pinch stimulation was blocked by medial thalamic lesion, but the increase in α values in the S1 induced by brush and pinch stimulation was not affected. Conclusions The neuronal avalanche model shows a critical state in the cortical network for noxious-related signal processing. The α value may provide an index of brain network activity that distinguishes the responses to somatic stimuli from the control state. These network dynamics may be valuable for the evaluation of acute nociceptive processes and may be applied to chronic pathological pain conditions.

Stochastic Wilson–Cowan models of neuronal network dynamics with memory and delay

International Nuclear Information System (INIS)

Goychuk, Igor; Goychuk, Andriy

2015-01-01

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

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

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Liu, Kevin X; Edwards, Benjamin; Lee, Sheena; Finelli, Mattéa J; Davies, Ben; Davies, Kay E; Oliver, Peter L

2015-05-01

Amyotrophic lateral sclerosis is a devastating neurodegenerative disorder characterized by the progressive loss of spinal motor neurons. While the aetiological mechanisms underlying the disease remain poorly understood, oxidative stress is a central component of amyotrophic lateral sclerosis and contributes to motor neuron injury. Recently, oxidation resistance 1 (OXR1) has emerged as a critical regulator of neuronal survival in response to oxidative stress, and is upregulated in the spinal cord of patients with amyotrophic lateral sclerosis. Here, we tested the hypothesis that OXR1 is a key neuroprotective factor during amyotrophic lateral sclerosis pathogenesis by crossing a new transgenic mouse line that overexpresses OXR1 in neurons with the SOD1(G93A) mouse model of amyotrophic lateral sclerosis. Interestingly, we report that overexpression of OXR1 significantly extends survival, improves motor deficits, and delays pathology in the spinal cord and in muscles of SOD1(G93A) mice. Furthermore, we find that overexpression of OXR1 in neurons significantly delays non-cell-autonomous neuroinflammatory response, classic complement system activation, and STAT3 activation through transcriptomic analysis of spinal cords of SOD1(G93A) mice. Taken together, these data identify OXR1 as the first neuron-specific antioxidant modulator of pathogenesis and disease progression in SOD1-mediated amyotrophic lateral sclerosis, and suggest that OXR1 may serve as a novel target for future therapeutic strategies. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain.

Noise and neuronal populations conspire to encode simple waveforms reliably

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Parnas, B. R.

1996-01-01

Sensory systems rely on populations of neurons to encode information transduced at the periphery into meaningful patterns of neuronal population activity. This transduction occurs in the presence of intrinsic neuronal noise. This is fortunate. The presence of noise allows more reliable encoding of the temporal structure present in the stimulus than would be possible in a noise-free environment. Simulations with a parallel model of signal processing at the auditory periphery have been used to explore the effects of noise and a neuronal population on the encoding of signal information. The results show that, for a given set of neuronal modeling parameters and stimulus amplitude, there is an optimal amount of noise for stimulus encoding with maximum fidelity.

The Importance of Non-neuronal Cell Types in hiPSC-Based Disease Modeling and Drug Screening

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David M. Gonzalez

2017-12-01

Full Text Available Current applications of human induced pluripotent stem cell (hiPSC technologies in patient-specific models of neurodegenerative and neuropsychiatric disorders tend to focus on neuronal phenotypes. Here, we review recent efforts toward advancing hiPSCs toward non-neuronal cell types of the central nervous system (CNS and highlight their potential use for the development of more complex in vitro models of neurodevelopment and disease. We present evidence from previous works in both rodents and humans of the importance of these cell types (oligodendrocytes, microglia, astrocytes in neurological disease and highlight new hiPSC-based models that have sought to explore these relationships in vitro. Lastly, we summarize efforts toward conducting high-throughput screening experiments with hiPSCs and propose methods by which new screening platforms could be designed to better capture complex relationships between neural cell populations in health and disease.

Optimizing NEURON Simulation Environment Using Remote Memory Access with Recursive Doubling on Distributed Memory Systems

OpenAIRE

Shehzad, Danish; Bozkuş, Zeki

2016-01-01

Increase in complexity of neuronal network models escalated the efforts to make NEURON simulation environment efficient. The computational neuroscientists divided the equations into subnets amongst multiple processors for achieving better hardware performance. On parallel machines for neuronal networks, interprocessor spikes exchange consumes large section of overall simulation time. In NEURON for communication between processors Message Passing Interface (MPI) is used. MPI_Allgather collecti...

Physiological Characterization of Vestibular Efferent Brainstem Neurons Using a Transgenic Mouse Model

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Leijon, Sara; Magnusson, Anna K.

2014-01-01

The functional role of efferent innervation of the vestibular end-organs in the inner ear remains elusive. This study provides the first physiological characterization of the cholinergic vestibular efferent (VE) neurons in the brainstem by utilizing a transgenic mouse model, expressing eGFP under a choline-acetyltransferase (ChAT)-locus spanning promoter in combination with targeted patch clamp recordings. The intrinsic electrical properties of the eGFP-positive VE neurons were compared to the properties of the lateral olivocochlear (LOC) brainstem neurons, which gives rise to efferent innervation of the cochlea. Both VE and the LOC neurons were marked by their negative resting membrane potential neurons differed significantly in the depolarizing range. When injected with positive currents, VE neurons fired action potentials faithfully to the onset of depolarization followed by sparse firing with long inter-spike intervals. This response gave rise to a low response gain. The LOC neurons, conversely, responded with a characteristic delayed tonic firing upon depolarizing stimuli, giving rise to higher response gain than the VE neurons. Depolarization triggered large TEA insensitive outward currents with fast inactivation kinetics, indicating A-type potassium currents, in both the inner ear-projecting neuronal types. Immunohistochemistry confirmed expression of Kv4.3 and 4.2 ion channel subunits in both the VE and LOC neurons. The difference in spiking responses to depolarization is related to a two-fold impact of these transient outward currents on somatic integration in the LOC neurons compared to in VE neurons. It is speculated that the physiological properties of the VE neurons might be compatible with a wide-spread control over motion and gravity sensation in the inner ear, providing likewise feed-back amplification of abrupt and strong phasic signals from the semi-circular canals and of tonic signals from the gravito-sensitive macular organs. PMID:24867596

Dysregulated neuronal activity patterns implicate corticostriatal circuit dysfunction in multiple rodent models of Huntington’s disease

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Benjamin R. Miller

2011-05-01

Full Text Available Huntington’s disease (HD is an autosomal dominant neurodegenerative disorder that targets the corticostriatal system and results in progressive deterioration of cognitive, emotional, and motor skills. Although cortical and striatal neurons are widely studied in animal models of HD, there is little information on neuronal function during expression of the HD behavioral phenotype. To address this knowledge gap, we used chronically implanted micro-wire bundles to record extracellular spikes and local field potentials (LFPs in truncated (R6/1 and R6/2 and full-length (knock-in, KI mouse models as well as in tgHD rats behaving in an open-field arena. Spike activity was recorded in the striatum of all models and in prefrontal cortex (PFC of R6/2 and KI mice, and in primary motor cortex (M1 of R6/2 mice. We also recorded LFP activity in R6/2 striatum. All HD models exhibited altered neuronal activity relative to wild-type (WT controls. Although there was no consistent effect on firing rate across models and brain areas, burst firing was reduced in striatum, PFC, and M1 of R6/2 mice, and in striatum of KI mice. Consistent with a decline in bursting, the interspike-interval coefficient of variation was reduced in all regions of all models, except PFC of KI mice and striatum of tgHD rats. Among simultaneously recorded neuron pairs, correlated firing was reduced in all brain regions of all models, while coincident bursting, which measures the temporal overlap between bursting pairs, was reduced in striatum of all models as well as in M1 of R6/2's. Preliminary analysis of striatal LFPs revealed aberrant behavior-related oscillations in the delta to theta range and in gamma activity. Collectively, our results indicate that disrupted corticostriatal processing occurs across multiple HD models despite differences in the severity of the behavioral phenotype. Efforts aimed at normalizing corticostriatal activity may hold the key to developing new HD

A Robust Feedforward Model of the Olfactory System.

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Yilun Zhang

2016-04-01

Full Text Available Most natural odors have sparse molecular composition. This makes the principles of compressed sensing potentially relevant to the structure of the olfactory code. Yet, the largely feedforward organization of the olfactory system precludes reconstruction using standard compressed sensing algorithms. To resolve this problem, recent theoretical work has shown that signal reconstruction could take place as a result of a low dimensional dynamical system converging to one of its attractor states. However, the dynamical aspects of optimization slowed down odor recognition and were also found to be susceptible to noise. Here we describe a feedforward model of the olfactory system that achieves both strong compression and fast reconstruction that is also robust to noise. A key feature of the proposed model is a specific relationship between how odors are represented at the glomeruli stage, which corresponds to a compression, and the connections from glomeruli to third-order neurons (neurons in the olfactory cortex of vertebrates or Kenyon cells in the mushroom body of insects, which in the model corresponds to reconstruction. We show that should this specific relationship hold true, the reconstruction will be both fast and robust to noise, and in particular to the false activation of glomeruli. The predicted connectivity rate from glomeruli to third-order neurons can be tested experimentally.

Competition model for aperiodic stochastic resonance in a Fitzhugh-Nagumo model of cardiac sensory neurons.

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Kember, G C; Fenton, G A; Armour, J A; Kalyaniwalla, N

2001-04-01

Regional cardiac control depends upon feedback of the status of the heart from afferent neurons responding to chemical and mechanical stimuli as transduced by an array of sensory neurites. Emerging experimental evidence shows that neural control in the heart may be partially exerted using subthreshold inputs that are amplified by noisy mechanical fluctuations. This amplification is known as aperiodic stochastic resonance (ASR). Neural control in the noisy, subthreshold regime is difficult to see since there is a near absence of any correlation between input and the output, the latter being the average firing (spiking) rate of the neuron. This lack of correlation is unresolved by traditional energy models of ASR since these models are unsuitable for identifying "cause and effect" between such inputs and outputs. In this paper, the "competition between averages" model is used to determine what portion of a noisy, subthreshold input is responsible, on average, for the output of sensory neurons as represented by the Fitzhugh-Nagumo equations. A physiologically relevant conclusion of this analysis is that a nearly constant amount of input is responsible for a spike, on average, and this amount is approximately independent of the firing rate. Hence, correlation measures are generally reduced as the firing rate is lowered even though neural control under this model is actually unaffected.

The Hypocretin/Orexin Neuronal Networks in Zebrafish.

