WorldWideScience

Sample records for neuronal population dynamics

  1. Spiking, Bursting, and Population Dynamics in a Network of Growth Transform Neurons.

    Science.gov (United States)

    Gangopadhyay, Ahana; Chakrabartty, Shantanu

    2017-04-27

    This paper investigates the dynamical properties of a network of neurons, each of which implements an asynchronous mapping based on polynomial growth transforms. In the first part of this paper, we present a geometric approach for visualizing the dynamics of the network where each of the neurons traverses a trajectory in a dual optimization space, whereas the network itself traverses a trajectory in an equivalent primal optimization space. We show that as the network learns to solve basic classification tasks, different choices of primal-dual mapping produce unique but interpretable neural dynamics like noise shaping, spiking, and bursting. While the proposed framework is general enough, in this paper, we demonstrate its use for designing support vector machines (SVMs) that exhibit noise-shaping properties similar to those of ΣΔ modulators, and for designing SVMs that learn to encode information using spikes and bursts. It is demonstrated that the emergent switching, spiking, and burst dynamics produced by each neuron encodes its respective margin of separation from a classification hyperplane whose parameters are encoded by the network population dynamics. We believe that the proposed growth transform neuron model and the underlying geometric framework could serve as an important tool to connect well-established machine learning algorithms like SVMs to neuromorphic principles like spiking, bursting, population encoding, and noise shaping.

  2. Metastable states and quasicycles in a stochastic Wilson-Cowan model of neuronal population dynamics

    KAUST Repository

    Bressloff, Paul C.

    2010-01-01

    We analyze a stochastic model of neuronal population dynamics with intrinsic noise. In the thermodynamic limit N→∞, where N determines the size of each population, the dynamics is described by deterministic Wilson-Cowan equations. On the other hand

  3. Dynamics of a structured neuron population

    International Nuclear Information System (INIS)

    Pakdaman, Khashayar; Salort, Delphine; Perthame, Benoît

    2010-01-01

    We study the dynamics of assemblies of interacting neurons. For large fully connected networks, the dynamics of the system can be described by a partial differential equation reminiscent of age-structure models used in mathematical ecology, where the 'age' of a neuron represents the time elapsed since its last discharge. The nonlinearity arises from the connectivity J of the network. We prove some mathematical properties of the model that are directly related to qualitative properties. On the one hand, we prove that it is well-posed and that it admits stationary states which, depending upon the connectivity, can be unique or not. On the other hand, we study the long time behaviour of solutions; both for small and large J, we prove the relaxation to the steady state describing asynchronous firing of the neurons. In the middle range, numerical experiments show that periodic solutions appear expressing re-synchronization of the network and asynchronous firing

  4. Representation of dynamical stimuli in populations of threshold neurons.

    Directory of Open Access Journals (Sweden)

    Tatjana Tchumatchenko

    2011-10-01

    Full Text Available Many sensory or cognitive events are associated with dynamic current modulations in cortical neurons. This raises an urgent demand for tractable model approaches addressing the merits and limits of potential encoding strategies. Yet, current theoretical approaches addressing the response to mean- and variance-encoded stimuli rarely provide complete response functions for both modes of encoding in the presence of correlated noise. Here, we investigate the neuronal population response to dynamical modifications of the mean or variance of the synaptic bombardment using an alternative threshold model framework. In the variance and mean channel, we provide explicit expressions for the linear and non-linear frequency response functions in the presence of correlated noise and use them to derive population rate response to step-like stimuli. For mean-encoded signals, we find that the complete response function depends only on the temporal width of the input correlation function, but not on other functional specifics. Furthermore, we show that both mean- and variance-encoded signals can relay high-frequency inputs, and in both schemes step-like changes can be detected instantaneously. Finally, we obtain the pairwise spike correlation function and the spike triggered average from the linear mean-evoked response function. These results provide a maximally tractable limiting case that complements and extends previous results obtained in the integrate and fire framework.

  5. Stochastic Nonlinear Evolutional Model of the Large-Scaled Neuronal Population and Dynamic Neural Coding Subject to Stimulation

    International Nuclear Information System (INIS)

    Wang Rubin; Yu Wei

    2005-01-01

    In this paper, we investigate how the population of neuronal oscillators deals with information and the dynamic evolution of neural coding when the external stimulation acts on it. Numerically computing method is used to describe the evolution process of neural coding in three-dimensioned space. The numerical result proves that only the suitable stimulation can change the coupling structure and plasticity of neurons

  6. Emergent properties of interacting populations of spiking neurons.

    Science.gov (United States)

    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.

  7. Metastable states and quasicycles in a stochastic Wilson-Cowan model of neuronal population dynamics

    KAUST Repository

    Bressloff, Paul C.

    2010-11-03

    We analyze a stochastic model of neuronal population dynamics with intrinsic noise. In the thermodynamic limit N→∞, where N determines the size of each population, the dynamics is described by deterministic Wilson-Cowan equations. On the other hand, for finite N the dynamics is described by a master equation that determines the probability of spiking activity within each population. We first consider a single excitatory population that exhibits bistability in the deterministic limit. The steady-state probability distribution of the stochastic network has maxima at points corresponding to the stable fixed points of the deterministic network; the relative weighting of the two maxima depends on the system size. For large but finite N, we calculate the exponentially small rate of noise-induced transitions between the resulting metastable states using a Wentzel-Kramers- Brillouin (WKB) approximation and matched asymptotic expansions. We then consider a two-population excitatory or inhibitory network that supports limit cycle oscillations. Using a diffusion approximation, we reduce the dynamics to a neural Langevin equation, and show how the intrinsic noise amplifies subthreshold oscillations (quasicycles). © 2010 The American Physical Society.

  8. Mean-field dynamics of a population of stochastic map neurons

    Science.gov (United States)

    Franović, Igor; Maslennikov, Oleg V.; Bačić, Iva; Nekorkin, Vladimir I.

    2017-07-01

    We analyze the emergent regimes and the stimulus-response relationship of a population of noisy map neurons by means of a mean-field model, derived within the framework of cumulant approach complemented by the Gaussian closure hypothesis. It is demonstrated that the mean-field model can qualitatively account for stability and bifurcations of the exact system, capturing all the generic forms of collective behavior, including macroscopic excitability, subthreshold oscillations, periodic or chaotic spiking, and chaotic bursting dynamics. Apart from qualitative analogies, we find a substantial quantitative agreement between the exact and the approximate system, as reflected in matching of the parameter domains admitting the different dynamical regimes, as well as the characteristic properties of the associated time series. The effective model is further shown to reproduce with sufficient accuracy the phase response curves of the exact system and the assembly's response to external stimulation of finite amplitude and duration.

  9. Emergent properties of interacting populations of spiking neurons

    Directory of Open Access Journals (Sweden)

    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.

  10. Kappe neurons, a novel population of olfactory sensory neurons

    OpenAIRE

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

    2014-01-01

    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 ar...

  11. Attractor dynamics in local neuronal networks

    Directory of Open Access Journals (Sweden)

    Jean-Philippe eThivierge

    2014-03-01

    Full Text Available Patterns of synaptic connectivity in various regions of the brain are characterized by the presence of synaptic motifs, defined as unidirectional and bidirectional synaptic contacts that follow a particular configuration and link together small groups of neurons. Recent computational work proposes that a relay network (two populations communicating via a third, relay population of neurons can generate precise patterns of neural synchronization. Here, we employ two distinct models of neuronal dynamics and show that simulated neural circuits designed in this way are caught in a global attractor of activity that prevents neurons from modulating their response on the basis of incoming stimuli. To circumvent the emergence of a fixed global attractor, we propose a mechanism of selective gain inhibition that promotes flexible responses to external stimuli. We suggest that local neuronal circuits may employ this mechanism to generate precise patterns of neural synchronization whose transient nature delimits the occurrence of a brief stimulus.

  12. Discontinuous Galerkin finite element method for solving population density functions of cortical pyramidal and thalamic neuronal populations.

    Science.gov (United States)

    Huang, Chih-Hsu; Lin, Chou-Ching K; Ju, Ming-Shaung

    2015-02-01

    Compared with the Monte Carlo method, the population density method is efficient for modeling collective dynamics of neuronal populations in human brain. In this method, a population density function describes the probabilistic distribution of states of all neurons in the population and it is governed by a hyperbolic partial differential equation. In the past, the problem was mainly solved by using the finite difference method. In a previous study, a continuous Galerkin finite element method was found better than the finite difference method for solving the hyperbolic partial differential equation; however, the population density function often has discontinuity and both methods suffer from a numerical stability problem. The goal of this study is to improve the numerical stability of the solution using discontinuous Galerkin finite element method. To test the performance of the new approach, interaction of a population of cortical pyramidal neurons and a population of thalamic neurons was simulated. The numerical results showed good agreement between results of discontinuous Galerkin finite element and Monte Carlo methods. The convergence and accuracy of the solutions are excellent. The numerical stability problem could be resolved using the discontinuous Galerkin finite element method which has total-variation-diminishing property. The efficient approach will be employed to simulate the electroencephalogram or dynamics of thalamocortical network which involves three populations, namely, thalamic reticular neurons, thalamocortical neurons and cortical pyramidal neurons. Copyright © 2014 Elsevier Ltd. All rights reserved.

  13. Bayesian population decoding of spiking neurons.

    Science.gov (United States)

    Gerwinn, Sebastian; Macke, Jakob; Bethge, Matthias

    2009-01-01

    The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs and contains information about temporal fluctuations in the stimulus. Leaky integrate-and-fire neurons constitute a popular class of encoding models, in which spike times depend directly on the temporal structure of the inputs. However, optimal decoding rules for these models have only been studied explicitly in the noiseless case. Here, we study decoding rules for probabilistic inference of a continuous stimulus from the spike times of a population of leaky integrate-and-fire neurons with threshold noise. We derive three algorithms for approximating the posterior distribution over stimuli as a function of the observed spike trains. In addition to a reconstruction of the stimulus we thus obtain an estimate of the uncertainty as well. Furthermore, we derive a 'spike-by-spike' online decoding scheme that recursively updates the posterior with the arrival of each new spike. We use these decoding rules to reconstruct time-varying stimuli represented by a Gaussian process from spike trains of single neurons as well as neural populations.

  14. Bayesian population decoding of spiking neurons

    Directory of Open Access Journals (Sweden)

    Sebastian Gerwinn

    2009-10-01

    Full Text Available The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs and contains information about temporal fluctuations in the stimulus. Leaky integrate-and-fire neurons constitute a popular class of encoding models, in which spike times depend directly on the temporal structure of the inputs. However, optimal decoding rules for these models have only been studied explicitly in the noiseless case. Here, we study decoding rules for probabilistic inference of a continuous stimulus from the spike times of a population of leaky integrate-and-fire neurons with threshold noise. We derive three algorithms for approximating the posterior distribution over stimuli as a function of the observed spike trains. In addition to a reconstruction of the stimulus we thus obtain an estimate of the uncertainty as well. Furthermore, we derive a `spike-by-spike' online decoding scheme that recursively updates the posterior with the arrival of each new spike. We use these decoding rules to reconstruct time-varying stimuli represented by a Gaussian process from spike trains of single neurons as well as neural populations.

  15. Kappe neurons, a novel population of olfactory sensory neurons.

    Science.gov (United States)

    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.

  16. Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size.

    Science.gov (United States)

    Schwalger, Tilo; Deger, Moritz; Gerstner, Wulfram

    2017-04-01

    Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50-2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations.

  17. Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size

    Science.gov (United States)

    Gerstner, Wulfram

    2017-01-01

    Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50–2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations. PMID:28422957

  18. Proteolytic Remodeling of Perineuronal Nets: Effects on Synaptic Plasticity and Neuronal Population Dynamics

    Directory of Open Access Journals (Sweden)

    P. Lorenzo Bozzelli

    2018-01-01

    Full Text Available The perineuronal net (PNN represents a lattice-like structure that is prominently expressed along the soma and proximal dendrites of parvalbumin- (PV- positive interneurons in varied brain regions including the cortex and hippocampus. It is thus apposed to sites at which PV neurons receive synaptic input. Emerging evidence suggests that changes in PNN integrity may affect glutamatergic input to PV interneurons, a population that is critical for the expression of synchronous neuronal population discharges that occur with gamma oscillations and sharp-wave ripples. The present review is focused on the composition of PNNs, posttranslation modulation of PNN components by sulfation and proteolysis, PNN alterations in disease, and potential effects of PNN remodeling on neuronal plasticity at the single-cell and population level.

  19. From Quasiperiodic Partial Synchronization to Collective Chaos in Populations of Inhibitory Neurons with Delay.

    Science.gov (United States)

    Pazó, Diego; Montbrió, Ernest

    2016-06-10

    Collective chaos is shown to emerge, via a period-doubling cascade, from quasiperiodic partial synchronization in a population of identical inhibitory neurons with delayed global coupling. This system is thoroughly investigated by means of an exact model of the macroscopic dynamics, valid in the thermodynamic limit. The collective chaotic state is reproduced numerically with a finite population, and persists in the presence of weak heterogeneities. Finally, the relationship of the model's dynamics with fast neuronal oscillations is discussed.

  20. Fluctuations and information filtering in coupled populations of spiking neurons with adaptation.

    Science.gov (United States)

    Deger, Moritz; Schwalger, Tilo; Naud, Richard; Gerstner, Wulfram

    2014-12-01

    Finite-sized populations of spiking elements are fundamental to brain function but also are used in many areas of physics. Here we present a theory of the dynamics of finite-sized populations of spiking units, based on a quasirenewal description of neurons with adaptation. We derive an integral equation with colored noise that governs the stochastic dynamics of the population activity in response to time-dependent stimulation and calculate the spectral density in the asynchronous state. We show that systems of coupled populations with adaptation can generate a frequency band in which sensory information is preferentially encoded. The theory is applicable to fully as well as randomly connected networks and to leaky integrate-and-fire as well as to generalized spiking neurons with adaptation on multiple time scales.

  1. Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning.

    Science.gov (United States)

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

    2016-11-01

    Recent models of movement generation in motor cortex have sought to explain neural activity not as a function of movement parameters, known as representational models, but as a dynamical system acting at the level of the population. Despite evidence supporting this framework, the evaluation of representational models and their integration with dynamical systems is incomplete in the literature. Using a representational velocity-tuning based simulation of center-out reaching, we show that incorporating variable latency offsets between neural activity and kinematics is sufficient to generate rotational dynamics at the level of neural populations, a phenomenon observed in motor cortex. However, we developed a covariance-matched permutation test (CMPT) that reassigns neural data between task conditions independently for each neuron while maintaining overall neuron-to-neuron relationships, revealing that rotations based on the representational model did not uniquely depend on the underlying condition structure. In contrast, rotations based on either a dynamical model or motor cortex data depend on this relationship, providing evidence that the dynamical model more readily explains motor cortex activity. Importantly, implementing a recurrent neural network we demonstrate that both representational tuning properties and rotational dynamics emerge, providing evidence that a dynamical system can reproduce previous findings of representational tuning. Finally, using motor cortex data in combination with the CMPT, we show that results based on small numbers of neurons or conditions should be interpreted cautiously, potentially informing future experimental design. Together, our findings reinforce the view that representational models lack the explanatory power to describe complex aspects of single neuron and population level activity.

  2. 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. .

  3. Stochastic synchronization of neuronal populations with intrinsic and extrinsic noise.

    KAUST Repository

    Bressloff, Paul C; Lai, Yi Ming

    2011-01-01

    We extend the theory of noise-induced phase synchronization to the case of a neural master equation describing the stochastic dynamics of an ensemble of uncoupled neuronal population oscillators with intrinsic and extrinsic noise. The master

  4. Symmetry breaking in two interacting populations of quadratic integrate-and-fire neurons

    Science.gov (United States)

    Ratas, Irmantas; Pyragas, Kestutis

    2017-10-01

    We analyze the dynamics of two coupled identical populations of quadratic integrate-and-fire neurons, which represent the canonical model for class I neurons near the spiking threshold. The populations are heterogeneous; they include both inherently spiking and excitable neurons. The coupling within and between the populations is global via synapses that take into account the finite width of synaptic pulses. Using a recently developed reduction method based on the Lorentzian ansatz, we derive a closed system of equations for the neuron's firing rates and the mean membrane potentials in both populations. The reduced equations are exact in the infinite-size limit. The bifurcation analysis of the equations reveals a rich variety of nonsymmetric patterns, including a splay state, antiphase periodic oscillations, chimera-like states, and chaotic oscillations as well as bistabilities between various states. The validity of the reduced equations is confirmed by direct numerical simulations of the finite-size networks.

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

    Science.gov (United States)

    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.

  6. Population activity structure of excitatory and inhibitory neurons.

    Science.gov (United States)

    Bittner, Sean R; Williamson, Ryan C; Snyder, Adam C; Litwin-Kumar, Ashok; Doiron, Brent; Chase, Steven M; Smith, Matthew A; Yu, Byron M

    2017-01-01

    Many studies use population analysis approaches, such as dimensionality reduction, to characterize the activity of large groups of neurons. To date, these methods have treated each neuron equally, without taking into account whether neurons are excitatory or inhibitory. We studied population activity structure as a function of neuron type by applying factor analysis to spontaneous activity from spiking networks with balanced excitation and inhibition. Throughout the study, we characterized population activity structure by measuring its dimensionality and the percentage of overall activity variance that is shared among neurons. First, by sampling only excitatory or only inhibitory neurons, we found that the activity structures of these two populations in balanced networks are measurably different. We also found that the population activity structure is dependent on the ratio of excitatory to inhibitory neurons sampled. Finally we classified neurons from extracellular recordings in the primary visual cortex of anesthetized macaques as putative excitatory or inhibitory using waveform classification, and found similarities with the neuron type-specific population activity structure of a balanced network with excitatory clustering. These results imply that knowledge of neuron type is important, and allows for stronger statistical tests, when interpreting population activity structure.

  7. Population activity structure of excitatory and inhibitory neurons.

    Directory of Open Access Journals (Sweden)

    Sean R Bittner

    Full Text Available Many studies use population analysis approaches, such as dimensionality reduction, to characterize the activity of large groups of neurons. To date, these methods have treated each neuron equally, without taking into account whether neurons are excitatory or inhibitory. We studied population activity structure as a function of neuron type by applying factor analysis to spontaneous activity from spiking networks with balanced excitation and inhibition. Throughout the study, we characterized population activity structure by measuring its dimensionality and the percentage of overall activity variance that is shared among neurons. First, by sampling only excitatory or only inhibitory neurons, we found that the activity structures of these two populations in balanced networks are measurably different. We also found that the population activity structure is dependent on the ratio of excitatory to inhibitory neurons sampled. Finally we classified neurons from extracellular recordings in the primary visual cortex of anesthetized macaques as putative excitatory or inhibitory using waveform classification, and found similarities with the neuron type-specific population activity structure of a balanced network with excitatory clustering. These results imply that knowledge of neuron type is important, and allows for stronger statistical tests, when interpreting population activity structure.

  8. Population activity structure of excitatory and inhibitory neurons

    Science.gov (United States)

    Doiron, Brent

    2017-01-01

    Many studies use population analysis approaches, such as dimensionality reduction, to characterize the activity of large groups of neurons. To date, these methods have treated each neuron equally, without taking into account whether neurons are excitatory or inhibitory. We studied population activity structure as a function of neuron type by applying factor analysis to spontaneous activity from spiking networks with balanced excitation and inhibition. Throughout the study, we characterized population activity structure by measuring its dimensionality and the percentage of overall activity variance that is shared among neurons. First, by sampling only excitatory or only inhibitory neurons, we found that the activity structures of these two populations in balanced networks are measurably different. We also found that the population activity structure is dependent on the ratio of excitatory to inhibitory neurons sampled. Finally we classified neurons from extracellular recordings in the primary visual cortex of anesthetized macaques as putative excitatory or inhibitory using waveform classification, and found similarities with the neuron type-specific population activity structure of a balanced network with excitatory clustering. These results imply that knowledge of neuron type is important, and allows for stronger statistical tests, when interpreting population activity structure. PMID:28817581

  9. Dynamics of Competition between Subnetworks of Spiking Neuronal Networks in the Balanced State

    Science.gov (United States)

    Lagzi, Fereshteh; Rotter, Stefan

    2015-01-01

    We explore and analyze the nonlinear switching dynamics of neuronal networks with non-homogeneous connectivity. The general significance of such transient dynamics for brain function is unclear; however, for instance decision-making processes in perception and cognition have been implicated with it. The network under study here is comprised of three subnetworks of either excitatory or inhibitory leaky integrate-and-fire neurons, of which two are of the same type. The synaptic weights are arranged to establish and maintain a balance between excitation and inhibition in case of a constant external drive. Each subnetwork is randomly connected, where all neurons belonging to a particular population have the same in-degree and the same out-degree. Neurons in different subnetworks are also randomly connected with the same probability; however, depending on the type of the pre-synaptic neuron, the synaptic weight is scaled by a factor. We observed that for a certain range of the “within” versus “between” connection weights (bifurcation parameter), the network activation spontaneously switches between the two sub-networks of the same type. This kind of dynamics has been termed “winnerless competition”, which also has a random component here. In our model, this phenomenon is well described by a set of coupled stochastic differential equations of Lotka-Volterra type that imply a competition between the subnetworks. The associated mean-field model shows the same dynamical behavior as observed in simulations of large networks comprising thousands of spiking neurons. The deterministic phase portrait is characterized by two attractors and a saddle node, its stochastic component is essentially given by the multiplicative inherent noise of the system. We find that the dwell time distribution of the active states is exponential, indicating that the noise drives the system randomly from one attractor to the other. A similar model for a larger number of populations might

  10. Dynamics of Competition between Subnetworks of Spiking Neuronal Networks in the Balanced State.

    Science.gov (United States)

    Lagzi, Fereshteh; Rotter, Stefan

    2015-01-01

    We explore and analyze the nonlinear switching dynamics of neuronal networks with non-homogeneous connectivity. The general significance of such transient dynamics for brain function is unclear; however, for instance decision-making processes in perception and cognition have been implicated with it. The network under study here is comprised of three subnetworks of either excitatory or inhibitory leaky integrate-and-fire neurons, of which two are of the same type. The synaptic weights are arranged to establish and maintain a balance between excitation and inhibition in case of a constant external drive. Each subnetwork is randomly connected, where all neurons belonging to a particular population have the same in-degree and the same out-degree. Neurons in different subnetworks are also randomly connected with the same probability; however, depending on the type of the pre-synaptic neuron, the synaptic weight is scaled by a factor. We observed that for a certain range of the "within" versus "between" connection weights (bifurcation parameter), the network activation spontaneously switches between the two sub-networks of the same type. This kind of dynamics has been termed "winnerless competition", which also has a random component here. In our model, this phenomenon is well described by a set of coupled stochastic differential equations of Lotka-Volterra type that imply a competition between the subnetworks. The associated mean-field model shows the same dynamical behavior as observed in simulations of large networks comprising thousands of spiking neurons. The deterministic phase portrait is characterized by two attractors and a saddle node, its stochastic component is essentially given by the multiplicative inherent noise of the system. We find that the dwell time distribution of the active states is exponential, indicating that the noise drives the system randomly from one attractor to the other. A similar model for a larger number of populations might suggest a

  11. 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

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

    Directory of Open Access Journals (Sweden)

    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

  13. Inferring eye position from populations of lateral intraparietal neurons.

    Science.gov (United States)

    Graf, Arnulf Ba; Andersen, Richard A

    2014-05-20

    Understanding how the brain computes eye position is essential to unraveling high-level visual functions such as eye movement planning, coordinate transformations and stability of spatial awareness. The lateral intraparietal area (LIP) is essential for this process. However, despite decades of research, its contribution to the eye position signal remains controversial. LIP neurons have recently been reported to inaccurately represent eye position during a saccadic eye movement, and to be too slow to support a role in high-level visual functions. We addressed this issue by predicting eye position and saccade direction from the responses of populations of LIP neurons. We found that both signals were accurately predicted before, during and after a saccade. Also, the dynamics of these signals support their contribution to visual functions. These findings provide a principled understanding of the coding of information in populations of neurons within an important node of the cortical network for visual-motor behaviors.DOI: http://dx.doi.org/10.7554/eLife.02813.001. Copyright © 2014, Graf and Andersen.

  14. Transistor analogs of emergent iono-neuronal dynamics.

    Science.gov (United States)

    Rachmuth, Guy; Poon, Chi-Sang

    2008-06-01

    Neuromorphic analog metal-oxide-silicon (MOS) transistor circuits promise compact, low-power, and high-speed emulations of iono-neuronal dynamics orders-of-magnitude faster than digital simulation. However, their inherently limited input voltage dynamic range vs power consumption and silicon die area tradeoffs makes them highly sensitive to transistor mismatch due to fabrication inaccuracy, device noise, and other nonidealities. This limitation precludes robust analog very-large-scale-integration (aVLSI) circuits implementation of emergent iono-neuronal dynamics computations beyond simple spiking with limited ion channel dynamics. Here we present versatile neuromorphic analog building-block circuits that afford near-maximum voltage dynamic range operating within the low-power MOS transistor weak-inversion regime which is ideal for aVLSI implementation or implantable biomimetic device applications. The fabricated microchip allowed robust realization of dynamic iono-neuronal computations such as coincidence detection of presynaptic spikes or pre- and postsynaptic activities. As a critical performance benchmark, the high-speed and highly interactive iono-neuronal simulation capability on-chip enabled our prompt discovery of a minimal model of chaotic pacemaker bursting, an emergent iono-neuronal behavior of fundamental biological significance which has hitherto defied experimental testing or computational exploration via conventional digital or analog simulations. These compact and power-efficient transistor analogs of emergent iono-neuronal dynamics open new avenues for next-generation neuromorphic, neuroprosthetic, and brain-machine interface applications.

  15. Diverse coupling of neurons to populations in sensory cortex.

    Science.gov (United States)

    Okun, Michael; Steinmetz, Nicholas; Cossell, Lee; Iacaruso, M Florencia; Ko, Ho; Barthó, Péter; Moore, Tirin; Hofer, Sonja B; Mrsic-Flogel, Thomas D; Carandini, Matteo; Harris, Kenneth D

    2015-05-28

    A large population of neurons can, in principle, produce an astronomical number of distinct firing patterns. In cortex, however, these patterns lie in a space of lower dimension, as if individual neurons were "obedient members of a huge orchestra". Here we use recordings from the visual cortex of mouse (Mus musculus) and monkey (Macaca mulatta) to investigate the relationship between individual neurons and the population, and to establish the underlying circuit mechanisms. We show that neighbouring neurons can differ in their coupling to the overall firing of the population, ranging from strongly coupled 'choristers' to weakly coupled 'soloists'. Population coupling is largely independent of sensory preferences, and it is a fixed cellular attribute, invariant to stimulus conditions. Neurons with high population coupling are more strongly affected by non-sensory behavioural variables such as motor intention. Population coupling reflects a causal relationship, predicting the response of a neuron to optogenetically driven increases in local activity. Moreover, population coupling indicates synaptic connectivity; the population coupling of a neuron, measured in vivo, predicted subsequent in vitro estimates of the number of synapses received from its neighbours. Finally, population coupling provides a compact summary of population activity; knowledge of the population couplings of n neurons predicts a substantial portion of their n(2) pairwise correlations. Population coupling therefore represents a novel, simple measure that characterizes the relationship of each neuron to a larger population, explaining seemingly complex network firing patterns in terms of basic circuit variables.

  16. Modulation of neuronal dynamic range using two different adaptation mechanisms

    Directory of Open Access Journals (Sweden)

    Lei Wang

    2017-01-01

    Full Text Available The capability of neurons to discriminate between intensity of external stimulus is measured by its dynamic range. A larger dynamic range indicates a greater probability of neuronal survival. In this study, the potential roles of adaptation mechanisms (ion currents in modulating neuronal dynamic range were numerically investigated. Based on the adaptive exponential integrate-and-fire model, which includes two different adaptation mechanisms, i.e. subthreshold and suprathreshold (spike-triggered adaptation, our results reveal that the two adaptation mechanisms exhibit rather different roles in regulating neuronal dynamic range. Specifically, subthreshold adaptation acts as a negative factor that observably decreases the neuronal dynamic range, while suprathreshold adaptation has little influence on the neuronal dynamic range. Moreover, when stochastic noise was introduced into the adaptation mechanisms, the dynamic range was apparently enhanced, regardless of what state the neuron was in, e.g. adaptive or non-adaptive. Our model results suggested that the neuronal dynamic range can be differentially modulated by different adaptation mechanisms. Additionally, noise was a non-ignorable factor, which could effectively modulate the neuronal dynamic range.

  17. Dynamical responses to external stimuli for both cases of excitatory and inhibitory synchronization in a complex neuronal network.

    Science.gov (United States)

    Kim, Sang-Yoon; Lim, Woochang

    2017-10-01

    For studying how dynamical responses to external stimuli depend on the synaptic-coupling type, we consider two types of excitatory and inhibitory synchronization (i.e., synchronization via synaptic excitation and inhibition) in complex small-world networks of excitatory regular spiking (RS) pyramidal neurons and inhibitory fast spiking (FS) interneurons. For both cases of excitatory and inhibitory synchronization, effects of synaptic couplings on dynamical responses to external time-periodic stimuli S ( t ) (applied to a fraction of neurons) are investigated by varying the driving amplitude A of S ( t ). Stimulated neurons are phase-locked to external stimuli for both cases of excitatory and inhibitory couplings. On the other hand, the stimulation effect on non-stimulated neurons depends on the type of synaptic coupling. The external stimulus S ( t ) makes a constructive effect on excitatory non-stimulated RS neurons (i.e., it causes external phase lockings in the non-stimulated sub-population), while S ( t ) makes a destructive effect on inhibitory non-stimulated FS interneurons (i.e., it breaks up original inhibitory synchronization in the non-stimulated sub-population). As results of these different effects of S ( t ), the type and degree of dynamical response (e.g., synchronization enhancement or suppression), characterized by the dynamical response factor [Formula: see text] (given by the ratio of synchronization degree in the presence and absence of stimulus), are found to vary in a distinctly different way, depending on the synaptic-coupling type. Furthermore, we also measure the matching degree between the dynamics of the two sub-populations of stimulated and non-stimulated neurons in terms of a "cross-correlation" measure [Formula: see text]. With increasing A , based on [Formula: see text], we discuss the cross-correlations between the two sub-populations, affecting the dynamical responses to S ( t ).

  18. A model based approach in observing the activity of neuronal populations for the prediction of epileptic seizures

    International Nuclear Information System (INIS)

    Chong, M.S.; Nesic, D.; Kuhlmann, L.; Postoyan, R.; Varsavsky, A.; Cook, M.

    2010-01-01

    Full text: Epilepsy is a common neurological disease that affects 0.5-1 % of the world's population. In cases where known treatments cannot achieve complete recovery, seizure prediction is essential so that preventive measures can be undertaken to prevent resultant injury. The elcctroencephalogram (EEG) is a widely used diagnostic tool for epilepsy. However, the EEG does not provide a detailed view of the underlying seizure causing neuronal mechanisms. Knowing the dynamics of the neuronal population is useful because tracking the evolution of the neuronal mechanisms will allow us to track the brain's progression from interictal to ictal state. Wendling and colleagues proposed a parameterised mathematical model that represents the activity of interconnected neuronal populations. By modifying the parameters, this model is able to reproduce signals that are very similar to the real EEG depicting commonly observed patterns during interictal and ictal periods. The transition from non-seizure to seizure activity, as seen in the EEG. is hypothesised to be due to the impairment of inhibition. Using Wendling's model, we designed a deterministic nonlinear estimator to recover the average membrane potential of the neuronal populations from a single channel EEG signal. for any fixed and known parameter values. Our nonlinear estimator is analytically proven to asymptotically converge to the true state of the model and illustrated in simulations. We were able to computationally observe the dynamics of the three neuronal populations described in the model: excitatory, fast and slow inhibitory populations. This forms a first step towards the prediction of epileptic seiwres. (author)

  19. Phase-coherence transitions and communication in the gamma range between delay-coupled neuronal populations.

    Directory of Open Access Journals (Sweden)

    Alessandro Barardi

    2014-07-01

    Full Text Available Synchronization between neuronal populations plays an important role in information transmission between brain areas. In particular, collective oscillations emerging from the synchronized activity of thousands of neurons can increase the functional connectivity between neural assemblies by coherently coordinating their phases. This synchrony of neuronal activity can take place within a cortical patch or between different cortical regions. While short-range interactions between neurons involve just a few milliseconds, communication through long-range projections between different regions could take up to tens of milliseconds. How these heterogeneous transmission delays affect communication between neuronal populations is not well known. To address this question, we have studied the dynamics of two bidirectionally delayed-coupled neuronal populations using conductance-based spiking models, examining how different synaptic delays give rise to in-phase/anti-phase transitions at particular frequencies within the gamma range, and how this behavior is related to the phase coherence between the two populations at different frequencies. We have used spectral analysis and information theory to quantify the information exchanged between the two networks. For different transmission delays between the two coupled populations, we analyze how the local field potential and multi-unit activity calculated from one population convey information in response to a set of external inputs applied to the other population. The results confirm that zero-lag synchronization maximizes information transmission, although out-of-phase synchronization allows for efficient communication provided the coupling delay, the phase lag between the populations, and the frequency of the oscillations are properly matched.