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Elbaz, Idan; Levitas-Djerbi, Talia; Appelbaum, Lior

2017-01-01

The hypothalamic Hypocretin/Orexin (Hcrt) neurons secrete two Hcrt neuropeptides. These neurons and peptides play a major role in the regulation of feeding, sleep wake cycle, reward-seeking, addiction, and stress. Loss of Hcrt neurons causes the sleep disorder narcolepsy. The zebrafish has become an attractive model to study the Hcrt neuronal network because it is a transparent vertebrate that enables simple genetic manipulation, imaging of the structure and function of neuronal circuits in live animals, and high-throughput monitoring of behavioral performance during both day and night. The zebrafish Hcrt network comprises ~16-60 neurons, which similar to mammals, are located in the hypothalamus and widely innervate the brain and spinal cord, and regulate various fundamental behaviors such as feeding, sleep, and wakefulness. Here we review how the zebrafish contributes to the study of the Hcrt neuronal system molecularly, anatomically, physiologically, and pathologically.

Human iPSC-Derived Neuronal Model of Tau-A152T Frontotemporal Dementia Reveals Tau-Mediated Mechanisms of Neuronal Vulnerability

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M. Catarina Silva

2016-09-01

Full Text Available Frontotemporal dementia (FTD and other tauopathies characterized by focal brain neurodegeneration and pathological accumulation of proteins are commonly associated with tau mutations. However, the mechanism of neuronal loss is not fully understood. To identify molecular events associated with tauopathy, we studied induced pluripotent stem cell (iPSC-derived neurons from individuals carrying the tau-A152T variant. We highlight the potential of in-depth phenotyping of human neuronal cell models for pre-clinical studies and identification of modulators of endogenous tau toxicity. Through a panel of biochemical and cellular assays, A152T neurons showed accumulation, redistribution, and decreased solubility of tau. Upregulation of tau was coupled to enhanced stress-inducible markers and cell vulnerability to proteotoxic, excitotoxic, and mitochondrial stressors, which was rescued upon CRISPR/Cas9-mediated targeting of tau or by pharmacological activation of autophagy. Our findings unmask tau-mediated perturbations of specific pathways associated with neuronal vulnerability, revealing potential early disease biomarkers and therapeutic targets for FTD and other tauopathies.

Neuron-derived IgG protects neurons from complement-dependent cytotoxicity.

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Zhang, Jie; Niu, Na; Li, Bingjie; McNutt, Michael A

2013-12-01

Passive immunity of the nervous system has traditionally been thought to be predominantly due to the blood-brain barrier. This concept must now be revisited based on the existence of neuron-derived IgG. The conventional concept is that IgG is produced solely by mature B lymphocytes, but it has now been found to be synthesized by murine and human neurons. However, the function of this endogenous IgG is poorly understood. In this study, we confirm IgG production by rat cortical neurons at the protein and mRNA levels, with 69.0 ± 5.8% of cortical neurons IgG-positive. Injury to primary-culture neurons was induced by complement leading to increases in IgG production. Blockage of neuron-derived IgG resulted in more neuronal death and early apoptosis in the presence of complement. In addition, FcγRI was found in microglia and astrocytes. Expression of FcγR I in microglia was increased by exposure to neuron-derived IgG. Release of NO from microglia triggered by complement was attenuated by neuron-derived IgG, and this attenuation could be reversed by IgG neutralization. These data demonstrate that neuron-derived IgG is protective of neurons against injury induced by complement and microglial activation. IgG appears to play an important role in maintaining the stability of the nervous system.

Subsampling effects in neuronal avalanche distributions recorded in vivo

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Munk Matthias HJ

2009-04-01

Full Text Available Abstract Background Many systems in nature are characterized by complex behaviour where large cascades of events, or avalanches, unpredictably alternate with periods of little activity. Snow avalanches are an example. Often the size distribution f(s of a system's avalanches follows a power law, and the branching parameter sigma, the average number of events triggered by a single preceding event, is unity. A power law for f(s, and sigma = 1, are hallmark features of self-organized critical (SOC systems, and both have been found for neuronal activity in vitro. Therefore, and since SOC systems and neuronal activity both show large variability, long-term stability and memory capabilities, SOC has been proposed to govern neuronal dynamics in vivo. Testing this hypothesis is difficult because neuronal activity is spatially or temporally subsampled, while theories of SOC systems assume full sampling. To close this gap, we investigated how subsampling affects f(s and sigma by imposing subsampling on three different SOC models. We then compared f(s and sigma of the subsampled models with those of multielectrode local field potential (LFP activity recorded in three macaque monkeys performing a short term memory task. Results Neither the LFP nor the subsampled SOC models showed a power law for f(s. Both, f(s and sigma, depended sensitively on the subsampling geometry and the dynamics of the model. Only one of the SOC models, the Abelian Sandpile Model, exhibited f(s and sigma similar to those calculated from LFP activity. Conclusion Since subsampling can prevent the observation of the characteristic power law and sigma in SOC systems, misclassifications of critical systems as sub- or supercritical are possible. Nevertheless, the system specific scaling of f(s and sigma under subsampling conditions may prove useful to select physiologically motivated models of brain function. Models that better reproduce f(s and sigma calculated from the physiological

Systematic Three-Dimensional Coculture Rapidly Recapitulates Interactions between Human Neurons and Astrocytes

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Robert Krencik

2017-12-01

Full Text Available Summary: Human astrocytes network with neurons in dynamic ways that are still poorly defined. Our ability to model this relationship is hampered by the lack of relevant and convenient tools to recapitulate this complex interaction. To address this barrier, we have devised efficient coculture systems utilizing 3D organoid-like spheres, termed asteroids, containing pre-differentiated human pluripotent stem cell (hPSC-derived astrocytes (hAstros combined with neurons generated from hPSC-derived neural stem cells (hNeurons or directly induced via Neurogenin 2 overexpression (iNeurons. Our systematic methods rapidly produce structurally complex hAstros and synapses in high-density coculture with iNeurons in precise numbers, allowing for improved studies of neural circuit function, disease modeling, and drug screening. We conclude that these bioengineered neural circuit model systems are reliable and scalable tools to accurately study aspects of human astrocyte-neuron functional properties while being easily accessible for cell-type-specific manipulations and observations. : In this article, Krencik and colleagues show that high-density cocultures of pre-differentiated human astrocytes with induced neurons, from pluripotent stem cells, elicit mature characteristics by 3–5 weeks. This provides a faster and more defined alternative method to organoid cultures for investigating human neural circuit function. Keywords: human pluripotent stem cells, neurons, astrocytes, synapses, coculture, three-dimensional spheres, organoids, disease modeling

Visualization of Sensory Neurons and Their Projections in an Upper Motor Neuron Reporter Line.

Science.gov (United States)

Genç, Barış; Lagrimas, Amiko Krisa Bunag; Kuru, Pınar; Hess, Robert; Tu, Michael William; Menichella, Daniela Maria; Miller, Richard J; Paller, Amy S; Özdinler, P Hande

2015-01-01

Visualization of peripheral nervous system axons and cell bodies is important to understand their development, target recognition, and integration into complex circuitries. Numerous studies have used protein gene product (PGP) 9.5 [a.k.a. ubiquitin carboxy-terminal hydrolase L1 (UCHL1)] expression as a marker to label sensory neurons and their axons. Enhanced green fluorescent protein (eGFP) expression, under the control of UCHL1 promoter, is stable and long lasting in the UCHL1-eGFP reporter line. In addition to the genetic labeling of corticospinal motor neurons in the motor cortex and degeneration-resistant spinal motor neurons in the spinal cord, here we report that neurons of the peripheral nervous system are also fluorescently labeled in the UCHL1-eGFP reporter line. eGFP expression is turned on at embryonic ages and lasts through adulthood, allowing detailed studies of cell bodies, axons and target innervation patterns of all sensory neurons in vivo. In addition, visualization of both the sensory and the motor neurons in the same animal offers many advantages. In this report, we used UCHL1-eGFP reporter line in two different disease paradigms: diabetes and motor neuron disease. eGFP expression in sensory axons helped determine changes in epidermal nerve fiber density in a high-fat diet induced diabetes model. Our findings corroborate previous studies, and suggest that more than five months is required for significant skin denervation. Crossing UCHL1-eGFP with hSOD1G93A mice generated hSOD1G93A-UeGFP reporter line of amyotrophic lateral sclerosis, and revealed sensory nervous system defects, especially towards disease end-stage. Our studies not only emphasize the complexity of the disease in ALS, but also reveal that UCHL1-eGFP reporter line would be a valuable tool to visualize and study various aspects of sensory nervous system development and degeneration in the context of numerous diseases.

Ablating ErbB4 in PV neurons attenuates synaptic and cognitive deficits in an animal model of Alzheimer's disease.

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Zhang, Heng; Zhang, Ling; Zhou, Dongming; He, Xiao; Wang, Dongpi; Pan, Hongyu; Zhang, Xiaoqin; Mei, Yufei; Qian, Qi; Zheng, Tingting; Jones, Frank E; Sun, Binggui

2017-10-01

Accumulation of amyloid β (Aβ) induces neuronal, synaptic, and cognitive deficits in patients and animal models of Alzheimer's disease (AD). The underlying mechanisms, however, remain to be fully elucidated. In the present study, we found that Aβ interacted with ErbB4, a member of the receptor tyrosine kinase family and mainly expressed in GABAergic interneurons. Deleting ErbB4 in parvalbumin-expressing neurons (PV neurons) significantly attenuated oligomeric Aβ-induced suppression of long term potentiation (LTP). Furthermore, specific ablation of ErbB4 in PV neurons via Cre/loxP system greatly improved spatial memory and synaptic plasticity in the hippocampus of hAPP-J20 mice. The deposition of Aβ detected by 3D6 and Thioflavin S staining and the proteolytic processing of hAPP analyzed by western blotting were not affected in the hippocampus of hAPP-J20 mice by deleting ErbB4 in PV neurons. Our data suggested that ErbB4 in PV neurons mediated Aβ-induced synaptic and cognitive dysfunctions without affecting Aβ levels. Copyright © 2017 Elsevier Inc. All rights reserved.

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

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Pecevski, Dejan; Buesing, Lars; Maass, Wolfgang

2011-01-01

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

Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons.

Directory of Open Access Journals (Sweden)

Dejan Pecevski

2011-12-01

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

Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons.

Science.gov (United States)

Pecevski, Dejan; Buesing, Lars; Maass, Wolfgang

2011-12-01

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

A human mirror neuron system for language: Perspectives from signed languages of the deaf.

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Knapp, Heather Patterson; Corina, David P

2010-01-01

Language is proposed to have developed atop the human analog of the macaque mirror neuron system for action perception and production [Arbib M.A. 2005. From monkey-like action recognition to human language: An evolutionary framework for neurolinguistics (with commentaries and author's response). Behavioral and Brain Sciences, 28, 105-167; Arbib M.A. (2008). From grasp to language: Embodied concepts and the challenge of abstraction. Journal de Physiologie Paris 102, 4-20]. Signed languages of the deaf are fully-expressive, natural human languages that are perceived visually and produced manually. We suggest that if a unitary mirror neuron system mediates the observation and production of both language and non-linguistic action, three prediction can be made: (1) damage to the human mirror neuron system should non-selectively disrupt both sign language and non-linguistic action processing; (2) within the domain of sign language, a given mirror neuron locus should mediate both perception and production; and (3) the action-based tuning curves of individual mirror neurons should support the highly circumscribed set of motions that form the "vocabulary of action" for signed languages. In this review we evaluate data from the sign language and mirror neuron literatures and find that these predictions are only partially upheld. 2009 Elsevier Inc. All rights reserved.

Multi-timescale Modeling of Activity-Dependent Metabolic Coupling in the Neuron-Glia-Vasculature Ensemble

KAUST Repository

Jolivet, Renaud; Coggan, Jay S.; Allaman, Igor; Magistretti, Pierre J.

2015-01-01

time integrates the respective timescales at which energy metabolism and neuronal excitability occur. The model is constrained by relative neuronal and astrocytic oxygen and glucose utilization, by the concentration of metabolites at rest

From music making to speaking: Engaging the mirror neuron system in autism

OpenAIRE

Wan, Catherine Y.; Demaine, Krystal; Zipse, Lauryn; Norton, Andrea; Schlaug, Gottfried

2010-01-01

Individuals with autism show impairments in emotional tuning, social interactions and communication. These are functions that have been attributed to the putative human mirror neuron system (MNS), which contains neurons that respond to the actions of self and others. It has been proposed that a dysfunction of that system underlies some of the characteristics of autism. Here, we review behavioral and imaging studies that implicate the MNS (or a brain network with similar functions) in sensory-...

Vagus nerve stimulation improves locomotion and neuronal populations in a model of Parkinson's disease.

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Farrand, Ariana Q; Helke, Kristi L; Gregory, Rebecca A; Gooz, Monika; Hinson, Vanessa K; Boger, Heather A

Parkinson's disease (PD) is a progressive, neurodegenerative disorder with no disease-modifying therapies, and symptomatic treatments are often limited by debilitating side effects. In PD, locus coeruleus noradrenergic (LC-NE) neurons degenerate prior to substantia nigra dopaminergic (SN-DA) neurons. Vagus nerve stimulation (VNS) activates LC neurons, and decreases pro-inflammatory markers, allowing improvement of LC targets, making it a potential PD therapeutic. To assess therapeutic potential of VNS in a PD model. To mimic the progression of PD degeneration, rats received a systemic injection of noradrenergic neurotoxin DSP-4, followed one week later by bilateral intrastriatal injection of dopaminergic neurotoxin 6-hydroxydopamine. At this time, a subset of rats also had vagus cuffs implanted. After eleven days, rats received a precise VNS regimen twice a day for ten days, and locomotion was measured during each afternoon session. Immediately following final stimulation, rats were euthanized, and left dorsal striatum, bilateral SN and LC were sectioned for immunohistochemical detection of monoaminergic neurons (tyrosine hydroxylase, TH), α-synuclein, astrocytes (GFAP) and microglia (Iba-1). VNS significantly increased locomotion of lesioned rats. VNS also resulted in increased expression of TH in striatum, SN, and LC; decreased SN α-synuclein expression; and decreased expression of glial markers in the SN and LC of lesioned rats. Additionally, saline-treated rats after VNS, had higher LC TH and lower SN Iba-1. Our findings of increased locomotion, beneficial effects on LC-NE and SN-DA neurons, decreased α-synuclein density in SN TH-positive neurons, and neuroinflammation suggest VNS has potential as a novel PD therapeutic. Copyright © 2017 Elsevier Inc. All rights reserved.

Multiplicative multifractal modeling and discrimination of human neuronal activity

International Nuclear Information System (INIS)

Zheng Yi; Gao Jianbo; Sanchez, Justin C.; Principe, Jose C.; Okun, Michael S.

2005-01-01

Understanding neuronal firing patterns is one of the most important problems in theoretical neuroscience. It is also very important for clinical neurosurgery. In this Letter, we introduce a computational procedure to examine whether neuronal firing recordings could be characterized by cascade multiplicative multifractals. By analyzing raw recording data as well as generated spike train data from 3 patients collected in two brain areas, the globus pallidus externa (GPe) and the globus pallidus interna (GPi), we show that the neural firings are consistent with a multifractal process over certain time scale range (t 1 ,t 2 ), where t 1 is argued to be not smaller than the mean inter-spike-interval of neuronal firings, while t 2 may be related to the time that neuronal signals propagate in the major neural branching structures pertinent to GPi and GPe. The generalized dimension spectrum D q effectively differentiates the two brain areas, both intra- and inter-patients. For distinguishing between GPe and GPi, it is further shown that the cascade model is more effective than the methods recently examined by Schiff et al. as well as the Fano factor analysis. Therefore, the methodology may be useful in developing computer aided tools to help clinicians perform precision neurosurgery in the operating room

Cell Death, Neuronal Plasticity and Functional Loading in the Development of the Central Nervous System

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Keefe, J. R.

1985-01-01

Research on the precise timing and regulation of neuron production and maturation in the vestibular and visual systems of Wistar rats and several inbred strains of mice (C57B16 and Pallid mutant) concentrated upon establishing a timing baseline for mitotic development of the neurons of the vestibular nuclei and the peripheral vestibular sensory structures (maculae, cristae). This involved studies of the timing and site of neuronal cell birth and preliminary studies of neuronal cell death in both central and peripheral elements of the mammalian vestibular system. Studies on neuronal generation and maturation in the retina were recently added to provide a mechanism for more properly defining the in utero' developmental age of the individual fetal subject and to closely monitor potential transplacental effects of environmentally stressed maternal systems. Information is given on current efforts concentrating upon the (1) perinatal period of development (E18 thru P14) and (2) the role of cell death in response to variation in the functional loading of the vestibular and proprioreceptive systems in developing mammalian organisms.

Kappe neurons, a novel population of olfactory sensory neurons.

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Ahuja, Gaurav; Bozorg Nia, Shahrzad; Zapilko, Veronika; Shiriagin, Vladimir; Kowatschew, Daniel; Oka, Yuichiro; Korsching, Sigrun I

2014-02-10

Perception of olfactory stimuli is mediated by distinct populations of olfactory sensory neurons, each with a characteristic set of morphological as well as functional parameters. Beyond two large populations of ciliated and microvillous neurons, a third population, crypt neurons, has been identified in teleost and cartilaginous fishes. We report here a novel, fourth olfactory sensory neuron population in zebrafish, which we named kappe neurons for their characteristic shape. Kappe neurons are identified by their Go-like immunoreactivity, and show a distinct spatial distribution within the olfactory epithelium, similar to, but significantly different from that of crypt neurons. Furthermore, kappe neurons project to a single identified target glomerulus within the olfactory bulb, mdg5 of the mediodorsal cluster, whereas crypt neurons are known to project exclusively to the mdg2 glomerulus. Kappe neurons are negative for established markers of ciliated, microvillous and crypt neurons, but appear to have microvilli. Kappe neurons constitute the fourth type of olfactory sensory neurons reported in teleost fishes and their existence suggests that encoding of olfactory stimuli may require a higher complexity than hitherto assumed already in the peripheral olfactory system.

The Morris-Lecar neuron model embeds a leaky integrate-and-fire model

DEFF Research Database (Denmark)

Ditlevsen, Susanne; Greenwood, Priscilla

2013-01-01

We showthat the stochastic Morris–Lecar neuron, in a neighborhood of its stable point, can be approximated by a two-dimensional Ornstein Uhlenbeck (OU) modulation of a constant circular motion. The associated radial OU process is an example of a leaky integrate-and-fire (LIF) model prior to firing...

Discrimination of communication vocalizations by single neurons and groups of neurons in the auditory midbrain.

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Schneider, David M; Woolley, Sarah M N

2010-06-01

Many social animals including songbirds use communication vocalizations for individual recognition. The perception of vocalizations depends on the encoding of complex sounds by neurons in the ascending auditory system, each of which is tuned to a particular subset of acoustic features. Here, we examined how well the responses of single auditory neurons could be used to discriminate among bird songs and we compared discriminability to spectrotemporal tuning. We then used biologically realistic models of pooled neural responses to test whether the responses of groups of neurons discriminated among songs better than the responses of single neurons and whether discrimination by groups of neurons was related to spectrotemporal tuning and trial-to-trial response variability. The responses of single auditory midbrain neurons could be used to discriminate among vocalizations with a wide range of abilities, ranging from chance to 100%. The ability to discriminate among songs using single neuron responses was not correlated with spectrotemporal tuning. Pooling the responses of pairs of neurons generally led to better discrimination than the average of the two inputs and the most discriminating input. Pooling the responses of three to five single neurons continued to improve neural discrimination. The increase in discriminability was largest for groups of neurons with similar spectrotemporal tuning. Further, we found that groups of neurons with correlated spike trains achieved the largest gains in discriminability. We simulated neurons with varying levels of temporal precision and measured the discriminability of responses from single simulated neurons and groups of simulated neurons. Simulated neurons with biologically observed levels of temporal precision benefited more from pooling correlated inputs than did neurons with highly precise or imprecise spike trains. These findings suggest that pooling correlated neural responses with the levels of precision observed in the

Emergent properties of interacting populations of spiking neurons

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Stefano eCardanobile

2011-12-01

Full Text Available Dynamic neuronal networks are a key paradigm of increasing importance in brain research, concerned with the functional analysis of biological neuronal networks and, at the same time, with the synthesis of artificial brain-like systems. In this context, neuronal network models serve as mathematical tools to understand the function of brains, but they might as well develop into future tools for enhancing certain functions of our nervous system.Here, we discuss our recent achievements in developing multiplicative point processes into a viable mathematical framework for spiking network modeling. The perspective is that the dynamic behavior of these neuronal networks on the population level is faithfully reflected by a set of non-linear rate equations, describing all interactions on this level. These equations, in turn, are similar in structure to the Lotka-Volterra equations, well known by their use in modeling predator-prey relationships in population biology, but abundant applications to economic theory have also been described.We present a number of biologically relevant examples for spiking network function, which can be studied with the help of the aforementioned correspondence between spike trains and specific systems of non-linear coupled ordinary differential equations. We claim that, enabled by the use of multiplicative point processes, we can make essential contributions to a more thorough understanding of the dynamical properties of neural populations.

Transgenic tools to characterize neuronal properties of discrete populations of zebrafish neurons.