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

    Directory of Open Access Journals (Sweden)

    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

  1. Population coding in sparsely connected networks of noisy neurons

    OpenAIRE

    Tripp, Bryan P.; Orchard, Jeff

    2012-01-01

    This study examines the relationship between population coding and spatial connection statistics in networks of noisy neurons. Encoding of sensory information in the neocortex is thought to require coordinated neural populations, because individual cortical neurons respond to a wide range of stimuli, and exhibit highly variable spiking in response to repeated stimuli. Population coding is rooted in network structure, because cortical neurons receive information only from other neurons, and be...

  2. Brain-wide neuronal dynamics during motor adaptation in zebrafish.

    Science.gov (United States)

    Ahrens, Misha B; Li, Jennifer M; Orger, Michael B; Robson, Drew N; Schier, Alexander F; Engert, Florian; Portugues, Ruben

    2012-05-09

    A fundamental question in neuroscience is how entire neural circuits generate behaviour and adapt it to changes in sensory feedback. Here we use two-photon calcium imaging to record the activity of large populations of neurons at the cellular level, throughout the brain of larval zebrafish expressing a genetically encoded calcium sensor, while the paralysed animals interact fictively with a virtual environment and rapidly adapt their motor output to changes in visual feedback. We decompose the network dynamics involved in adaptive locomotion into four types of neuronal response properties, and provide anatomical maps of the corresponding sites. A subset of these signals occurred during behavioural adjustments and are candidates for the functional elements that drive motor learning. Lesions to the inferior olive indicate a specific functional role for olivocerebellar circuitry in adaptive locomotion. This study enables the analysis of brain-wide dynamics at single-cell resolution during behaviour.

  3. Connectivity and dynamics of neuronal networks as defined by the shape of individual neurons

    International Nuclear Information System (INIS)

    Ahnert, Sebastian E; A N Travencolo, Bruno; Costa, Luciano da Fontoura

    2009-01-01

    Biological neuronal networks constitute a special class of dynamical systems, as they are formed by individual geometrical components, namely the neurons. In the existing literature, relatively little attention has been given to the influence of neuron shape on the overall connectivity and dynamics of the emerging networks. The current work addresses this issue by considering simplified neuronal shapes consisting of circular regions (soma/axons) with spokes (dendrites). Networks are grown by placing these patterns randomly in the two-dimensional (2D) plane and establishing connections whenever a piece of dendrite falls inside an axon. Several topological and dynamical properties of the resulting graph are measured, including the degree distribution, clustering coefficients, symmetry of connections, size of the largest connected component, as well as three hierarchical measurements of the local topology. By varying the number of processes of the individual basic patterns, we can quantify relationships between the individual neuronal shape and the topological and dynamical features of the networks. Integrate-and-fire dynamics on these networks is also investigated with respect to transient activation from a source node, indicating that long-range connections play an important role in the propagation of avalanches.

  4. Population coding in sparsely connected networks of noisy neurons.

    Science.gov (United States)

    Tripp, Bryan P; Orchard, Jeff

    2012-01-01

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

  5. Population Coding in Sparsely Connected Networks of Noisy Neurons

    Directory of Open Access Journals (Sweden)

    Bryan Patrick Tripp

    2012-05-01

    Full Text Available This study examines the relationship between population coding and spatial connection statistics in networks of noisy neurons. Encoding of sensory information in the neocortex is thought to require coordinated neural populations, because individual cortical neurons respond to a wide range of stimuli, and exhibit highly variable spiking in response to repeated stimuli. Population coding is rooted in network structure, because cortical neurons receive information only from other neurons, and because the information they encode must be decoded by other neurons, if it is to affect behaviour. However, population coding theory has often ignored network structure, or assumed discrete, fully-connected populations (in contrast with the sparsely connected, continuous sheet of the cortex. In this study, we model a sheet of cortical neurons with sparse, primarily local connections, and find that a network with this structure can encode multiple internal state variables with high signal-to-noise ratio. However, in our model, although connection probability varies with the distance between neurons, we find that the connections cannot be instantiated at random according to these probabilities, but must have additional structure if information is to be encoded with high fidelity.

  6. Stochastic synchronization of neuronal populations with intrinsic and extrinsic noise.

    KAUST Repository

    Bressloff, Paul C

    2011-05-03

    We extend the theory of noise-induced phase synchronization to the case of a neural master equation describing the stochastic dynamics of an ensemble of uncoupled neuronal population oscillators with intrinsic and extrinsic noise. The master equation formulation of stochastic neurodynamics represents the state of each population by the number of currently active neurons, and the state transitions are chosen so that deterministic Wilson-Cowan rate equations are recovered in the mean-field limit. We apply phase reduction and averaging methods to a corresponding Langevin approximation of the master equation in order to determine how intrinsic noise disrupts synchronization of the population oscillators driven by a common extrinsic noise source. We illustrate our analysis by considering one of the simplest networks known to generate limit cycle oscillations at the population level, namely, a pair of mutually coupled excitatory (E) and inhibitory (I) subpopulations. We show how the combination of intrinsic independent noise and extrinsic common noise can lead to clustering of the population oscillators due to the multiplicative nature of both noise sources under the Langevin approximation. Finally, we show how a similar analysis can be carried out for another simple population model that exhibits limit cycle oscillations in the deterministic limit, namely, a recurrent excitatory network with synaptic depression; inclusion of synaptic depression into the neural master equation now generates a stochastic hybrid system.

  7. 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.

  8. Dynamics of Phosphoinositide-Dependent Signaling in Sympathetic Neurons

    OpenAIRE

    Kruse, Martin; Vivas, Oscar; Traynor-Kaplan, Alexis; Hille, Bertil

    2016-01-01

    In neurons, loss of plasma membrane phosphatidylinositol 4,5-bisphosphate [PI(4,5)P2] leads to a decrease in exocytosis and changes in electrical excitability. Restoration of PI(4,5)P2 levels after phospholipase C activation is therefore essential for a return to basal neuronal activity. However, the dynamics of phosphoinositide metabolism have not been analyzed in neurons. We measured dynamic changes of PI(4,5)P2, phosphatidylinositol 4-phosphate, diacylglycerol, inositol 1,4,5-trisphosphate...

  9. Information processing by neuronal populations

    National Research Council Canada - National Science Library

    Hölscher, Christian; Munk, Matthias

    2009-01-01

    ... simultaneously recorded spike trains 120 Mark Laubach, Nandakumar S. Narayanan, and Eyal Y. Kimchi Part III Neuronal population information coding and plasticity in specific brain areas 149 7 F...

  10. Defining POMC neurons using transgenic reagents: impact of transient Pomc expression in diverse immature neuronal populations.

    Science.gov (United States)

    Padilla, Stephanie L; Reef, Daniel; Zeltser, Lori M

    2012-03-01

    Melanocortin signaling plays a central role in the regulation of phenotypes related to body weight and energy homeostasis. To specifically target and study the function of proopiomelanocortin (POMC) neurons, Pomc promoter elements have been utilized to generate reporter and Cre recombinase transgenic reagents. Across gestation, we find that Pomc is dynamically expressed in many sites in the developing mouse forebrain, midbrain, hindbrain, spinal cord, and retina. Although Pomc expression in most embryonic brain regions is transient, it is sufficient to direct Cre-mediated recombination of floxed alleles. We visualize the populations affected by this transgene by crossing Pomc-Cre mice to ROSA reporter strains and identify 62 sites of recombination throughout the adult brain, including several nuclei implicated in energy homeostasis regulation. To compare the relationship between acute Pomc promoter activity and Pomc-Cre-mediated recombination at the single cell level, we crossed Pomc-enhanced green fluorescent protein (eGFP) and Pomc-Cre;ROSA-tdTomato lines. We detect the highest concentration of Pomc-eGFP+ cells in the arcuate nucleus of the hypothalamus and dentate gyrus but also observe smaller populations of labeled cells in the nucleus of the solitary tract, periventricular zone of the third ventricle, and cerebellum. Consistent with the dynamic nature of Pomc expression in the embryo, the vast majority of neurons marked with the tdTomato reporter do not express eGFP in the adult. Thus, recombination in off-target sites could contribute to physiological phenotypes using Pomc-Cre transgenics. For example, we find that approximately 83% of the cells in the arcuate nucleus of the hypothalamus immunoreactive for leptin-induced phosphorylated signal transducer and activator of transcription 3 are marked with Pomc-Cre;ROSA-tdTomato; only 13% of these are eGFP+ POMC neurons.

  11. Dynamical networks: Finding, measuring, and tracking neural population activity using network science

    Directory of Open Access Journals (Sweden)

    Mark D. Humphries

    2017-12-01

    Full Text Available Systems neuroscience is in a headlong rush to record from as many neurons at the same time as possible. As the brain computes and codes using neuron populations, it is hoped these data will uncover the fundamentals of neural computation. But with hundreds, thousands, or more simultaneously recorded neurons come the inescapable problems of visualizing, describing, and quantifying their interactions. Here I argue that network science provides a set of scalable, analytical tools that already solve these problems. By treating neurons as nodes and their interactions as links, a single network can visualize and describe an arbitrarily large recording. I show that with this description we can quantify the effects of manipulating a neural circuit, track changes in population dynamics over time, and quantitatively define theoretical concepts of neural populations such as cell assemblies. Using network science as a core part of analyzing population recordings will thus provide both qualitative and quantitative advances to our understanding of neural computation.

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

    Science.gov (United States)

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

    2014-03-11

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

  13. Onset Dynamics of Action Potentials in Rat Neocortical Neurons and Identified Snail Neurons: Quantification of the Difference

    OpenAIRE

    Volgushev, Maxim; Malyshev, Aleksey; Balaban, Pavel; Chistiakova, Marina; Volgushev, Stanislav; Wolf, Fred

    2008-01-01

    The generation of action potentials (APs) is a key process in the operation of nerve cells and the communication between neurons. Action potentials in mammalian central neurons are characterized by an exceptionally fast onset dynamics, which differs from the typically slow and gradual onset dynamics seen in identified snail neurons. Here we describe a novel method of analysis which provides a quantitative measure of the onset dynamics of action potentials. This method captures the...

  14. Firing dynamics of an autaptic neuron

    International Nuclear Information System (INIS)

    Wang Heng-Tong; Chen Yong

    2015-01-01

    Autapses are synapses that connect a neuron to itself in the nervous system. Previously, both experimental and theoretical studies have demonstrated that autaptic connections in the nervous system have a significant physiological function. Autapses in nature provide self-delayed feedback, thus introducing an additional timescale to neuronal activities and causing many dynamic behaviors in neurons. Recently, theoretical studies have revealed that an autapse provides a control option for adjusting the response of a neuron: e.g., an autaptic connection can cause the electrical activities of the Hindmarsh–Rose neuron to switch between quiescent, periodic, and chaotic firing patterns; an autapse can enhance or suppress the mode-locking status of a neuron injected with sinusoidal current; and the firing frequency and interspike interval distributions of the response spike train can also be modified by the autapse. In this paper, we review recent studies that showed how an autapse affects the response of a single neuron. (topical review)

  15. Noise and neuronal populations conspire to encode simple waveforms reliably

    Science.gov (United States)

    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.

  16. The effect of correlated neuronal firing and neuronal heterogeneity on population coding accuracy in guinea pig inferior colliculus.

    Directory of Open Access Journals (Sweden)

    Oran Zohar

    Full Text Available It has been suggested that the considerable noise in single-cell responses to a stimulus can be overcome by pooling information from a large population. Theoretical studies indicated that correlations in trial-to-trial fluctuations in the responses of different neurons may limit the improvement due to pooling. Subsequent theoretical studies have suggested that inherent neuronal diversity, i.e., the heterogeneity of tuning curves and other response properties of neurons preferentially tuned to the same stimulus, can provide a means to overcome this limit. Here we study the effect of spike-count correlations and the inherent neuronal heterogeneity on the ability to extract information from large neural populations. We use electrophysiological data from the guinea pig Inferior-Colliculus to capture inherent neuronal heterogeneity and single cell statistics, and introduce response correlations artificially. To this end, we generate pseudo-population responses, based on single-cell recording of neurons responding to auditory stimuli with varying binaural correlations. Typically, when pseudo-populations are generated from single cell data, the responses within the population are statistically independent. As a result, the information content of the population will increase indefinitely with its size. In contrast, here we apply a simple algorithm that enables us to generate pseudo-population responses with variable spike-count correlations. This enables us to study the effect of neuronal correlations on the accuracy of conventional rate codes. We show that in a homogenous population, in the presence of even low-level correlations, information content is bounded. In contrast, utilizing a simple linear readout, that takes into account the natural heterogeneity, even of neurons preferentially tuned to the same stimulus, within the neural population, one can overcome the correlated noise and obtain a readout whose accuracy grows linearly with the size of

  17. Not a single but multiple populations of GABAergic neurons control sleep.

    Science.gov (United States)

    Luppi, Pierre-Hervé; Peyron, Christelle; Fort, Patrice

    2017-04-01

    The role of gamma-amino butyric acid (GABA) in sleep induction and maintenance is well accepted since most insomnia treatments target GABAa receptors. However, the population(s) of GABAergic neurons involved in the beneficial effect of GABA on sleep remains to be identified. This is not an easy task since GABAergic neurons are widely distributed in all brain structures. A recently growing number of populations of GABAergic neurons have been involved in sleep control. We first review here possible candidates for inducing non-rapid eye movement (NREM) sleep including the GABAergic neurons of the ventrolateral preoptic area, the parafacial zone in the brainstem, the nucleus accumbens and the cortex. We also discuss the role of several populations of GABAergic neurons in rapid eye movement (REM) sleep control. Indeed, it is well accepted that muscle atonia occurring during REM sleep is due to a GABA/glycinergic hyperpolarization of motoneurons. Recent evidence strongly suggests that these neurons are located in the ventral medullary reticular formation. It has also recently been shown that neurons containing the neuropeptide melanin concentrating hormone and GABA located in the lateral hypothalamic area control REM sleep expression. Finally, a population of REM-off GABAergic neurons located in the ventrolateral periaqueductal gray has been shown to gate REM sleep by inhibiting glutamatergic neurons located in the sublaterodorsal tegmental nucleus. In summary, recent data clearly indicate that multiple populations of GABAergic neurons located throughout the brain from the cortex to the medulla oblongata control NREM and REM sleep. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. Assimilation of Biophysical Neuronal Dynamics in Neuromorphic VLSI.

    Science.gov (United States)

    Wang, Jun; Breen, Daniel; Akinin, Abraham; Broccard, Frederic; Abarbanel, Henry D I; Cauwenberghs, Gert

    2017-12-01

    Representing the biophysics of neuronal dynamics and behavior offers a principled analysis-by-synthesis approach toward understanding mechanisms of nervous system functions. We report on a set of procedures assimilating and emulating neurobiological data on a neuromorphic very large scale integrated (VLSI) circuit. The analog VLSI chip, NeuroDyn, features 384 digitally programmable parameters specifying for 4 generalized Hodgkin-Huxley neurons coupled through 12 conductance-based chemical synapses. The parameters also describe reversal potentials, maximal conductances, and spline regressed kinetic functions for ion channel gating variables. In one set of experiments, we assimilated membrane potential recorded from one of the neurons on the chip to the model structure upon which NeuroDyn was designed using the known current input sequence. We arrived at the programmed parameters except for model errors due to analog imperfections in the chip fabrication. In a related set of experiments, we replicated songbird individual neuron dynamics on NeuroDyn by estimating and configuring parameters extracted using data assimilation from intracellular neural recordings. Faithful emulation of detailed biophysical neural dynamics will enable the use of NeuroDyn as a tool to probe electrical and molecular properties of functional neural circuits. Neuroscience applications include studying the relationship between molecular properties of neurons and the emergence of different spike patterns or different brain behaviors. Clinical applications include studying and predicting effects of neuromodulators or neurodegenerative diseases on ion channel kinetics.

  19. Single neuron dynamics during experimentally induced anoxic depolarization

    NARCIS (Netherlands)

    Zandt, B.; Stigen, Tyler; ten Haken, Bernard; Netoff, Theoden; van Putten, Michel Johannes Antonius Maria

    2013-01-01

    We studied single neuron dynamics during anoxic depolarizations, which are often observed in cases of neuronal energy depletion. Anoxic and similar depolarizations play an important role in several pathologies, notably stroke, migraine, and epilepsy. One of the effects of energy depletion was

  20. Role of Mitochondrial Dynamics in Neuronal Development: Mechanism for Wolfram Syndrome.

    Science.gov (United States)

    Cagalinec, Michal; Liiv, Mailis; Hodurova, Zuzana; Hickey, Miriam Ann; Vaarmann, Annika; Mandel, Merle; Zeb, Akbar; Choubey, Vinay; Kuum, Malle; Safiulina, Dzhamilja; Vasar, Eero; Veksler, Vladimir; Kaasik, Allen

    2016-07-01

    Deficiency of the protein Wolfram syndrome 1 (WFS1) is associated with multiple neurological and psychiatric abnormalities similar to those observed in pathologies showing alterations in mitochondrial dynamics. The aim of this study was to examine the hypothesis that WFS1 deficiency affects neuronal function via mitochondrial abnormalities. We show that down-regulation of WFS1 in neurons leads to dramatic changes in mitochondrial dynamics (inhibited mitochondrial fusion, altered mitochondrial trafficking, and augmented mitophagy), delaying neuronal development. WFS1 deficiency induces endoplasmic reticulum (ER) stress, leading to inositol 1,4,5-trisphosphate receptor (IP3R) dysfunction and disturbed cytosolic Ca2+ homeostasis, which, in turn, alters mitochondrial dynamics. Importantly, ER stress, impaired Ca2+ homeostasis, altered mitochondrial dynamics, and delayed neuronal development are causatively related events because interventions at all these levels improved the downstream processes. Our data shed light on the mechanisms of neuronal abnormalities in Wolfram syndrome and point out potential therapeutic targets. This work may have broader implications for understanding the role of mitochondrial dynamics in neuropsychiatric diseases.

  1. Role of Mitochondrial Dynamics in Neuronal Development: Mechanism for Wolfram Syndrome.

    Directory of Open Access Journals (Sweden)

    Michal Cagalinec

    2016-07-01

    Full Text Available Deficiency of the protein Wolfram syndrome 1 (WFS1 is associated with multiple neurological and psychiatric abnormalities similar to those observed in pathologies showing alterations in mitochondrial dynamics. The aim of this study was to examine the hypothesis that WFS1 deficiency affects neuronal function via mitochondrial abnormalities. We show that down-regulation of WFS1 in neurons leads to dramatic changes in mitochondrial dynamics (inhibited mitochondrial fusion, altered mitochondrial trafficking, and augmented mitophagy, delaying neuronal development. WFS1 deficiency induces endoplasmic reticulum (ER stress, leading to inositol 1,4,5-trisphosphate receptor (IP3R dysfunction and disturbed cytosolic Ca2+ homeostasis, which, in turn, alters mitochondrial dynamics. Importantly, ER stress, impaired Ca2+ homeostasis, altered mitochondrial dynamics, and delayed neuronal development are causatively related events because interventions at all these levels improved the downstream processes. Our data shed light on the mechanisms of neuronal abnormalities in Wolfram syndrome and point out potential therapeutic targets. This work may have broader implications for understanding the role of mitochondrial dynamics in neuropsychiatric diseases.

  2. Using neuronal populations to study the mechanisms underlying spatial and feature attention

    Science.gov (United States)

    Cohen, Marlene R.; Maunsell, John H.R.

    2012-01-01

    Summary Visual attention affects both perception and neuronal responses. Whether the same neuronal mechanisms mediate spatial attention, which improves perception of attended locations, and non-spatial forms of attention has been a subject of considerable debate. Spatial and feature attention have similar effects on individual neurons. Because visual cortex is retinotopically organized, however, spatial attention can co-modulate local neuronal populations, while feature attention generally requires more selective modulation. We compared the effects of feature and spatial attention on local and spatially separated populations by recording simultaneously from dozens of neurons in both hemispheres of V4. Feature and spatial attention affect the activity of local populations similarly, modulating both firing rates and correlations between pairs of nearby neurons. However, while spatial attention appears to act on local populations, feature attention is coordinated across hemispheres. Our results are consistent with a unified attentional mechanism that can modulate the responses of arbitrary subgroups of neurons. PMID:21689604

  3. Rate response of neurons subject to fast or frozen noise: from stochastic and homogeneous to deterministic and heterogeneous populations.

    Science.gov (United States)

    Alijani, Azadeh Khajeh; Richardson, Magnus J E

    2011-07-01

    The response of a neuronal population to afferent drive can be expected to be sensitive to both the distribution and dynamics of membrane voltages within the population. Voltage fluctuations can be driven by synaptic noise, neuromodulators, or cellular inhomogeneities: processes ranging from millisecond autocorrelation times to effectively static or "frozen" noise. Here we extend previous studies of filtered fluctuations to the experimentally verified exponential integrate-and-fire model. How fast or frozen fluctuations affect the steady-state rate and firing-rate response are both examined using perturbative solutions and limits of a 1 + 2 dimensional Fokker-Planck equation. The central finding is that, under conditions of a more-or-less constant population voltage variance, the firing-rate response is only weakly dependent on the fluctuation filter constant: The voltage distribution is the principal determinant of the population response. This result is unexpected given the nature of the systems underlying the extreme limits of fast and frozen fluctuations; the first limit represents a homogeneous population of neurons firing stochastically, whereas the second limit is equivalent to a heterogeneous population of neurons firing deterministically.

  4. Automated quantification of neuronal networks and single-cell calcium dynamics using calcium imaging.

    Science.gov (United States)

    Patel, Tapan P; Man, Karen; Firestein, Bonnie L; Meaney, David F

    2015-03-30

    Recent advances in genetically engineered calcium and membrane potential indicators provide the potential to estimate the activation dynamics of individual neurons within larger, mesoscale networks (100s-1000+neurons). However, a fully integrated automated workflow for the analysis and visualization of neural microcircuits from high speed fluorescence imaging data is lacking. Here we introduce FluoroSNNAP, Fluorescence Single Neuron and Network Analysis Package. FluoroSNNAP is an open-source, interactive software developed in MATLAB for automated quantification of numerous biologically relevant features of both the calcium dynamics of single-cells and network activity patterns. FluoroSNNAP integrates and improves upon existing tools for spike detection, synchronization analysis, and inference of functional connectivity, making it most useful to experimentalists with little or no programming knowledge. We apply FluoroSNNAP to characterize the activity patterns of neuronal microcircuits undergoing developmental maturation in vitro. Separately, we highlight the utility of single-cell analysis for phenotyping a mixed population of neurons expressing a human mutant variant of the microtubule associated protein tau and wild-type tau. We show the performance of semi-automated cell segmentation using spatiotemporal independent component analysis and significant improvement in detecting calcium transients using a template-based algorithm in comparison to peak-based or wavelet-based detection methods. Our software further enables automated analysis of microcircuits, which is an improvement over existing methods. We expect the dissemination of this software will facilitate a comprehensive analysis of neuronal networks, promoting the rapid interrogation of circuits in health and disease. Copyright © 2015. Published by Elsevier B.V.

  5. Estimating the biophysical properties of neurons with intracellular calcium dynamics.

    Science.gov (United States)

    Ye, Jingxin; Rozdeba, Paul J; Morone, Uriel I; Daou, Arij; Abarbanel, Henry D I

    2014-06-01

    We investigate the dynamics of a conductance-based neuron model coupled to a model of intracellular calcium uptake and release by the endoplasmic reticulum. The intracellular calcium dynamics occur on a time scale that is orders of magnitude slower than voltage spiking behavior. Coupling these mechanisms sets the stage for the appearance of chaotic dynamics, which we observe within certain ranges of model parameter values. We then explore the question of whether one can, using observed voltage data alone, estimate the states and parameters of the voltage plus calcium (V+Ca) dynamics model. We find the answer is negative. Indeed, we show that voltage plus another observed quantity must be known to allow the estimation to be accurate. We show that observing both the voltage time course V(t) and the intracellular Ca time course will permit accurate estimation, and from the estimated model state, accurate prediction after observations are completed. This sets the stage for how one will be able to use a more detailed model of V+Ca dynamics in neuron activity in the analysis of experimental data on individual neurons as well as functional networks in which the nodes (neurons) have these biophysical properties.

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

    Science.gov (United States)

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

    2017-09-01

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

  7. 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.

  8. Temporal dynamics of glyoxalase 1 in secondary neuronal injury.

    Directory of Open Access Journals (Sweden)

    Philipp Pieroh

    Full Text Available BACKGROUND: Enhanced glycolysis leads to elevated levels of the toxic metabolite methylglyoxal which contributes to loss of protein-function, metabolic imbalance and cell death. Neurons were shown being highly susceptible to methylglyoxal toxicity. Glyoxalase 1 as an ubiquitous enzyme reflects the main detoxifying enzyme of methylglyoxal and underlies changes during aging and neurodegeneration. However, little is known about dynamics of Glyoxalase 1 following neuronal lesions so far. METHODS: To determine a possible involvement of Glyoxalase 1 in acute brain injury, we analysed the temporal dynamics of Glyoxalase 1 distribution and expression by immunohistochemistry and Western Blot analysis. Organotypic hippocampal slice cultures were excitotoxically (N-methyl-D-aspartate, 50 µM for 4 hours lesioned in vitro (5 minutes to 72 hours. Additionally, permanent middle cerebral artery occlusion was performed (75 minutes to 60 days. RESULTS: We found (i a predominant localisation of Glyoxalase 1 in endothelial cells in non-lesioned brains (ii a time-dependent up-regulation and re-distribution of Glyoxalase 1 in neurons and astrocytes and (iii a strong increase in Glyoxalase 1 dimers after neuronal injury (24 hours to 72 hours when compared to monomers of the protein. CONCLUSIONS: The high dynamics of Glyoxalase 1 expression and distribution following neuronal injury may indicate a novel role of Glyoxalase 1.

  9. Emergent dynamics of spiking neurons with fluctuating threshold

    Science.gov (United States)

    Bhattacharjee, Anindita; Das, M. K.

    2017-05-01

    Role of fluctuating threshold on neuronal dynamics is investigated. The threshold function is assumed to follow a normal probability distribution. Standard deviation of inter-spike interval of the response is computed as an indicator of irregularity in spike emission. It has been observed that, the irregularity in spiking is more if the threshold variation is more. A significant change in modal characteristics of Inter Spike Intervals (ISI) is seen to occur as a function of fluctuation parameter. Investigation is further carried out for coupled system of neurons. Cooperative dynamics of coupled neurons are discussed in view of synchronization. Total and partial synchronization regimes are depicted with the help of contour plots of synchrony measure under various conditions. Results of this investigation may provide a basis for exploring the complexities of neural communication and brain functioning.

  10. The Limited Utility of Multiunit Data in Differentiating Neuronal Population Activity.

    Directory of Open Access Journals (Sweden)

    Corey J Keller

    Full Text Available To date, single neuron recordings remain the gold standard for monitoring the activity of neuronal populations. Since obtaining single neuron recordings is not always possible, high frequency or 'multiunit activity' (MUA is often used as a surrogate. Although MUA recordings allow one to monitor the activity of a large number of neurons, they do not allow identification of specific neuronal subtypes, the knowledge of which is often critical for understanding electrophysiological processes. Here, we explored whether prior knowledge of the single unit waveform of specific neuron types is sufficient to permit the use of MUA to monitor and distinguish differential activity of individual neuron types. We used an experimental and modeling approach to determine if components of the MUA can monitor medium spiny neurons (MSNs and fast-spiking interneurons (FSIs in the mouse dorsal striatum. We demonstrate that when well-isolated spikes are recorded, the MUA at frequencies greater than 100Hz is correlated with single unit spiking, highly dependent on the waveform of each neuron type, and accurately reflects the timing and spectral signature of each neuron. However, in the absence of well-isolated spikes (the norm in most MUA recordings, the MUA did not typically contain sufficient information to permit accurate prediction of the respective population activity of MSNs and FSIs. Thus, even under ideal conditions for the MUA to reliably predict the moment-to-moment activity of specific local neuronal ensembles, knowledge of the spike waveform of the underlying neuronal populations is necessary, but not sufficient.

  11. The effect of noise correlations in populations of diversely tuned neurons.

    Science.gov (United States)

    Ecker, Alexander S; Berens, Philipp; Tolias, Andreas S; Bethge, Matthias

    2011-10-05

    The amount of information encoded by networks of neurons critically depends on the correlation structure of their activity. Neurons with similar stimulus preferences tend to have higher noise correlations than others. In homogeneous populations of neurons, this limited range correlation structure is highly detrimental to the accuracy of a population code. Therefore, reduced spike count correlations under attention, after adaptation, or after learning have been interpreted as evidence for a more efficient population code. Here, we analyze the role of limited range correlations in more realistic, heterogeneous population models. We use Fisher information and maximum-likelihood decoding to show that reduced correlations do not necessarily improve encoding accuracy. In fact, in populations with more than a few hundred neurons, increasing the level of limited range correlations can substantially improve encoding accuracy. We found that this improvement results from a decrease in noise entropy that is associated with increasing correlations if the marginal distributions are unchanged. Surprisingly, for constant noise entropy and in the limit of large populations, the encoding accuracy is independent of both structure and magnitude of noise correlations.

  12. Extracting neuronal functional network dynamics via adaptive Granger causality analysis.

    Science.gov (United States)

    Sheikhattar, Alireza; Miran, Sina; Liu, Ji; Fritz, Jonathan B; Shamma, Shihab A; Kanold, Patrick O; Babadi, Behtash

    2018-04-24

    Quantifying the functional relations between the nodes in a network based on local observations is a key challenge in studying complex systems. Most existing time series analysis techniques for this purpose provide static estimates of the network properties, pertain to stationary Gaussian data, or do not take into account the ubiquitous sparsity in the underlying functional networks. When applied to spike recordings from neuronal ensembles undergoing rapid task-dependent dynamics, they thus hinder a precise statistical characterization of the dynamic neuronal functional networks underlying adaptive behavior. We develop a dynamic estimation and inference paradigm for extracting functional neuronal network dynamics in the sense of Granger, by integrating techniques from adaptive filtering, compressed sensing, point process theory, and high-dimensional statistics. We demonstrate the utility of our proposed paradigm through theoretical analysis, algorithm development, and application to synthetic and real data. Application of our techniques to two-photon Ca 2+ imaging experiments from the mouse auditory cortex reveals unique features of the functional neuronal network structures underlying spontaneous activity at unprecedented spatiotemporal resolution. Our analysis of simultaneous recordings from the ferret auditory and prefrontal cortical areas suggests evidence for the role of rapid top-down and bottom-up functional dynamics across these areas involved in robust attentive behavior.

  13. Inhibitory coherence in a heterogeneous population of subthreshold and suprathreshold type-I neurons

    International Nuclear Information System (INIS)

    Kim, Sang-Yoon; Hong, Duk-Geun; Kim, Jean; Lim, Woochang

    2012-01-01

    We study inhibitory coherence (i.e. collective coherence by synaptic inhibition) in a population of globally coupled type-I neurons, which can fire at arbitrarily low frequency. No inhibitory coherence is observed in a homogeneous population composed of only subthreshold neurons, which exhibit noise-induced firings. In addition to subthreshold neurons, there exist spontaneously firing suprathreshold neurons in a noisy environment of a real brain. To take into consideration the effect of suprathreshold neurons on inhibitory coherence, we consider a heterogeneous population of subthreshold and suprathreshold neurons and investigate the inhibitory coherence by increasing the fraction of suprathreshold neurons P supra . As P supra passes a threshold P* supra , suprathreshold neurons begin to synchronize and play the role of coherent inhibitors for the emergence of inhibitory coherence. Thus, regularly oscillating population-averaged global potential appears for P supra > P* supra . For this coherent case, suprathreshold neurons exhibit sparse spike synchronization (i.e. individual potentials of suprathreshold neurons consist of coherent sparse spikings and coherent subthreshold small-amplitude hoppings). By virtue of their coherent inhibition, sparsely synchronized suprathreshold neurons suppress the noisy activity of subthreshold neurons. Thus, subthreshold neurons exhibit hopping synchronization (i.e. only coherent subthreshold hopping oscillations without spikings appear in the individual potentials of subthreshold neurons). We also characterize the inhibitory coherence in terms of the ‘statistical-mechanical’ spike-based and correlation-based measures, which quantify the average contributions of the microscopic individual spikes and individual potentials to the macroscopic global potential. Finally, the effect of sparse randomness of synaptic connectivity on the inhibitory coherence is briefly discussed. (paper)

  14. A New Population of Parvocellular Oxytocin Neurons Controlling Magnocellular Neuron Activity and Inflammatory Pain Processing.