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Satou, Chie; Kimura, Yukiko; Hirata, Hiromi; Suster, Maximiliano L; Kawakami, Koichi; Higashijima, Shin-ichi

2013-09-01

The developing nervous system consists of a variety of cell types. Transgenic animals expressing reporter genes in specific classes of neuronal cells are powerful tools for the study of neuronal network formation. We generated a wide variety of transgenic zebrafish that expressed reporter genes in specific classes of neurons or neuronal progenitors. These include lines in which neurons of specific neurotransmitter phenotypes expressed fluorescent proteins or Gal4, and lines in which specific subsets of the dorsal progenitor domain in the spinal cord expressed fluorescent proteins. Using these, we examined domain organization in the developing dorsal spinal cord, and found that there are six progenitor domains in zebrafish, which is similar to the domain organization in mice. We also systematically characterized neurotransmitter properties of the neurons that are produced from each domain. Given that reporter gene expressions occurs in a wide area of the nervous system in the lines generated, these transgenic fish should serve as powerful tools for the investigation of not only the neurons in the dorsal spinal cord but also neuronal structures and functions in many other regions of the nervous system.

Bidirectional communication between sensory neurons and osteoblasts in an in vitro coculture system.

Science.gov (United States)

Kodama, Daisuke; Hirai, Takao; Kondo, Hisataka; Hamamura, Kazunori; Togari, Akifumi

2017-02-01

Recent studies have revealed that the sensory nervous system is involved in bone metabolism. However, the mechanism of communication between neurons and osteoblasts is yet to be elucidated. In this study, we investigated the signaling pathways between sensory neurons of the dorsal root ganglion (DRG) and the osteoblast-like MC3T3-E1 cells using an in vitro coculture system. Our findings indicate that signal transduction from DRG-derived neurons to MC3T3-E1 cells is suppressed by antagonists of the AMPA receptor and the NK 1 receptor. Conversely, signal transduction from MC3T3-E1 cells to DRG-derived neurons is suppressed by a P2X 7 receptor antagonist. Our results suggest that these cells communicate with each other by exocytosis of glutamate, substance P in the efferent signal, and ATP in the afferent signal. © 2017 Federation of European Biochemical Societies.

A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models

OpenAIRE

Hanuschkin, A.; Ganguli, S.; Hahnloser, R. H. R.

2013-01-01

Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map-desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop...

The PM1 neurons, movement sensitive centrifugal visual brain neurons in the locust: anatomy, physiology, and modulation by identified octopaminergic neurons.

Science.gov (United States)

Stern, Michael

2009-02-01

The locust's optic lobe contains a system of wide-field, multimodal, centrifugal neurons. Two of these cells, the protocerebrum-medulla-neurons PM4a and b, are octopaminergic. This paper describes a second pair of large centrifugal neurons (the protocerebrum-medulla-neurons PM1a and PM1b) from the brain of Locusta migratoria based on intracellular cobalt fills, electrophysiology, and immunocytochemistry. They originate and arborise in the central brain and send processes into the medulla of the optic lobe. Double intracellular recording from the same cell suggests input in the central brain and output in the optic lobe. The neurons show immunoreactivity to gamma-amino-butyric acid and its synthesising enzyme, glutamate decarboxylase. The PM1 cells are movement sensitive and show habituation to repeated visual stimulation. Bath application of octopamine causes the response to dishabituate. A very similar effect is produced by electrical stimulation of one of an octopaminergic PM4 neuron. This effect can be blocked by application of the octopamine antagonists, mianserin and phentolamine. This readily accessible system of four wide-field neurons provides a system suitable for the investigation of octopaminergic effects on the visual system at the cellular level.

Excitability of Aβ sensory neurons is altered in an animal model of peripheral neuropathy

Directory of Open Access Journals (Sweden)

Zhu Yong

2012-01-01

Full Text Available Abstract Background Causes of neuropathic pain following nerve injury remain unclear, limiting the development of mechanism-based therapeutic approaches. Animal models have provided some directions, but little is known about the specific sensory neurons that undergo changes in such a way as to induce and maintain activation of sensory pain pathways. Our previous studies implicated changes in the Aβ, normally non-nociceptive neurons in activating spinal nociceptive neurons in a cuff-induced animal model of neuropathic pain and the present study was directed specifically at determining any change in excitability of these neurons. Thus, the present study aimed at recording intracellularly from Aβ-fiber dorsal root ganglion (DRG neurons and determining excitability of the peripheral receptive field, of the cell body and of the dorsal roots. Methods A peripheral neuropathy was induced in Sprague Dawley rats by inserting two thin polyethylene cuffs around the right sciatic nerve. All animals were confirmed to exhibit tactile hypersensitivity to von Frey filaments three weeks later, before the acute electrophysiological experiments. Under stable intracellular recording conditions neurons were classified functionally on the basis of their response to natural activation of their peripheral receptive field. In addition, conduction velocity of the dorsal roots, configuration of the action potential and rate of adaptation to stimulation were also criteria for classification. Excitability was measured as the threshold to activation of the peripheral receptive field, the response to intracellular injection of depolarizing current into the soma and the response to electrical stimulation of the dorsal roots. Results In control animals mechanical thresholds of all neurons were within normal ranges. Aβ DRG neurons in neuropathic rats demonstrated a mean mechanical threshold to receptive field stimulation that were significantly lower than in control rats, a

Contribution of synchronized GABAergic neurons to dopaminergic neuron firing and bursting.

Science.gov (United States)

Morozova, Ekaterina O; Myroshnychenko, Maxym; Zakharov, Denis; di Volo, Matteo; Gutkin, Boris; Lapish, Christopher C; Kuznetsov, Alexey

2016-10-01

In the ventral tegmental area (VTA), interactions between dopamine (DA) and γ-aminobutyric acid (GABA) neurons are critical for regulating DA neuron activity and thus DA efflux. To provide a mechanistic explanation of how GABA neurons influence DA neuron firing, we developed a circuit model of the VTA. The model is based on feed-forward inhibition and recreates canonical features of the VTA neurons. Simulations revealed that γ-aminobutyric acid (GABA) receptor (GABAR) stimulation can differentially influence the firing pattern of the DA neuron, depending on the level of synchronization among GABA neurons. Asynchronous activity of GABA neurons provides a constant level of inhibition to the DA neuron and, when removed, produces a classical disinhibition burst. In contrast, when GABA neurons are synchronized by common synaptic input, their influence evokes additional spikes in the DA neuron, resulting in increased measures of firing and bursting. Distinct from previous mechanisms, the increases were not based on lowered firing rate of the GABA neurons or weaker hyperpolarization by the GABAR synaptic current. This phenomenon was induced by GABA-mediated hyperpolarization of the DA neuron that leads to decreases in intracellular calcium (Ca 2+ ) concentration, thus reducing the Ca 2+ -dependent potassium (K + ) current. In this way, the GABA-mediated hyperpolarization replaces Ca 2+ -dependent K + current; however, this inhibition is pulsatile, which allows the DA neuron to fire during the rhythmic pauses in inhibition. Our results emphasize the importance of inhibition in the VTA, which has been discussed in many studies, and suggest a novel mechanism whereby computations can occur locally. Copyright © 2016 the American Physiological Society.

NEURON and Python.

Science.gov (United States)

Hines, Michael L; Davison, Andrew P; Muller, Eilif

2009-01-01

The NEURON simulation program now allows Python to be used, alone or in combination with NEURON's traditional Hoc interpreter. Adding Python to NEURON has the immediate benefit of making available a very extensive suite of analysis tools written for engineering and science. It also catalyzes NEURON software development by offering users a modern programming tool that is recognized for its flexibility and power to create and maintain complex programs. At the same time, nothing is lost because all existing models written in Hoc, including graphical user interface tools, continue to work without change and are also available within the Python context. An example of the benefits of Python availability is the use of the xml module in implementing NEURON's Import3D and CellBuild tools to read MorphML and NeuroML model specifications.

Pathophysiological role of prostaglandin E2-induced up-regulation of the EP2 receptor in motor neuron-like NSC-34 cells and lumbar motor neurons in ALS model mice.

Science.gov (United States)

Kosuge, Yasuhiro; Miyagishi, Hiroko; Yoneoka, Yuki; Yoneda, Keiko; Nango, Hiroshi; Ishige, Kumiko; Ito, Yoshihisa

2017-07-04

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease characterized by selective degeneration of motor neurons. The primary triggers for motor neuronal death are still unknown, but inflammation is considered to be an important factor contributing to the pathophysiology of ALS both clinically and in ALS models. Prostaglandin E2 (PGE2) and its corresponding four E-prostanoid receptors play a pivotal role in the degeneration of motor neurons in human and transgenic models of ALS. It has also been shown that PGE2-EP2 signaling in glial cells (astrocytes or microglia) promotes motor neuronal death in G93A mice. The present study was designed to investigate the levels of expression of EP receptors in the spinal motor neurons of ALS model mice and to examine whether PGE2 alters the expression of EP receptors in differentiated NSC-34 cells, a motor neuron-like cell line. Immunohistochemical staining demonstrated that EP2 and EP3 immunoreactivity was localized in NeuN-positive large cells showing the typical morphology of motor neurons in mice. Semi-quantitative analysis showed that the immunoreactivity of EP2 in motor neurons was significantly increased in the early symptomatic stage in ALS model mice. In contrast, the level of EP3 expression remained constant, irrespective of age. In differentiated NSC-34 cells, bath application of PGE2 resulted in a concentration-dependent decrease of MTT reduction. Although PGE2 had no effect on cell survival at concentrations of less than 10 μM, pretreatment with 10 μM PGE2 significantly up-regulated EP2 and concomitantly potentiated cell death induced by 30 μM PGE2. These results suggest that PGE2 is an important effector for induction of the EP2 subtype in differentiated NSC-34 cells, and that not only EP2 up-regulation in glial cells but also EP2 up-regulation in motor neurons plays a pivotal role in the vulnerability of motor neurons in ALS model mice. Copyright © 2017 Elsevier Ltd. All rights

Plasticity-modulated seizure dynamics for seizure termination in realistic neuronal models

NARCIS (Netherlands)

Koppert, M.M.J.; Kalitzin, S.; Lopes da Silva, F.H.; Viergever, M.A.

2011-01-01

In previous studies we showed that autonomous absence seizure generation and termination can be explained by realistic neuronal models eliciting bi-stable dynamics. In these models epileptic seizures are triggered either by external stimuli (reflex epilepsies) or by internal fluctuations. This

Routes to Chaos Induced by a Discontinuous Resetting Process in a Hybrid Spiking Neuron Model.

Science.gov (United States)

Nobukawa, Sou; Nishimura, Haruhiko; Yamanishi, Teruya

2018-01-10

Several hybrid spiking neuron models combining continuous spike generation mechanisms and discontinuous resetting processes following spiking have been proposed. The Izhikevich neuron model, for example, can reproduce many spiking patterns. This model clearly possesses various types of bifurcations and routes to chaos under the effect of a state-dependent jump in the resetting process. In this study, we focus further on the relation between chaotic behaviour and the state-dependent jump, approaching the subject by comparing spiking neuron model versions with and without the resetting process. We first adopt a continuous two-dimensional spiking neuron model in which the orbit in the spiking state does not exhibit divergent behaviour. We then insert the resetting process into the model. An evaluation using the Lyapunov exponent with a saltation matrix and a characteristic multiplier of the Poincar'e map reveals that two types of chaotic behaviour (i.e. bursting chaotic spikes and near-period-two chaotic spikes) are induced by the resetting process. In addition, we confirm that this chaotic bursting state is generated from the periodic spiking state because of the slow- and fast-scale dynamics that arise when jumping to the hyperpolarization and depolarization regions, respectively.

Optogenetically enhanced axon regeneration: motor versus sensory neuron-specific stimulation.

Science.gov (United States)

Ward, Patricia J; Clanton, Scott L; English, Arthur W

2018-02-01

Brief neuronal activation in injured peripheral nerves is both necessary and sufficient to enhance motor axon regeneration, and this effect is specific to the activated motoneurons. It is less clear whether sensory neurons respond in a similar manner to neuronal activation following peripheral axotomy. Further, it is unknown to what extent enhancement of axon regeneration with increased neuronal activity relies on a reflexive interaction within the spinal circuitry. We used mouse genetics and optical tools to evaluate the precision and selectivity of system-specific neuronal activation to enhance axon regeneration in a mixed nerve. We evaluated sensory and motor axon regeneration in two different mouse models expressing the light-sensitive cation channel, channelrhodopsin (ChR2). We selectively activated either sensory or motor axons using light stimulation combined with transection and repair of the sciatic nerve. Regardless of genotype, the number of ChR2-positive neurons whose axons had regenerated successfully was greater following system-specific optical treatment, with no effect on the number of ChR2-negative neurons (whether motor or sensory neurons). We conclude that acute system-specific neuronal activation is sufficient to enhance both motor and sensory axon regeneration. This regeneration-enhancing effect is likely cell autonomous. © 2018 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

System characterization of neuronal excitability in the hippocampus and its relevance to observed dynamics of spontaneous seizure-like transitions

Science.gov (United States)

Zalay, Osbert C.; Serletis, Demitre; Carlen, Peter L.; Bardakjian, Berj L.

2010-06-01

Most forms of epilepsy are marked by seizure episodes that arise spontaneously. The low-magnesium/high-potassium (low-Mg2+/high-K+) experimental model of epilepsy is an acute model that produces spontaneous, recurring seizure-like events (SLEs). To elucidate the nature of spontaneous seizure transitions and their relationship to neuronal excitability, whole-cell recordings from the intact hippocampus were undertaken in vitro, and the response of hippocampal CA3 neurons to Gaussian white noise injection was obtained before and after treatment with various concentrations of low-Mg2+/high-K+ solution. A second-order Volterra kernel model was estimated for each of the input-output response pairs. The spectral energy of the responses was also computed, providing a quantitative measure of neuronal excitability. Changes in duration and amplitude of the first-order kernel correlated positively with the spectral energy increase following treatment with low-Mg2+/high-K+ solution, suggesting that variations in neuronal excitability are coded by the system kernels, in part by differences to the profile of the first-order kernel. In particular, kernel duration was more sensitive than amplitude to changes in spectral energy, and correlated more strongly with kernel area. An oscillator network model of the hippocampal CA3 was constructed to investigate the relationship of kernel duration to network excitability, and the model was able to generate spontaneous, recurrent SLEs by increasing the duration of a mode function analogous to the first-order kernel. Results from the model indicated that disruption to the dynamic balance of feedback was responsible for seizure-like transitions and the observed intermittency of SLEs. A physiological candidate for feedback imbalance consistent with the network model is the destabilizing interaction of extracellular potassium and paroxysmal neuronal activation. Altogether, these results (1) validate a mathematical model for epileptiform

Computer Modelling of Functional Aspects of Noise in Endogenously Oscillating Neurons

Science.gov (United States)

Huber, M. T.; Dewald, M.; Voigt, K.; Braun, H. A.; Moss, F.

1998-03-01

Membrane potential oscillations are a widespread feature of neuronal activity. When such oscillations operate close to the spike-triggering threshold, noise can become an essential property of spike-generation. According to that, we developed a minimal Hodgkin-Huxley-type computer model which includes a noise term. This model accounts for experimental data from quite different cells ranging from mammalian cortical neurons to fish electroreceptors. With slight modifications of the parameters, the model's behavior can be tuned to bursting activity, which additionally allows it to mimick temperature encoding in peripheral cold receptors including transitions to apparently chaotic dynamics as indicated by methods for the detection of unstable periodic orbits. Under all conditions, cooperative effects between noise and nonlinear dynamics can be shown which, beyond stochastic resonance, might be of functional significance for stimulus encoding and neuromodulation.

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

Science.gov (United States)

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

2016-03-01

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

Reciprocal cholinergic and GABAergic modulation of the small ventrolateral pacemaker neurons of Drosophila's circadian clock neuron network.

Science.gov (United States)

Lelito, Katherine R; Shafer, Orie T

2012-04-01

The relatively simple clock neuron network of Drosophila is a valuable model system for the neuronal basis of circadian timekeeping. Unfortunately, many key neuronal classes of this network are inaccessible to electrophysiological analysis. We have therefore adopted the use of genetically encoded sensors to address the physiology of the fly's circadian clock network. Using genetically encoded Ca(2+) and cAMP sensors, we have investigated the physiological responses of two specific classes of clock neuron, the large and small ventrolateral neurons (l- and s-LN(v)s), to two neurotransmitters implicated in their modulation: acetylcholine (ACh) and γ-aminobutyric acid (GABA). Live imaging of l-LN(v) cAMP and Ca(2+) dynamics in response to cholinergic agonist and GABA application were well aligned with published electrophysiological data, indicating that our sensors were capable of faithfully reporting acute physiological responses to these transmitters within single adult clock neuron soma. We extended these live imaging methods to s-LN(v)s, critical neuronal pacemakers whose physiological properties in the adult brain are largely unknown. Our s-LN(v) experiments revealed the predicted excitatory responses to bath-applied cholinergic agonists and the predicted inhibitory effects of GABA and established that the antagonism of ACh and GABA extends to their effects on cAMP signaling. These data support recently published but physiologically untested models of s-LN(v) modulation and lead to the prediction that cholinergic and GABAergic inputs to s-LN(v)s will have opposing effects on the phase and/or period of the molecular clock within these critical pacemaker neurons.

Heterogeneous delay-induced asynchrony and resonance in a small-world neuronal network system

Science.gov (United States)

Yu, Wen-Ting; Tang, Jun; Ma, Jun; Yang, Xianqing

2016-06-01

A neuronal network often involves time delay caused by the finite signal propagation time in a given biological network. This time delay is not a homogenous fluctuation in a biological system. The heterogeneous delay-induced asynchrony and resonance in a noisy small-world neuronal network system are numerically studied in this work by calculating synchronization measure and spike interval distribution. We focus on three different delay conditions: double-values delay, triple-values delay, and Gaussian-distributed delay. Our results show the following: 1) the heterogeneity in delay results in asynchronous firing in the neuronal network, and 2) maximum synchronization could be achieved through resonance given that the delay values are integer or half-integer times of each other.

Does dysfunction of the mirror neuron system contribute to symptoms in amyotrophic lateral sclerosis?

Science.gov (United States)

Eisen, Andrew; Lemon, Roger; Kiernan, Matthew C; Hornberger, Michael; Turner, Martin R

2015-07-01

There is growing evidence that mirror neurons, initially discovered over two decades ago in the monkey, are present in the human brain. In the monkey, mirror neurons characteristically fire not only when it is performing an action, such as grasping an object, but also when observing a similar action performed by another agent (human or monkey). In this review we discuss the origin, cortical distribution and possible functions of mirror neurons as a background to exploring their potential relevance in amyotrophic lateral sclerosis (ALS). We have recently proposed that ALS (and the related condition of frontotemporal dementia) may be viewed as a failure of interlinked functional complexes having their origins in key evolutionary adaptations. This can include loss of the direct projections from the corticospinal tract, and this is at least part of the explanation for impaired motor control in ALS. Since, in the monkey, corticospinal neurons also show mirror properties, ALS in humans might also affect the mirror neuron system. We speculate that a defective mirror neuron system might contribute to other ALS deficits affecting motor imagery, gesture, language and empathy. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

From Neurons to Newtons

DEFF Research Database (Denmark)

Nielsen, Bjørn Gilbert

2001-01-01

proteins generate forces, to the macroscopic levels where overt arm movements are vol- untarily controlled within an unpredictable environment by legions of neurons¯ring in orderly fashion. An extensive computer simulation system has been developed for this thesis, which at present contains a neural...... network scripting language for specifying arbitrary neural architectures, de¯nition ¯les for detailed spinal networks, various biologically realistic models of neurons, and dynamic synapses. Also included are structurally accurate models of intrafusal and extra-fusal muscle ¯bers and a general body...... that an explicit function may be derived which expresses the force that the spindle contractile elements must produce to exactly counter spindle unloading during muscle shortening. This information was used to calculate the corresponding "optimal" °-motoneuronal activity level. For some simple arm movement tasks...

Antho-RFamide-containing neurons in the primitive nervous system of the anthozoan Renilla koellikeri

DEFF Research Database (Denmark)

Pernet, Vincent; Anctil, Michel; Grimmelikhuijzen, Cornelis J P

2004-01-01

-RFamide-containing neurons in this species would contribute to our understanding of the early evolution of nervous systems. Using antisera raised against RFamide and FMRFamide, we detected immunostaining in numerous neurons throughout the nervous system of the sea pansy. The antisera revealed ectodermal nerve......-nets on the upper and lower sides of the colony and on the oral side of tentacles, in the oral disk, and in the pharynx of feeding polyps. Neurons were immunostained also in the mesogleal nerve-net of feeding polyps and in the through-conducting mesogleal nerve-net of the colonial mass. Varying densities of stained...

A Computational Model Based on Multi-Regional Calcium Imaging Represents the Spatio-Temporal Dynamics in a Caenorhabditis elegans Sensory Neuron.

Directory of Open Access Journals (Sweden)

Masahiro Kuramochi

Full Text Available Due to the huge number of neuronal cells in the brain and their complex circuit formation, computer simulation of neuronal activity is indispensable to understanding whole brain dynamics. Recently, various computational models have been developed based on whole-brain calcium imaging data. However, these analyses monitor only the activity of neuronal cell bodies and treat the cells as point unit. This point-neuron model is inexpensive in computational costs, but the model is unrealistically simplistic at representing intact neural activities in the brain. Here, we describe a novel three-unit Ordinary Differential Equation (ODE model based on the neuronal responses derived from a Caenorhabditis elegans salt-sensing neuron. We recorded calcium responses in three regions of the ASER neuron using a simple downstep of NaCl concentration. Our simple ODE model generated from a single recording can adequately reproduce and predict the temporal responses of each part of the neuron to various types of NaCl concentration changes. Our strategy which combines a simple recording data and an ODE mathematical model may be extended to realistically understand whole brain dynamics by computational simulation.