    Science.gov (United States)

    Eliava, Marina; Melchior, Meggane; Knobloch-Bollmann, H Sophie; Wahis, Jérôme; da Silva Gouveia, Miriam; Tang, Yan; Ciobanu, Alexandru Cristian; Triana Del Rio, Rodrigo; Roth, Lena C; Althammer, Ferdinand; Chavant, Virginie; Goumon, Yannick; Gruber, Tim; Petit-Demoulière, Nathalie; Busnelli, Marta; Chini, Bice; Tan, Linette L; Mitre, Mariela; Froemke, Robert C; Chao, Moses V; Giese, Günter; Sprengel, Rolf; Kuner, Rohini; Poisbeau, Pierrick; Seeburg, Peter H; Stoop, Ron; Charlet, Alexandre; Grinevich, Valery

    2016-03-16

    Oxytocin (OT) is a neuropeptide elaborated by the hypothalamic paraventricular (PVN) and supraoptic (SON) nuclei. Magnocellular OT neurons of these nuclei innervate numerous forebrain regions and release OT into the blood from the posterior pituitary. The PVN also harbors parvocellular OT cells that project to the brainstem and spinal cord, but their function has not been directly assessed. Here, we identified a subset of approximately 30 parvocellular OT neurons, with collateral projections onto magnocellular OT neurons and neurons of deep layers of the spinal cord. Evoked OT release from these OT neurons suppresses nociception and promotes analgesia in an animal model of inflammatory pain. Our findings identify a new population of OT neurons that modulates nociception in a two tier process: (1) directly by release of OT from axons onto sensory spinal cord neurons and inhibiting their activity and (2) indirectly by stimulating OT release from SON neurons into the periphery. Copyright © 2016 Elsevier Inc. All rights reserved.

  15. A Tractable Method for Describing Complex Couplings between Neurons and Population Rate.

    Science.gov (United States)

    Gardella, Christophe; Marre, Olivier; Mora, Thierry

    2016-01-01

    Neurons within a population are strongly correlated, but how to simply capture these correlations is still a matter of debate. Recent studies have shown that the activity of each cell is influenced by the population rate, defined as the summed activity of all neurons in the population. However, an explicit, tractable model for these interactions is still lacking. Here we build a probabilistic model of population activity that reproduces the firing rate of each cell, the distribution of the population rate, and the linear coupling between them. This model is tractable, meaning that its parameters can be learned in a few seconds on a standard computer even for large population recordings. We inferred our model for a population of 160 neurons in the salamander retina. In this population, single-cell firing rates depended in unexpected ways on the population rate. In particular, some cells had a preferred population rate at which they were most likely to fire. These complex dependencies could not be explained by a linear coupling between the cell and the population rate. We designed a more general, still tractable model that could fully account for these nonlinear dependencies. We thus provide a simple and computationally tractable way to learn models that reproduce the dependence of each neuron on the population rate.

  16. Biophysical synaptic dynamics in an analog VLSI network of Hodgkin-Huxley neurons.

    Science.gov (United States)

    Yu, Theodore; Cauwenberghs, Gert

    2009-01-01

    We study synaptic dynamics in a biophysical network of four coupled spiking neurons implemented in an analog VLSI silicon microchip. The four neurons implement a generalized Hodgkin-Huxley model with individually configurable rate-based kinetics of opening and closing of Na+ and K+ ion channels. The twelve synapses implement a rate-based first-order kinetic model of neurotransmitter and receptor dynamics, accounting for NMDA and non-NMDA type chemical synapses. The implemented models on the chip are fully configurable by 384 parameters accounting for conductances, reversal potentials, and pre/post-synaptic voltage-dependence of the channel kinetics. We describe the models and present experimental results from the chip characterizing single neuron dynamics, single synapse dynamics, and multi-neuron network dynamics showing phase-locking behavior as a function of synaptic coupling strength. The 3mm x 3mm microchip consumes 1.29 mW power making it promising for applications including neuromorphic modeling and neural prostheses.

  17. Universal Critical Dynamics in High Resolution Neuronal Avalanche Data

    Science.gov (United States)

    Friedman, Nir; Ito, Shinya; Brinkman, Braden A. W.; Shimono, Masanori; DeVille, R. E. Lee; Dahmen, Karin A.; Beggs, John M.; Butler, Thomas C.

    2012-05-01

    The tasks of neural computation are remarkably diverse. To function optimally, neuronal networks have been hypothesized to operate near a nonequilibrium critical point. However, experimental evidence for critical dynamics has been inconclusive. Here, we show that the dynamics of cultured cortical networks are critical. We analyze neuronal network data collected at the individual neuron level using the framework of nonequilibrium phase transitions. Among the most striking predictions confirmed is that the mean temporal profiles of avalanches of widely varying durations are quantitatively described by a single universal scaling function. We also show that the data have three additional features predicted by critical phenomena: approximate power law distributions of avalanche sizes and durations, samples in subcritical and supercritical phases, and scaling laws between anomalous exponents.

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

    Directory of Open Access Journals (Sweden)

    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.

  19. Dynamical analysis of Parkinsonian state emulated by hybrid Izhikevich neuron models

    Science.gov (United States)

    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

  20. Niche-dependent development of functional neuronal networks from embryonic stem cell-derived neural populations

    Directory of Open Access Journals (Sweden)

    Siebler Mario

    2009-08-01

    Full Text Available Abstract Background The present work was performed to investigate the ability of two different embryonic stem (ES cell-derived neural precursor populations to generate functional neuronal networks in vitro. The first ES cell-derived neural precursor population was cultivated as free-floating neural aggregates which are known to form a developmental niche comprising different types of neural cells, including neural precursor cells (NPCs, progenitor cells and even further matured cells. This niche provides by itself a variety of different growth factors and extracellular matrix proteins that influence the proliferation and differentiation of neural precursor and progenitor cells. The second population was cultivated adherently in monolayer cultures to control most stringently the extracellular environment. This population comprises highly homogeneous NPCs which are supposed to represent an attractive way to provide well-defined neuronal progeny. However, the ability of these different ES cell-derived immature neural cell populations to generate functional neuronal networks has not been assessed so far. Results While both precursor populations were shown to differentiate into sufficient quantities of mature NeuN+ neurons that also express GABA or vesicular-glutamate-transporter-2 (vGlut2, only aggregate-derived neuronal populations exhibited a synchronously oscillating network activity 2–4 weeks after initiating the differentiation as detected by the microelectrode array technology. Neurons derived from homogeneous NPCs within monolayer cultures did merely show uncorrelated spiking activity even when differentiated for up to 12 weeks. We demonstrated that these neurons exhibited sparsely ramified neurites and an embryonic vGlut2 distribution suggesting an inhibited terminal neuronal maturation. In comparison, neurons derived from heterogeneous populations within neural aggregates appeared as fully mature with a dense neurite network and punctuated

  1. Synchronization dynamics in a small pacemaker neuronal ensemble via a robust adaptive controller

    International Nuclear Information System (INIS)

    Cornejo-Pérez, O.; Solis-Perales, G.C.; Arenas-Prado, J.A.

    2012-01-01

    The synchronization dynamics of a pacemaker neuronal ensemble under the action of a control command is studied herein. The ensemble corresponds to the pyloric central pattern generator of the stomatogastric ganglion of lobster. The desired dynamics is provided by means of an external master neuron and it is induced via a nonlinear controller. Such a controller is composed of a linearizing-like controller and a high gain observer; the controller is able to counteract uncertainties and external perturbations in the controlled system. Numerical simulations of the robust synchronization dynamics of the master neuron and the pacemaker neuronal ensemble are displayed.

  2. Relating normalization to neuronal populations across cortical areas.

    Science.gov (United States)

    Ruff, Douglas A; Alberts, Joshua J; Cohen, Marlene R

    2016-09-01

    Normalization, which divisively scales neuronal responses to multiple stimuli, is thought to underlie many sensory, motor, and cognitive processes. In every study where it has been investigated, neurons measured in the same brain area under identical conditions exhibit a range of normalization, ranging from suppression by nonpreferred stimuli (strong normalization) to additive responses to combinations of stimuli (no normalization). Normalization has been hypothesized to arise from interactions between neuronal populations, either in the same or different brain areas, but current models of normalization are not mechanistic and focus on trial-averaged responses. To gain insight into the mechanisms underlying normalization, we examined interactions between neurons that exhibit different degrees of normalization. We recorded from multiple neurons in three cortical areas while rhesus monkeys viewed superimposed drifting gratings. We found that neurons showing strong normalization shared less trial-to-trial variability with other neurons in the same cortical area and more variability with neurons in other cortical areas than did units with weak normalization. Furthermore, the cortical organization of normalization was not random: neurons recorded on nearby electrodes tended to exhibit similar amounts of normalization. Together, our results suggest that normalization reflects a neuron's role in its local network and that modulatory factors like normalization share the topographic organization typical of sensory tuning properties. Copyright © 2016 the American Physiological Society.

  3. Mediodorsal Thalamic Neurons Mirror the Activity of Medial Prefrontal Neurons Responding to Movement and Reinforcement during a Dynamic DNMTP Task.

    Science.gov (United States)

    Miller, Rikki L A; Francoeur, Miranda J; Gibson, Brett M; Mair, Robert G

    2017-01-01

    The mediodorsal nucleus (MD) interacts with medial prefrontal cortex (mPFC) to support learning and adaptive decision-making. MD receives driver (layer 5) and modulatory (layer 6) projections from PFC and is the main source of driver thalamic projections to middle cortical layers of PFC. Little is known about the activity of MD neurons and their influence on PFC during decision-making. We recorded MD neurons in rats performing a dynamic delayed nonmatching to position (dDNMTP) task and compared results to a previous study of mPFC with the same task (Onos et al., 2016). Criterion event-related responses were observed for 22% (254/1179) of neurons recorded in MD, 237 (93%) of which exhibited activity consistent with mPFC response types. More MD than mPFC neurons exhibited responses related to movement (45% vs. 29%) and reinforcement (51% vs. 27%). MD had few responses related to lever presses, and none related to preparation or memory delay, which constituted 43% of event-related activity in mPFC. Comparison of averaged normalized population activity and population response times confirmed the broad similarity of common response types in MD and mPFC and revealed differences in the onset and offset of some response types. Our results show that MD represents information about actions and outcomes essential for decision-making during dDNMTP, consistent with evidence from lesion studies that MD supports reward-based learning and action-selection. These findings support the hypothesis that MD reinforces task-relevant neural activity in PFC that gives rise to adaptive behavior.

  4. 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

  5. PINP: a new method of tagging neuronal populations for identification during in vivo electrophysiological recording.

    Directory of Open Access Journals (Sweden)

    Susana Q Lima

    Full Text Available Neural circuits are exquisitely organized, consisting of many different neuronal subpopulations. However, it is difficult to assess the functional roles of these subpopulations using conventional extracellular recording techniques because these techniques do not easily distinguish spikes from different neuronal populations. To overcome this limitation, we have developed PINP (Photostimulation-assisted Identification of Neuronal Populations, a method of tagging neuronal populations for identification during in vivo electrophysiological recording. The method is based on expressing the light-activated channel channelrhodopsin-2 (ChR2 to restricted neuronal subpopulations. ChR2-tagged neurons can be detected electrophysiologically in vivo since illumination of these neurons with a brief flash of blue light triggers a short latency reliable action potential. We demonstrate the feasibility of this technique by expressing ChR2 in distinct populations of cortical neurons using two different strategies. First, we labeled a subpopulation of cortical neurons-mainly fast-spiking interneurons-by using adeno-associated virus (AAV to deliver ChR2 in a transgenic mouse line in which the expression of Cre recombinase was driven by the parvalbumin promoter. Second, we labeled subpopulations of excitatory neurons in the rat auditory cortex with ChR2 based on projection target by using herpes simplex virus 1 (HSV1, which is efficiently taken up by axons and transported retrogradely; we find that this latter population responds to acoustic stimulation differently from unlabeled neurons. Tagging neurons is a novel application of ChR2, used in this case to monitor activity instead of manipulating it. PINP can be readily extended to other populations of genetically identifiable neurons, and will provide a useful method for probing the functional role of different neuronal populations in vivo.

  6. Predicting oculomotor behaviour from correlated populations of posterior parietal neurons.

    Science.gov (United States)

    Graf, Arnulf B A; Andersen, Richard A

    2015-01-23

    Oculomotor function critically depends on how signals representing saccade direction and eye position are combined across neurons in the lateral intraparietal (LIP) area of the posterior parietal cortex. Here we show that populations of parietal neurons exhibit correlated variability, and that using these interneuronal correlations yields oculomotor predictions that are more accurate and also less uncertain. The structure of LIP population responses is therefore essential for reliable read-out of oculomotor behaviour.

  7. [Pulse flows of populations of cortical neurons under low-intensity pulsed microwave: interspike intervals].

    Science.gov (United States)

    Chizhenkova, R A

    2014-01-01

    Pulse flows of populations of cortical neurons were investigated on unanesthetized nonimmobilized rabbits prior, during, and after 1-min microwave irradiation (wavelength 37.5 cm, power density 0.5-1.0 mW/cm2) in continuous and pulse-modulated modes with a frequency of 5, 20 and 100 Hz. The changes in the characteristics of interspike intervals resulted from these exposures. The peculiarity of rearrangements of pulse flows and their dynamics was determined by modes of irradiation.

  8. Gene expression profiling of two distinct neuronal populations in the rodent spinal cord.

    Directory of Open Access Journals (Sweden)

    Jesper Ryge

    Full Text Available BACKGROUND: In the field of neuroscience microarray gene expression profiles on anatomically defined brain structures are being used increasingly to study both normal brain functions as well as pathological states. Fluorescent tracing techniques in brain tissue that identifies distinct neuronal populations can in combination with global gene expression profiling potentially increase the resolution and specificity of such studies to shed new light on neuronal functions at the cellular level. METHODOLOGY/PRINCIPAL FINDINGS: We examine the microarray gene expression profiles of two distinct neuronal populations in the spinal cord of the neonatal rat, the principal motor neurons and specific interneurons involved in motor control. The gene expression profiles of the respective cell populations were obtained from amplified mRNA originating from 50-250 fluorescently identified and laser microdissected cells. In the data analysis we combine a new microarray normalization procedure with a conglomerate measure of significant differential gene expression. Using our methodology we find 32 genes to be more expressed in the interneurons compared to the motor neurons that all except one have not previously been associated with this neuronal population. As a validation of our method we find 17 genes to be more expressed in the motor neurons than in the interneurons and of these only one had not previously been described in this population. CONCLUSIONS/SIGNIFICANCE: We provide an optimized experimental protocol that allows isolation of gene transcripts from fluorescent retrogradely labeled cell populations in fresh tissue, which can be used to generate amplified aRNA for microarray hybridization from as few as 50 laser microdissected cells. Using this optimized experimental protocol in combination with our microarray analysis methodology we find 49 differentially expressed genes between the motor neurons and the interneurons that reflect the functional

  9. Data collapse and critical dynamics in neuronal avalanche data

    Science.gov (United States)

    Butler, Thomas; Friedman, Nir; Dahmen, Karin; Beggs, John; Deville, Lee; Ito, Shinya

    2012-02-01

    The tasks of information processing, computation, and response to stimuli require neural computation to be remarkably flexible and diverse. To optimally satisfy the demands of neural computation, neuronal networks have been hypothesized to operate near a non-equilibrium critical point. In spite of their importance for neural dynamics, experimental evidence for critical dynamics has been primarily limited to power law statistics that can also emerge from non-critical mechanisms. By tracking the firing of large numbers of synaptically connected cortical neurons and comparing the resulting data to the predictions of critical phenomena, we show that cortical tissues in vitro can function near criticality. Among the most striking predictions of critical dynamics is that the mean temporal profiles of avalanches of widely varying durations are quantitatively described by a single universal scaling function (data collapse). We show for the first time that this prediction is confirmed in neuronal networks. We also show that the data have three additional features predicted by critical phenomena: approximate power law distributions of avalanche sizes and durations, samples in subcritical and supercritical phases, and scaling laws between anomalous exponents.

  10. Populations of striatal medium spiny neurons encode vibrotactile frequency in rats: modulation by slow wave oscillations.

    Science.gov (United States)

    Hawking, Thomas G; Gerdjikov, Todor V

    2013-01-01

    Dorsolateral striatum (DLS) is implicated in tactile perception and receives strong projections from somatosensory cortex. However, the sensory representations encoded by striatal projection neurons are not well understood. Here we characterized the contribution of DLS to the encoding of vibrotactile information in rats by assessing striatal responses to precise frequency stimuli delivered to a single vibrissa. We applied stimuli in a frequency range (45-90 Hz) that evokes discriminable percepts and carries most of the power of vibrissa vibration elicited by a range of complex fine textures. Both medium spiny neurons and evoked potentials showed tactile responses that were modulated by slow wave oscillations. Furthermore, medium spiny neuron population responses represented stimulus frequency on par with previously reported behavioral benchmarks. Our results suggest that striatum encodes frequency information of vibrotactile stimuli which is dynamically modulated by ongoing brain state.

  11. The Itch-Producing Agents Histamine and Cowhage Activate Separate Populations of Primate Spinothalamic Tract Neurons

    Science.gov (United States)

    Davidson, Steve; Zhang, Xijing; Yoon, Chul H.; Khasabov, Sergey G.; Simone, Donald A.; Giesler, Glenn J.

    2010-01-01

    Itch is an everyday sensation, but when associated with disease or infection it can be chronic and debilitating. Several forms of itch can be blocked using antihistamines, but others cannot and these constitute an important clinical problem. Little information is available on the mechanisms underlying itch that is produced by nonhistaminergic mechanisms. We examined the responses of spinothalamic tract neurons to histaminergic and, for the first time, nonhistaminergic forms of itch stimuli. Fifty-seven primate spinothalamic tract (STT) neurons were identified using antidromic activation techniques and examined for their responses to histamine and cowhage, the nonhistaminergic itch-producing spicules covering the pod of the legume Mucuna pruriens. Each examined neuron had a receptive field on the hairy skin of the hindlimb and responded to noxious mechanical stimulation. STT neurons were tested with both pruritogens applied in a random order and we found 12 that responded to histamine and seven to cowhage. Each pruritogen-responsive STT neuron was activated by the chemical algogen capsaicin and two-thirds responded to noxious heat stimuli, demonstrating that these neurons convey chemical, thermal, and mechanical nociceptive information as well. Histamine or cowhage responsive STT neurons were found in both the marginal zone and the deep dorsal horn and were classified as high threshold and wide dynamic range. Unexpectedly, histamine and cowhage never activated the same cell. Our results demonstrate that the spinothalamic tract contains mutually exclusive populations of neurons responsive to histamine or the nonhistaminergic itch-producing agent cowhage. PMID:17855615

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

    Science.gov (United States)

    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.

  13. Dynamics of Moment Neuronal Networks with Intra- and Inter-Interactions

    Directory of Open Access Journals (Sweden)

    Xuyan Xiang

    2015-01-01

    Full Text Available A framework of moment neuronal networks with intra- and inter-interactions is presented. It is to show how the spontaneous activity is propagated across the homogeneous and heterogeneous network. The input-output firing relationship and the stability are first explored for a homogeneous network. For heterogeneous network without the constraint of the correlation coefficients between neurons, a more sophisticated dynamics is then explored. With random interactions, the network gets easily synchronized. However, desynchronization is produced by a lateral interaction such as Mexico hat function. It is the external intralayer input unit that offers a more sophisticated and unexpected dynamics over the predecessors. Hence, the work further opens up the possibility of carrying out a stochastic computation in neuronal networks.

  14. Elucidating distinct ion channel populations on the surface of hippocampal neurons via single-particle tracking recurrence analysis

    Science.gov (United States)

    Sikora, Grzegorz; Wyłomańska, Agnieszka; Gajda, Janusz; Solé, Laura; Akin, Elizabeth J.; Tamkun, Michael M.; Krapf, Diego

    2017-12-01

    Protein and lipid nanodomains are prevalent on the surface of mammalian cells. In particular, it has been recently recognized that ion channels assemble into surface nanoclusters in the soma of cultured neurons. However, the interactions of these molecules with surface nanodomains display a considerable degree of heterogeneity. Here, we investigate this heterogeneity and develop statistical tools based on the recurrence of individual trajectories to identify subpopulations within ion channels in the neuronal surface. We specifically study the dynamics of the K+ channel Kv1.4 and the Na+ channel Nav1.6 on the surface of cultured hippocampal neurons at the single-molecule level. We find that both these molecules are expressed in two different forms with distinct kinetics with regards to surface interactions, emphasizing the complex proteomic landscape of the neuronal surface. Further, the tools presented in this work provide new methods for the analysis of membrane nanodomains, transient confinement, and identification of populations within single-particle trajectories.

  15. Temporal structure of neuronal population oscillations with empirical model decomposition

    International Nuclear Information System (INIS)

    Li Xiaoli

    2006-01-01

    Frequency analysis of neuronal oscillation is very important for understanding the neural information processing and mechanism of disorder in the brain. This Letter addresses a new method to analyze the neuronal population oscillations with empirical mode decomposition (EMD). Following EMD of neuronal oscillation, a series of intrinsic mode functions (IMFs) are obtained, then Hilbert transform of IMFs can be used to extract the instantaneous time frequency structure of neuronal oscillation. The method is applied to analyze the neuronal oscillation in the hippocampus of epileptic rats in vivo, the results show the neuronal oscillations have different descriptions during the pre-ictal, seizure onset and ictal periods of the epileptic EEG at the different frequency band. This new method is very helpful to provide a view for the temporal structure of neural oscillation

  16. 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

  17. The Dynamics of Networks of Identical Theta Neurons.

    Science.gov (United States)

    Laing, Carlo R

    2018-02-05

    We consider finite and infinite all-to-all coupled networks of identical theta neurons. Two types of synaptic interactions are investigated: instantaneous and delayed (via first-order synaptic processing). Extensive use is made of the Watanabe/Strogatz (WS) ansatz for reducing the dimension of networks of identical sinusoidally-coupled oscillators. As well as the degeneracy associated with the constants of motion of the WS ansatz, we also find continuous families of solutions for instantaneously coupled neurons, resulting from the reversibility of the reduced model and the form of the synaptic input. We also investigate a number of similar related models. We conclude that the dynamics of networks of all-to-all coupled identical neurons can be surprisingly complicated.

  18. Clustering promotes switching dynamics in networks of noisy neurons

    Science.gov (United States)

    Franović, Igor; Klinshov, Vladimir

    2018-02-01

    Macroscopic variability is an emergent property of neural networks, typically manifested in spontaneous switching between the episodes of elevated neuronal activity and the quiescent episodes. We investigate the conditions that facilitate switching dynamics, focusing on the interplay between the different sources of noise and heterogeneity of the network topology. We consider clustered networks of rate-based neurons subjected to external and intrinsic noise and derive an effective model where the network dynamics is described by a set of coupled second-order stochastic mean-field systems representing each of the clusters. The model provides an insight into the different contributions to effective macroscopic noise and qualitatively indicates the parameter domains where switching dynamics may occur. By analyzing the mean-field model in the thermodynamic limit, we demonstrate that clustering promotes multistability, which gives rise to switching dynamics in a considerably wider parameter region compared to the case of a non-clustered network with sparse random connection topology.

  19. Understanding how discrete populations of hypothalamic neurons orchestrate complicated behavioral states

    Directory of Open Access Journals (Sweden)

    Allison eGraebner

    2015-08-01

    Full Text Available A major question in systems neuroscience is how a single population of neurons can interact with the rest of the brain to orchestrate complex behavioral states. The hypothalamus contains many such discrete neuronal populations that individually regulate arousal, feeding, and drinking. For example, hypothalamic neurons that express hypocretin (Hcrt neuropeptides can sense homeostatic and metabolic factors affecting wakefulness and orchestrate organismal arousal. Neurons that express agouti-related protein (AgRP can sense the metabolic needs of the body and orchestrate a state of hunger. The organum vasculosum of the lamina terminalis (OVLT can detect the hypertonicity of blood and orchestrate a state of thirst. Each hypothalamic population is sufficient to generate complicated behavioral states through the combined efforts of distinct efferent projections. The principal challenge to understanding these brain systems is therefore to determine the individual roles of each downstream projection for each behavioral state. In recent years, the development and application of temporally precise, genetically encoded tools have greatly improved our understanding of the structure and function of these neural systems. This review will survey recent advances in our understanding of how these individual hypothalamic populations can orchestrate complicated behavioral states due to the combined efforts of individual downstream projections.

  20. Populations of subplate and interstitial neurons in fetal and adult human telencephalon.

    Science.gov (United States)

    Judaš, Miloš; Sedmak, Goran; Pletikos, Mihovil; Jovanov-Milošević, Nataša

    2010-10-01

    In the adult human telencephalon, subcortical (gyral) white matter contains a special population of interstitial neurons considered to be surviving descendants of fetal subplate neurons [Kostovic & Rakic (1980) Cytology and the time of origin of interstitial neurons in the white matter in infant and adult human and monkey telencephalon. J Neurocytol9, 219]. We designate this population of cells as superficial (gyral) interstitial neurons and describe their morphology and distribution in the postnatal and adult human cerebrum. Human fetal subplate neurons cannot be regarded as interstitial, because the subplate zone is an essential part of the fetal cortex, the major site of synaptogenesis and the 'waiting' compartment for growing cortical afferents, and contains both projection neurons and interneurons with distinct input-output connectivity. However, although the subplate zone is a transient fetal structure, many subplate neurons survive postnatally as superficial (gyral) interstitial neurons. The fetal white matter is represented by the intermediate zone and well-defined deep periventricular tracts of growing axons, such as the corpus callosum, anterior commissure, internal and external capsule, and the fountainhead of the corona radiata. These tracts gradually occupy the territory of transient fetal subventricular and ventricular zones.The human fetal white matter also contains distinct populations of deep fetal interstitial neurons, which, by virtue of their location, morphology, molecular phenotypes and advanced level of dendritic maturation, remain distinct from subplate neurons and neurons in adjacent structures (e.g. basal ganglia, basal forebrain). We describe the morphological, histochemical (nicotinamide-adenine dinucleotide phosphate-diaphorase) and immunocytochemical (neuron-specific nuclear protein, microtubule-associated protein-2, calbindin, calretinin, neuropeptide Y) features of both deep fetal interstitial neurons and deep (periventricular

  1. Ordering Dynamics in Neuron Activity Pattern Model: An Insight to Brain Functionality.

    Directory of Open Access Journals (Sweden)

    Jasleen Gundh

    Full Text Available We study the domain ordering kinetics in d = 2 ferromagnets which corresponds to populated neuron activities with both long-ranged interactions, V(r ∼ r-n and short-ranged interactions. We present the results from comprehensive Monte Carlo (MC simulations for the nonconserved Ising model with n ≥ 2, interaction range considering near and far neighbors. Our model results could represent the long-ranged neuron kinetics (n ≤ 4 in consistent with the same dynamical behaviour of short-ranged case (n ≥ 4 at far below and near criticality. We found that emergence of fast and slow kinetics of long and short ranged case could imitate the formation of connections among near and distant neurons. The calculated characteristic length scale in long-ranged interaction is found to be n independent (L(t ∼ t1/(n-2, whereas short-ranged interaction follows L(t ∼ t1/2 law and approximately preserve universality in domain kinetics. Further, we did the comparative study of phase ordering near the critical temperature which follows different behaviours of domain ordering near and far critical temperature but follows universal scaling law.

  2. A computational paradigm for dynamic logic-gates in neuronal activity

    Directory of Open Access Journals (Sweden)

    Amir eGoldental

    2014-04-01

    Full Text Available In 1943 McCulloch and Pitts suggested that the brain is composed of reliable logic-gates similar to the logic at the core of today's computers. This framework had a limited impact on neuroscience, since neurons exhibit far richer dynamics. Here we propose a new experimentally corroborated paradigm in which the truth tables of the brain's logic-gates are time dependent, i.e. dynamic logic-gates (DLGs. The truth tables of the DLGs depend on the history of their activity and the stimulation frequencies of their input neurons. Our experimental results are based on a procedure where conditioned stimulations were enforced on circuits of neurons embedded within a large-scale network of cortical cells in-vitro. We demonstrate that the underlying biological mechanism is the unavoidable increase of neuronal response latencies to ongoing stimulations, which imposes a non-uniform gradual stretching of network delays. The limited experimental results are confirmed and extended by simulations and theoretical arguments based on identical neurons with a fixed increase of the neuronal response latency per evoked spike. We anticipate our results to lead to better understanding of the suitability of this computational paradigm to account for the brain's functionalities and will require the development of new systematic mathematical methods beyond the methods developed for traditional Boolean algebra.

  3. 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...

  4. Encoding of Naturalistic Optic Flow by a Population of Blowfly Motion-Sensitive Neurons

    NARCIS (Netherlands)

    Karmeier, K.; Hateren, J.H. van; Kern, R.; Egelhaaf, M.

    In sensory systems information is encoded by the activity of populations of neurons. To analyze the coding properties of neuronal populations sensory stimuli have usually been used that were much simpler than those encountered in real life. It has been possible only recently to stimulate visual

  5. Input-output relation and energy efficiency in the neuron with different spike threshold dynamics.

    Science.gov (United States)

    Yi, Guo-Sheng; Wang, Jiang; Tsang, Kai-Ming; Wei, Xi-Le; Deng, Bin

    2015-01-01

    Neuron encodes and transmits information through generating sequences of output spikes, which is a high energy-consuming process. The spike is initiated when membrane depolarization reaches a threshold voltage. In many neurons, threshold is dynamic and depends on the rate of membrane depolarization (dV/dt) preceding a spike. Identifying the metabolic energy involved in neural coding and their relationship to threshold dynamic is critical to understanding neuronal function and evolution. Here, we use a modified Morris-Lecar model to investigate neuronal input-output property and energy efficiency associated with different spike threshold dynamics. We find that the neurons with dynamic threshold sensitive to dV/dt generate discontinuous frequency-current curve and type II phase response curve (PRC) through Hopf bifurcation, and weak noise could prohibit spiking when bifurcation just occurs. The threshold that is insensitive to dV/dt, instead, results in a continuous frequency-current curve, a type I PRC and a saddle-node on invariant circle bifurcation, and simultaneously weak noise cannot inhibit spiking. It is also shown that the bifurcation, frequency-current curve and PRC type associated with different threshold dynamics arise from the distinct subthreshold interactions of membrane currents. Further, we observe that the energy consumption of the neuron is related to its firing characteristics. The depolarization of spike threshold improves neuronal energy efficiency by reducing the overlap of Na(+) and K(+) currents during an action potential. The high energy efficiency is achieved at more depolarized spike threshold and high stimulus current. These results provide a fundamental biophysical connection that links spike threshold dynamics, input-output relation, energetics and spike initiation, which could contribute to uncover neural encoding mechanism.

  6. Dynamical behaviour of the firing in coupled neuronal system

    International Nuclear Information System (INIS)

    Wei Wang; Perez, G.; Cerdeira, H.A.

    1993-03-01

    The time interval sequences and the spatio-temporal patterns of the firings of a coupled neuronal network are investigated in this paper. For a single neuron stimulated by an external stimulus I, the time interval sequences show a low frequency firing of bursts of spikes, and reversed period-doubling cascade to a high frequency repetitive firing state as the stimulus I is increased. For two neurons coupled to each other through the firing of the spikes, the complexity of the time interval sequences becomes simple as the coupling strength increases. A network with large numbers of neurons shows a complex spatio-temporal pattern structure. As the coupling strength increases, the numbers of phase locked neurons increase and the time interval diagram shows temporal chaos and a bifurcation in the space. The dynamical behaviour is also verified by the Lyapunov exponent. (author). 17 refs, 6 figs

  7. Reconstructing the population activity of olfactory output neurons that innervate identifiable processing units

    Directory of Open Access Journals (Sweden)

    Shigehiro Namiki

    2008-06-01

    Full Text Available We investigated the functional organization of the moth antennal lobe (AL, the primary olfactory network, using in vivo electrophysiological recordings and anatomical identification. The moth AL contains about 60 processing units called glomeruli that are identifiable from one animal to another. We were able to monitor the output information of the AL by recording the activity of a population of output neurons, each of which innervated a single glomerulus. Using compiled intracellular recordings and staining data from different animals, we mapped the odor-evoked dynamics on a digital atlas of the AL and geometrically reconstructed the population activity. We examined the quantitative relationship between the similarity of olfactory responses and the anatomical distance between glomeruli. Globally, the olfactory response profile was independent of the anatomical distance, although some local features were present.

  8. Linking macroscopic with microscopic neuroanatomy using synthetic neuronal populations.

    Science.gov (United States)

    Schneider, Calvin J; Cuntz, Hermann; Soltesz, Ivan

    2014-10-01

    Dendritic morphology has been shown to have a dramatic impact on neuronal function. However, population features such as the inherent variability in dendritic morphology between cells belonging to the same neuronal type are often overlooked when studying computation in neural networks. While detailed models for morphology and electrophysiology exist for many types of single neurons, the role of detailed single cell morphology in the population has not been studied quantitatively or computationally. Here we use the structural context of the neural tissue in which dendritic trees exist to drive their generation in silico. We synthesize the entire population of dentate gyrus granule cells, the most numerous cell type in the hippocampus, by growing their dendritic trees within their characteristic dendritic fields bounded by the realistic structural context of (1) the granule cell layer that contains all somata and (2) the molecular layer that contains the dendritic forest. This process enables branching statistics to be linked to larger scale neuroanatomical features. We find large differences in dendritic total length and individual path length measures as a function of location in the dentate gyrus and of somatic depth in the granule cell layer. We also predict the number of unique granule cell dendrites invading a given volume in the molecular layer. This work enables the complete population-level study of morphological properties and provides a framework to develop complex and realistic neural network models.