Normal and abnormal neuronal migration in the developing cerebral cortex.

Science.gov (United States)

Sun, Xue-Zhi; Takahashi, Sentaro; Cui, Chun; Zhang, Rui; Sakata-Haga, Hiromi; Sawada, Kazuhiko; Fukui, Yoshihiro

2002-08-01

Neuronal migration is the critical cellular process which initiates histogenesis of cerebral cortex. Migration involves a series of complex cell interactions and transformation. After completing their final mitosis, neurons migrate from the ventricular zone into the cortical plate, and then establish neuronal lamina and settle onto the outermost layer, forming an "inside-out" gradient of maturation. This process is guided by radial glial fibers, requires proper receptors, ligands, other unknown extracellular factors, and local signaling to stop neuronal migration. This process is also highly sensitive to various physical, chemical and biological agents as well as to genetic mutations. Any disturbance of the normal process may result in neuronal migration disorder. Such neuronal migration disorder is believed as major cause of both gross brain malformation and more special cerebral structural and functional abnormalities in experimental animals and in humans. An increasing number of instructive studies on experimental models and several genetic model systems of neuronal migration disorder have established the foundation of cortex formation and provided deeper insights into the genetic and molecular mechanisms underlying normal and abnormal neuronal migration.

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

Science.gov (United States)

Llinas, Rodolfo R.

1988-12-01

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

An electronic implementation for Liao's chaotic delayed neuron model with non-monotonous activation function

International Nuclear Information System (INIS)

Duan Shukai; Liao Xiaofeng

2007-01-01

A new chaotic delayed neuron model with non-monotonously increasing transfer function, called as chaotic Liao's delayed neuron model, was recently reported and analyzed. An electronic implementation of this model is described in detail. At the same time, some methods in circuit design, especially for circuit with time delayed unit and non-monotonously increasing activation unit, are also considered carefully. We find that the dynamical behaviors of the designed circuits are closely similar to the results predicted by numerical experiments

An in silico agent-based model demonstrates Reelin function in directing lamination of neurons during cortical development.

Science.gov (United States)

Caffrey, James R; Hughes, Barry D; Britto, Joanne M; Landman, Kerry A

2014-01-01

The characteristic six-layered appearance of the neocortex arises from the correct positioning of pyramidal neurons during development and alterations in this process can cause intellectual disabilities and developmental delay. Malformations in cortical development arise when neurons either fail to migrate properly from the germinal zones or fail to cease migration in the correct laminar position within the cortical plate. The Reelin signalling pathway is vital for correct neuronal positioning as loss of Reelin leads to a partially inverted cortex. The precise biological function of Reelin remains controversial and debate surrounds its role as a chemoattractant or stop signal for migrating neurons. To investigate this further we developed an in silico agent-based model of cortical layer formation. Using this model we tested four biologically plausible hypotheses for neuron motility and four biologically plausible hypotheses for the loss of neuron motility (conversion from migration). A matrix of 16 combinations of motility and conversion rules was applied against the known structure of mouse cortical layers in the wild-type cortex, the Reelin-null mutant, the Dab1-null mutant and a conditional Dab1 mutant. Using this approach, many combinations of motility and conversion mechanisms can be rejected. For example, the model does not support Reelin acting as a repelling or as a stopping signal. In contrast, the study lends very strong support to the notion that the glycoprotein Reelin acts as a chemoattractant for neurons. Furthermore, the most viable proposition for the conversion mechanism is one in which conversion is affected by a motile neuron sensing in the near vicinity neurons that have already converted. Therefore, this model helps elucidate the function of Reelin during neuronal migration and cortical development.

An in silico agent-based model demonstrates Reelin function in directing lamination of neurons during cortical development.

Directory of Open Access Journals (Sweden)

James R Caffrey

Full Text Available The characteristic six-layered appearance of the neocortex arises from the correct positioning of pyramidal neurons during development and alterations in this process can cause intellectual disabilities and developmental delay. Malformations in cortical development arise when neurons either fail to migrate properly from the germinal zones or fail to cease migration in the correct laminar position within the cortical plate. The Reelin signalling pathway is vital for correct neuronal positioning as loss of Reelin leads to a partially inverted cortex. The precise biological function of Reelin remains controversial and debate surrounds its role as a chemoattractant or stop signal for migrating neurons. To investigate this further we developed an in silico agent-based model of cortical layer formation. Using this model we tested four biologically plausible hypotheses for neuron motility and four biologically plausible hypotheses for the loss of neuron motility (conversion from migration. A matrix of 16 combinations of motility and conversion rules was applied against the known structure of mouse cortical layers in the wild-type cortex, the Reelin-null mutant, the Dab1-null mutant and a conditional Dab1 mutant. Using this approach, many combinations of motility and conversion mechanisms can be rejected. For example, the model does not support Reelin acting as a repelling or as a stopping signal. In contrast, the study lends very strong support to the notion that the glycoprotein Reelin acts as a chemoattractant for neurons. Furthermore, the most viable proposition for the conversion mechanism is one in which conversion is affected by a motile neuron sensing in the near vicinity neurons that have already converted. Therefore, this model helps elucidate the function of Reelin during neuronal migration and cortical development.

A high mitochondrial transport rate characterizes CNS neurons with high axonal regeneration capacity.

Directory of Open Access Journals (Sweden)

Romain Cartoni

Full Text Available Improving axonal transport in the injured and diseased central nervous system has been proposed as a promising strategy to improve neuronal repair. However, the contribution of each cargo to the repair mechanism is unknown. DRG neurons globally increase axonal transport during regeneration. Because the transport of specific cargos after axonal insult has not been examined systematically in a model of enhanced regenerative capacity, it is unknown whether the transport of all cargos would be modulated equally in injured central nervous system neurons. Here, using a microfluidic culture system we compared neurons co-deleted for PTEN and SOCS3, an established model of high axonal regeneration capacity, to control neurons. We measured the axonal transport of three cargos (mitochondria, synaptic vesicles and late endosomes in regenerating axons and found that the transport of mitochondria, but not the other cargos, was increased in PTEN/SOCS3 co-deleted axons relative to controls. The results reported here suggest a pivotal role for this organelle during axonal regeneration.

A map of octopaminergic neurons in the Drosophila brain.

Science.gov (United States)

Busch, Sebastian; Selcho, Mareike; Ito, Kei; Tanimoto, Hiromu

2009-04-20

The biogenic amine octopamine modulates diverse behaviors in invertebrates. At the single neuron level, the mode of action is well understood in the peripheral nervous system owing to its simple structure and accessibility. For elucidating the role of individual octopaminergic neurons in the modulation of complex behaviors, a detailed analysis of the connectivity in the central nervous system is required. Here we present a comprehensive anatomical map of candidate octopaminergic neurons in the adult Drosophila brain: including the supra- and subesophageal ganglia. Application of the Flp-out technique enabled visualization of 27 types of individual octopaminergic neurons. Based on their morphology and distribution of genetic markers, we found that most octopaminergic neurons project to multiple brain structures with a clear separation of dendritic and presynaptic regions. Whereas their major dendrites are confined to specific brain regions, each cell type targets different, yet defined, neuropils distributed throughout the central nervous system. This would allow them to constitute combinatorial modules assigned to the modulation of distinct neuronal processes. The map may provide an anatomical framework for the functional constitution of the octopaminergic system. It also serves as a model for the single-cell organization of a particular neurotransmitter in the brain. 2009 Wiley-Liss, Inc.

A principled dimension-reduction method for the population density approach to modeling networks of neurons with synaptic dynamics.

Science.gov (United States)

Ly, Cheng

2013-10-01

The population density approach to neural network modeling has been utilized in a variety of contexts. The idea is to group many similar noisy neurons into populations and track the probability density function for each population that encompasses the proportion of neurons with a particular state rather than simulating individual neurons (i.e., Monte Carlo). It is commonly used for both analytic insight and as a time-saving computational tool. The main shortcoming of this method is that when realistic attributes are incorporated in the underlying neuron model, the dimension of the probability density function increases, leading to intractable equations or, at best, computationally intensive simulations. Thus, developing principled dimension-reduction methods is essential for the robustness of these powerful methods. As a more pragmatic tool, it would be of great value for the larger theoretical neuroscience community. For exposition of this method, we consider a single uncoupled population of leaky integrate-and-fire neurons receiving external excitatory synaptic input only. We present a dimension-reduction method that reduces a two-dimensional partial differential-integral equation to a computationally efficient one-dimensional system and gives qualitatively accurate results in both the steady-state and nonequilibrium regimes. The method, termed modified mean-field method, is based entirely on the governing equations and not on any auxiliary variables or parameters, and it does not require fine-tuning. The principles of the modified mean-field method have potential applicability to more realistic (i.e., higher-dimensional) neural networks.

Morphometric synaptology of a whole neuron profile using a semiautomatic interactive computer system.

Science.gov (United States)

Saito, K; Niki, K

1983-07-01

We propose a new method of dealing with morphometric synaptology that processes all synapses and boutons around the HRP marked neuron on a large composite electron micrograph, rather than a qualitative or a piecemeal quantitative study of a particular synapse and/or bouton that is not positioned on the surface of the neuron. This approach requires the development of both neuroanatomical procedures, by which a specific whole neuronal profile is identified, and valuable specialized tools, which support the collection and analysis of a great volume of morphometric data from composite electron micrographs, in order to reduce the burden of the morphologist. The present report is also concerned with the total and reliable semi-automatic interactive computer system for gathering and analyzing morphometric data that has been under development in our laboratory. A morphologist performs the pattern recognition portion by using a large-sized tablet digitizer and a menu-sheet command, and the system registers the various morphometric values of many different neurons and performs statistical analysis. Some examples of morphometric measurements and analysis show the usefulness and efficiency of the proposed system and method.