  9. Extracting functionally feedforward networks from a population of spiking neurons.

    Science.gov (United States)

    Vincent, Kathleen; Tauskela, Joseph S; Thivierge, Jean-Philippe

    2012-01-01

    Neuronal avalanches are a ubiquitous form of activity characterized by spontaneous bursts whose size distribution follows a power-law. Recent theoretical models have replicated power-law avalanches by assuming the presence of functionally feedforward connections (FFCs) in the underlying dynamics of the system. Accordingly, avalanches are generated by a feedforward chain of activation that persists despite being embedded in a larger, massively recurrent circuit. However, it is unclear to what extent networks of living neurons that exhibit power-law avalanches rely on FFCs. Here, we employed a computational approach to reconstruct the functional connectivity of cultured cortical neurons plated on multielectrode arrays (MEAs) and investigated whether pharmacologically induced alterations in avalanche dynamics are accompanied by changes in FFCs. This approach begins by extracting a functional network of directed links between pairs of neurons, and then evaluates the strength of FFCs using Schur decomposition. In a first step, we examined the ability of this approach to extract FFCs from simulated spiking neurons. The strength of FFCs obtained in strictly feedforward networks diminished monotonically as links were gradually rewired at random. Next, we estimated the FFCs of spontaneously active cortical neuron cultures in the presence of either a control medium, a GABA(A) receptor antagonist (PTX), or an AMPA receptor antagonist combined with an NMDA receptor antagonist (APV/DNQX). The distribution of avalanche sizes in these cultures was modulated by this pharmacology, with a shallower power-law under PTX (due to the prominence of larger avalanches) and a steeper power-law under APV/DNQX (due to avalanches recruiting fewer neurons) relative to control cultures. The strength of FFCs increased in networks after application of PTX, consistent with an amplification of feedforward activity during avalanches. Conversely, FFCs decreased after application of APV

  10. Dense reconstruction of brain-wide neuronal population close to the ground truth

    OpenAIRE

    Li, Yun; Zhou, Hang; Li, Shiwei; Li, Jing; Su, Lei; Li, Anan; Feng, Xiong; Li, Ning; Han, Jiacheng; Kang, Hongtao; Chen, Yijun; Fang, Wenqian; Liu, Yidong; Lin, Huimin; Jin, Sen

    2017-01-01

    Neuron is the basic structure and functional unit of the brain, its projection and connections with other neurons provide a basic physical infrastructure for neural signal storage, allocation, processing, and integration. Recent technique progresses allow for labeling and imaging specific neuronal populations at single axonal level across a whole mouse brain. However, digital reconstruction of these neuron individuals needs months of human labor or sometimes is even an impossible task. Here w...

  11. 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)

  12. 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

  13. Inflammatory mediator bradykinin increases population of sensory neurons expressing functional T-type Ca(2+) channels.

    Science.gov (United States)

    Huang, Dongyang; Liang, Ce; Zhang, Fan; Men, Hongchao; Du, Xiaona; Gamper, Nikita; Zhang, Hailin

    2016-04-29

    T-type Ca(2+) channels are important regulators of peripheral sensory neuron excitability. Accordingly, T-type Ca(2+) currents are often increased in various pathological pain conditions, such as inflammation or nerve injury. Here we investigated effects of inflammation on functional expression of T-type Ca(2+) channels in small-diameter cultured dorsal root ganglion (DRG) neurons. We found that overnight treatment of DRG cultures with a cocktail of inflammatory mediators bradykinin (BK), adenosine triphosphate (ATP), norepinephrine (NE) and prostaglandin E2 (PGE2) strongly increased the population size of the small-diameter neurons displaying low-voltage activated (LVA, T-type) Ca(2+) currents while having no effect on the peak LVA current amplitude. When applied individually, BK and ATP also increased the population size of LVA-positive neurons while NE and PGE2 had no effect. The PLC inhibitor U-73122 and B2 receptor antagonist, Hoe-140, both abolished the increase of the population of LVA-positive DRG neurons. Inflammatory treatment did not affect CaV3.2 mRNA or protein levels in DRG cultures. Furthermore, an ubiquitination inhibitor, MG132, did not increase the population of LVA-positive neurons. Our data suggest that inflammatory mediators BK and ATP increase the abundance of LVA-positive DRG neurons in total neuronal population by stimulating the recruitment of a 'reserve pool' of CaV3.2 channels, particularly in neurons that do not display measurable LVA currents under control conditions. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  14. Different requirements for GFRα2-signaling in three populations of cutaneous sensory neurons.

    Science.gov (United States)

    Kupari, Jussi; Airaksinen, Matti S

    2014-01-01

    Many primary sensory neurons in mouse dorsal root ganglia (DRG) express one or several GFRα's, the ligand-binding receptors of the GDNF family, and their common signaling receptor Ret. GFRα2, the principal receptor for neurturin, is expressed in most of the small nonpeptidergic DRG neurons, but also in some large DRG neurons that start to express Ret earlier. Previously, GFRα2 has been shown to be crucial for the soma size of small nonpeptidergic nociceptors and for their target innervation of glabrous epidermis. However, little is known about this receptor in other Ret-expressing DRG neuron populations. Here we have investigated two populations of Ret-positive low-threshold mechanoreceptors that innervate different types of hair follicles on mouse back skin: the small C-LTMRs and the large Aβ-LTMRs. Using GFRα2-KO mice and immunohistochemistry we found that, similar to the nonpeptidergic nociceptors, GFRα2 controls the cell size but not the survival of both C-LTMRs and Aβ-LTMRs. In contrast to the nonpeptidergic neurons, GFRα2 is not required for the target innervation of C-LTMRs and Aβ-LTMRs in the back skin. These results suggest that different factors drive target innervation in these three populations of neurons. In addition, the observation that the large Ret-positive DRG neurons lack GFRα2 immunoreactivity in mature animals suggests that these neurons switch their GFRα signaling pathways during postnatal development.

  15. Populations of auditory cortical neurons can accurately encode acoustic space across stimulus intensity.

    Science.gov (United States)

    Miller, Lee M; Recanzone, Gregg H

    2009-04-07

    The auditory cortex is critical for perceiving a sound's location. However, there is no topographic representation of acoustic space, and individual auditory cortical neurons are often broadly tuned to stimulus location. It thus remains unclear how acoustic space is represented in the mammalian cerebral cortex and how it could contribute to sound localization. This report tests whether the firing rates of populations of neurons in different auditory cortical fields in the macaque monkey carry sufficient information to account for horizontal sound localization ability. We applied an optimal neural decoding technique, based on maximum likelihood estimation, to populations of neurons from 6 different cortical fields encompassing core and belt areas. We found that the firing rate of neurons in the caudolateral area contain enough information to account for sound localization ability, but neurons in other tested core and belt cortical areas do not. These results provide a detailed and plausible population model of how acoustic space could be represented in the primate cerebral cortex and support a dual stream processing model of auditory cortical processing.

  16. Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception.

    Science.gov (United States)

    Kutschireiter, Anna; Surace, Simone Carlo; Sprekeler, Henning; Pfister, Jean-Pascal

    2017-08-18

    The robust estimation of dynamical hidden features, such as the position of prey, based on sensory inputs is one of the hallmarks of perception. This dynamical estimation can be rigorously formulated by nonlinear Bayesian filtering theory. Recent experimental and behavioral studies have shown that animals' performance in many tasks is consistent with such a Bayesian statistical interpretation. However, it is presently unclear how a nonlinear Bayesian filter can be efficiently implemented in a network of neurons that satisfies some minimum constraints of biological plausibility. Here, we propose the Neural Particle Filter (NPF), a sampling-based nonlinear Bayesian filter, which does not rely on importance weights. We show that this filter can be interpreted as the neuronal dynamics of a recurrently connected rate-based neural network receiving feed-forward input from sensory neurons. Further, it captures properties of temporal and multi-sensory integration that are crucial for perception, and it allows for online parameter learning with a maximum likelihood approach. The NPF holds the promise to avoid the 'curse of dimensionality', and we demonstrate numerically its capability to outperform weighted particle filters in higher dimensions and when the number of particles is limited.

  17. Engineering connectivity by multiscale micropatterning of individual populations of neurons.

    Science.gov (United States)

    Albers, Jonas; Toma, Koji; Offenhäusser, Andreas

    2015-02-01

    Functional networks are the basis of information processing in the central nervous system. Essential for their formation are guided neuronal growth as well as controlled connectivity and information flow. The basis of neuronal development is generated by guiding cues and geometric constraints. To investigate the neuronal growth and connectivity of adjacent neuronal networks, two-dimensional protein patterns were created. A mixture of poly-L-lysine and laminin was transferred onto a silanized glass surface by microcontact printing. The structures were populated with dissociated primary cortical embryonic rat neurons. Triangular structures with diverse opening angles, height, and design were chosen as two-dimensional structures to allow network formation with constricted gateways. Neuronal development was observed by immunohistochemistry to pursue the influence of the chosen structures on the neuronal outgrowth. Neurons were stained for MAP2, while poly-L-lysine was FITC labeled. With this study we present an easy-to-use technique to engineer two-dimensional networks in vitro with defined gateways. The presented micropatterning method is used to generate daisy-chained neuronal networks with predefined connectivity. Signal propagation among geometrically constrained networks can easily be monitored by calcium-sensitive dyes, providing insights into network communication in vitro. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  18. Unmasking of spiral ganglion neuron firing dynamics by membrane potential and neurotrophin-3.

    Science.gov (United States)

    Crozier, Robert A; Davis, Robin L

    2014-07-16

    Type I spiral ganglion neurons have a unique role relative to other sensory afferents because, as a single population, they must convey the richness, complexity, and precision of auditory information as they shape signals transmitted to the brain. To understand better the sophistication of spiral ganglion response properties, we compared somatic whole-cell current-clamp recordings from basal and apical neurons obtained during the first 2 postnatal weeks from CBA/CaJ mice. We found that during this developmental time period neuron response properties changed from uniformly excitable to differentially plastic. Low-frequency, apical and high-frequency basal neurons at postnatal day 1 (P1)-P3 were predominantly slowly accommodating (SA), firing at low thresholds with little alteration in accommodation response mode induced by changes in resting membrane potential (RMP) or added neurotrophin-3 (NT-3). In contrast, P10-P14 apical and basal neurons were predominately rapidly accommodating (RA), had higher firing thresholds, and responded to elevation of RMP and added NT-3 by transitioning to the SA category without affecting the instantaneous firing rate. Therefore, older neurons appeared to be uniformly less excitable under baseline conditions yet displayed a previously unrecognized capacity to change response modes dynamically within a remarkably stable accommodation framework. Because the soma is interposed in the signal conduction pathway, these specializations can potentially lead to shaping and filtering of the transmitted signal. These results suggest that spiral ganglion neurons possess electrophysiological mechanisms that enable them to adapt their response properties to the characteristics of incoming stimuli and thus have the capacity to encode a wide spectrum of auditory information. Copyright © 2014 the authors 0270-6474/14/349688-15$15.00/0.

  19. Asynchronous Rate Chaos in Spiking Neuronal Circuits.

    Directory of Open Access Journals (Sweden)

    Omri Harish

    2015-07-01

    Full Text Available The brain exhibits temporally complex patterns of activity with features similar to those of chaotic systems. Theoretical studies over the last twenty years have described various computational advantages for such regimes in neuronal systems. Nevertheless, it still remains unclear whether chaos requires specific cellular properties or network architectures, or whether it is a generic property of neuronal circuits. We investigate the dynamics of networks of excitatory-inhibitory (EI spiking neurons with random sparse connectivity operating in the regime of balance of excitation and inhibition. Combining Dynamical Mean-Field Theory with numerical simulations, we show that chaotic, asynchronous firing rate fluctuations emerge generically for sufficiently strong synapses. Two different mechanisms can lead to these chaotic fluctuations. One mechanism relies on slow I-I inhibition which gives rise to slow subthreshold voltage and rate fluctuations. The decorrelation time of these fluctuations is proportional to the time constant of the inhibition. The second mechanism relies on the recurrent E-I-E feedback loop. It requires slow excitation but the inhibition can be fast. In the corresponding dynamical regime all neurons exhibit rate fluctuations on the time scale of the excitation. Another feature of this regime is that the population-averaged firing rate is substantially smaller in the excitatory population than in the inhibitory population. This is not necessarily the case in the I-I mechanism. Finally, we discuss the neurophysiological and computational significance of our results.

  20. Asynchronous Rate Chaos in Spiking Neuronal Circuits

    Science.gov (United States)

    Harish, Omri; Hansel, David

    2015-01-01

    The brain exhibits temporally complex patterns of activity with features similar to those of chaotic systems. Theoretical studies over the last twenty years have described various computational advantages for such regimes in neuronal systems. Nevertheless, it still remains unclear whether chaos requires specific cellular properties or network architectures, or whether it is a generic property of neuronal circuits. We investigate the dynamics of networks of excitatory-inhibitory (EI) spiking neurons with random sparse connectivity operating in the regime of balance of excitation and inhibition. Combining Dynamical Mean-Field Theory with numerical simulations, we show that chaotic, asynchronous firing rate fluctuations emerge generically for sufficiently strong synapses. Two different mechanisms can lead to these chaotic fluctuations. One mechanism relies on slow I-I inhibition which gives rise to slow subthreshold voltage and rate fluctuations. The decorrelation time of these fluctuations is proportional to the time constant of the inhibition. The second mechanism relies on the recurrent E-I-E feedback loop. It requires slow excitation but the inhibition can be fast. In the corresponding dynamical regime all neurons exhibit rate fluctuations on the time scale of the excitation. Another feature of this regime is that the population-averaged firing rate is substantially smaller in the excitatory population than in the inhibitory population. This is not necessarily the case in the I-I mechanism. Finally, we discuss the neurophysiological and computational significance of our results. PMID:26230679

  1. Membrane potential and response properties of populations of cortical neurons in the high conductance state

    International Nuclear Information System (INIS)

    Moreno-Bote, Ruben; Parga, Nestor

    2005-01-01

    Because of intense synaptic activity, cortical neurons are in a high conductance state. We show that this state has important consequences on the properties of a population of independent model neurons with conductance-based synapses. Using an adiabaticlike approximation we study both the membrane potential and the firing probability distributions across the population. We find that the latter is bimodal in such a way that at any particular moment some neurons are inactive while others are active. The population rate and the response variability are also characterized

  2. Learning to Estimate Dynamical State with Probabilistic Population Codes.

    Directory of Open Access Journals (Sweden)

    Joseph G Makin

    2015-11-01

    Full Text Available Tracking moving objects, including one's own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF, the parameters of which can be learned via latent-variable density estimation (the EM algorithm. The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, "probabilistic population codes." We show that a recurrent neural network-a modified form of an exponential family harmonium (EFH-that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states.

  3. Laser capture microdissection of enriched populations of neurons or single neurons for gene expression analysis after traumatic brain injury.

    Science.gov (United States)

    Boone, Deborah R; Sell, Stacy L; Hellmich, Helen Lee

    2013-04-10

    Long-term cognitive disability after TBI is associated with injury-induced neurodegeneration in the hippocampus-a region in the medial temporal lobe that is critical for learning, memory and executive function. Hence our studies focus on gene expression analysis of specific neuronal populations in distinct subregions of the hippocampus. The technique of laser capture microdissection (LCM), introduced in 1996 by Emmert-Buck, et al., has allowed for significant advances in gene expression analysis of single cells and enriched populations of cells from heterogeneous tissues such as the mammalian brain that contains thousands of functional cell types. We use LCM and a well established rat model of traumatic brain injury (TBI) to investigate the molecular mechanisms that underlie the pathogenesis of TBI. Following fluid-percussion TBI, brains are removed at pre-determined times post-injury, immediately frozen on dry ice, and prepared for sectioning in a cryostat. The rat brains can be embedded in OCT and sectioned immediately, or stored several months at -80 °C before sectioning for laser capture microdissection. Additionally, we use LCM to study the effects of TBI on circadian rhythms. For this, we capture neurons from the suprachiasmatic nuclei that contain the master clock of the mammalian brain. Here, we demonstrate the use of LCM to obtain single identified neurons (injured and degenerating, Fluoro-Jade-positive, or uninjured, Fluoro-Jade-negative) and enriched populations of hippocampal neurons for subsequent gene expression analysis by real time PCR and/or whole-genome microarrays. These LCM-enabled studies have revealed that the selective vulnerability of anatomically distinct regions of the rat hippocampus are reflected in the different gene expression profiles of different populations of neurons obtained by LCM from these distinct regions. The results from our single-cell studies, where we compare the transcriptional profiles of dying and adjacent surviving

  4. Dynamic binding of visual features by neuronal/stimulus synchrony.

    Science.gov (United States)

    Iwabuchi, A

    1998-05-01

    When people see a visual scene, certain parts of the visual scene are treated as belonging together and we regard them as a perceptual unit, which is called a "figure". People focus on figures, and the remaining parts of the scene are disregarded as "ground". In Gestalt psychology this process is called "figure-ground segregation". According to current perceptual psychology, a figure is formed by binding various visual features in a scene, and developments in neuroscience have revealed that there are many feature-encoding neurons, which respond to such features specifically. It is not known, however, how the brain binds different features of an object into a coherent visual object representation. Recently, the theory of binding by neuronal synchrony, which argues that feature binding is dynamically mediated by neuronal synchrony of feature-encoding neurons, has been proposed. This review article portrays the problem of figure-ground segregation and features binding, summarizes neurophysiological and psychophysical experiments and theory relevant to feature binding by neuronal/stimulus synchrony, and suggests possible directions for future research on this topic.

  5. Inverse stochastic resonance in networks of spiking neurons.

    Science.gov (United States)

    Uzuntarla, Muhammet; Barreto, Ernest; Torres, Joaquin J

    2017-07-01

    Inverse Stochastic Resonance (ISR) is a phenomenon in which the average spiking rate of a neuron exhibits a minimum with respect to noise. ISR has been studied in individual neurons, but here, we investigate ISR in scale-free networks, where the average spiking rate is calculated over the neuronal population. We use Hodgkin-Huxley model neurons with channel noise (i.e., stochastic gating variable dynamics), and the network connectivity is implemented via electrical or chemical connections (i.e., gap junctions or excitatory/inhibitory synapses). We find that the emergence of ISR depends on the interplay between each neuron's intrinsic dynamical structure, channel noise, and network inputs, where the latter in turn depend on network structure parameters. We observe that with weak gap junction or excitatory synaptic coupling, network heterogeneity and sparseness tend to favor the emergence of ISR. With inhibitory coupling, ISR is quite robust. We also identify dynamical mechanisms that underlie various features of this ISR behavior. Our results suggest possible ways of experimentally observing ISR in actual neuronal systems.

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

    Science.gov (United States)

    Yan, Hao; Sun, Xiaojuan

    2017-06-01

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

  7. Mathematical Relationships between Neuron Morphology and Neurite Growth Dynamics in Drosophila melanogaster Larva Class IV Sensory Neurons

    Science.gov (United States)

    Ganguly, Sujoy; Liang, Xin; Grace, Michael; Lee, Daniel; Howard, Jonathon

    The morphology of neurons is diverse and reflects the diversity of neuronal functions, yet the principles that govern neuronal morphogenesis are unclear. In an effort to better understand neuronal morphogenesis we will be focusing on the development of the dendrites of class IV sensory neuron in Drosophila melanogaster. In particular we attempt to determine how the the total length, and the number of branches of dendrites are mathematically related to the dynamics of neurite growth and branching. By imaging class IV neurons during early embryogenesis we are able to measure the change in neurite length l (t) as a function of time v (t) = dl / dt . We found that the distribution of v (t) is well characterized by a hyperbolic secant distribution, and that the addition of new branches per unit time is well described by a Poisson process. Combining these measurements with the assumption that branching occurs with equal probability anywhere along the dendrite we were able to construct a mathematical model that provides reasonable agreement with the observed number of branches, and total length of the dendrites of the class IV sensory neuron.

  8. Deep Learning with Dynamic Spiking Neurons and Fixed Feedback Weights.

    Science.gov (United States)

    Samadi, Arash; Lillicrap, Timothy P; Tweed, Douglas B

    2017-03-01

    Recent work in computer science has shown the power of deep learning driven by the backpropagation algorithm in networks of artificial neurons. But real neurons in the brain are different from most of these artificial ones in at least three crucial ways: they emit spikes rather than graded outputs, their inputs and outputs are related dynamically rather than by piecewise-smooth functions, and they have no known way to coordinate arrays of synapses in separate forward and feedback pathways so that they change simultaneously and identically, as they do in backpropagation. Given these differences, it is unlikely that current deep learning algorithms can operate in the brain, but we that show these problems can be solved by two simple devices: learning rules can approximate dynamic input-output relations with piecewise-smooth functions, and a variation on the feedback alignment algorithm can train deep networks without having to coordinate forward and feedback synapses. Our results also show that deep spiking networks learn much better if each neuron computes an intracellular teaching signal that reflects that cell's nonlinearity. With this mechanism, networks of spiking neurons show useful learning in synapses at least nine layers upstream from the output cells and perform well compared to other spiking networks in the literature on the MNIST digit recognition task.

  9. Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons.

    Science.gov (United States)

    Mensi, Skander; Hagens, Olivier; Gerstner, Wulfram; Pozzorini, Christian

    2016-02-01

    The way in which single neurons transform input into output spike trains has fundamental consequences for network coding. Theories and modeling studies based on standard Integrate-and-Fire models implicitly assume that, in response to increasingly strong inputs, neurons modify their coding strategy by progressively reducing their selective sensitivity to rapid input fluctuations. Combining mathematical modeling with in vitro experiments, we demonstrate that, in L5 pyramidal neurons, the firing threshold dynamics adaptively adjust the effective timescale of somatic integration in order to preserve sensitivity to rapid signals over a broad range of input statistics. For that, a new Generalized Integrate-and-Fire model featuring nonlinear firing threshold dynamics and conductance-based adaptation is introduced that outperforms state-of-the-art neuron models in predicting the spiking activity of neurons responding to a variety of in vivo-like fluctuating currents. Our model allows for efficient parameter extraction and can be analytically mapped to a Generalized Linear Model in which both the input filter--describing somatic integration--and the spike-history filter--accounting for spike-frequency adaptation--dynamically adapt to the input statistics, as experimentally observed. Overall, our results provide new insights on the computational role of different biophysical processes known to underlie adaptive coding in single neurons and support previous theoretical findings indicating that the nonlinear dynamics of the firing threshold due to Na+-channel inactivation regulate the sensitivity to rapid input fluctuations.

  10. Temperature manipulation of neuronal dynamics in a forebrain motor control nucleus.

    Directory of Open Access Journals (Sweden)

    Matías A Goldin

    2017-08-01

    Full Text Available Different neuronal types within brain motor areas contribute to the generation of complex motor behaviors. A widely studied songbird forebrain nucleus (HVC has been recognized as fundamental in shaping the precise timing characteristics of birdsong. This is based, among other evidence, on the stretching and the "breaking" of song structure when HVC is cooled. However, little is known about the temperature effects that take place in its neurons. To address this, we investigated the dynamics of HVC both experimentally and computationally. We developed a technique where simultaneous electrophysiological recordings were performed during temperature manipulation of HVC. We recorded spontaneous activity and found three effects: widening of the spike shape, decrease of the firing rate and change in the interspike interval distribution. All these effects could be explained with a detailed conductance based model of all the neurons present in HVC. Temperature dependence of the ionic channel time constants explained the first effect, while the second was based in the changes of the maximal conductance using single synaptic excitatory inputs. The last phenomenon, only emerged after introducing a more realistic synaptic input to the inhibitory interneurons. Two timescales were present in the interspike distributions. The behavior of one timescale was reproduced with different input balances received form the excitatory neurons, whereas the other, which disappears with cooling, could not be found assuming poissonian synaptic inputs. Furthermore, the computational model shows that the bursting of the excitatory neurons arises naturally at normal brain temperature and that they have an intrinsic delay at low temperatures. The same effect occurs at single synapses, which may explain song stretching. These findings shed light on the temperature dependence of neuronal dynamics and present a comprehensive framework to study neuronal connectivity. This study, which

  11. The Slow Dynamics of Intracellular Sodium Concentration Increase the Time Window of Neuronal Integration: A Simulation Study

    Directory of Open Access Journals (Sweden)

    Asaph Zylbertal

    2017-09-01

    Full Text Available Changes in intracellular Na+ concentration ([Na+]i are rarely taken into account when neuronal activity is examined. As opposed to Ca2+, [Na+]i dynamics are strongly affected by longitudinal diffusion, and therefore they are governed by the morphological structure of the neurons, in addition to the localization of influx and efflux mechanisms. Here, we examined [Na+]i dynamics and their effects on neuronal computation in three multi-compartmental neuronal models, representing three distinct cell types: accessory olfactory bulb (AOB mitral cells, cortical layer V pyramidal cells, and cerebellar Purkinje cells. We added [Na+]i as a state variable to these models, and allowed it to modulate the Na+ Nernst potential, the Na+-K+ pump current, and the Na+-Ca2+ exchanger rate. Our results indicate that in most cases [Na+]i dynamics are significantly slower than [Ca2+]i dynamics, and thus may exert a prolonged influence on neuronal computation in a neuronal type specific manner. We show that [Na+]i dynamics affect neuronal activity via three main processes: reduction of EPSP amplitude in repeatedly active synapses due to reduction of the Na+ Nernst potential; activity-dependent hyperpolarization due to increased activity of the Na+-K+ pump; specific tagging of active synapses by extended Ca2+ elevation, intensified by concurrent back-propagating action potentials or complex spikes. Thus, we conclude that [Na+]i dynamics should be considered whenever synaptic plasticity, extensive synaptic input, or bursting activity are examined.

  12. Intraspinal serotonergic neurons consist of two, temporally distinct populations in developing zebrafish.

    Science.gov (United States)

    Montgomery, Jacob E; Wiggin, Timothy D; Rivera-Perez, Luis M; Lillesaar, Christina; Masino, Mark A

    2016-06-01

    Zebrafish intraspinal serotonergic neuron (ISN) morphology and distribution have been examined in detail at different ages; however, some aspects of the development of these cells remain unclear. Although antibodies to serotonin (5-HT) have detected ISNs in the ventral spinal cord of embryos, larvae, and adults, the only tryptophan hydroxylase (tph) transcript that has been described in the spinal cord is tph1a. Paradoxically, spinal tph1a is only expressed transiently in embryos, which brings the source of 5-HT in the ISNs of larvae and adults into question. Because the pet1 and tph2 promoters drive transgene expression in the spinal cord, we hypothesized that tph2 is expressed in spinal cords of zebrafish larvae. We confirmed this hypothesis through in situ hybridization. Next, we used 5-HT antibody labeling and transgenic markers of tph2-expressing neurons to identify a transient population of ISNs in embryos that was distinct from ISNs that appeared later in development. The existence of separate ISN populations may not have been recognized previously due to their shared location in the ventral spinal cord. Finally, we used transgenic markers and immunohistochemical labeling to identify the transient ISN population as GABAergic Kolmer-Agduhr double-prime (KA″) neurons. Altogether, this study revealed a novel developmental paradigm in which KA″ neurons are transiently serotonergic before the appearance of a stable population of tph2-expressing ISNs. © 2015 Wiley Periodicals, Inc.

  13. Complex population response of dorsal putamen neurons predicts the ability to learn.

    Science.gov (United States)

    Laquitaine, Steeve; Piron, Camille; Abellanas, David; Loewenstein, Yonatan; Boraud, Thomas

    2013-01-01

    Day-to-day variability in performance is a common experience. We investigated its neural correlate by studying learning behavior of monkeys in a two-alternative forced choice task, the two-armed bandit task. We found substantial session-to-session variability in the monkeys' learning behavior. Recording the activity of single dorsal putamen neurons we uncovered a dual function of this structure. It has been previously shown that a population of neurons in the DLP exhibits firing activity sensitive to the reward value of chosen actions. Here, we identify putative medium spiny neurons in the dorsal putamen that are cue-selective and whose activity builds up with learning. Remarkably we show that session-to-session changes in the size of this population and in the intensity with which this population encodes cue-selectivity is correlated with session-to-session changes in the ability to learn the task. Moreover, at the population level, dorsal putamen activity in the very beginning of the session is correlated with the performance at the end of the session, thus predicting whether the monkey will have a "good" or "bad" learning day. These results provide important insights on the neural basis of inter-temporal performance variability.

  14. Voltage Imaging of Waking Mouse Cortex Reveals Emergence of Critical Neuronal Dynamics

    Science.gov (United States)

    Scott, Gregory; Fagerholm, Erik D.; Mutoh, Hiroki; Leech, Robert; Sharp, David J.; Shew, Woodrow L.

    2014-01-01

    Complex cognitive processes require neuronal activity to be coordinated across multiple scales, ranging from local microcircuits to cortex-wide networks. However, multiscale cortical dynamics are not well understood because few experimental approaches have provided sufficient support for hypotheses involving multiscale interactions. To address these limitations, we used, in experiments involving mice, genetically encoded voltage indicator imaging, which measures cortex-wide electrical activity at high spatiotemporal resolution. Here we show that, as mice recovered from anesthesia, scale-invariant spatiotemporal patterns of neuronal activity gradually emerge. We show for the first time that this scale-invariant activity spans four orders of magnitude in awake mice. In contrast, we found that the cortical dynamics of anesthetized mice were not scale invariant. Our results bridge empirical evidence from disparate scales and support theoretical predictions that the awake cortex operates in a dynamical regime known as criticality. The criticality hypothesis predicts that small-scale cortical dynamics are governed by the same principles as those governing larger-scale dynamics. Importantly, these scale-invariant principles also optimize certain aspects of information processing. Our results suggest that during the emergence from anesthesia, criticality arises as information processing demands increase. We expect that, as measurement tools advance toward larger scales and greater resolution, the multiscale framework offered by criticality will continue to provide quantitative predictions and insight on how neurons, microcircuits, and large-scale networks are dynamically coordinated in the brain. PMID:25505314

  15. Transcriptional profiling at whole population and single cell levels reveals somatosensory neuron molecular diversity

    Science.gov (United States)

    Chiu, Isaac M; Barrett, Lee B; Williams, Erika K; Strochlic, David E; Lee, Seungkyu; Weyer, Andy D; Lou, Shan; Bryman, Gregory S; Roberson, David P; Ghasemlou, Nader; Piccoli, Cara; Ahat, Ezgi; Wang, Victor; Cobos, Enrique J; Stucky, Cheryl L; Ma, Qiufu; Liberles, Stephen D; Woolf, Clifford J

    2014-01-01

    The somatosensory nervous system is critical for the organism's ability to respond to mechanical, thermal, and nociceptive stimuli. Somatosensory neurons are functionally and anatomically diverse but their molecular profiles are not well-defined. Here, we used transcriptional profiling to analyze the detailed molecular signatures of dorsal root ganglion (DRG) sensory neurons. We used two mouse reporter lines and surface IB4 labeling to purify three major non-overlapping classes of neurons: 1) IB4+SNS-Cre/TdTomato+, 2) IB4−SNS-Cre/TdTomato+, and 3) Parv-Cre/TdTomato+ cells, encompassing the majority of nociceptive, pruriceptive, and proprioceptive neurons. These neurons displayed distinct expression patterns of ion channels, transcription factors, and GPCRs. Highly parallel qRT-PCR analysis of 334 single neurons selected by membership of the three populations demonstrated further diversity, with unbiased clustering analysis identifying six distinct subgroups. These data significantly increase our knowledge of the molecular identities of known DRG populations and uncover potentially novel subsets, revealing the complexity and diversity of those neurons underlying somatosensation. DOI: http://dx.doi.org/10.7554/eLife.04660.001 PMID:25525749

  16. Dynamic transition on the seizure-like neuronal activity by astrocytic calcium channel block

    International Nuclear Information System (INIS)

    Li, Jiajia; Wang, Rong; Du, Mengmeng; Tang, Jun; Wu, Ying

    2016-01-01

    The involvement of astrocytes in neuronal firing dynamics is becoming increasingly evident. In this study, we used a classical hippocampal tripartite synapse model consisting of soma-dendrite coupled neuron models and a Hodgkin–Huxley-like astrocyte model, to investigate the seizure-like firing in the somatic neuron induced by the over-expressed neuronal N-methyl-d-aspartate (NMDA) receptors. Based on this model, we further investigated the effect of the astrocytic channel block on the neuronal firing through a bifurcation analysis. Results show that blocking inositol-1,4,5-triphosphate(IP3)-dependent calcium channel in astrocytes efficiently suppresses the astrocytic calcium oscillation, which in turn suppresses the seizure-like firing in the neuron.