Role of Serotonin Neurons in L-DOPA- and Graft-Induced Dyskinesia in a Rat Model of Parkinson's Disease

Directory of Open Access Journals (Sweden)

Eunju Shin

2012-01-01

Full Text Available L-DOPA, the most effective drug to treat motor symptoms of Parkinson's disease, causes abnormal involuntary movements, limiting its use in advanced stages of the disease. An increasing body of evidence points to the serotonin system as a key player in the appearance of L-DOPA-induced dyskinesia (LID. In fact, exogenously administered L-DOPA can be taken up by serotonin neurons, converted to dopamine and released as a false transmitter, contributing to pulsatile stimulation of striatal dopamine receptors. Accordingly, destruction of serotonin fibers or silencing serotonin neurons by serotonin agonists could counteract LID in animal models. Recent clinical work has also shown that serotonin neurons are present in the caudate/putamen of patients grafted with embryonic ventral mesencephalic cells, producing intense serotonin hyperinnervation. These patients experience graft-induced dyskinesia (GID, a type of dyskinesia phenotypically similar to the one induced by L-DOPA but independent from its administration. Interestingly, the 5-HT1A receptor agonist buspirone has been shown to suppress GID in these patients, suggesting that serotonin neurons might be involved in the etiology of GID as for LID. In this paper we will discuss the experimental and clinical evidence supporting the involvement of the serotonin system in both LID and GID.

Associative learning is necessary but not sufficient for mirror neuron development

OpenAIRE

Bonaiuto, James

2014-01-01

Existing computational models of the mirror system demonstrate the additional circuitry needed for mirror neurons to display the range of properties that they exhibit. Such models emphasize the need for existing connectivity to form visuomotor associations, processing to reduce the space of possible inputs, and demonstrate the role neurons with mirror properties might play in monitoring one's own actions.

Associative learning is necessary but not sufficient for mirror neuron development.

Science.gov (United States)

Bonaiuto, James

2014-04-01

Existing computational models of the mirror system demonstrate the additional circuitry needed for mirror neurons to display the range of properties that they exhibit. Such models emphasize the need for existing connectivity to form visuomotor associations, processing to reduce the space of possible inputs, and demonstrate the role neurons with mirror properties might play in monitoring one's own actions.

Memory-induced resonancelike suppression of spike generation in a resonate-and-fire neuron model

Science.gov (United States)

Mankin, Romi; Paekivi, Sander

2018-01-01

The behavior of a stochastic resonate-and-fire neuron model based on a reduction of a fractional noise-driven generalized Langevin equation (GLE) with a power-law memory kernel is considered. The effect of temporally correlated random activity of synaptic inputs, which arise from other neurons forming local and distant networks, is modeled as an additive fractional Gaussian noise in the GLE. Using a first-passage-time formulation, in certain system parameter domains exact expressions for the output interspike interval (ISI) density and for the survival probability (the probability that a spike is not generated) are derived and their dependence on input parameters, especially on the memory exponent, is analyzed. In the case of external white noise, it is shown that at intermediate values of the memory exponent the survival probability is significantly enhanced in comparison with the cases of strong and weak memory, which causes a resonancelike suppression of the probability of spike generation as a function of the memory exponent. Moreover, an examination of the dependence of multimodality in the ISI distribution on input parameters shows that there exists a critical memory exponent αc≈0.402 , which marks a dynamical transition in the behavior of the system. That phenomenon is illustrated by a phase diagram describing the emergence of three qualitatively different structures of the ISI distribution. Similarities and differences between the behavior of the model at internal and external noises are also discussed.

Signals and Circuits in the Purkinje Neuron

Directory of Open Access Journals (Sweden)

Ze'ev R Abrams

2011-09-01

Full Text Available Purkinje neurons in the cerebellum have over 100,000 inputs organized in an orthogonal geometry, and a single output channel. As the sole output of the cerebellar cortex layer, their complex firing pattern has been associated with motor control and learning. As such they have been extensively modeled and measured using tools ranging from electrophysiology and neuroanatomy, to dynamic systems and artificial intelligence methods. However, there is an alternative approach to analyze and describe the neuronal output of these cells using concepts from Electrical Engineering, particularly signal processing and digital/analog circuits. By viewing the Purkinje neuron as an unknown circuit to be reverse-engineered, we can use the tools that provide the foundations of today’s integrated circuits and communication systems to analyze the Purkinje system at the circuit level. We use Fourier transforms to analyze and isolate the inherent frequency modes in the Purkinje neuron and define 3 unique frequency ranges associated with the cells’ output. Comparing the Purkinje neuron to a signal generator that can be externally modulated adds an entire level of complexity to the functional role of these neurons both in terms of data analysis and information processing, relying on Fourier analysis methods in place of statistical ones. We also re-describe some of the recent literature in the field, using the nomenclature of signal processing. Furthermore, by comparing the experimental data of the past decade with basic electronic circuitry, we can resolve the outstanding controversy in the field, by recognizing that the Purkinje neuron can act as a multivibrator circuit.

A mouse model of DEPDC5-related epilepsy: Neuronal loss of Depdc5 causes dysplastic and ectopic neurons, increased mTOR signaling, and seizure susceptibility.

Science.gov (United States)

Yuskaitis, Christopher J; Jones, Brandon M; Wolfson, Rachel L; Super, Chloe E; Dhamne, Sameer C; Rotenberg, Alexander; Sabatini, David M; Sahin, Mustafa; Poduri, Annapurna

2018-03-01

DEPDC5 is a newly identified epilepsy-related gene implicated in focal epilepsy, brain malformations, and Sudden Unexplained Death in Epilepsy (SUDEP). In vitro, DEPDC5 negatively regulates amino acid sensing by the mTOR complex 1 (mTORC1) pathway, but the role of DEPDC5 in neurodevelopment and epilepsy has not been described. No animal model of DEPDC5-related epilepsy has recapitulated the neurological phenotypes seen in patients, and germline knockout rodent models are embryonic lethal. Here, we establish a neuron-specific Depdc5 conditional knockout mouse by cre-recombination under the Synapsin1 promotor. Depdc5 flox/flox -Syn1 Cre (Depdc5cc+) mice survive to adulthood with a progressive neurologic phenotype that includes motor abnormalities (i.e., hind limb clasping) and reduced survival compared to littermate control mice. Depdc5cc+ mice have larger brains with increased cortical neuron size and dysplastic neurons throughout the cortex, comparable to the abnormal neurons seen in human focal cortical dysplasia specimens. Depdc5 results in constitutive mTORC1 hyperactivation exclusively in neurons as measured by the increased phosphorylation of the downstream ribosomal protein S6. Despite a lack of increased mTORC1 signaling within astrocytes, Depdc5cc+ brains show reactive astrogliosis. We observed two Depdc5cc+ mice to have spontaneous seizures, including a terminal seizure. We demonstrate that as a group Depdc5cc+ mice have lowered seizure thresholds, as evidenced by decreased latency to seizures after chemoconvulsant injection and increased mortality from pentylenetetrazole-induced seizures. In summary, our neuron-specific Depdc5 knockout mouse model recapitulates clinical, pathological, and biochemical features of human DEPDC5-related epilepsy and brain malformations. We thereby present an important model in which to study targeted therapeutic strategies for DEPDC5-related conditions. Copyright © 2017 Elsevier Inc. All rights reserved.

Comparison of independent screens on differentially vulnerable motor neurons reveals alpha-synuclein as a common modifier in motor neuron diseases.

Science.gov (United States)

Kline, Rachel A; Kaifer, Kevin A; Osman, Erkan Y; Carella, Francesco; Tiberi, Ariana; Ross, Jolill; Pennetta, Giuseppa; Lorson, Christian L; Murray, Lyndsay M

2017-03-01

The term "motor neuron disease" encompasses a spectrum of disorders in which motor neurons are the primary pathological target. However, in both patients and animal models of these diseases, not all motor neurons are equally vulnerable, in that while some motor neurons are lost very early in disease, others remain comparatively intact, even at late stages. This creates a valuable system to investigate the factors that regulate motor neuron vulnerability. In this study, we aim to use this experimental paradigm to identify potential transcriptional modifiers. We have compared the transcriptome of motor neurons from healthy wild-type mice, which are differentially vulnerable in the childhood motor neuron disease Spinal Muscular Atrophy (SMA), and have identified 910 transcriptional changes. We have compared this data set with published microarray data sets on other differentially vulnerable motor neurons. These neurons were differentially vulnerable in the adult onset motor neuron disease Amyotrophic Lateral Sclerosis (ALS), but the screen was performed on the equivalent population of neurons from neurologically normal human, rat and mouse. This cross species comparison has generated a refined list of differentially expressed genes, including CELF5, Col5a2, PGEMN1, SNCA, Stmn1 and HOXa5, alongside a further enrichment for synaptic and axonal transcripts. As an in vivo validation, we demonstrate that the manipulation of a significant number of these transcripts can modify the neurodegenerative phenotype observed in a Drosophila line carrying an ALS causing mutation. Finally, we demonstrate that vector-mediated expression of alpha-synuclein (SNCA), a transcript decreased in selectively vulnerable motor neurons in all four screens, can extend life span, increase weight and decrease neuromuscular junction pathology in a mouse model of SMA. In summary, we have combined multiple data sets to identify transcripts, which are strong candidates for being phenotypic modifiers

Multifaceted effects of oligodendroglial exosomes on neurons: impact on neuronal firing rate, signal transduction and gene regulation.

Science.gov (United States)

Fröhlich, Dominik; Kuo, Wen Ping; Frühbeis, Carsten; Sun, Jyh-Jang; Zehendner, Christoph M; Luhmann, Heiko J; Pinto, Sheena; Toedling, Joern; Trotter, Jacqueline; Krämer-Albers, Eva-Maria

2014-09-26

Exosomes are small membranous vesicles of endocytic origin that are released by almost every cell type. They exert versatile functions in intercellular communication important for many physiological and pathological processes. Recently, exosomes attracted interest with regard to their role in cell-cell communication in the nervous system. We have shown that exosomes released from oligodendrocytes upon stimulation with the neurotransmitter glutamate are internalized by neurons and enhance the neuronal stress tolerance. Here, we demonstrate that oligodendroglial exosomes also promote neuronal survival during oxygen-glucose deprivation, a model of cerebral ischaemia. We show the transfer from oligodendrocytes to neurons of superoxide dismutase and catalase, enzymes which are known to help cells to resist oxidative stress. Additionally, we identify various effects of oligodendroglial exosomes on neuronal physiology. Electrophysiological analysis using in vitro multi-electrode arrays revealed an increased firing rate of neurons exposed to oligodendroglial exosomes. Moreover, gene expression analysis and phosphorylation arrays uncovered differentially expressed genes and altered signal transduction pathways in neurons after exosome treatment. Our study thus provides new insight into the broad spectrum of action of oligodendroglial exosomes and their effects on neuronal physiology. The exchange of extracellular vesicles between neural cells may exhibit remarkable potential to impact brain performance. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Mirror neuron system: basic findings and clinical applications.