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

  18. Role of Delays in Shaping Spatiotemporal Dynamics of Neuronal Activity in Large Networks

    International Nuclear Information System (INIS)

    Roxin, Alex; Brunel, Nicolas; Hansel, David

    2005-01-01

    We study the effect of delays on the dynamics of large networks of neurons. We show that delays give rise to a wealth of bifurcations and to a rich phase diagram, which includes oscillatory bumps, traveling waves, lurching waves, standing waves arising via a period-doubling bifurcation, aperiodic regimes, and regimes of multistability. We study the existence and the stability of the various dynamical patterns analytically and numerically in a simplified rate model as a function of the interaction parameters. The results derived in that framework allow us to understand the origin of the diversity of dynamical states observed in large networks of spiking neurons

  19. A Specific Population of Reticulospinal Neurons Controls the Termination of Locomotion.

    Science.gov (United States)

    Juvin, Laurent; Grätsch, Swantje; Trillaud-Doppia, Emilie; Gariépy, Jean-François; Büschges, Ansgar; Dubuc, Réjean

    2016-06-14

    Locomotion requires the proper sequencing of neural activity to start, maintain, and stop it. Recently, brainstem neurons were shown to specifically stop locomotion in mammals. However, the cellular properties of these neurons and their activity during locomotion are still unknown. Here, we took advantage of the lamprey model to characterize the activity of a cell population that we now show to be involved in stopping locomotion. We find that these neurons display a burst of spikes that coincides with the end of swimming activity. Their pharmacological activation ends ongoing swimming, whereas the inactivation of these neurons dramatically impairs the rapid termination of swimming. These neurons are henceforth referred to as stop cells, because they play a crucial role in the termination of locomotion. Our findings contribute to the fundamental understanding of motor control and provide important details about the cellular mechanisms involved in locomotor termination. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

  20. Mouse V1 population correlates of visual detection rely on heterogeneity within neuronal response patterns

    Science.gov (United States)

    Montijn, Jorrit S; Goltstein, Pieter M; Pennartz, Cyriel MA

    2015-01-01

    Previous studies have demonstrated the importance of the primary sensory cortex for the detection, discrimination, and awareness of visual stimuli, but it is unknown how neuronal populations in this area process detected and undetected stimuli differently. Critical differences may reside in the mean strength of responses to visual stimuli, as reflected in bulk signals detectable in functional magnetic resonance imaging, electro-encephalogram, or magnetoencephalography studies, or may be more subtly composed of differentiated activity of individual sensory neurons. Quantifying single-cell Ca2+ responses to visual stimuli recorded with in vivo two-photon imaging, we found that visual detection correlates more strongly with population response heterogeneity rather than overall response strength. Moreover, neuronal populations showed consistencies in activation patterns across temporally spaced trials in association with hit responses, but not during nondetections. Contrary to models relying on temporally stable networks or bulk signaling, these results suggest that detection depends on transient differentiation in neuronal activity within cortical populations. DOI: http://dx.doi.org/10.7554/eLife.10163.001 PMID:26646184

  1. Towards a Population Dynamics Theory for Evolutionary Computing: Learning from Biological Population Dynamics in Nature

    Science.gov (United States)

    Ma, Zhanshan (Sam)

    In evolutionary computing (EC), population size is one of the critical parameters that a researcher has to deal with. Hence, it was no surprise that the pioneers of EC, such as De Jong (1975) and Holland (1975), had already studied the population sizing from the very beginning of EC. What is perhaps surprising is that more than three decades later, we still largely depend on the experience or ad-hoc trial-and-error approach to set the population size. For example, in a recent monograph, Eiben and Smith (2003) indicated: "In almost all EC applications, the population size is constant and does not change during the evolutionary search." Despite enormous research on this issue in recent years, we still lack a well accepted theory for population sizing. In this paper, I propose to develop a population dynamics theory forEC with the inspiration from the population dynamics theory of biological populations in nature. Essentially, the EC population is considered as a dynamic system over time (generations) and space (search space or fitness landscape), similar to the spatial and temporal dynamics of biological populations in nature. With this conceptual mapping, I propose to 'transplant' the biological population dynamics theory to EC via three steps: (i) experimentally test the feasibility—whether or not emulating natural population dynamics improves the EC performance; (ii) comparatively study the underlying mechanisms—why there are improvements, primarily via statistical modeling analysis; (iii) conduct theoretical analysis with theoretical models such as percolation theory and extended evolutionary game theory that are generally applicable to both EC and natural populations. This article is a summary of a series of studies we have performed to achieve the general goal [27][30]-[32]. In the following, I start with an extremely brief introduction on the theory and models of natural population dynamics (Sections 1 & 2). In Sections 4 to 6, I briefly discuss three

  2. The Isolation of Pure Populations of Neurons by Laser Capture Microdissection: Methods and Application in Neuroscience.

    Science.gov (United States)

    Morris, Renée; Mehta, Prachi

    2018-01-01

    In mammals, the central nervous system (CNS) is constituted of various cellular elements, posing a challenge to isolating specific cell types to investigate their expression profile. As a result, tissue homogenization is not amenable to analyses of motor neurons profiling as these represent less than 10% of the total spinal cord cell population. One way to tackle the problem of tissue heterogeneity and obtain meaningful genomic, proteomic, and transcriptomic profiling is to use laser capture microdissection technology (LCM). In this chapter, we describe protocols for the capture of isolated populations of motor neurons from spinal cord tissue sections and for downstream transcriptomic analysis of motor neurons with RT-PCR. We have also included a protocol for the immunological confirmation that the captured neurons are indeed motor neurons. Although focused on spinal cord motor neurons, these protocols can be easily optimized for the isolation of any CNS neurons.

  3. Network dynamics in nociceptive pathways assessed by the neuronal avalanche model

    Directory of Open Access Journals (Sweden)

    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.

  4. Population dynamics

    Directory of Open Access Journals (Sweden)

    Cooch, E. G.

    2004-06-01

    Full Text Available Increases or decreases in the size of populations over space and time are, arguably, the motivation for much of pure and applied ecological research. The fundamental model for the dynamics of any population is straightforward: the net change over time in the abundance of some population is the simple difference between the number of additions (individuals entering the population minus the number of subtractions (individuals leaving the population. Of course, the precise nature of the pattern and process of these additions and subtractions is often complex, and population biology is often replete with fairly dense mathematical representations of both processes. While there is no doubt that analysis of such abstract descriptions of populations has been of considerable value in advancing our, there has often existed a palpable discomfort when the ‘beautiful math’ is faced with the often ‘ugly realities’ of empirical data. In some cases, this attempted merger is abandoned altogether, because of the paucity of ‘good empirical data’ with which the theoretician can modify and evaluate more conceptually–based models. In some cases, the lack of ‘data’ is more accurately represented as a lack of robust estimates of one or more parameters. It is in this arena that methods developed to analyze multiple encounter data from individually marked organisms has seen perhaps the greatest advances. These methods have rapidly evolved to facilitate not only estimation of one or more vital rates, critical to population modeling and analysis, but also to allow for direct estimation of both the dynamics of populations (e.g., Pradel, 1996, and factors influencing those dynamics (e.g., Nichols et al., 2000. The interconnections between the various vital rates, their estimation, and incorporation into models, was the general subject of our plenary presentation by Hal Caswell (Caswell & Fujiwara, 2004. Caswell notes that although interest has traditionally

  5. Dynamic Control of Synchronous Activity in Networks of Spiking Neurons.

    Directory of Open Access Journals (Sweden)

    Axel Hutt

    Full Text Available Oscillatory brain activity is believed to play a central role in neural coding. Accumulating evidence shows that features of these oscillations are highly dynamic: power, frequency and phase fluctuate alongside changes in behavior and task demands. The role and mechanism supporting this variability is however poorly understood. We here analyze a network of recurrently connected spiking neurons with time delay displaying stable synchronous dynamics. Using mean-field and stability analyses, we investigate the influence of dynamic inputs on the frequency of firing rate oscillations. We show that afferent noise, mimicking inputs to the neurons, causes smoothing of the system's response function, displacing equilibria and altering the stability of oscillatory states. Our analysis further shows that these noise-induced changes cause a shift of the peak frequency of synchronous oscillations that scales with input intensity, leading the network towards critical states. We lastly discuss the extension of these principles to periodic stimulation, in which externally applied driving signals can trigger analogous phenomena. Our results reveal one possible mechanism involved in shaping oscillatory activity in the brain and associated control principles.

  6. Dynamic Control of Synchronous Activity in Networks of Spiking Neurons.

    Science.gov (United States)

    Hutt, Axel; Mierau, Andreas; Lefebvre, Jérémie

    Oscillatory brain activity is believed to play a central role in neural coding. Accumulating evidence shows that features of these oscillations are highly dynamic: power, frequency and phase fluctuate alongside changes in behavior and task demands. The role and mechanism supporting this variability is however poorly understood. We here analyze a network of recurrently connected spiking neurons with time delay displaying stable synchronous dynamics. Using mean-field and stability analyses, we investigate the influence of dynamic inputs on the frequency of firing rate oscillations. We show that afferent noise, mimicking inputs to the neurons, causes smoothing of the system's response function, displacing equilibria and altering the stability of oscillatory states. Our analysis further shows that these noise-induced changes cause a shift of the peak frequency of synchronous oscillations that scales with input intensity, leading the network towards critical states. We lastly discuss the extension of these principles to periodic stimulation, in which externally applied driving signals can trigger analogous phenomena. Our results reveal one possible mechanism involved in shaping oscillatory activity in the brain and associated control principles.

  7. Convergent processing of both positive and negative motivational signals by the VTA dopamine neuronal populations.

    Directory of Open Access Journals (Sweden)

    Dong V Wang

    2011-02-01

    Full Text Available Dopamine neurons in the ventral tegmental area (VTA have been traditionally studied for their roles in reward-related motivation or drug addiction. Here we study how the VTA dopamine neuron population may process fearful and negative experiences as well as reward information in freely behaving mice. Using multi-tetrode recording, we find that up to 89% of the putative dopamine neurons in the VTA exhibit significant activation in response to the conditioned tone that predict food reward, while the same dopamine neuron population also respond to the fearful experiences such as free fall and shake events. The majority of these VTA putative dopamine neurons exhibit suppression and offset-rebound excitation, whereas ∼25% of the recorded putative dopamine neurons show excitation by the fearful events. Importantly, VTA putative dopamine neurons exhibit parametric encoding properties: their firing change durations are proportional to the fearful event durations. In addition, we demonstrate that the contextual information is crucial for these neurons to respectively elicit positive or negative motivational responses by the same conditioned tone. Taken together, our findings suggest that VTA dopamine neurons may employ the convergent encoding strategy for processing both positive and negative experiences, intimately integrating with cues and environmental context.

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

    Science.gov (United States)

    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.

  9. Dynamical Encoding by Networks of Competing Neuron Groups: Winnerless Competition

    International Nuclear Information System (INIS)

    Rabinovich, M.; Volkovskii, A.; Lecanda, P.; Huerta, R.; Abarbanel, H. D. I.; Laurent, G.

    2001-01-01

    Following studies of olfactory processing in insects and fish, we investigate neural networks whose dynamics in phase space is represented by orbits near the heteroclinic connections between saddle regions (fixed points or limit cycles). These networks encode input information as trajectories along the heteroclinic connections. If there are N neurons in the network, the capacity is approximately e(N-1) ! , i.e., much larger than that of most traditional network structures. We show that a small winnerless competition network composed of FitzHugh-Nagumo spiking neurons efficiently transforms input information into a spatiotemporal output

  10. 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...

  11. A Discrete Population of Neurons in the Lateral Amygdala Is Specifically Activated by Contextual Fear Conditioning

    Science.gov (United States)

    Wilson, Yvette M.; Murphy, Mark

    2009-01-01

    There is no clear identification of the neurons involved in fear conditioning in the amygdala. To search for these neurons, we have used a genetic approach, the "fos-tau-lacZ" (FTL) mouse, to map functionally activated expression in neurons following contextual fear conditioning. We have identified a discrete population of neurons in the lateral…

  12. 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....

  13. Functionalized anatomical models for EM-neuron Interaction modeling

    Science.gov (United States)

    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.

  14. Studies of phase return map and symbolic dynamics in a periodically driven Hodgkin—Huxley neuron

    International Nuclear Information System (INIS)

    Ding Jiong; Zhang Hong; Tong Qin-Ye; Chen Zhuo

    2014-01-01

    How neuronal spike trains encode external information is a hot topic in neurodynamics studies. In this paper, we investigate the dynamical states of the Hodgkin—Huxley neuron under periodic forcing. Depending on the parameters of the stimulus, the neuron exhibits periodic, quasiperiodic and chaotic spike trains. In order to analyze these spike trains quantitatively, we use the phase return map to describe the dynamical behavior on a one-dimensional (1D) map. According to the monotonicity or discontinuous point of the 1D map, the spike trains are transformed into symbolic sequences by implementing a coarse-grained algorithm — symbolic dynamics. Based on the ordering rules of symbolic dynamics, the parameters of the external stimulus can be measured in high resolution with finite length symbolic sequences. A reasonable explanation for why the nervous system can discriminate or cognize the small change of the external signals in a short time is also presented. (general)

  15. Context Fear Learning Specifically Activates Distinct Populations of Neurons in Amygdala and Hypothalamus

    Science.gov (United States)

    Trogrlic, Lidia; Wilson, Yvette M.; Newman, Andrew G.; Murphy, Mark

    2011-01-01

    The identity and distribution of neurons that are involved in any learning or memory event is not known. In previous studies, we identified a discrete population of neurons in the lateral amygdala that show learning-specific activation of a c-"fos"-regulated transgene following context fear conditioning. Here, we have extended these studies to…

  16. Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations.

    Science.gov (United States)

    Bragg, Elise M; Briggs, Farran

    2017-02-15

    This protocol outlines large-scale reconstructions of neurons combined with the use of independent and unbiased clustering analyses to create a comprehensive survey of the morphological characteristics observed among a selective neuronal population. Combination of these techniques constitutes a novel approach for the collection and analysis of neuroanatomical data. Together, these techniques enable large-scale, and therefore more comprehensive, sampling of selective neuronal populations and establish unbiased quantitative methods for describing morphologically unique neuronal classes within a population. The protocol outlines the use of modified rabies virus to selectively label neurons. G-deleted rabies virus acts like a retrograde tracer following stereotaxic injection into a target brain structure of interest and serves as a vehicle for the delivery and expression of EGFP in neurons. Large numbers of neurons are infected using this technique and express GFP throughout their dendrites, producing "Golgi-like" complete fills of individual neurons. Accordingly, the virus-mediated retrograde tracing method improves upon traditional dye-based retrograde tracing techniques by producing complete intracellular fills. Individual well-isolated neurons spanning all regions of the brain area under study are selected for reconstruction in order to obtain a representative sample of neurons. The protocol outlines procedures to reconstruct cell bodies and complete dendritic arborization patterns of labeled neurons spanning multiple tissue sections. Morphological data, including positions of each neuron within the brain structure, are extracted for further analysis. Standard programming functions were utilized to perform independent cluster analyses and cluster evaluations based on morphological metrics. To verify the utility of these analyses, statistical evaluation of a cluster analysis performed on 160 neurons reconstructed in the thalamic reticular nucleus of the thalamus

  17. Hungry Neurons: Metabolic Insights on Seizure Dynamics

    OpenAIRE

    Paolo Bazzigaluppi; Azin Ebrahim Amini; Iliya Weisspapir; Bojana Stefanovic; Peter L. Carlen

    2017-01-01

    Epilepsy afflicts up to 1.6% of the population and the mechanisms underlying the appearance of seizures are still not understood. In past years, many efforts have been spent trying to understand the mechanisms underlying the excessive and synchronous firing of neurons. Traditionally, attention was pointed towards synaptic (dys)function and extracellular ionic species (dys)regulation. Recently, novel clinical and preclinical studies explored the role of brain metabolism (i.e., glucose utilizat...

  18. Relationship between the mechanisms of gamma rhythm generation and the magnitude of the macroscopic phase response function in a population of excitatory and inhibitory modified quadratic integrate-and-fire neurons

    Science.gov (United States)

    Akao, Akihiko; Ogawa, Yutaro; Jimbo, Yasuhiko; Ermentrout, G. Bard; Kotani, Kiyoshi

    2018-01-01

    Gamma oscillations are thought to play an important role in brain function. Interneuron gamma (ING) and pyramidal interneuron gamma (PING) mechanisms have been proposed as generation mechanisms for these oscillations. However, the relation between the generation mechanisms and the dynamical properties of the gamma oscillation are still unclear. Among the dynamical properties of the gamma oscillation, the phase response function (PRF) is important because it encodes the response of the oscillation to inputs. Recently, the PRF for an inhibitory population of modified theta neurons that generate an ING rhythm was computed by the adjoint method applied to the associated Fokker-Planck equation (FPE) for the model. The modified theta model incorporates conductance-based synapses as well as the voltage and current dynamics. Here, we extended this previous work by creating an excitatory-inhibitory (E-I) network using the modified theta model and described the population dynamics with the corresponding FPE. We conducted a bifurcation analysis of the FPE to find parameter regions which generate gamma oscillations. In order to label the oscillatory parameter regions by their generation mechanisms, we defined ING- and PING-type gamma oscillation in a mathematically plausible way based on the driver of the inhibitory population. We labeled the oscillatory parameter regions by these generation mechanisms and derived PRFs via the adjoint method on the FPE in order to investigate the differences in the responses of each type of oscillation to inputs. PRFs for PING and ING mechanisms are derived and compared. We found the amplitude of the PRF for the excitatory population is larger in the PING case than in the ING case. Finally, the E-I population of the modified theta neuron enabled us to analyze the PRFs of PING-type gamma oscillation and the entrainment ability of E and I populations. We found a parameter region in which PRFs of E and I are both purely positive in the case of

  19. Dynamic SERS nanosensor for neurotransmitter sensing near neurons.

    Science.gov (United States)

    Lussier, Félix; Brulé, Thibault; Bourque, Marie-Josée; Ducrot, Charles; Trudeau, Louis-Éric; Masson, Jean-François

    2017-12-04

    Current electrophysiology and electrochemistry techniques have provided unprecedented understanding of neuronal activity. However, these techniques are suited to a small, albeit important, panel of neurotransmitters such as glutamate, GABA and dopamine, and these constitute only a subset of the broader range of neurotransmitters involved in brain chemistry. Surface-enhanced Raman scattering (SERS) provides a unique opportunity to detect a broader range of neurotransmitters in close proximity to neurons. Dynamic SERS (D-SERS) nanosensors based on patch-clamp-like nanopipettes decorated with gold nanoraspberries can be located accurately under a microscope using techniques analogous to those used in current electrophysiology or electrochemistry experiments. In this manuscript, we demonstrate that D-SERS can measure in a single experiment ATP, glutamate (glu), acetylcholine (ACh), GABA and dopamine (DA), among other neurotransmitters, with the potential for detecting a greater number of neurotransmitters. The SERS spectra of these neurotransmitters were identified with a barcoding data processing method and time series of the neurotransmitter levels were constructed. The D-SERS nanosensor was then located near cultured mouse dopaminergic neurons. The detection of neurotransmitters was performed in response to a series of K + depolarisations, and allowed the detection of elevated levels of both ATP and dopamine. Control experiments were also performed near glial cells, showing only very low basal detection neurotransmitter events. This paper demonstrates the potential of D-SERS to detect neurotransmitter secretion events near living neurons, but also constitutes a strong proof-of-concept for the broad application of SERS to the detection of secretion events by neurons or other cell types in order to study normal or pathological cell functions.

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

    Science.gov (United States)

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

    2018-03-01

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

  1. Permanent genetic access to transiently active neurons via TRAP: targeted recombination in active populations.

    Science.gov (United States)

    Guenthner, Casey J; Miyamichi, Kazunari; Yang, Helen H; Heller, H Craig; Luo, Liqun

    2013-06-05

    Targeting genetically encoded tools for neural circuit dissection to relevant cellular populations is a major challenge in neurobiology. We developed an approach, targeted recombination in active populations (TRAP), to obtain genetic access to neurons that were activated by defined stimuli. This method utilizes mice in which the tamoxifen-dependent recombinase CreER(T2) is expressed in an activity-dependent manner from the loci of the immediate early genes Arc and Fos. Active cells that express CreER(T2) can only undergo recombination when tamoxifen is present, allowing genetic access to neurons that are active during a time window of less than 12 hr. We show that TRAP can provide selective access to neurons activated by specific somatosensory, visual, and auditory stimuli and by experience in a novel environment. When combined with tools for labeling, tracing, recording, and manipulating neurons, TRAP offers a powerful approach for understanding how the brain processes information and generates behavior. Copyright © 2013 Elsevier Inc. All rights reserved.

  2. Plasticity-induced characteristic changes of pattern dynamics and the related phase transitions in small-world neuronal networks

    International Nuclear Information System (INIS)

    Huang Xu-Hui; Hu Gang

    2014-01-01

    Phase transitions widely exist in nature and occur when some control parameters are changed. In neural systems, their macroscopic states are represented by the activity states of neuron populations, and phase transitions between different activity states are closely related to corresponding functions in the brain. In particular, phase transitions to some rhythmic synchronous firing states play significant roles on diverse brain functions and disfunctions, such as encoding rhythmical external stimuli, epileptic seizure, etc. However, in previous studies, phase transitions in neuronal networks are almost driven by network parameters (e.g., external stimuli), and there has been no investigation about the transitions between typical activity states of neuronal networks in a self-organized way by applying plastic connection weights. In this paper, we discuss phase transitions in electrically coupled and lattice-based small-world neuronal networks (LBSW networks) under spike-timing-dependent plasticity (STDP). By applying STDP on all electrical synapses, various known and novel phase transitions could emerge in LBSW networks, particularly, the phenomenon of self-organized phase transitions (SOPTs): repeated transitions between synchronous and asynchronous firing states. We further explore the mechanics generating SOPTs on the basis of synaptic weight dynamics. (interdisciplinary physics and related areas of science and technology)

  3. Neurons derived from patients with bipolar disorder divide into intrinsically different sub-populations of neurons, predicting the patients' responsiveness to lithium.

    Science.gov (United States)

    Stern, S; Santos, R; Marchetto, M C; Mendes, A P D; Rouleau, G A; Biesmans, S; Wang, Q-W; Yao, J; Charnay, P; Bang, A G; Alda, M; Gage, F H

    2017-02-28

    Bipolar disorder (BD) is a progressive psychiatric disorder with more than 3% prevalence worldwide. Affected individuals experience recurrent episodes of depression and mania, disrupting normal life and increasing the risk of suicide greatly. The complexity and genetic heterogeneity of psychiatric disorders have challenged the development of animal and cellular models. We recently reported that hippocampal dentate gyrus (DG) neurons differentiated from induced pluripotent stem cell (iPSC)-derived fibroblasts of BD patients are electrophysiologically hyperexcitable. Here we used iPSCs derived from Epstein-Barr virus-immortalized B-lymphocytes to verify that the hyperexcitability of DG-like neurons is reproduced in this different cohort of patients and cells. Lymphocytes are readily available for research with a large number of banked lines with associated patient clinical description. We used whole-cell patch-clamp recordings of over 460 neurons to characterize neurons derived from control individuals and BD patients. Extensive functional analysis showed that intrinsic cell parameters are very different between the two groups of BD neurons, those derived from lithium (Li)-responsive (LR) patients and those derived from Li-non-responsive (NR) patients, which led us to partition our BD neurons into two sub-populations of cells and suggested two different subdisorders. Training a Naïve Bayes classifier with the electrophysiological features of patients whose responses to Li are known allows for accurate classification with more than 92% success rate for a new patient whose response to Li is unknown. Despite their very different functional profiles, both populations of neurons share a large, fast after-hyperpolarization (AHP). We therefore suggest that the large, fast AHP is a key feature of BD and a main contributor to the fast, sustained spiking abilities of BD neurons. Confirming our previous report with fibroblast-derived DG neurons, chronic Li treatment reduced

  4. Deciphering neuronal population codes for acute thermal pain

    Science.gov (United States)

    Chen, Zhe; Zhang, Qiaosheng; Phuong Sieu Tong, Ai; Manders, Toby R.; Wang, Jing

    2017-06-01

    Objective. Pain is defined as an unpleasant sensory and emotional experience associated with actual or potential tissue damage, or described in terms of such damage. Current pain research mostly focuses on molecular and synaptic changes at the spinal and peripheral levels. However, a complete understanding of pain mechanisms requires the physiological study of the neocortex. Our goal is to apply a neural decoding approach to read out the onset of acute thermal pain signals, which can be used for brain-machine interface. Approach. We used micro wire arrays to record ensemble neuronal activities from the primary somatosensory cortex (S1) and anterior cingulate cortex (ACC) in freely behaving rats. We further investigated neural codes for acute thermal pain at both single-cell and population levels. To detect the onset of acute thermal pain signals, we developed a novel latent state-space framework to decipher the sorted or unsorted S1 and ACC ensemble spike activities, which reveal information about the onset of pain signals. Main results. The state space analysis allows us to uncover a latent state process that drives the observed ensemble spike activity, and to further detect the ‘neuronal threshold’ for acute thermal pain on a single-trial basis. Our method achieved good detection performance in sensitivity and specificity. In addition, our results suggested that an optimal strategy for detecting the onset of acute thermal pain signals may be based on combined evidence from S1 and ACC population codes. Significance. Our study is the first to detect the onset of acute pain signals based on neuronal ensemble spike activity. It is important from a mechanistic viewpoint as it relates to the significance of S1 and ACC activities in the regulation of the acute pain onset.

  5. Primary Lateral Sclerosis and Early Upper Motor Neuron Disease: Characteristics of a Cross-Sectional Population.

    Science.gov (United States)

    Fournier, Christina N; Murphy, Alyssa; Loci, Lorena; Mitsumoto, Hiroshi; Lomen-Hoerth, Catherine; Kisanuki, Yasushi; Simmons, Zachary; Maragakis, Nicholas J; McVey, April L; Al-Lahham, Tawfiq; Heiman-Patterson, Terry D; Andrews, Jinsy; McDonnell, Erin; Cudkowicz, Merit; Atassi, Nazem

    2016-03-01

    The goals of this study were to characterize clinical and electrophysiologic findings of subjects with upper motor neuron disease and to explore feasibility of clinical trials in this population. Twenty northeast amyotrophic lateral sclerosis consortium (northeast amyotrophic lateral sclerosis) sites performed chart reviews to identify active clinical pure upper motor neuron disease patients. Patients with hereditary spastic paraplegia or meeting revised El Escorial electrodiagnostic criteria for amyotrophic lateral sclerosis were excluded. Patients were classified into 2 groups according to the presence or absence of minor electromyography (EMG) abnormalities. Two hundred thirty-three subjects with upper motor neuron disease were identified; 217 had available EMG data. Normal EMGs were seen in 140 subjects, and 77 had minor denervation. Mean disease duration was 84 (±80) months for the entire cohort with no difference seen between the 2 groups. No difference was seen in clinical symptoms, disability, or outcome measures between the 2 groups after correcting for multiple comparisons. Minor EMG abnormalities were not associated with phenotypic differences in a clinical upper motor neuron disease population. These findings suggest that subtle EMG abnormalities can not necessarily be used as a prognostic tool in patients with clinical upper motor neuron disease. This study also demonstrates the availability of a large number of patients with upper motor neuron diseases within the northeast amyotrophic lateral sclerosis network and suggests feasibility for conducting clinical trials in this population.

  6. Mitochondrial Dynamics Mediated by Mitofusin 1 Is Required for POMC Neuron Glucose-Sensing and Insulin Release Control.

    Science.gov (United States)

    Ramírez, Sara; Gómez-Valadés, Alicia G; Schneeberger, Marc; Varela, Luis; Haddad-Tóvolli, Roberta; Altirriba, Jordi; Noguera, Eduard; Drougard, Anne; Flores-Martínez, Álvaro; Imbernón, Mónica; Chivite, Iñigo; Pozo, Macarena; Vidal-Itriago, Andrés; Garcia, Ainhoa; Cervantes, Sara; Gasa, Rosa; Nogueiras, Ruben; Gama-Pérez, Pau; Garcia-Roves, Pablo M; Cano, David A; Knauf, Claude; Servitja, Joan-Marc; Horvath, Tamas L; Gomis, Ramon; Zorzano, Antonio; Claret, Marc

    2017-06-06

    Proopiomelanocortin (POMC) neurons are critical sensors of nutrient availability implicated in energy balance and glucose metabolism control. However, the precise mechanisms underlying nutrient sensing in POMC neurons remain incompletely understood. We show that mitochondrial dynamics mediated by Mitofusin 1 (MFN1) in POMC neurons couple nutrient sensing with systemic glucose metabolism. Mice lacking MFN1 in POMC neurons exhibited defective mitochondrial architecture remodeling and attenuated hypothalamic gene expression programs during the fast-to-fed transition. This loss of mitochondrial flexibility in POMC neurons bidirectionally altered glucose sensing, causing abnormal glucose homeostasis due to defective insulin secretion by pancreatic β cells. Fed mice lacking MFN1 in POMC neurons displayed enhanced hypothalamic mitochondrial oxygen flux and reactive oxygen species generation. Central delivery of antioxidants was able to normalize the phenotype. Collectively, our data posit MFN1-mediated mitochondrial dynamics in POMC neurons as an intrinsic nutrient-sensing mechanism and unveil an unrecognized link between this subset of neurons and insulin release. Copyright © 2017 Elsevier Inc. All rights reserved.

  7. The dynamic brain: from spiking neurons to neural masses and cortical fields.

    Directory of Open Access Journals (Sweden)

    Gustavo Deco

    2008-08-01

    Full Text Available The cortex is a complex system, characterized by its dynamics and architecture, which underlie many functions such as action, perception, learning, language, and cognition. Its structural architecture has been studied for more than a hundred years; however, its dynamics have been addressed much less thoroughly. In this paper, we review and integrate, in a unifying framework, a variety of computational approaches that have been used to characterize the dynamics of the cortex, as evidenced at different levels of measurement. Computational models at different space-time scales help us understand the fundamental mechanisms that underpin neural processes and relate these processes to neuroscience data. Modeling at the single neuron level is necessary because this is the level at which information is exchanged between the computing elements of the brain; the neurons. Mesoscopic models tell us how neural elements interact to yield emergent behavior at the level of microcolumns and cortical columns. Macroscopic models can inform us about whole brain dynamics and interactions between large-scale neural systems such as cortical regions, the thalamus, and brain stem. Each level of description relates uniquely to neuroscience data, from single-unit recordings, through local field potentials to functional magnetic resonance imaging (fMRI, electroencephalogram (EEG, and magnetoencephalogram (MEG. Models of the cortex can establish which types of large-scale neuronal networks can perform computations and characterize their emergent properties. Mean-field and related formulations of dynamics also play an essential and complementary role as forward models that can be inverted given empirical data. This makes dynamic models critical in integrating theory and experiments. We argue that elaborating principled and informed models is a prerequisite for grounding empirical neuroscience in a cogent theoretical framework, commensurate with the achievements in the

  8. Burst analysis tool for developing neuronal networks exhibiting highly varying action potential dynamics

    Directory of Open Access Journals (Sweden)

    Fikret Emre eKapucu

    2012-06-01

    Full Text Available In this paper we propose a firing statistics based neuronal network burst detection algorithm for neuronal networks exhibiting highly variable action potential dynamics. Electrical activity of neuronal networks is generally analyzed by the occurrences of spikes and bursts both in time and space. Commonly accepted analysis tools employ burst detection algorithms based on predefined criteria. However, maturing neuronal networks, such as those originating from human embryonic stem cells (hESC, exhibit highly variable network structure and time-varying dynamics. To explore the developing burst/spike activities of such networks, we propose a burst detection algorithm which utilizes the firing statistics based on interspike interval (ISI histograms. Moreover, the algorithm calculates interspike interval thresholds for burst spikes as well as for pre-burst spikes and burst tails by evaluating the cumulative moving average and skewness of the ISI histogram. Because of the adaptive nature of the proposed algorithm, its analysis power is not limited by the type of neuronal cell network at hand. We demonstrate the functionality of our algorithm with two different types of microelectrode array (MEA data recorded from spontaneously active hESC-derived neuronal cell networks. The same data was also analyzed by two commonly employed burst detection algorithms and the differences in burst detection results are illustrated. The results demonstrate that our method is both adaptive to the firing statistics of the network and yields successful burst detection from the data. In conclusion, the proposed method is a potential tool for analyzing of hESC-derived neuronal cell networks and thus can be utilized in studies aiming to understand the development and functioning of human neuronal networks and as an analysis tool for in vitro drug screening and neurotoxicity assays.

  9. Separate populations of neurons in ventral striatum encode value and motivation.

    Science.gov (United States)

    Bissonette, Gregory B; Burton, Amanda C; Gentry, Ronny N; Goldstein, Brandon L; Hearn, Taylor N; Barnett, Brian R; Kashtelyan, Vadim; Roesch, Matthew R

    2013-01-01

    Neurons in the ventral striatum (VS) fire to cues that predict differently valued rewards. It is unclear whether this activity represents the value associated with the expected reward or the level of motivation induced by reward anticipation. To distinguish between the two, we trained rats on a task in which we varied value independently from motivation by manipulating the size of the reward expected on correct trials and the threat of punishment expected upon errors. We found that separate populations of neurons in VS encode expected value and motivation.

  10. Consistency and diversity of spike dynamics in the neurons of bed nucleus of stria terminalis of the rat: a dynamic clamp study.

    Directory of Open Access Journals (Sweden)

    Attila Szücs

    Full Text Available Neurons display a high degree of variability and diversity in the expression and regulation of their voltage-dependent ionic channels. Under low level of synaptic background a number of physiologically distinct cell types can be identified in most brain areas that display different responses to standard forms of intracellular current stimulation. Nevertheless, it is not well understood how biophysically different neurons process synaptic inputs in natural conditions, i.e., when experiencing intense synaptic bombardment in vivo. While distinct cell types might process synaptic inputs into different patterns of action potentials representing specific "motifs" of network activity, standard methods of electrophysiology are not well suited to resolve such questions. In the current paper we performed dynamic clamp experiments with simulated synaptic inputs that were presented to three types of neurons in the juxtacapsular bed nucleus of stria terminalis (jcBNST of the rat. Our analysis on the temporal structure of firing showed that the three types of jcBNST neurons did not produce qualitatively different spike responses under identical patterns of input. However, we observed consistent, cell type dependent variations in the fine structure of firing, at the level of single spikes. At the millisecond resolution structure of firing we found high degree of diversity across the entire spectrum of neurons irrespective of their type. Additionally, we identified a new cell type with intrinsic oscillatory properties that produced a rhythmic and regular firing under synaptic stimulation that distinguishes it from the previously described jcBNST cell types. Our findings suggest a sophisticated, cell type dependent regulation of spike dynamics of neurons when experiencing a complex synaptic background. The high degree of their dynamical diversity has implications to their cooperative dynamics and synchronization.

  11. 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

  12. Dynamic analysis of the conditional oscillator underlying slow waves in thalamocortical neurons

    Directory of Open Access Journals (Sweden)

    Francois eDavid

    2016-02-01

    Full Text Available During non-REM sleep the EEG shows characteristics waves that are generated by the dynamic interactions between cortical and thalamic oscillators. In thalamic neurons, low-threshold T-type Ca2+ channels play a pivotal role in almost every type of neuronal oscillations, including slow (<1 Hz waves, sleep spindles and delta waves. The transient opening of T channels gives rise to the low threshold spikes (LTSs, and associated high frequency bursts of action potentials, that are characteristically present during sleep spindles and delta waves, whereas the persistent opening of a small fraction of T channels, (i.e. ITwindow is responsible for the membrane potential bistability underlying sleep slow oscillations. Surprisingly thalamocortical (TC neurons express a very high density of T channels that largely exceed the amount required to generate LTSs and therefore, to support certain, if not all, sleep oscillations. Here, to clarify the relationship between T current density and sleep oscillations, we systematically investigated the impact of the T conductance level on the intrinsic rhythmic activities generated in TC neurons, combining in vitro experiments and TC neuron simulation. Using bifurcation analysis, we provide insights into the dynamical processes taking place at the transition between slow and delta oscillations. Our results show that although stable delta oscillations can be evoked with minimal T conductance, the full range of slow oscillation patterns, including groups of delta oscillations separated by Up states (grouped-delta slow waves requires a high density of T channels. Moreover, high levels of T conductance ensure the robustness of different types of slow oscillations.

  13. 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.

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

    Science.gov (United States)

    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.

  15. Neuronal synchrony: peculiarity and generality.

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

  17. Segregated populations of hippocampal principal CA1 neurons mediating conditioning and extinction of contextual fear.

    Science.gov (United States)

    Tronson, Natalie C; Schrick, Christina; Guzman, Yomayra F; Huh, Kyu Hwan; Srivastava, Deepak P; Penzes, Peter; Guedea, Anita L; Gao, Can; Radulovic, Jelena

    2009-03-18

    Learning processes mediating conditioning and extinction of contextual fear require activation of several key signaling pathways in the hippocampus. Principal hippocampal CA1 neurons respond to fear conditioning by a coordinated activation of multiple protein kinases and immediate early genes, such as cFos, enabling rapid and lasting consolidation of contextual fear memory. The extracellular signal-regulated kinase (Erk) additionally acts as a central mediator of fear extinction. It is not known however, whether these molecular events take place in overlapping or nonoverlapping neuronal populations. By using mouse models of conditioning and extinction of fear, we set out to determine the time course of cFos and Erk activity, their cellular overlap, and regulation by afferent cholinergic input from the medial septum. Analyses of cFos(+) and pErk(+) cells by immunofluorescence revealed predominant nuclear activation of either protein during conditioning and extinction of fear, respectively. Transgenic cFos-LacZ mice were further used to label in vivo Fos(+) hippocampal cells during conditioning followed by pErk immunostaining after extinction. The results showed that these signaling molecules were activated in segregated populations of hippocampal principal neurons. Furthermore, immunotoxin-induced lesions of medial septal neurons, providing cholinergic input into the hippocampus, selectively abolished Erk activation and extinction of fear without affecting cFos responses and conditioning. These results demonstrate that extinction mechanisms based on Erk signaling involve a specific population of CA1 principal neurons distinctively regulated by afferent cholinergic input from the medial septum.

  18. Tracking the fear memory engram: discrete populations of neurons within amygdala, hypothalamus, and lateral septum are specifically activated by auditory fear conditioning

    Science.gov (United States)

    Wilson, Yvette M.; Gunnersen, Jenny M.; Murphy, Mark

    2015-01-01

    Memory formation is thought to occur via enhanced synaptic connectivity between populations of neurons in the brain. However, it has been difficult to localize and identify the neurons that are directly involved in the formation of any specific memory. We have previously used fos-tau-lacZ (FTL) transgenic mice to identify discrete populations of neurons in amygdala and hypothalamus, which were specifically activated by fear conditioning to a context. Here we have examined neuronal activation due to fear conditioning to a more specific auditory cue. Discrete populations of learning-specific neurons were identified in only a small number of locations in the brain, including those previously found to be activated in amygdala and hypothalamus by context fear conditioning. These populations, each containing only a relatively small number of neurons, may be directly involved in fear learning and memory. PMID:26179231

  19. Code-specific learning rules improve action selection by populations of spiking neurons.

    Science.gov (United States)

    Friedrich, Johannes; Urbanczik, Robert; Senn, Walter

    2014-08-01

    Population coding is widely regarded as a key mechanism for achieving reliable behavioral decisions. We previously introduced reinforcement learning for population-based decision making by spiking neurons. Here we generalize population reinforcement learning to spike-based plasticity rules that take account of the postsynaptic neural code. We consider spike/no-spike, spike count and spike latency codes. The multi-valued and continuous-valued features in the postsynaptic code allow for a generalization of binary decision making to multi-valued decision making and continuous-valued action selection. We show that code-specific learning rules speed up learning both for the discrete classification and the continuous regression tasks. The suggested learning rules also speed up with increasing population size as opposed to standard reinforcement learning rules. Continuous action selection is further shown to explain realistic learning speeds in the Morris water maze. Finally, we introduce the concept of action perturbation as opposed to the classical weight- or node-perturbation as an exploration mechanism underlying reinforcement learning. Exploration in the action space greatly increases the speed of learning as compared to exploration in the neuron or weight space.

  20. Leader neurons in population bursts of 2D living neural networks

    International Nuclear Information System (INIS)

    Eckmann, J-P; Zbinden, Cyrille; Jacobi, Shimshon; Moses, Elisha; Marom, Shimon

    2008-01-01

    Eytan and Marom (2006 J. Neurosci. 26 8465-76) recently showed that the spontaneous bursting activity of rat neuron cultures includes 'first-to-fire' cells that consistently fire earlier than others. Here, we analyze the behavior of these neurons in long-term recordings of spontaneous activity of rat hippocampal and rat cortical neuron cultures from three different laboratories. We identify precursor events that may either subside ('aborted bursts') or can lead to a full-blown burst ('pre-bursts'). We find that the activation in the pre-burst typically has a first neuron ('leader'), followed by a localized response in its neighborhood. Locality is diminished in the bursts themselves. The long-term dynamics of the leaders is relatively robust, evolving with a half-life of 23-34 h. Stimulation of the culture alters the leader distribution, but the distribution stabilizes within about 1 h. We show that the leaders carry information about the identity of the burst, as measured by the signature of the number of spikes per neuron in a burst. The number of spikes from leaders in the first few spikes of a precursor event is furthermore shown to be predictive with regard to the transition into a burst (pre-burst versus aborted burst). We conclude that the leaders play a role in the development of the bursts and conjecture that they are part of an underlying sub-network that is excited first and then acts as a nucleation center for the burst

  1. Interstitial cells of the adult neocortical white matter are the remnant of the early generated subplate neuron population

    International Nuclear Information System (INIS)

    Chun, J.J.; Shatz, C.J.

    1989-01-01

    The postnatal fate of the first-generated neurons of the cat cerebral cortex was examined. These neurons can be identified uniquely by 3H-thymidine exposure during the week preceding the neurogenesis of cortical layer 6. Previous studies in which 3H-thymidine birthdating at embryonic day 27 (E27) was combined with immunohistochemistry have shown that these neurons are present in large numbers during fetal and early postnatal life within the subplate (future white matter), that they are immunoreactive for the neuron-specific protein MAP2 and for the putative neurotransmitters GABA, NPY, SRIF, and CCK. Here, the same techniques were used to follow the postnatal location and disappearance of the early generated subplate neuron population. At birth (P0), subplate neurons showing immunoreactivity for GABA, NPY, SRIF, or CCK are present in large numbers and at high density within the white matter throughout the neocortex, and the entire population can be observed as a dense MAP2-immunoreactive band situated beneath cortical layer 6. Between P0 and P401 (adulthood), the MAP2-immunostained band disappears so that comparatively few MAP2-immunoreactive neurons remain within the white matter. There is a corresponding decrease in the number and density of neurons stained with antibodies against neurotransmitters. In each instance, these neurons could be double-labeled by the administration of 3H-thymidine at E27, indicating that they are the remnants of the early generated subplate neuron population. The major period of decrease occurs during the first 4 postnatal weeks, and adult values are attained by 5 months. Within the white matter of the lateral gyrus (visual cortex), the density of immunostained neurons decreases dramatically: MAP2, 82%, SRIF, 81%, and NPY, 96%

  2. Conditional Viral Tract Tracing Delineates the Projections of the Distinct Kisspeptin Neuron Populations to Gonadotropin-Releasing Hormone (GnRH) Neurons in the Mouse.

    Science.gov (United States)

    Yip, Siew Hoong; Boehm, Ulrich; Herbison, Allan E; Campbell, Rebecca E

    2015-07-01

    Kisspeptin neurons play an essential role in the regulation of fertility through direct regulation of the GnRH neurons. However, the relative contributions of the two functionally distinct kisspeptin neuron subpopulations to this critical regulation are not fully understood. Here we analyzed the specific projection patterns of kisspeptin neurons originating from either the rostral periventricular nucleus of the third ventricle (RP3V) or the arcuate nucleus (ARN) using a cell-specific, viral-mediated tract-tracing approach. We stereotaxically injected a Cre-dependent recombinant adenovirus encoding farnesylated enhanced green fluorescent protein into the ARN or RP3V of adult male and female mice expressing Cre recombinase in kisspeptin neurons. Fibers from ARN kisspeptin neurons projected widely; however, we did not find any evidence for direct contact with GnRH neuron somata or proximal dendrites in either sex. In contrast, we identified RP3V kisspeptin fibers in close contact with GnRH neuron somata and dendrites in both sexes. Fibers originating from both the RP3V and ARN were observed in close contact with distal GnRH neuron processes in the ARN and in the lateral and internal aspects of the median eminence. Furthermore, GnRH nerve terminals were found in close contact with the proximal dendrites of ARN kisspeptin neurons in the ARN, and ARN kisspeptin fibers were found contacting RP3V kisspeptin neurons in both sexes. Together these data delineate selective zones of kisspeptin neuron inputs to GnRH neurons and demonstrate complex interconnections between the distinct kisspeptin populations and GnRH neurons.

  3. Basal Forebrain Gating by Somatostatin Neurons Drives Prefrontal Cortical Activity.

    Science.gov (United States)

    Espinosa, Nelson; Alonso, Alejandra; Morales, Cristian; Espinosa, Pedro; Chávez, Andrés E; Fuentealba, Pablo

    2017-11-17

    The basal forebrain provides modulatory input to the cortex regulating brain states and cognitive processing. Somatostatin-expressing neurons constitute a heterogeneous GABAergic population known to functionally inhibit basal forebrain cortically projecting cells thus favoring sleep and cortical synchronization. However, it remains unclear if somatostatin cells can regulate population activity patterns in the basal forebrain and modulate cortical dynamics. Here, we demonstrate that somatostatin neurons regulate the corticopetal synaptic output of the basal forebrain impinging on cortical activity and behavior. Optogenetic inactivation of somatostatin neurons in vivo rapidly modified neural activity in the basal forebrain, with the consequent enhancement and desynchronization of activity in the prefrontal cortex, reflected in both neuronal spiking and network oscillations. Cortical activation was partially dependent on cholinergic transmission, suppressing slow waves and potentiating gamma oscillations. In addition, recruitment dynamics was cell type-specific, with interneurons showing similar temporal profiles, but stronger responses than pyramidal cells. Finally, optogenetic stimulation of quiescent animals during resting periods prompted locomotor activity, suggesting generalized cortical activation and increased arousal. Altogether, we provide physiological and behavioral evidence indicating that somatostatin neurons are pivotal in gating the synaptic output of the basal forebrain, thus indirectly controlling cortical operations via both cholinergic and non-cholinergic mechanisms. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  4. 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.

  5. A neuron-astrocyte transistor-like model for neuromorphic dressed neurons.

    Science.gov (United States)

    Valenza, G; Pioggia, G; Armato, A; Ferro, M; Scilingo, E P; De Rossi, D

    2011-09-01

    Experimental evidences on the role of the synaptic glia as an active partner together with the bold synapse in neuronal signaling and dynamics of neural tissue strongly suggest to investigate on a more realistic neuron-glia model for better understanding human brain processing. Among the glial cells, the astrocytes play a crucial role in the tripartite synapsis, i.e. the dressed neuron. A well-known two-way astrocyte-neuron interaction can be found in the literature, completely revising the purely supportive role for the glia. The aim of this study is to provide a computationally efficient model for neuron-glia interaction. The neuron-glia interactions were simulated by implementing the Li-Rinzel model for an astrocyte and the Izhikevich model for a neuron. Assuming the dressed neuron dynamics similar to the nonlinear input-output characteristics of a bipolar junction transistor, we derived our computationally efficient model. This model may represent the fundamental computational unit for the development of real-time artificial neuron-glia networks opening new perspectives in pattern recognition systems and in brain neurophysiology. Copyright © 2011 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

    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.

  7. Effects of dynamic synapses on noise-delayed response latency of a single neuron

    Science.gov (United States)

    Uzuntarla, M.; Ozer, M.; Ileri, U.; Calim, A.; Torres, J. J.

    2015-12-01

    The noise-delayed decay (NDD) phenomenon emerges when the first-spike latency of a periodically forced stochastic neuron exhibits a maximum for a particular range of noise intensity. Here, we investigate the latency response dynamics of a single Hodgkin-Huxley neuron that is subject to both a suprathreshold periodic stimulus and a background activity arriving through dynamic synapses. We study the first-spike latency response as a function of the presynaptic firing rate f . This constitutes a more realistic scenario than previous works, since f provides a suitable biophysically realistic parameter to control the level of activity in actual neural systems. We first report on the emergence of classical NDD behavior as a function of f for the limit of static synapses. Second, we show that when short-term depression and facilitation mechanisms are included at the synapses, different NDD features can be found due to their modulatory effect on synaptic current fluctuations. For example, an intriguing double NDD (DNDD) behavior occurs for different sets of relevant synaptic parameters. Moreover, depending on the balance between synaptic depression and synaptic facilitation, single NDD or DNDD can prevail, in such a way that synaptic facilitation favors the emergence of DNDD whereas synaptic depression favors the existence of single NDD. Here we report the existence of the DNDD effect in the response latency dynamics of a neuron.

  8. Coding and decoding with adapting neurons: a population approach to the peri-stimulus time histogram.

    Science.gov (United States)

    Naud, Richard; Gerstner, Wulfram

    2012-01-01

    The response of a neuron to a time-dependent stimulus, as measured in a Peri-Stimulus-Time-Histogram (PSTH), exhibits an intricate temporal structure that reflects potential temporal coding principles. Here we analyze the encoding and decoding of PSTHs for spiking neurons with arbitrary refractoriness and adaptation. As a modeling framework, we use the spike response model, also known as the generalized linear neuron model. Because of refractoriness, the effect of the most recent spike on the spiking probability a few milliseconds later is very strong. The influence of the last spike needs therefore to be described with high precision, while the rest of the neuronal spiking history merely introduces an average self-inhibition or adaptation that depends on the expected number of past spikes but not on the exact spike timings. Based on these insights, we derive a 'quasi-renewal equation' which is shown to yield an excellent description of the firing rate of adapting neurons. We explore the domain of validity of the quasi-renewal equation and compare it with other rate equations for populations of spiking neurons. The problem of decoding the stimulus from the population response (or PSTH) is addressed analogously. We find that for small levels of activity and weak adaptation, a simple accumulator of the past activity is sufficient to decode the original input, but when refractory effects become large decoding becomes a non-linear function of the past activity. The results presented here can be applied to the mean-field analysis of coupled neuron networks, but also to arbitrary point processes with negative self-interaction.

  9. Differential effects of cocaine on histone posttranslational modifications in identified populations of striatal neurons.

    Science.gov (United States)

    Jordi, Emmanuelle; Heiman, Myriam; Marion-Poll, Lucile; Guermonprez, Pierre; Cheng, Shuk Kei; Nairn, Angus C; Greengard, Paul; Girault, Jean-Antoine

    2013-06-04

    Drugs of abuse, such as cocaine, induce changes in gene expression and epigenetic marks including alterations in histone posttranslational modifications in striatal neurons. These changes are thought to participate in physiological memory mechanisms and to be critical for long-term behavioral alterations. However, the striatum is composed of multiple cell types, including two distinct populations of medium-sized spiny neurons, and little is known concerning the cell-type specificity of epigenetic modifications. To address this question we used bacterial artificial chromosome transgenic mice, which express EGFP fused to the N-terminus of the large subunit ribosomal protein L10a driven by the D1 or D2 dopamine receptor (D1R, D2R) promoter, respectively. Fluorescence in nucleoli was used to sort nuclei from D1R- or D2R-expressing neurons and to quantify by flow cytometry the cocaine-induced changes in histone acetylation and methylation specifically in these two types of nuclei. The two populations of medium-sized spiny neurons displayed different patterns of histone modifications 15 min or 24 h after a single injection of cocaine or 24 h after seven daily injections. In particular, acetylation of histone 3 on Lys 14 and of histone 4 on Lys 5 and 12, and methylation of histone 3 on Lys 9 exhibited distinct and persistent changes in the two cell types. Our data provide insights into the differential epigenetic responses to cocaine in D1R- and D2R-positive neurons and their potential regulation, which may participate in the persistent effects of cocaine in these neurons. The method described should have general utility for studying nuclear modifications in different types of neuronal or nonneuronal cell types.

  10. APPLICATION OF UKRAINIAN GRID INFRASTRUCTURE FOR INVESTIGATION OF NONLINEAR DYNAMICS IN LARGE NEURONAL NETWORKS

    Directory of Open Access Journals (Sweden)

    O. О. Sudakov

    2015-12-01

    Full Text Available In present work the Ukrainian National Grid (UNG infrastructure was applied for investigation of synchronization in large networks of interacting neurons. This application is important for solving of modern neuroscience problems related to mechanisms of nervous system activities (memory, cognition etc. and nervous pathologies (epilepsy, Parkinsonism, etc.. Modern non-linear dynamics theories and applications provides powerful basis for computer simulations of biological neuronal networks and investigation of phenomena which mechanisms hardly could be clarified by other approaches. Cubic millimeter of brain tissue contains about 105 neurons, so realistic (Hodgkin-Huxley model and phenomenological (Kuramoto-Sakaguchi, FitzHugh-Nagumo, etc. models simulations require consideration of large neurons numbers.

  11. Different populations of prostaglandin EP3 receptor-expressing preoptic neurons project to two fever-mediating sympathoexcitatory brain regions.

    Science.gov (United States)

    Nakamura, Y; Nakamura, K; Morrison, S F

    2009-06-30

    The central mechanism of fever induction is triggered by an action of prostaglandin E(2) (PGE(2)) on neurons in the preoptic area (POA) through the EP3 subtype of prostaglandin E receptor. EP3 receptor (EP3R)-expressing POA neurons project directly to the dorsomedial hypothalamus (DMH) and to the rostral raphe pallidus nucleus (rRPa), key sites for the control of thermoregulatory effectors. Based on physiological findings, we hypothesize that the febrile responses in brown adipose tissue (BAT) and those in cutaneous vasoconstrictors are controlled independently by separate neuronal pathways: PGE(2) pyrogenic signaling is transmitted from EP3R-expressing POA neurons via a projection to the DMH to activate BAT thermogenesis and via another projection to the rRPa to increase cutaneous vasoconstriction. In this case, DMH-projecting and rRPa-projecting neurons would constitute segregated populations within the EP3R-expressing neuronal group in the POA. Here, we sought direct anatomical evidence to test this hypothesis with a double-tracing experiment in which two types of the retrograde tracer, cholera toxin b-subunit (CTb), conjugated with different fluorophores were injected into the DMH and the rRPa of rats and the resulting retrogradely labeled populations of EP3R-immunoreactive neurons in the POA were identified with confocal microscopy. We found substantial numbers of EP3R-immunoreactive neurons in both the DMH-projecting and the rRPa-projecting populations. However, very few EP3R-immunoreactive POA neurons were labeled with both the CTb from the DMH and that from the rRPa, although a substantial number of neurons that were not immunoreactive for EP3R were double-labeled with both CTbs. The paucity of the EP3R-expressing neurons that send collaterals to both the DMH and the rRPa suggests that pyrogenic signals are sent independently to these caudal brain regions from the POA and that such pyrogenic outputs from the POA reflect different control mechanisms for BAT

  12. Phase synchronization of neuronal noise in mouse hippocampal epileptiform dynamics.

    Science.gov (United States)

    Serletis, Demitre; Carlen, Peter L; Valiante, Taufik A; Bardakjian, Berj L

    2013-02-01

    Organized brain activity is the result of dynamical, segregated neuronal signals that may be used to investigate synchronization effects using sophisticated neuroengineering techniques. Phase synchrony analysis, in particular, has emerged as a promising methodology to study transient and frequency-specific coupling effects across multi-site signals. In this study, we investigated phase synchronization in intracellular recordings of interictal and ictal epileptiform events recorded from pairs of cells in the whole (intact) mouse hippocampus. In particular, we focused our analysis on the background noise-like activity (NLA), previously reported to exhibit complex neurodynamical properties. Our results show evidence for increased linear and nonlinear phase coupling in NLA across three frequency bands [theta (4-10 Hz), beta (12-30 Hz) and gamma (30-80 Hz)] in the ictal compared to interictal state dynamics. We also present qualitative and statistical evidence for increased phase synchronization in the theta, beta and gamma frequency bands from paired recordings of ictal NLA. Overall, our results validate the use of background NLA in the neurodynamical study of epileptiform transitions and suggest that what is considered "neuronal noise" is amenable to synchronization effects in the spatiotemporal domain.

  13. Synchronous spikes are necessary but not sufficient for a synchrony code in populations of spiking neurons.

    Science.gov (United States)

    Grewe, Jan; Kruscha, Alexandra; Lindner, Benjamin; Benda, Jan

    2017-03-07

    Synchronous activity in populations of neurons potentially encodes special stimulus features. Selective readout of either synchronous or asynchronous activity allows formation of two streams of information processing. Theoretical work predicts that such a synchrony code is a fundamental feature of populations of spiking neurons if they operate in specific noise and stimulus regimes. Here we experimentally test the theoretical predictions by quantifying and comparing neuronal response properties in tuberous and ampullary electroreceptor afferents of the weakly electric fish Apteronotus leptorhynchus These related systems show similar levels of synchronous activity, but only in the more irregularly firing tuberous afferents a synchrony code is established, whereas in the more regularly firing ampullary afferents it is not. The mere existence of synchronous activity is thus not sufficient for a synchrony code. Single-cell features such as the irregularity of spiking and the frequency dependence of the neuron's transfer function determine whether synchronous spikes possess a distinct meaning for the encoding of time-dependent signals.

  14. Mapping human temporal and parietal neuronal population activity and functional coupling during mathematical cognition

    Science.gov (United States)

    Daitch, Amy L.; Foster, Brett L.; Schrouff, Jessica; Rangarajan, Vinitha; Kaşikçi, Itır; Gattas, Sandra; Parvizi, Josef

    2016-01-01

    Brain areas within the lateral parietal cortex (LPC) and ventral temporal cortex (VTC) have been shown to code for abstract quantity representations and for symbolic numerical representations, respectively. To explore the fast dynamics of activity within each region and the interaction between them, we used electrocorticography recordings from 16 neurosurgical subjects implanted with grids of electrodes over these two regions and tracked the activity within and between the regions as subjects performed three different numerical tasks. Although our results reconfirm the presence of math-selective hubs within the VTC and LPC, we report here a remarkable heterogeneity of neural responses within each region at both millimeter and millisecond scales. Moreover, we show that the heterogeneity of response profiles within each hub mirrors the distinct patterns of functional coupling between them. Our results support the existence of multiple bidirectional functional loops operating between discrete populations of neurons within the VTC and LPC during the visual processing of numerals and the performance of arithmetic functions. These findings reveal information about the dynamics of numerical processing in the brain and also provide insight into the fine-grained functional architecture and connectivity within the human brain. PMID:27821758

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

    Science.gov (United States)

    Sadeh, Sadra; Rotter, Stefan

    2015-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Sadra Sadeh

    2015-01-01

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

  17. Effects of self-coupling and asymmetric output on metastable dynamical transient firing patterns in arrays of neurons with bidirectional inhibitory coupling.

    Science.gov (United States)

    Horikawa, Yo

    2016-04-01

    Metastable dynamical transient patterns in arrays of bidirectionally coupled neurons with self-coupling and asymmetric output were studied. First, an array of asymmetric sigmoidal neurons with symmetric inhibitory bidirectional coupling and self-coupling was considered and the bifurcations of its steady solutions were shown. Metastable dynamical transient spatially nonuniform states existed in the presence of a pair of spatially symmetric stable solutions as well as unstable spatially nonuniform solutions in a restricted range of the output gain of a neuron. The duration of the transients increased exponentially with the number of neurons up to the maximum number at which the spatially nonuniform steady solutions were stabilized. The range of the output gain for which they existed reduced as asymmetry in a sigmoidal output function of a neuron increased, while the existence range expanded as the strength of inhibitory self-coupling increased. Next, arrays of spiking neuron models with slow synaptic inhibitory bidirectional coupling and self-coupling were considered with computer simulation. In an array of Class 1 Hindmarsh-Rose type models, in which each neuron showed a graded firing rate, metastable dynamical transient firing patterns were observed in the presence of inhibitory self-coupling. This agreed with the condition for the existence of metastable dynamical transients in an array of sigmoidal neurons. In an array of Class 2 Bonhoeffer-van der Pol models, in which each neuron had a clear threshold between firing and resting, long-lasting transient firing patterns with bursting and irregular motion were observed. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. Obtaining Arbitrary Prescribed Mean Field Dynamics for Recurrently Coupled Networks of Type-I Spiking Neurons with Analytically Determined Weights

    Directory of Open Access Journals (Sweden)

    Wilten eNicola

    2016-02-01

    Full Text Available A fundamental question in computational neuroscience is how to connect a network of spiking neurons to produce desired macroscopic or mean field dynamics. One possible approach is through the Neural Engineering Framework (NEF. The NEF approach requires quantities called decoders which are solved through an optimization problem requiring large matrix inversion. Here, we show how a decoder can be obtained analytically for type I and certain type II firing rates as a function of the heterogeneity of its associated neuron. These decoders generate approximants for functions that converge to the desired function in mean-squared error like 1/N, where N is the number of neurons in the network. We refer to these decoders as scale-invariant decoders due to their structure. These decoders generate weights for a network of neurons through the NEF formula for weights. These weights force the spiking network to have arbitrary and prescribed mean field dynamics. The weights generated with scale-invariant decoders all lie on low dimensional hypersurfaces asymptotically. We demonstrate the applicability of these scale-invariant decoders and weight surfaces by constructing networks of spiking theta neurons that replicate the dynamics of various well known dynamical systems such as the neural integrator, Van der Pol system and the Lorenz system. As these decoders are analytically determined and non-unique, the weights are also analytically determined and non-unique. We discuss the implications for measured weights of neuronal networks

  19. Obtaining Arbitrary Prescribed Mean Field Dynamics for Recurrently Coupled Networks of Type-I Spiking Neurons with Analytically Determined Weights.

    Science.gov (United States)

    Nicola, Wilten; Tripp, Bryan; Scott, Matthew

    2016-01-01

    A fundamental question in computational neuroscience is how to connect a network of spiking neurons to produce desired macroscopic or mean field dynamics. One possible approach is through the Neural Engineering Framework (NEF). The NEF approach requires quantities called decoders which are solved through an optimization problem requiring large matrix inversion. Here, we show how a decoder can be obtained analytically for type I and certain type II firing rates as a function of the heterogeneity of its associated neuron. These decoders generate approximants for functions that converge to the desired function in mean-squared error like 1/N, where N is the number of neurons in the network. We refer to these decoders as scale-invariant decoders due to their structure. These decoders generate weights for a network of neurons through the NEF formula for weights. These weights force the spiking network to have arbitrary and prescribed mean field dynamics. The weights generated with scale-invariant decoders all lie on low dimensional hypersurfaces asymptotically. We demonstrate the applicability of these scale-invariant decoders and weight surfaces by constructing networks of spiking theta neurons that replicate the dynamics of various well known dynamical systems such as the neural integrator, Van der Pol system and the Lorenz system. As these decoders are analytically determined and non-unique, the weights are also analytically determined and non-unique. We discuss the implications for measured weights of neuronal networks.

  20. Study of GABAergic extra-synaptic tonic inhibition in single neurons and neural populations by traversing neural scales: application to propofol-induced anaesthesia.

    Science.gov (United States)

    Hutt, Axel; Buhry, Laure

    2014-12-01

    Anaesthetic agents are known to affect extra-synaptic GABAergic receptors, which induce tonic inhibitory currents. Since these receptors are very sensitive to small concentrations of agents, they are supposed to play an important role in the underlying neural mechanism of general anaesthesia. Moreover anaesthetic agents modulate the encephalographic activity (EEG) of subjects and hence show an effect on neural populations. To understand better the tonic inhibition effect in single neurons on neural populations and hence how it affects the EEG, the work considers single neurons and neural populations in a steady-state and studies numerically and analytically the modulation of their firing rate and nonlinear gain with respect to different levels of tonic inhibition. We consider populations of both type-I (Leaky Integrate-and-Fire model) and type-II (Morris-Lecar model) neurons. To bridge the single neuron description to the population description analytically, a recently proposed statistical approach is employed which allows to derive new analytical expressions for the population firing rate for type-I neurons. In addition, the work shows the derivation of a novel transfer function for type-I neurons as considered in neural mass models and studies briefly the interaction of synaptic and extra-synaptic inhibition. We reveal a strong subtractive and divisive effect of tonic inhibition in type-I neurons, i.e. a shift of the firing rate to higher excitation levels accompanied by a change of the nonlinear gain. Tonic inhibition shortens the excitation window of type-II neurons and their populations while maintaining the nonlinear gain. The gained results are interpreted in the context of recent experimental findings under propofol-induced anaesthesia.

  1. Different populations of subthalamic neurons encode cocaine vs. sucrose reward and predict future error.

    Science.gov (United States)

    Lardeux, Sylvie; Paleressompoulle, Dany; Pernaud, Remy; Cador, Martine; Baunez, Christelle

    2013-10-01

    The search for treatment of cocaine addiction raises the challenge to find a way to diminish motivation for the drug without decreasing it for natural rewards. Subthalamic nucleus (STN) inactivation decreases motivation for cocaine while increasing motivation for food, suggesting that STN can dissociate different rewards. Here, we investigated how rat STN neurons respond to cues predicting cocaine or sucrose and to reward delivery while rats are performing a discriminative stimuli task. We show that different neuronal populations of STN neurons encode cocaine and sucrose. In addition, we show that STN activity at the cue onset predicts future error. When changing the reward predicted unexpectedly, STN neurons show capacities of adaptation, suggesting a role in reward-prediction error. Furthermore, some STN neurons show a response to executive error (i.e., "oops neurons") that is specific to the missed reward. These results position the STN as a nexus where natural rewards and drugs of abuse are coded differentially and can influence the performance. Therefore, STN can be viewed as a structure where action could be taken for the treatment of cocaine addiction.

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

    Science.gov (United States)

    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.

  3. How pattern formation in ring networks of excitatory and inhibitoryspiking neurons depends on the input current regime

    Directory of Open Access Journals (Sweden)

    Birgit eKriener

    2014-01-01

    Full Text Available Pattern formation, i.e., the generation of an inhomogeneous spatial activity distribution in a dynamical system with translation invariant structure, is a well-studied phenomenon in neuronal network dynamics,specifically in neural field models. These are population models to describe the spatio-temporal dynamics of large groups of neurons in terms of macroscopic variables such as population firing rates. Though neural field models are often deduced from and equipped with biophysically meaningfulproperties, a direct mapping to simulations of individual spiking neuron populations is rarely considered. Neurons have a distinct identity defined by their action on their postsynaptic targets. In its simplest form they act either excitatorily or inhibitorily.When the distribution of neuron identities is assumed to be periodic, pattern formation can be observed, given the coupling strength is supercritical, i.e., larger than a critical weight. We find that this critical weight is strongly dependent on the characteristics of the neuronal input, i.e., depends on whether neurons are mean- orfluctuation driven, and different limits in linearizing the full non-linear system apply in order to assess stability.In particular, if neurons are mean-driven, the linearization has a very simple form and becomesindependent of both the fixed point firing rate and the variance of the input current, while in the very strongly fluctuation-driven regime the fixed point rate, as well as the input mean and variance areimportant parameters in the determination of the critical weight.We demonstrate that interestingly even in ``intermediate'' regimes, when the system is technically fluctuation-driven, the simple linearization neglecting the variance of the input can yield the better prediction of the critical couplingstrength. We moreover analyze the effects of structural randomness by rewiring individualsynapses or redistributing weights, as well as coarse-graining on pattern

  4. Fish population dynamics

    National Research Council Canada - National Science Library

    Gulland, J. A

    1977-01-01

    This book describes how the dynamics of fish populations can be analysed in terms of the factors affecting their rates of growth, mortality and reproduction, with particular emphasis on the effects of fishing...