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Iacoboni, Marco; Mazziotta, John C

2007-09-01

In primates, ventral premotor and rostral inferior parietal neurons fire during the execution of hand and mouth actions. Some cells (called mirror neurons) also fire when hand and mouth actions are just observed. Mirror neurons provide a simple neural mechanism for understanding the actions of others. In humans, posterior inferior frontal and rostral inferior parietal areas have mirror properties. These human areas are relevant to imitative learning and social behavior. Indeed, the socially isolating condition of autism is associated with a deficit in mirror neuron areas. Strategies inspired by mirror neuron research recently have been used in the treatment of autism and in motor rehabilitation after stroke.

Controlling Chaos in Neuron Based on Lasalle Invariance Principle

International Nuclear Information System (INIS)

Wei Duqu; Qin Yinghua

2011-01-01

A new control law is proposed to asymptotically stabilize the chaotic neuron system based on LaSalle invariant principle. The control technique does not require analytical knowledge of the system dynamics and operates without an explicit knowledge of the desired steady-state position. The well-known modified Hodgkin-Huxley (MHH) and Hindmarsh-Rose (HR) model neurons are taken as examples to verify the implementation of our method. Simulation results show the proposed control law is effective. The outcome of this study is significant since it is helpful to understand the learning process of a human brain towards the information processing, memory and abnormal discharge of the brain neurons. (general)

Studies on functional roles of the histaminergic neuron system by using pharmacological agents, knockout mice and positron emission tomography

International Nuclear Information System (INIS)

Watanabe, Takehiko; Yanai, Kazuhiko

2001-01-01

Since one of us, Takehiko Watanabe (TW), elucidated the location and distribution of the histaminergic neuron system in the brain with antibody raised against L-histidine decarboxylase (a histamine-forming enzyme, HDC) as a marker in 1984 and came to Tohoku University School of Medicine in Sendai, we have been collaborating on the functions of this neuron system by using pharmacological agents, knockout mice of the histamine-related genes, and, in some cases, positron emission tomography (PET). Many of our graduate students and colleagues have been actively involved in histamine research since 1985. Our extensive studies have clarified some of the functions of histamine neurons using methods from molecular techniques to non-invasive human PET imaging. Histamine neurons are involved in many brain functions, such as spontaneous locomotion, arousal in wake-sleep cycle, appetite control, seizures, learning and memory, aggressive behavior and emotion. Particularly, the histaminergic neuron system is one of the most important neuron systems to maintain and stimulate wakefulness. Histamine also functions as a biprotection system against various noxious and unfavorable stimuli (for examples, convulsion, nociception, drug sensitization, ischemic lesions, and stress). Although activators of histamine neurons have not been clinically available until now, we would like to point out that the activation of the histaminergic neuron system is important to maintain mental health. Here, we summarize the newly-discovered functions of histamine neurons mainly on the basis of results from our research groups. (author)

Studies on functional roles of the histaminergic neuron system by using pharmacological agents, knockout mice and positron emission tomography

Energy Technology Data Exchange (ETDEWEB)

Watanabe, Takehiko; Yanai, Kazuhiko [Tohoku Univ., Sendai (Japan). Graduate School of Medicine

2001-12-01

Since one of us, Takehiko Watanabe (TW), elucidated the location and distribution of the histaminergic neuron system in the brain with antibody raised against L-histidine decarboxylase (a histamine-forming enzyme, HDC) as a marker in 1984 and came to Tohoku University School of Medicine in Sendai, we have been collaborating on the functions of this neuron system by using pharmacological agents, knockout mice of the histamine-related genes, and, in some cases, positron emission tomography (PET). Many of our graduate students and colleagues have been actively involved in histamine research since 1985. Our extensive studies have clarified some of the functions of histamine neurons using methods from molecular techniques to non-invasive human PET imaging. Histamine neurons are involved in many brain functions, such as spontaneous locomotion, arousal in wake-sleep cycle, appetite control, seizures, learning and memory, aggressive behavior and emotion. Particularly, the histaminergic neuron system is one of the most important neuron systems to maintain and stimulate wakefulness. Histamine also functions as a biprotection system against various noxious and unfavorable stimuli (for examples, convulsion, nociception, drug sensitization, ischemic lesions, and stress). Although activators of histamine neurons have not been clinically available until now, we would like to point out that the activation of the histaminergic neuron system is important to maintain mental health. Here, we summarize the newly-discovered functions of histamine neurons mainly on the basis of results from our research groups. (author)

Real-time computing platform for spiking neurons (RT-spike).

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Ros, Eduardo; Ortigosa, Eva M; Agís, Rodrigo; Carrillo, Richard; Arnold, Michael

2006-07-01

A computing platform is described for simulating arbitrary networks of spiking neurons in real time. A hybrid computing scheme is adopted that uses both software and hardware components to manage the tradeoff between flexibility and computational power; the neuron model is implemented in hardware and the network model and the learning are implemented in software. The incremental transition of the software components into hardware is supported. We focus on a spike response model (SRM) for a neuron where the synapses are modeled as input-driven conductances. The temporal dynamics of the synaptic integration process are modeled with a synaptic time constant that results in a gradual injection of charge. This type of model is computationally expensive and is not easily amenable to existing software-based event-driven approaches. As an alternative we have designed an efficient time-based computing architecture in hardware, where the different stages of the neuron model are processed in parallel. Further improvements occur by computing multiple neurons in parallel using multiple processing units. This design is tested using reconfigurable hardware and its scalability and performance evaluated. Our overall goal is to investigate biologically realistic models for the real-time control of robots operating within closed action-perception loops, and so we evaluate the performance of the system on simulating a model of the cerebellum where the emulation of the temporal dynamics of the synaptic integration process is important.

Human iPSC-Derived Endothelial Cells and Microengineered Organ-Chip Enhance Neuronal Development

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Samuel Sances

2018-04-01

Full Text Available Summary: Human stem cell-derived models of development and neurodegenerative diseases are challenged by cellular immaturity in vitro. Microengineered organ-on-chip (or Organ-Chip systems are designed to emulate microvolume cytoarchitecture and enable co-culture of distinct cell types. Brain microvascular endothelial cells (BMECs share common signaling pathways with neurons early in development, but their contribution to human neuronal maturation is largely unknown. To study this interaction and influence of microculture, we derived both spinal motor neurons and BMECs from human induced pluripotent stem cells and observed increased calcium transient function and Chip-specific gene expression in Organ-Chips compared with 96-well plates. Seeding BMECs in the Organ-Chip led to vascular-neural interaction and specific gene activation that further enhanced neuronal function and in vivo-like signatures. The results show that the vascular system has specific maturation effects on spinal cord neural tissue, and the use of Organ-Chips can move stem cell models closer to an in vivo condition. : Sances et al. combine Organ-Chip technology with human induced pluripotent stem cell-derived spinal motor neurons to study the maturation effects of Organ-Chip culture. By including microvascular cells also derived from the same patient line, the authors show enhancement of neuronal function, reproduction of vascular-neuron pathways, and specific gene activation that resembles in vivo spinal cord development. Keywords: organ-on-chip, spinal cord, iPSC, disease modeling, amyotrophic lateral sclerosis, microphysiological system, brain microvascular endothelial cells, spinal motor neurons, vasculature, microfluidic device

A mathematical model of communication between groups of circadian neurons in Drosophila melanogaster.

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Risau-Gusman, Sebastián; Gleiser, Pablo M

2014-12-01

In the fruit fly, circadian behavior is controlled by a small number of specialized neurons, whose molecular clocks are relatively well known. However, much less is known about how these neurons communicate among themselves. In particular, only 1 circadian neuropeptide, pigment-dispersing factor (PDF), has been identified, and most aspects of its interaction with the molecular clock remain to be elucidated. Furthermore, it is speculated that many other peptides should contribute to circadian communication. We have developed a relatively detailed model of the 2 main groups of circadian pacemaker neurons (sLNvs and LNds) to investigate these issues. We have proposed many possible mechanisms for the interaction between the synchronization factors and the molecular clock, and we have compared the outputs with the experimental results reported in the literature both for the wild-type and PDF-null mutant. We have studied how different the properties of each neuron should be to account for the observations reported for the sLNvs in the mutant. We have found that only a few mechanisms, mostly related to the slowing down of nuclear entry of a circadian protein, can synchronize neurons that present these differences. Detailed immunofluorescent recordings have suggested that, whereas in the mutant, LNd neurons are synchronized, in the wild-type, a subset of the LNds oscillate faster than the rest. With our model, we find that a more likely explanation for the same observations is that this subset is being driven outside its synchronization range and displays therefore a complex pattern of oscillation.

Performance limitations of relay neurons.

Directory of Open Access Journals (Sweden)

Rahul Agarwal

Full Text Available Relay cells are prevalent throughout sensory systems and receive two types of inputs: driving and modulating. The driving input contains receptive field properties that must be transmitted while the modulating input alters the specifics of transmission. For example, the visual thalamus contains relay neurons that receive driving inputs from the retina that encode a visual image, and modulating inputs from reticular activating system and layer 6 of visual cortex that control what aspects of the image will be relayed back to visual cortex for perception. What gets relayed depends on several factors such as attentional demands and a subject's goals. In this paper, we analyze a biophysical based model of a relay cell and use systems theoretic tools to construct analytic bounds on how well the cell transmits a driving input as a function of the neuron's electrophysiological properties, the modulating input, and the driving signal parameters. We assume that the modulating input belongs to a class of sinusoidal signals and that the driving input is an irregular train of pulses with inter-pulse intervals obeying an exponential distribution. Our analysis applies to any [Formula: see text] order model as long as the neuron does not spike without a driving input pulse and exhibits a refractory period. Our bounds on relay reliability contain performance obtained through simulation of a second and third order model, and suggest, for instance, that if the frequency of the modulating input increases or the DC offset decreases, then relay increases. Our analysis also shows, for the first time, how the biophysical properties of the neuron (e.g. ion channel dynamics define the oscillatory patterns needed in the modulating input for appropriately timed relay of sensory information. In our discussion, we describe how our bounds predict experimentally observed neural activity in the basal ganglia in (i health, (ii in Parkinson's disease (PD, and (iii in PD during

Timing of neuron development in the rodent vestibular system

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Keefe, J. R.

1982-01-01

The timing of cell generation (onset and duration) in the developing rat vestibular and proprioceptive systems is investigated. The results clearly indicate a defined time-span for generation of all neurons in the central nervous system nuclei studied. This cytogenetic period in both vestibular and proprioceptive sensory nuclei is determined to occur during and immediately after placentation, a potentially critical period for spaceflight exposure due to alterations in maternal physiology.

On Empathy: The Mirror Neuron System and Art Education

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Jeffers, Carol S.

2009-01-01

This paper re/considers empathy and its implications for learning in the art classroom, particularly in light of relevant neuroscientific investigations of the mirror neuron system recently discovered in the human brain. These investigations reinterpret the meaning of perception, resonance, and connection, and point to the fundamental importance…

Optogenetic dissection of neuronal circuits in zebrafish using viral gene transfer and the Tet system