  5. Decoding a Decision Process in the Neuronal Population of Dorsal Premotor Cortex.

    Science.gov (United States)

    Rossi-Pool, Román; Zainos, Antonio; Alvarez, Manuel; Zizumbo, Jerónimo; Vergara, José; Romo, Ranulfo

    2017-12-20

    When trained monkeys discriminate the temporal structure of two sequential vibrotactile stimuli, dorsal premotor cortex (DPC) showed high heterogeneity among its neuronal responses. Notably, DPC neurons coded stimulus patterns as broader categories and signaled them during working memory, comparison, and postponed decision periods. Here, we show that such population activity can be condensed into two major coding components: one that persistently represented in working memory both the first stimulus identity and the postponed informed choice and another that transiently coded the initial sensory information and the result of the comparison between the two stimuli. Additionally, we identified relevant signals that coded the timing of task events. These temporal and task-parameter readouts were shown to be strongly linked to the monkeys' behavior when contrasted to those obtained in a non-demanding cognitive control task and during error trials. These signals, hidden in the heterogeneity, were prominently represented by the DPC population response. Copyright © 2017 Elsevier Inc. All rights reserved.

  6. Leading-process actomyosin coordinates organelle positioning and adhesion receptor dynamics in radially migrating cerebellar granule neurons.

    Science.gov (United States)

    Trivedi, Niraj; Ramahi, Joseph S; Karakaya, Mahmut; Howell, Danielle; Kerekes, Ryan A; Solecki, David J

    2014-12-02

    During brain development, neurons migrate from germinal zones to their final positions to assemble neural circuits. A unique saltatory cadence involving cyclical organelle movement (e.g., centrosome motility) and leading-process actomyosin enrichment prior to nucleokinesis organizes neuronal migration. While functional evidence suggests that leading-process actomyosin is essential for centrosome motility, the role of the actin-enriched leading process in globally organizing organelle transport or traction forces remains unexplored. We show that myosin ii motors and F-actin dynamics are required for Golgi apparatus positioning before nucleokinesis in cerebellar granule neurons (CGNs) migrating along glial fibers. Moreover, we show that primary cilia are motile organelles, localized to the leading-process F-actin-rich domain and immobilized by pharmacological inhibition of myosin ii and F-actin dynamics. Finally, leading process adhesion dynamics are dependent on myosin ii and F-actin. We propose that actomyosin coordinates the overall polarity of migrating CGNs by controlling asymmetric organelle positioning and cell-cell contacts as these cells move along their glial guides.

  7. A Unique "Angiotensin-Sensitive" Neuronal Population Coordinates Neuroendocrine, Cardiovascular, and Behavioral Responses to Stress.

    Science.gov (United States)

    de Kloet, Annette D; Wang, Lei; Pitra, Soledad; Hiller, Helmut; Smith, Justin A; Tan, Yalun; Nguyen, Dani; Cahill, Karlena M; Sumners, Colin; Stern, Javier E; Krause, Eric G

    2017-03-29

    Stress elicits neuroendocrine, autonomic, and behavioral responses that mitigate homeostatic imbalance and ensure survival. However, chronic engagement of such responses promotes psychological, cardiovascular, and metabolic impairments. In recent years, the renin-angiotensin system has emerged as a key mediator of stress responding and its related pathologies, but the neuronal circuits that orchestrate these interactions are not known. These studies combine the use of the Cre-recombinase/loxP system in mice with optogenetics to structurally and functionally characterize angiotensin type-1a receptor-containing neurons of the paraventricular nucleus of the hypothalamus, the goal being to determine the extent of their involvement in the regulation of stress responses. Initial studies use neuroanatomical techniques to reveal that angiotensin type-1a receptors are localized predominantly to the parvocellular neurosecretory neurons of the paraventricular nucleus of the hypothalamus. These neurons are almost exclusively glutamatergic and send dense projections to the exterior portion of the median eminence. Furthermore, these neurons largely express corticotrophin-releasing hormone or thyrotropin-releasing hormone and do not express arginine vasopressin or oxytocin. Functionally, optogenetic stimulation of these neurons promotes the activation of the hypothalamic-pituitary-adrenal and hypothalamic-pituitary-thyroid axes, as well as a rise in systolic blood pressure. When these neurons are optogenetically inhibited, the activity of these neuroendocrine axes are suppressed and anxiety-like behavior in the elevated plus maze is dampened. Collectively, these studies implicate this neuronal population in the integration and coordination of the physiological responses to stress and may therefore serve as a potential target for therapeutic intervention for stress-related pathology. SIGNIFICANCE STATEMENT Chronic stress leads to an array of physiological responses that ultimately

  8. Population dynamics at high Reynolds number

    NARCIS (Netherlands)

    Perlekar, P.; Benzi, R.; Nelson, D.R.; Toschi, F.

    2010-01-01

    We study the statistical properties of population dynamics evolving in a realistic two-dimensional compressible turbulent velocity field. We show that the interplay between turbulent dynamics and population growth and saturation leads to quasi-localization and a remarkable reduction in the carrying

  9. Efficient network reconstruction from dynamical cascades identifies small-world topology of neuronal avalanches.

    Directory of Open Access Journals (Sweden)

    Sinisa Pajevic

    2009-01-01

    Full Text Available Cascading activity is commonly found in complex systems with directed interactions such as metabolic networks, neuronal networks, or disease spreading in social networks. Substantial insight into a system's organization can be obtained by reconstructing the underlying functional network architecture from the observed activity cascades. Here we focus on Bayesian approaches and reduce their computational demands by introducing the Iterative Bayesian (IB and Posterior Weighted Averaging (PWA methods. We introduce a special case of PWA, cast in nonparametric form, which we call the normalized count (NC algorithm. NC efficiently reconstructs random and small-world functional network topologies and architectures from subcritical, critical, and supercritical cascading dynamics and yields significant improvements over commonly used correlation methods. With experimental data, NC identified a functional and structural small-world topology and its corresponding traffic in cortical networks with neuronal avalanche dynamics.

  10. Neurons other than motor neurons in motor neuron disease.

    Science.gov (United States)

    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.

  11. Regulation of Cortical Dynamic Range by Background Synaptic Noise and Feedforward Inhibition.

    Science.gov (United States)

    Khubieh, Ayah; Ratté, Stéphanie; Lankarany, Milad; Prescott, Steven A

    2016-08-01

    The cortex encodes a broad range of inputs. This breadth of operation requires sensitivity to weak inputs yet non-saturating responses to strong inputs. If individual pyramidal neurons were to have a narrow dynamic range, as previously claimed, then staggered all-or-none recruitment of those neurons would be necessary for the population to achieve a broad dynamic range. Contrary to this explanation, we show here through dynamic clamp experiments in vitro and computer simulations that pyramidal neurons have a broad dynamic range under the noisy conditions that exist in the intact brain due to background synaptic input. Feedforward inhibition capitalizes on those noise effects to control neuronal gain and thereby regulates the population dynamic range. Importantly, noise allows neurons to be recruited gradually and occludes the staggered recruitment previously attributed to heterogeneous excitation. Feedforward inhibition protects spike timing against the disruptive effects of noise, meaning noise can enable the gain control required for rate coding without compromising the precise spike timing required for temporal coding. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  12. Spatiotemporal dynamics on small-world neuronal networks: The roles of two types of time-delayed coupling

    Energy Technology Data Exchange (ETDEWEB)

    Wu Hao; Jiang Huijun [Hefei National Laboratory for Physical Sciences at the Microscale and Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026 (China); Hou Zhonghuai, E-mail: hzhlj@ustc.edu.cn [Hefei National Laboratory for Physical Sciences at the Microscale and Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026 (China)

    2011-10-15

    Highlights: > We compare neuronal dynamics in dependence on two types of delayed coupling. > Distinct results induced by different delayed coupling can be achieved. > Time delays in type 1 coupling can induce a most spatiotemporal ordered state. > For type 2 coupling, the systems exhibit synchronization transitions with delay. - Abstract: We investigate temporal coherence and spatial synchronization on small-world networks consisting of noisy Terman-Wang (TW) excitable neurons in dependence on two types of time-delayed coupling: {l_brace}x{sub j}(t - {tau}) - x{sub i}(t){r_brace} and {l_brace}x{sub j}(t - {tau}) - x{sub i}(t - {tau}){r_brace}. For the former case, we show that time delay in the coupling can dramatically enhance temporal coherence and spatial synchrony of the noise-induced spike trains. In addition, if the delay time {tau} is tuned to nearly match the intrinsic spike period of the neuronal network, the system dynamics reaches a most ordered state, which is both periodic in time and nearly synchronized in space, demonstrating an interesting resonance phenomenon with delay. For the latter case, however, we cannot achieve a similar spatiotemporal ordered state, but the neuronal dynamics exhibits interesting synchronization transitions with time delay from zigzag fronts of excitations to dynamic clustering anti-phase synchronization (APS), and further to clustered chimera states which have spatially distributed anti-phase coherence separated by incoherence. Furthermore, we also show how these findings are influenced by the change of the noise intensity and the rewiring probability of the small-world networks. Finally, qualitative analysis is given to illustrate the numerical results.

  13. Spatiotemporal dynamics on small-world neuronal networks: The roles of two types of time-delayed coupling

    International Nuclear Information System (INIS)

    Wu Hao; Jiang Huijun; Hou Zhonghuai

    2011-01-01

    Highlights: → We compare neuronal dynamics in dependence on two types of delayed coupling. → Distinct results induced by different delayed coupling can be achieved. → Time delays in type 1 coupling can induce a most spatiotemporal ordered state. → For type 2 coupling, the systems exhibit synchronization transitions with delay. - Abstract: We investigate temporal coherence and spatial synchronization on small-world networks consisting of noisy Terman-Wang (TW) excitable neurons in dependence on two types of time-delayed coupling: {x j (t - τ) - x i (t)} and {x j (t - τ) - x i (t - τ)}. For the former case, we show that time delay in the coupling can dramatically enhance temporal coherence and spatial synchrony of the noise-induced spike trains. In addition, if the delay time τ is tuned to nearly match the intrinsic spike period of the neuronal network, the system dynamics reaches a most ordered state, which is both periodic in time and nearly synchronized in space, demonstrating an interesting resonance phenomenon with delay. For the latter case, however, we cannot achieve a similar spatiotemporal ordered state, but the neuronal dynamics exhibits interesting synchronization transitions with time delay from zigzag fronts of excitations to dynamic clustering anti-phase synchronization (APS), and further to clustered chimera states which have spatially distributed anti-phase coherence separated by incoherence. Furthermore, we also show how these findings are influenced by the change of the noise intensity and the rewiring probability of the small-world networks. Finally, qualitative analysis is given to illustrate the numerical results.

  14. Fast two-photon imaging of subcellular voltage dynamics in neuronal tissue with genetically encoded indicators.

    Science.gov (United States)

    Chamberland, Simon; Yang, Helen H; Pan, Michael M; Evans, Stephen W; Guan, Sihui; Chavarha, Mariya; Yang, Ying; Salesse, Charleen; Wu, Haodi; Wu, Joseph C; Clandinin, Thomas R; Toth, Katalin; Lin, Michael Z; St-Pierre, François

    2017-07-27

    Monitoring voltage dynamics in defined neurons deep in the brain is critical for unraveling the function of neuronal circuits but is challenging due to the limited performance of existing tools. In particular, while genetically encoded voltage indicators have shown promise for optical detection of voltage transients, many indicators exhibit low sensitivity when imaged under two-photon illumination. Previous studies thus fell short of visualizing voltage dynamics in individual neurons in single trials. Here, we report ASAP2s, a novel voltage indicator with improved sensitivity. By imaging ASAP2s using random-access multi-photon microscopy, we demonstrate robust single-trial detection of action potentials in organotypic slice cultures. We also show that ASAP2s enables two-photon imaging of graded potentials in organotypic slice cultures and in Drosophila . These results demonstrate that the combination of ASAP2s and fast two-photon imaging methods enables detection of neural electrical activity with subcellular spatial resolution and millisecond-timescale precision.

  15. A sodium afterdepolarization in rat superior colliculus neurons and its contribution to population activity.

    Science.gov (United States)

    Ghitani, Nima; Bayguinov, Peter O; Basso, Michele A; Jackson, Meyer B

    2016-07-01

    The mammalian superior colliculus (SC) is a midbrain structure that integrates multimodal sensory inputs and computes commands to initiate rapid eye movements. SC neurons burst with the sudden onset of a visual stimulus, followed by persistent activity that may underlie shifts of attention and decision making. Experiments in vitro suggest that circuit reverberations play a role in the burst activity in the SC, but the origin of persistent activity is unclear. In the present study we characterized an afterdepolarization (ADP) that follows action potentials in slices of rat SC. Population responses seen with voltage-sensitive dye imaging consisted of rapid spikes followed immediately by a second distinct depolarization of lower amplitude and longer duration. Patch-clamp recordings showed qualitatively similar behavior: in nearly all neurons throughout the SC, rapid spikes were followed by an ADP. Ionic and pharmacological manipulations along with experiments with current and voltage steps indicated that the ADP of SC neurons arises from Na(+) current that either persists or resurges following Na(+) channel inactivation at the end of an action potential. Comparisons of pharmacological properties and frequency dependence revealed a clear parallel between patch-clamp recordings and voltage imaging experiments, indicating a common underlying membrane mechanism for the ADP in both single neurons and populations. The ADP can initiate repetitive spiking at intervals consistent with the frequency of persistent activity in the SC. These results indicate that SC neurons have intrinsic membrane properties that can contribute to electrical activity that underlies shifts of attention and decision making. Copyright © 2016 the American Physiological Society.

  16. Dynamical systems in population biology

    CERN Document Server

    Zhao, Xiao-Qiang

    2017-01-01

    This research monograph provides an introduction to the theory of nonautonomous semiflows with applications to population dynamics. It develops dynamical system approaches to various evolutionary equations such as difference, ordinary, functional, and partial differential equations, and pays more attention to periodic and almost periodic phenomena. The presentation includes persistence theory, monotone dynamics, periodic and almost periodic semiflows, basic reproduction ratios, traveling waves, and global analysis of prototypical population models in ecology and epidemiology. Research mathematicians working with nonlinear dynamics, particularly those interested in applications to biology, will find this book useful. It may also be used as a textbook or as supplementary reading for a graduate special topics course on the theory and applications of dynamical systems. Dr. Xiao-Qiang Zhao is a University Research Professor at Memorial University of Newfoundland, Canada. His main research interests involve applied...

  17. Synaptic Dynamics and Neuronal Network Connectivity are reflected in the Distribution of Times in Up states

    Directory of Open Access Journals (Sweden)

    Khanh eDao Duc

    2015-07-01

    Full Text Available The dynamics of neuronal networks connected by synaptic dynamics can sustain long periods of depolarization that can last for hundreds of milliseconds such as Up states recorded during sleep or anesthesia. Yet the underlying mechanism driving these periods remain unclear. We show here within a mean-field model that the residence times of the neuronal membrane potential in cortical Up states does not follow a Poissonian law, but presents several peaks. Furthermore, the present modeling approach allows extracting some information about the neuronal network connectivity from the time distribution histogram. Based on a synaptic-depression model, we find that these peaks, that can be observed in histograms of patch-clamp recordings are not artifacts of electrophysiological measurements, but rather are an inherent property of the network dynamics. Analysis of the equations reveals a stable focus located close to the unstable limit cycle, delimiting a region that defines the Up state. The model further shows that the peaks observed in the Up state time distribution are due to winding around the focus before escaping from the basin of attraction. Finally, we use in vivo recordings of intracellular membrane potential and we recover from the peak distribution, some information about the network connectivity. We conclude that it is possible to recover the network connectivity from the distribution of times that the neuronal membrane voltage spends in Up states.

  18. The mirror-neuron system and observational learning: Implications for the effectiveness of dynamic visualizations.

    OpenAIRE

    Van Gog, Tamara; Paas, Fred; Marcus, Nadine; Ayres, Paul; Sweller, John

    2009-01-01

    Van Gog, T., Paas, F., Marcus, N., Ayres, P., & Sweller, J. (2009). The mirror-neuron system and observational learning: Implications for the effectiveness of dynamic visualizations. Educational Psychology Review, 21, 21-30.

  19. How pattern formation in ring networks of excitatory and inhibitory spiking neurons depends on the input current regime.

    Science.gov (United States)

    Kriener, Birgit; Helias, Moritz; Rotter, Stefan; Diesmann, Markus; Einevoll, Gaute T

    2013-01-01

    Pattern formation, i.e., the generation of an inhomogeneous spatial activity distribution in a dynamical system with translation invariant structure, is a well-studied phenomenon in neuronal network dynamics, specifically in neural field models. These are population models to describe the spatio-temporal dynamics of large groups of neurons in terms of macroscopic variables such as population firing rates. Though neural field models are often deduced from and equipped with biophysically meaningful properties, a direct mapping to simulations of individual spiking neuron populations is rarely considered. Neurons have a distinct identity defined by their action on their postsynaptic targets. In its simplest form they act either excitatorily or inhibitorily. When the distribution of neuron identities is assumed to be periodic, pattern formation can be observed, given the coupling strength is supracritical, i.e., larger than a critical weight. We find that this critical weight is strongly dependent on the characteristics of the neuronal input, i.e., depends on whether neurons are mean- or fluctuation driven, and different limits in linearizing the full non-linear system apply in order to assess stability. In particular, if neurons are mean-driven, the linearization has a very simple form and becomes independent of both the fixed point firing rate and the variance of the input current, while in the very strongly fluctuation-driven regime the fixed point rate, as well as the input mean and variance are important parameters in the determination of the critical weight. We demonstrate that interestingly even in "intermediate" regimes, when the system is technically fluctuation-driven, the simple linearization neglecting the variance of the input can yield the better prediction of the critical coupling strength. We moreover analyze the effects of structural randomness by rewiring individual synapses or redistributing weights, as well as coarse-graining on the formation of

  20. A Population of Indirect Pathway Striatal Projection Neurons Is Selectively Entrained to Parkinsonian Beta Oscillations.

    Science.gov (United States)

    Sharott, Andrew; Vinciati, Federica; Nakamura, Kouichi C; Magill, Peter J

    2017-10-11

    Classical schemes of basal ganglia organization posit that parkinsonian movement difficulties presenting after striatal dopamine depletion stem from the disproportionate firing rates of spiny projection neurons (SPNs) therein. There remains, however, a pressing need to elucidate striatal SPN firing in the context of the synchronized network oscillations that are abnormally exaggerated in cortical-basal ganglia circuits in parkinsonism. To address this, we recorded unit activities in the dorsal striatum of dopamine-intact and dopamine-depleted rats during two brain states, respectively defined by cortical slow-wave activity (SWA) and activation. Dopamine depletion escalated striatal net output but had contrasting effects on "direct pathway" SPNs (dSPNs) and "indirect pathway" SPNs (iSPNs); their firing rates became imbalanced, and they disparately engaged in network oscillations. Disturbed striatal activity dynamics relating to the slow (∼1 Hz) oscillations prevalent during SWA partly generalized to the exaggerated beta-frequency (15-30 Hz) oscillations arising during cortical activation. In both cases, SPNs exhibited higher incidences of phase-locked firing to ongoing cortical oscillations, and SPN ensembles showed higher levels of rhythmic correlated firing, after dopamine depletion. Importantly, in dopamine-depleted striatum, a widespread population of iSPNs, which often displayed excessive firing rates and aberrant phase-locked firing to cortical beta oscillations, preferentially and excessively synchronized their firing at beta frequencies. Conversely, dSPNs were neither hyperactive nor synchronized to a large extent during cortical activation. These data collectively demonstrate a cell type-selective entrainment of SPN firing to parkinsonian beta oscillations. We conclude that a population of overactive, excessively synchronized iSPNs could orchestrate these pathological rhythms in basal ganglia circuits. SIGNIFICANCE STATEMENT Chronic depletion of dopamine

  1. Input dependent cell assembly dynamics in a model of the striatal medium spiny neuron network

    Directory of Open Access Journals (Sweden)

    Adam ePonzi

    2012-03-01

    Full Text Available The striatal medium spiny neuron (MSNs network is sparsely connected with fairly weak GABAergic collaterals receiving an excitatory glutamatergic cortical projection. Peri stimulus time histograms (PSTH of MSN population response investigated in various experimental studies display strong firing rate modulations distributed throughout behavioural task epochs. In previous work we have shown by numerical simulation that sparse random networks of inhibitory spiking neurons with characteristics appropriate for UP state MSNs form cell assemblies which fire together coherently in sequences on long behaviourally relevant timescales when the network receives a fixed pattern of constant input excitation. Here we first extend that model to the case where cortical excitation is composed of many independent noisy Poisson processes and demonstrate that cell assembly dynamics is still observed when the input is sufficiently weak. However if cortical excitation strength is increased more regularly firing and completely quiescent cells are found, which depend on the cortical stimulation. Subsequently we further extend previous work to consider what happens when the excitatory input varies as it would in when the animal is engaged in behavior. We investigate how sudden switches in excitation interact with network generated patterned activity. We show that sequences of cell assembly activations can be locked to the excitatory input sequence and delineate the range of parameters where this behaviour is shown. Model cell population PSTH display both stimulus and temporal specificity, with large population firing rate modulations locked to elapsed time from task events. Thus the random network can generate a large diversity of temporally evolving stimulus dependent responses even though the input is fixed between switches. We suggest the MSN network is well suited to the generation of such slow coherent task dependent response

  2. Input dependent cell assembly dynamics in a model of the striatal medium spiny neuron network.

    Science.gov (United States)

    Ponzi, Adam; Wickens, Jeff

    2012-01-01

    The striatal medium spiny neuron (MSN) network is sparsely connected with fairly weak GABAergic collaterals receiving an excitatory glutamatergic cortical projection. Peri-stimulus time histograms (PSTH) of MSN population response investigated in various experimental studies display strong firing rate modulations distributed throughout behavioral task epochs. In previous work we have shown by numerical simulation that sparse random networks of inhibitory spiking neurons with characteristics appropriate for UP state MSNs form cell assemblies which fire together coherently in sequences on long behaviorally relevant timescales when the network receives a fixed pattern of constant input excitation. Here we first extend that model to the case where cortical excitation is composed of many independent noisy Poisson processes and demonstrate that cell assembly dynamics is still observed when the input is sufficiently weak. However if cortical excitation strength is increased more regularly firing and completely quiescent cells are found, which depend on the cortical stimulation. Subsequently we further extend previous work to consider what happens when the excitatory input varies as it would when the animal is engaged in behavior. We investigate how sudden switches in excitation interact with network generated patterned activity. We show that sequences of cell assembly activations can be locked to the excitatory input sequence and outline the range of parameters where this behavior is shown. Model cell population PSTH display both stimulus and temporal specificity, with large population firing rate modulations locked to elapsed time from task events. Thus the random network can generate a large diversity of temporally evolving stimulus dependent responses even though the input is fixed between switches. We suggest the MSN network is well suited to the generation of such slow coherent task dependent response which could be utilized by the animal in behavior.

  3. Genetic variation in total number and locations of GnRH neurons identified using in situ hybridization in a wild-source population.

    Science.gov (United States)

    Kaugars, Katherine E; Rivers, Charlotte I; Saha, Margaret S; Heideman, Paul D

    2016-02-01

    The evolution of brain function in the regulation of physiology may depend in part upon the numbers and locations of neurons. Wild populations of rodents contain natural genetic variation in the inhibition of reproduction by winter-like short photoperiod, and it has been hypothesized that this functional variation might be due in part to heritable variation in the numbers or location of gonadotropin releasing hormone (GnRH) neurons. A naturally variable wild-source population of white-footed mice was used to develop lines artificially selected for or against mature gonads in short, winter-like photoperiods. We compared a selection line that is reproductively inhibited in short photoperiod (Responsive) to a line that is weakly inhibited by short photoperiod (Nonresponsive) for differences in counts of neurons identified using in situ hybridization for GnRH mRNA. There was no effect of photoperiod, but there were 60% more GnRH neurons in total in the Nonresponsive selection line than the Responsive selection line. The lines differed specifically in numbers of GnRH neurons in more anterior regions, whereas numbers of GnRH neurons in posterior areas were not statistically different between lines. We compare these results to those of an earlier study that used immunohistochemical labeling for GnRH neurons. The results are consistent with the hypothesis that the selection lines and natural source population contain significant genetic variation in the number and location of GnRH neurons. The variation in GnRH neurons may contribute to functional variation in fertility that occurs in short photoperiods in the laboratory and in the wild source population in winter. © 2015 Wiley Periodicals, Inc.

  4. Effects of kisspeptin1 on electrical activity of an extrahypothalamic population of gonadotropin-releasing hormone neurons in medaka (Oryzias latipes).

    Science.gov (United States)

    Zhao, Yali; Wayne, Nancy L

    2012-01-01

    Kisspeptin (product of the kiss1 gene) is the most potent known activator of the hypothalamo-pituitary-gonadal axis. Both kiss1 and the kisspeptin receptor are highly expressed in the hypothalamus of vertebrates, and low doses of kisspeptin have a robust and long-lasting stimulatory effect on the rate of action potential firing of hypophysiotropic gonadotropin releasing hormone-1 (GnRH1) neurons in mice. Fish have multiple populations of GnRH neurons distinguished by their location in the brain and the GnRH gene that they express. GnRH3 neurons located in the terminal nerve (TN) associated with the olfactory bulb are neuromodulatory and do not play a direct role in regulating pituitary-gonadal function. In medaka fish, the electrical activity of TN-GnRH3 neurons is modulated by visual cues from conspecifics, and is thought to act as a transmitter of information from the external environment to the central nervous system. TN-GnRH3 neurons also play a role in sexual motivation and arousal states, making them an important population of neurons to study for understanding coordination of complex behaviors. We investigated the role of kisspeptin in regulating electrical activity of TN-GnRH3 neurons in adult medaka. Using electrophysiology in an intact brain preparation, we show that a relatively brief treatment with 100 nM of kisspeptin had a long-lasting stimulatory effect on the electrical activity of an extrahypothalamic population of GnRH neurons. Dose-response analysis suggests a relatively narrow activational range of this neuropeptide. Further, blocking action potential firing with tetrodotoxin and blocking synaptic transmission with a low Ca(2+)/high Mg(2+) solution inhibited the stimulatory action of kisspeptin on electrical activity, indicating that kisspeptin is acting indirectly through synaptic regulation to excite TN-GnRH3 neurons. Our findings provide a new perspective on kisspeptin's broader functions within the central nervous system, through its

  5. Population dynamics in vasopressin cells.

    Science.gov (United States)

    Leng, Gareth; Brown, Colin; Sabatier, Nancy; Scott, Victoria

    2008-01-01

    Most neurons sense and code change, and when presented with a constant stimulus they adapt, so as to be able to detect a fresh change. However, for some things it is important to know their absolute level; to encode such information, neurons must sustain their response to an unchanging stimulus while remaining able to respond to a change in that stimulus. One system that encodes the absolute level of a stimulus is the vasopressin system, which generates a hormonal signal that is proportional to plasma osmolality. Vasopressin cells sense plasma osmolality and secrete appropriate levels of vasopressin from the neurohypophysis as needed to control water excretion; this requires sustained secretion under basal conditions and the ability to increase (or decrease) secretion should plasma osmolality change. Here we explore the mechanisms that enable vasopressin cells to fulfill this function, and consider how coordination between the cells might distribute the secretory load across the population of vasopressin cells. 2008 S. Karger AG, Basel.

  6. Neuronal coupling by endogenous electric fields: cable theory and applications to coincidence detector neurons in the auditory brain stem.

    Science.gov (United States)

    Goldwyn, Joshua H; Rinzel, John

    2016-04-01

    The ongoing activity of neurons generates a spatially and time-varying field of extracellular voltage (Ve). This Ve field reflects population-level neural activity, but does it modulate neural dynamics and the function of neural circuits? We provide a cable theory framework to study how a bundle of model neurons generates Ve and how this Ve feeds back and influences membrane potential (Vm). We find that these "ephaptic interactions" are small but not negligible. The model neural population can generate Ve with millivolt-scale amplitude, and this Ve perturbs the Vm of "nearby" cables and effectively increases their electrotonic length. After using passive cable theory to systematically study ephaptic coupling, we explore a test case: the medial superior olive (MSO) in the auditory brain stem. The MSO is a possible locus of ephaptic interactions: sounds evoke large (millivolt scale)Vein vivo in this nucleus. The Ve response is thought to be generated by MSO neurons that perform a known neuronal computation with submillisecond temporal precision (coincidence detection to encode sound source location). Using a biophysically based model of MSO neurons, we find millivolt-scale ephaptic interactions consistent with the passive cable theory results. These subtle membrane potential perturbations induce changes in spike initiation threshold, spike time synchrony, and time difference sensitivity. These results suggest that ephaptic coupling may influence MSO function. Copyright © 2016 the American Physiological Society.

  7. On the stability and dynamics of stochastic spiking neuron models: Nonlinear Hawkes process and point process GLMs.

    Science.gov (United States)

    Gerhard, Felipe; Deger, Moritz; Truccolo, Wilson

    2017-02-01

    Point process generalized linear models (PP-GLMs) provide an important statistical framework for modeling spiking activity in single-neurons and neuronal networks. Stochastic stability is essential when sampling from these models, as done in computational neuroscience to analyze statistical properties of neuronal dynamics and in neuro-engineering to implement closed-loop applications. Here we show, however, that despite passing common goodness-of-fit tests, PP-GLMs estimated from data are often unstable, leading to divergent firing rates. The inclusion of absolute refractory periods is not a satisfactory solution since the activity then typically settles into unphysiological rates. To address these issues, we derive a framework for determining the existence and stability of fixed points of the expected conditional intensity function (CIF) for general PP-GLMs. Specifically, in nonlinear Hawkes PP-GLMs, the CIF is expressed as a function of the previous spike history and exogenous inputs. We use a mean-field quasi-renewal (QR) approximation that decomposes spike history effects into the contribution of the last spike and an average of the CIF over all spike histories prior to the last spike. Fixed points for stationary rates are derived as self-consistent solutions of integral equations. Bifurcation analysis and the number of fixed points predict that the original models can show stable, divergent, and metastable (fragile) dynamics. For fragile models, fluctuations of the single-neuron dynamics predict expected divergence times after which rates approach unphysiologically high values. This metric can be used to estimate the probability of rates to remain physiological for given time periods, e.g., for simulation purposes. We demonstrate the use of the stability framework using simulated single-neuron examples and neurophysiological recordings. Finally, we show how to adapt PP-GLM estimation procedures to guarantee model stability. Overall, our results provide a

  8. Allee effects on population dynamics with delay

    International Nuclear Information System (INIS)

    Celik, C.; Merdan, H.; Duman, O.; Akin, O.

    2008-01-01

    In this paper, we study the stability analysis of equilibrium points of population dynamics with delay when the Allee effect occurs at low population density. Mainly, our mathematical results and numerical simulations point to the stabilizing effect of the Allee effects on population dynamics with delay

  9. Cortico-cortical communication dynamics

    Directory of Open Access Journals (Sweden)

    Per E Roland

    2014-05-01

    Full Text Available IIn principle, cortico-cortical communication dynamics is simple: neurons in one cortical area communicate by sending action potentials that release glutamate and excite their target neurons in other cortical areas. In practice, knowledge about cortico-cortical communication dynamics is minute. One reason is that no current technique can capture the fast spatio-temporal cortico-cortical evolution of action potential transmission and membrane conductances with sufficient spatial resolution. A combination of optogenetics and monosynaptic tracing with virus can reveal the spatio-temporal cortico-cortical dynamics of specific neurons and their targets, but does not reveal how the dynamics evolves under natural conditions. Spontaneous ongoing action potentials also spread across cortical areas and are difficult to separate from structured evoked and intrinsic brain activity such as thinking. At a certain state of evolution, the dynamics may engage larger populations of neurons to drive the brain to decisions, percepts and behaviors. For example, successfully evolving dynamics to sensory transients can appear at the mesoscopic scale revealing how the transient is perceived. As a consequence of these methodological and conceptual difficulties, studies in this field comprise a wide range of computational models, large-scale measurements (e.g., by MEG, EEG, and a combination of invasive measurements in animal experiments. Further obstacles and challenges of studying cortico-cortical communication dynamics are outlined in this critical review.

  10. Contribution of LFP dynamics to single-neuron spiking variability in motor cortex during movement execution

    Science.gov (United States)

    Rule, Michael E.; Vargas-Irwin, Carlos; Donoghue, John P.; Truccolo, Wilson

    2015-01-01

    Understanding the sources of variability in single-neuron spiking responses is an important open problem for the theory of neural coding. This variability is thought to result primarily from spontaneous collective dynamics in neuronal networks. Here, we investigate how well collective dynamics reflected in motor cortex local field potentials (LFPs) can account for spiking variability during motor behavior. Neural activity was recorded via microelectrode arrays implanted in ventral and dorsal premotor and primary motor cortices of non-human primates performing naturalistic 3-D reaching and grasping actions. Point process models were used to quantify how well LFP features accounted for spiking variability not explained by the measured 3-D reach and grasp kinematics. LFP features included the instantaneous magnitude, phase and analytic-signal components of narrow band-pass filtered (δ,θ,α,β) LFPs, and analytic signal and amplitude envelope features in higher-frequency bands. Multiband LFP features predicted single-neuron spiking (1ms resolution) with substantial accuracy as assessed via ROC analysis. Notably, however, models including both LFP and kinematics features displayed marginal improvement over kinematics-only models. Furthermore, the small predictive information added by LFP features to kinematic models was redundant to information available in fast-timescale (spiking history. Overall, information in multiband LFP features, although predictive of single-neuron spiking during movement execution, was redundant to information available in movement parameters and spiking history. Our findings suggest that, during movement execution, collective dynamics reflected in motor cortex LFPs primarily relate to sensorimotor processes directly controlling movement output, adding little explanatory power to variability not accounted by movement parameters. PMID:26157365

  11. Structural stability of nonlinear population dynamics.

    Science.gov (United States)

    Cenci, Simone; Saavedra, Serguei

    2018-01-01

    In population dynamics, the concept of structural stability has been used to quantify the tolerance of a system to environmental perturbations. Yet, measuring the structural stability of nonlinear dynamical systems remains a challenging task. Focusing on the classic Lotka-Volterra dynamics, because of the linearity of the functional response, it has been possible to measure the conditions compatible with a structurally stable system. However, the functional response of biological communities is not always well approximated by deterministic linear functions. Thus, it is unclear the extent to which this linear approach can be generalized to other population dynamics models. Here, we show that the same approach used to investigate the classic Lotka-Volterra dynamics, which is called the structural approach, can be applied to a much larger class of nonlinear models. This class covers a large number of nonlinear functional responses that have been intensively investigated both theoretically and experimentally. We also investigate the applicability of the structural approach to stochastic dynamical systems and we provide a measure of structural stability for finite populations. Overall, we show that the structural approach can provide reliable and tractable information about the qualitative behavior of many nonlinear dynamical systems.

  12. Structural stability of nonlinear population dynamics

    Science.gov (United States)

    Cenci, Simone; Saavedra, Serguei

    2018-01-01

    In population dynamics, the concept of structural stability has been used to quantify the tolerance of a system to environmental perturbations. Yet, measuring the structural stability of nonlinear dynamical systems remains a challenging task. Focusing on the classic Lotka-Volterra dynamics, because of the linearity of the functional response, it has been possible to measure the conditions compatible with a structurally stable system. However, the functional response of biological communities is not always well approximated by deterministic linear functions. Thus, it is unclear the extent to which this linear approach can be generalized to other population dynamics models. Here, we show that the same approach used to investigate the classic Lotka-Volterra dynamics, which is called the structural approach, can be applied to a much larger class of nonlinear models. This class covers a large number of nonlinear functional responses that have been intensively investigated both theoretically and experimentally. We also investigate the applicability of the structural approach to stochastic dynamical systems and we provide a measure of structural stability for finite populations. Overall, we show that the structural approach can provide reliable and tractable information about the qualitative behavior of many nonlinear dynamical systems.

  13. 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.

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

    Science.gov (United States)

    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.

  15. Neurons for hunger and thirst transmit a negative-valence teaching signal

    Science.gov (United States)

    Gong, Rong; Magnus, Christopher J.; Yu, Yang; Sternson, Scott M.

    2015-01-01

    Homeostasis is a biological principle for regulation of essential physiological parameters within a set range. Behavioural responses due to deviation from homeostasis are critical for survival, but motivational processes engaged by physiological need states are incompletely understood. We examined motivational characteristics and dynamics of two separate neuron populations that regulate energy and fluid homeostasis by using cell type-specific activity manipulations in mice. We found that starvation-sensitive AGRP neurons exhibit properties consistent with a negative-valence teaching signal. Mice avoided activation of AGRP neurons, indicating that AGRP neuron activity has negative valence. AGRP neuron inhibition conditioned preference for flavours and places. Correspondingly, deep-brain calcium imaging revealed that AGRP neuron activity rapidly reduced in response to food-related cues. Complementary experiments activating thirst-promoting neurons also conditioned avoidance. Therefore, these need-sensing neurons condition preference for environmental cues associated with nutrient or water ingestion, which is learned through reduction of negative-valence signals during restoration of homeostasis. PMID:25915020

  16. Neurochemistry of neurons in the ventrolateral medulla activated by hypotension: Are the same neurons activated by glucoprivation?

    Science.gov (United States)

    Parker, Lindsay M; Le, Sheng; Wearne, Travis A; Hardwick, Kate; Kumar, Natasha N; Robinson, Katherine J; McMullan, Simon; Goodchild, Ann K

    2017-06-15

    Previous studies have demonstrated that a range of stimuli activate neurons, including catecholaminergic neurons, in the ventrolateral medulla. Not all catecholaminergic neurons are activated and other neurochemical content is largely unknown hence whether stimulus specific populations exist is unclear. Here we determine the neurochemistry (using in situ hybridization) of catecholaminergic and noncatecholaminergic neurons which express c-Fos immunoreactivity throughout the rostrocaudal extent of the ventrolateral medulla, in Sprague Dawley rats treated with hydralazine or saline. Distinct neuronal populations containing PPCART, PPPACAP, and PPNPY mRNAs, which were largely catecholaminergic, were activated by hydralazine but not saline. Both catecholaminergic and noncatecholaminergic neurons containing preprotachykinin and prepro-enkephalin (PPE) mRNAs were also activated, with the noncatecholaminergic population located in the rostral C1 region. Few GlyT2 neurons were activated. A subset of these data was then used to compare the neuronal populations activated by 2-deoxyglucose evoked glucoprivation (Brain Structure and Function (2015) 220:117). Hydralazine activated more neurons than 2-deoxyglucose but similar numbers of catecholaminergic neurons. Commonly activated populations expressing PPNPY and PPE mRNAs were defined. These likely include PPNPY expressing catecholaminergic neurons projecting to vasopressinergic and corticotrophin releasing factor neurons in the paraventricular nucleus, which when activated result in elevated plasma vasopressin and corticosterone. Stimulus specific neurons included noncatecholaminergic neurons and a few PPE positive catecholaminergic neuron but neurochemical codes were largely unidentified. Reasons for the lack of identification of stimulus specific neurons, readily detectable using electrophysiology in anaesthetized preparations and for which neural circuits can be defined, are discussed. © 2017 Wiley Periodicals, Inc.

  17. Integration of silicon-based neural probes and micro-drive arrays for chronic recording of large populations of neurons in behaving animals.

    Science.gov (United States)

    Michon, Frédéric; Aarts, Arno; Holzhammer, Tobias; Ruther, Patrick; Borghs, Gustaaf; McNaughton, Bruce; Kloosterman, Fabian

    2016-08-01

    Understanding how neuronal assemblies underlie cognitive function is a fundamental question in system neuroscience. It poses the technical challenge to monitor the activity of populations of neurons, potentially widely separated, in relation to behaviour. In this paper, we present a new system which aims at simultaneously recording from a large population of neurons from multiple separated brain regions in freely behaving animals. The concept of the new device is to combine the benefits of two existing electrophysiological techniques, i.e. the flexibility and modularity of micro-drive arrays and the high sampling ability of electrode-dense silicon probes. Newly engineered long bendable silicon probes were integrated into a micro-drive array. The resulting device can carry up to 16 independently movable silicon probes, each carrying 16 recording sites. Populations of neurons were recorded simultaneously in multiple cortical and/or hippocampal sites in two freely behaving implanted rats. Current approaches to monitor neuronal activity either allow to flexibly record from multiple widely separated brain regions (micro-drive arrays) but with a limited sampling density or to provide denser sampling at the expense of a flexible placement in multiple brain regions (neural probes). By combining these two approaches and their benefits, we present an alternative solution for flexible and simultaneous recordings from widely distributed populations of neurons in freely behaving rats.

  18. Vertical binocular disparity is encoded implicitly within a model neuronal population tuned to horizontal disparity and orientation.

    Directory of Open Access Journals (Sweden)

    Jenny C A Read

    2010-04-01

    Full Text Available Primary visual cortex is often viewed as a "cyclopean retina", performing the initial encoding of binocular disparities between left and right images. Because the eyes are set apart horizontally in the head, binocular disparities are predominantly horizontal. Yet, especially in the visual periphery, a range of non-zero vertical disparities do occur and can influence perception. It has therefore been assumed that primary visual cortex must contain neurons tuned to a range of vertical disparities. Here, I show that this is not necessarily the case. Many disparity-selective neurons are most sensitive to changes in disparity orthogonal to their preferred orientation. That is, the disparity tuning surfaces, mapping their response to different two-dimensional (2D disparities, are elongated along the cell's preferred orientation. Because of this, even if a neuron's optimal 2D disparity has zero vertical component, the neuron will still respond best to a non-zero vertical disparity when probed with a sub-optimal horizontal disparity. This property can be used to decode 2D disparity, even allowing for realistic levels of neuronal noise. Even if all V1 neurons at a particular retinotopic location are tuned to the expected vertical disparity there (for example, zero at the fovea, the brain could still decode the magnitude and sign of departures from that expected value. This provides an intriguing counter-example to the common wisdom that, in order for a neuronal population to encode a quantity, its members must be tuned to a range of values of that quantity. It demonstrates that populations of disparity-selective neurons encode much richer information than previously appreciated. It suggests a possible strategy for the brain to extract rarely-occurring stimulus values, while concentrating neuronal resources on the most commonly-occurring situations.

  19. 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.

  20. Anatomical and molecular consequences of Unilateral Naris Closure on two populations of olfactory sensory neurons expressing defined odorant receptors.

    Science.gov (United States)

    Molinas, Adrien; Aoudé, Imad; Soubeyre, Vanessa; Tazir, Bassim; Cadiou, Hervé; Grosmaitre, Xavier

    2016-07-28

    Mammalian olfactory sensory neurons (OSNs), the primary elements of the olfactory system, are located in the olfactory epithelium lining the nasal cavity. Exposed to the environment, their lifespan is short. Consequently, OSNs are regularly regenerated and several reports show that activity strongly modulates their development and regeneration: the peripheral olfactory system can adjust to the amount of stimulus through compensatory mechanisms. Unilateral naris occlusion (UNO) was frequently used to investigate this mechanism at the entire epithelium level. However, there is little data regarding the effects of UNO at the cellular level, especially on individual neuronal populations expressing a defined odorant receptor. Here, using UNO during the first three postnatal weeks, we analyzed the anatomical and molecular consequences of sensory deprivation in OSNs populations expressing the MOR23 and M71 receptors. The density of MOR23-expressing neurons is decreased in the closed side while UNO does not affect the density of M71-expressing neurons. Using Real Time qPCR on isolated neurons, we observed that UNO modulates the transcript levels for transduction pathway proteins (odorant receptors, CNGA2, PDE1c). The transcripts modulated by UNO will differ between populations depending on the receptor expressed. These results suggest that sensory deprivation will have different effects on different OSNs' populations. As a consequence, early experience will shape the functional properties of OSNs differently depending on the type of odorant receptor they express. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  1. Population dynamics on heterogeneous bacterial substrates

    Science.gov (United States)

    Mobius, Wolfram; Murray, Andrew W.; Nelson, David R.

    2012-02-01

    How species invade new territories and how these range expansions influence the population's genotypes are important questions in the field of population genetics. The majority of work addressing these questions focuses on homogeneous environments. Much less is known about the population dynamics and population genetics when the environmental conditions are heterogeneous in space. To better understand range expansions in two-dimensional heterogeneous environments, we employ a system of bacteria and bacteriophage, the viruses of bacteria. Thereby, the bacteria constitute the environment in which a population of bacteriophages expands. The spread of phage constitutes itself in lysis of bacteria and thus formation of clear regions on bacterial lawns, called plaques. We study the population dynamics and genetics of the expanding page for various patterns of environments.

  2. Transition Dynamics of a Dentate Gyrus-CA3 Neuronal Network during Temporal Lobe Epilepsy

    Directory of Open Access Journals (Sweden)

    Liyuan Zhang

    2017-07-01

    Full Text Available In temporal lobe epilepsy (TLE, the variation of chemical receptor expression underlies the basis of neural network activity shifts, resulting in neuronal hyperexcitability and epileptiform discharges. However, dynamical mechanisms involved in the transitions of TLE are not fully understood, because of the neuronal diversity and the indeterminacy of network connection. Hence, based on Hodgkin–Huxley (HH type neurons and Pinsky–Rinzel (PR type neurons coupling with glutamatergic and GABAergic synaptic connections respectively, we propose a computational framework which contains dentate gyrus (DG region and CA3 region. By regulating the concentration range of N-methyl-D-aspartate-type glutamate receptor (NMDAR, we demonstrate the pyramidal neuron can generate transitions from interictal to seizure discharges. This suggests that enhanced endogenous activity of NMDAR contributes to excitability in pyramidal neuron. Moreover, we conclude that excitatory discharges in CA3 region vary considerably on account of the excitatory currents produced by the excitatory pyramidal neuron. Interestingly, by changing the backprojection connection, we find that glutamatergic type backprojection can promote the dominant frequency of firings and further motivate excitatory counterpropagation from CA3 region to DG region. However, GABAergic type backprojection can reduce firing rate and block morbid counterpropagation, which may be factored into the terminations of TLE. In addition, neuronal diversity dominated network shows weak correlation with different backprojections. Our modeling and simulation studies provide new insights into the mechanisms of seizures generation and connectionism in local hippocampus, along with the synaptic mechanisms of this disease.

  3. Transition Dynamics of a Dentate Gyrus-CA3 Neuronal Network during Temporal Lobe Epilepsy.

    Science.gov (United States)

    Zhang, Liyuan; Fan, Denggui; Wang, Qingyun

    2017-01-01

    In temporal lobe epilepsy (TLE), the variation of chemical receptor expression underlies the basis of neural network activity shifts, resulting in neuronal hyperexcitability and epileptiform discharges. However, dynamical mechanisms involved in the transitions of TLE are not fully understood, because of the neuronal diversity and the indeterminacy of network connection. Hence, based on Hodgkin-Huxley (HH) type neurons and Pinsky-Rinzel (PR) type neurons coupling with glutamatergic and GABAergic synaptic connections respectively, we propose a computational framework which contains dentate gyrus (DG) region and CA3 region. By regulating the concentration range of N-methyl-D-aspartate-type glutamate receptor (NMDAR), we demonstrate the pyramidal neuron can generate transitions from interictal to seizure discharges. This suggests that enhanced endogenous activity of NMDAR contributes to excitability in pyramidal neuron. Moreover, we conclude that excitatory discharges in CA3 region vary considerably on account of the excitatory currents produced by the excitatory pyramidal neuron. Interestingly, by changing the backprojection connection, we find that glutamatergic type backprojection can promote the dominant frequency of firings and further motivate excitatory counterpropagation from CA3 region to DG region. However, GABAergic type backprojection can reduce firing rate and block morbid counterpropagation, which may be factored into the terminations of TLE. In addition, neuronal diversity dominated network shows weak correlation with different backprojections. Our modeling and simulation studies provide new insights into the mechanisms of seizures generation and connectionism in local hippocampus, along with the synaptic mechanisms of this disease.

  4. A central pattern generator producing alternative outputs: pattern, strength, and dynamics of premotor synaptic input to leech heart motor neurons.

    Science.gov (United States)

    Norris, Brian J; Weaver, Adam L; Wenning, Angela; García, Paul S; Calabrese, Ronald L

    2007-11-01

    The central pattern generator (CPG) for heartbeat in medicinal leeches consists of seven identified pairs of segmental heart interneurons and one unidentified pair. Four of the identified pairs and the unidentified pair of interneurons make inhibitory synaptic connections with segmental heart motor neurons. The CPG produces a side-to-side asymmetric pattern of intersegmental coordination among ipsilateral premotor interneurons corresponding to a similarly asymmetric fictive motor pattern in heart motor neurons, and asymmetric constriction pattern of the two tubular hearts, synchronous and peristaltic. Using extracellular recordings from premotor interneurons and voltage-clamp recordings of ipsilateral segmental motor neurons in 69 isolated nerve cords, we assessed the strength and dynamics of premotor inhibitory synaptic output onto the entire ensemble of heart motor neurons and the associated conduction delays in both coordination modes. We conclude that premotor interneurons establish a stereotypical pattern of intersegmental synaptic connectivity, strengths, and dynamics that is invariant across coordination modes, despite wide variations among preparations. These data coupled with a previous description of the temporal pattern of premotor interneuron activity and relative phasing of motor neuron activity in the two coordination modes enable a direct assessment of how premotor interneurons through their temporal pattern of activity and their spatial pattern of synaptic connectivity, strengths, and dynamics coordinate segmental motor neurons into a functional pattern of activity.

  5. Population dynamics in variable environments

    CERN Document Server

    Tuljapurkar, Shripad

    1990-01-01

    Demography relates observable facts about individuals to the dynamics of populations. If the dynamics are linear and do not change over time, the classical theory of Lotka (1907) and Leslie (1945) is the central tool of demography. This book addresses the situation when the assumption of constancy is dropped. In many practical situations, a population will display unpredictable variation over time in its vital rates, which must then be described in statistical terms. Most of this book is concerned with the theory of populations which are subject to random temporal changes in their vital rates, although other kinds of variation (e. g. , cyclical) are also dealt with. The central questions are: how does temporal variation work its way into a population's future, and how does it affect our interpretation of a population's past. The results here are directed at demographers of humans and at popula­ tion biologists. The uneven mathematical level is dictated by the material, but the book should be accessible to re...

  6. Population dynamical responses to climate change

    DEFF Research Database (Denmark)

    Forchhammer, Mads; Schmidt, Niels Martin; Høye, Toke Thomas

    2008-01-01

    approaches, we analyse concurrently the influence of climatic variability and trophic interactions on the temporal population dynamics of species in the terrestrial vertebrate community at Zackenberg. We describe and contrast the population dynamics of three predator species (arctic fox Alopex lagopus, stoat...... of arctic fox were not significantly related to changes in lemming abundance, both the stoat and the breeding of long-tailed skua were mainly related to lemming dynamics. The predator-prey system at Zackenberg differentiates from previously described systems in high-arctic Greenland, which, we suggest...

  7. Oscillatory neuronal dynamics associated with manual acupuncture: a magnetoencephalography study using beamforming analysis

    Directory of Open Access Journals (Sweden)

    Aziz eAsghar

    2012-11-01

    Full Text Available Magnetoencephalography (MEG enables non-invasive recording of neuronal activity, with reconstruction methods providing estimates of underlying brain source locations and oscillatory dynamics from externally recorded neuromagnetic fields. The aim of our study was to use MEG to determine the effect of manual acupuncture on neuronal oscillatory dynamics. A major problem in MEG investigations of manual acupuncture is the absence of onset times for each needle manipulation. Given that beamforming (spatial filtering analysis is not dependent upon stimulus-driven responses being phase-locked to stimulus onset, we postulated that beamforming could reveal source locations and induced changes in neuronal activity during manual acupuncture. In a beamformer analysis, a two-minute period of manual acupuncture needle manipulation delivered to the ipsilateral right LI-4 (Hegu acupoint was contrasted with a two-minute baseline period. We considered oscillatory power changes in the theta (4-8Hz, alpha (8-13Hz, beta (13-30Hz and gamma (30-100Hz frequency bands. We found significant decreases in beta band power in the contralateral primary somatosensory cortex and superior frontal gyrus. In the ipsilateral cerebral hemisphere, we found significant power decreases in beta and gamma frequency bands in only the superior frontal gyrus. No significant power modulations were found in theta and alpha bands. Our results indicate that beamforming is a useful analytical tool to reconstruct underlying neuronal activity associated with manual acupuncture. Our main finding was of beta power decreases in primary somatosensory cortex and superior frontal gyrus, which opens up a line of future investigation regarding whether this contributes towards an underlying mechanism of acupuncture.

  8. Impact of sub and supra-threshold adaptation currents in networks of spiking neurons.

    Science.gov (United States)

    Colliaux, David; Yger, Pierre; Kaneko, Kunihiko

    2015-12-01

    Neuronal adaptation is the intrinsic capacity of the brain to change, by various mechanisms, its dynamical responses as a function of the context. Such a phenomena, widely observed in vivo and in vitro, is known to be crucial in homeostatic regulation of the activity and gain control. The effects of adaptation have already been studied at the single-cell level, resulting from either voltage or calcium gated channels both activated by the spiking activity and modulating the dynamical responses of the neurons. In this study, by disentangling those effects into a linear (sub-threshold) and a non-linear (supra-threshold) part, we focus on the the functional role of those two distinct components of adaptation onto the neuronal activity at various scales, starting from single-cell responses up to recurrent networks dynamics, and under stationary or non-stationary stimulations. The effects of slow currents on collective dynamics, like modulation of population oscillation and reliability of spike patterns, is quantified for various types of adaptation in sparse recurrent networks.

  9. Two distinct populations of projection neurons in the rat lateral parafascicular thalamic nucleus and their cholinergic responsiveness.

    Science.gov (United States)

    Beatty, J A; Sylwestrak, E L; Cox, C L

    2009-08-04

    The lateral parafascicular nucleus (lPf) is a member of the intralaminar thalamic nuclei, a collection of nuclei that characteristically provides widespread projections to the neocortex and basal ganglia and is associated with arousal, sensory, and motor functions. Recently, lPf neurons have been shown to possess different characteristics than other cortical-projecting thalamic relay neurons. We performed whole cell recordings from lPf neurons using an in vitro rat slice preparation and found two distinct neuronal subtypes that were differentiated by distinct morphological and physiological characteristics: diffuse and bushy. Diffuse neurons, which had been previously described, were the predominant neuronal subtype (66%). These neurons had few, poorly-branching, extended dendrites, and rarely displayed burst-like action potential discharge, a ubiquitous feature of thalamocortical relay neurons. Interestingly, we discovered a smaller population of bushy neurons (34%) that shared similar morphological and physiological characteristics with thalamocortical relay neurons of primary sensory thalamic nuclei. In contrast to other thalamocortical relay neurons, activation of muscarinic cholinergic receptors produced a membrane hyperpolarization via activation of M(2) receptors in most lPf neurons (60%). In a minority of lPf neurons (33%), muscarinic agonists produced a membrane depolarization via activation of predominantly M(3) receptors. The muscarinic receptor-mediated actions were independent of lPf neuronal subtype (i.e. diffuse or bushy neurons); however the cholinergic actions were correlated with lPf neurons with different efferent targets. Retrogradely-labeled lPf neurons from frontal cortical fluorescent bead injections primarily consisted of bushy type lPf neurons (78%), but more importantly, all of these neurons were depolarized by muscarinic agonists. On the other hand, lPf neurons labeled by striatal injections were predominantly hyperpolarized by muscarinic

  10. Inference of neuronal network spike dynamics and topology from calcium imaging data

    Directory of Open Access Journals (Sweden)

    Henry eLütcke

    2013-12-01

    Full Text Available Two-photon calcium imaging enables functional analysis of neuronal circuits by inferring action potential (AP occurrence ('spike trains' from cellular fluorescence signals. It remains unclear how experimental parameters such as signal-to-noise ratio (SNR and acquisition rate affect spike inference and whether additional information about network structure can be extracted. Here we present a simulation framework for quantitatively assessing how well spike dynamics and network topology can be inferred from noisy calcium imaging data. For simulated AP-evoked calcium transients in neocortical pyramidal cells, we analyzed the quality of spike inference as a function of SNR and data acquisition rate using a recently introduced peeling algorithm. Given experimentally attainable values of SNR and acquisition rate, neural spike trains could be reconstructed accurately and with up to millisecond precision. We then applied statistical neuronal network models to explore how remaining uncertainties in spike inference affect estimates of network connectivity and topological features of network organization. We define the experimental conditions suitable for inferring whether the network has a scale-free structure and determine how well hub neurons can be identified. Our findings provide a benchmark for future calcium imaging studies that aim to reliably infer neuronal network properties.

  11. Near scale-free dynamics in neural population activity of waking/sleeping rats revealed by multiscale analysis.

    Directory of Open Access Journals (Sweden)

    Leonid A Safonov

    Full Text Available A neuron embedded in an intact brain, unlike an isolated neuron, participates in network activity at various spatial resolutions. Such multiple scale spatial dynamics is potentially reflected in multiple time scales of temporal dynamics. We identify such multiple dynamical time scales of the inter-spike interval (ISI fluctuations of neurons of waking/sleeping rats by means of multiscale analysis. The time scale of large non-Gaussianity in the ISI fluctuations, measured with the Castaing method, ranges up to several minutes, markedly escaping the low-pass filtering characteristics of neurons. A comparison between neural activity during waking and sleeping reveals that non-Gaussianity is stronger during waking than sleeping throughout the entire range of scales observed. We find a remarkable property of near scale independence of the magnitude correlations as the primary cause of persistent non-Gaussianity. Such scale-invariance of correlations is characteristic of multiplicative cascade processes and raises the possibility of the existence of a scale independent memory preserving mechanism.

  12. Synchronization in a non-uniform network of excitatory spiking neurons

    Science.gov (United States)

    Echeveste, Rodrigo; Gros, Claudius

    Spontaneous synchronization of pulse coupled elements is ubiquitous in nature and seems to be of vital importance for life. Networks of pacemaker cells in the heart, extended populations of southeast asian fireflies, and neuronal oscillations in cortical networks, are examples of this. In the present work, a rich repertoire of dynamical states with different degrees of synchronization are found in a network of excitatory-only spiking neurons connected in a non-uniform fashion. In particular, uncorrelated and partially correlated states are found without the need for inhibitory neurons or external currents. The phase transitions between these states, as well the robustness, stability, and response of the network to external stimulus are studied.

  13. Postresuscitative Changes of Brain-Derived Neurotrophic Factor (BDNF Protein Expression: Association With Neuronal Death

    Directory of Open Access Journals (Sweden)

    M. Sh. Avrushchenko

    2017-01-01

    Full Text Available Aim of the study: to evaluate expression level of BDNF and its association with the postresuscitative neuronal death in highly hypoxia-sensitive brain regions.Materials and methods. Cardiac arrest in adult albino male rats was evoked by intrathoracic clamping of supracardiac bundle of vessels for 10 min. Pyramidal neurons of the hippocampus and Purkinje cells of the cerebellum were analyzed at various time points after resuscitation (days 1, 4, 7, 14. Shame-operated rats served as controls. The expression of BDNF protein was immunohistochemically determined. The BDNF expression level was determined by evalution on the base of the average optical density. The number of neurons with different BDNF expression levels and the total number of neurons per 1 mm of the layer length were computed. Image analysis systems (Intel personal computer, Olympus BX-41 microscope, ImageScopeM, ImageJ 1,48v and MS Excel 2007 software packages were used in the study. Data statistical processing was performed with the aid of Statistica 7.0 program and Kolmogorov-Smirnov λ-test, Mann-Whitney U-test and Student's t-test.Results. The dynamics of postresuscitative shifts of BDNF immunoreactivity in neuronal populations of hippocampal pyramidal cells and cerebellar Purkinje cells was established. It was shown that the level of BDNF expression within the two neuronal populations decreased, that was accompanied by neuronal death. In the Purkinje cell population the neuronal death occurred by the 4th day after resuscitation, while in the hippocampus, it occurs only by the 7th day. Notably, only BDNF-negative neurons or neurons with low level of BDNF expression died in both neuronal populations.Conclusion. The results of the study indicate the existence of an interrelation between the shifts in BDNF expression and the postresuscitative neuronal death. It was shown that only the cells with none or poor BDNF expression underwent death in highly hypoxia-sensitive neuronal

  14. Histamine influences body temperature by acting at H1 and H3 receptors on distinct populations of preoptic neurons.

    Science.gov (United States)

    Lundius, Ebba Gregorsson; Sanchez-Alavez, Manuel; Ghochani, Yasmin; Klaus, Joseph; Tabarean, Iustin V

    2010-03-24

    The preoptic area/anterior hypothalamus, a region that contains neurons that control thermoregulation, is the main locus at which histamine affects body temperature. Here we report that histamine reduced the spontaneous firing rate of GABAergic preoptic neurons by activating H3 subtype histamine receptors. This effect involved a decrease in the level of phosphorylation of the extracellular signal-regulated kinase and was not dependent on synaptic activity. Furthermore, a population of non-GABAergic neurons was depolarized, and their firing rate was enhanced by histamine acting at H1 subtype receptors. In our experiments, activation of the H1R receptors was linked to the PLC pathway and Ca(2+) release from intracellular stores. This depolarization persisted in TTX or when fast synaptic potentials were blocked, indicating that it represents a postsynaptic effect. Single-cell reverse transcription-PCR analysis revealed expression of H3 receptors in a population of GABAergic neurons, while H1 receptors were expressed in non-GABAergic cells. Histamine applied in the median preoptic nucleus induced a robust, long-lasting hyperthermia effect that was mimicked by either H1 or H3 histamine receptor subtype-specific agonists. Our data indicate that histamine modulates the core body temperature by acting at two distinct populations of preoptic neurons that express H1 and H3 receptor subtypes, respectively.

  15. SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks

    OpenAIRE

    Wang, Linnan; Ye, Jinmian; Zhao, Yiyang; Wu, Wei; Li, Ang; Song, Shuaiwen Leon; Xu, Zenglin; Kraska, Tim

    2018-01-01

    Going deeper and wider in neural architectures improves the accuracy, while the limited GPU DRAM places an undesired restriction on the network design domain. Deep Learning (DL) practitioners either need change to less desired network architectures, or nontrivially dissect a network across multiGPUs. These distract DL practitioners from concentrating on their original machine learning tasks. We present SuperNeurons: a dynamic GPU memory scheduling runtime to enable the network training far be...

  16. Market Squid Population Dynamics

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset contains population dynamics data on paralarvae, juvenile and adult market squid collected off California and the US Pacific Northwest. These data were...

  17. Probing the Electrode–Neuron Interface With Focused Cochlear Implant Stimulation

    Science.gov (United States)

    Bierer, Julie Arenberg

    2010-01-01

    Cochlear implants are highly successful neural prostheses for persons with severe or profound hearing loss who gain little benefit from hearing aid amplification. Although implants are capable of providing important spectral and temporal cues for speech perception, performance on speech tests is variable across listeners. Psychophysical measures obtained from individual implant subjects can also be highly variable across implant channels. This review discusses evidence that such variability reflects deviations in the electrode–neuron interface, which refers to an implant channel's ability to effectively stimulate the auditory nerve. It is proposed that focused electrical stimulation is ideally suited to assess channel-to-channel irregularities in the electrode–neuron interface. In implant listeners, it is demonstrated that channels with relatively high thresholds, as measured with the tripolar configuration, exhibit broader psychophysical tuning curves and smaller dynamic ranges than channels with relatively low thresholds. Broader tuning implies that frequency-specific information intended for one population of neurons in the cochlea may activate more distant neurons, and a compressed dynamic range could make it more difficult to resolve intensity-based information, particularly in the presence of competing noise. Degradation of both types of cues would negatively affect speech perception. PMID:20724356

  18. Understanding the Generation of Network Bursts by Adaptive Oscillatory Neurons

    Directory of Open Access Journals (Sweden)

    Tanguy Fardet

    2018-02-01

    Full Text Available Experimental and numerical studies have revealed that isolated populations of oscillatory neurons can spontaneously synchronize and generate periodic bursts involving the whole network. Such a behavior has notably been observed for cultured neurons in rodent's cortex or hippocampus. We show here that a sufficient condition for this network bursting is the presence of an excitatory population of oscillatory neurons which displays spike-driven adaptation. We provide an analytic model to analyze network bursts generated by coupled adaptive exponential integrate-and-fire neurons. We show that, for strong synaptic coupling, intrinsically tonic spiking neurons evolve to reach a synchronized intermittent bursting state. The presence of inhibitory neurons or plastic synapses can then modulate this dynamics in many ways but is not necessary for its appearance. Thanks to a simple self-consistent equation, our model gives an intuitive and semi-quantitative tool to understand the bursting behavior. Furthermore, it suggests that after-hyperpolarization currents are sufficient to explain bursting termination. Through a thorough mapping between the theoretical parameters and ion-channel properties, we discuss the biological mechanisms that could be involved and the relevance of the explored parameter-space. Such an insight enables us to propose experimentally-testable predictions regarding how blocking fast, medium or slow after-hyperpolarization channels would affect the firing rate and burst duration, as well as the interburst interval.

  19. Population dynamics and population control of Galium aparine L.

    NARCIS (Netherlands)

    Weide, van der R.Y.

    1993-01-01

    The population biology of Galium aparine L. needs to be better understood, in order to be able to rationalize decisions about the short- and long-term control of this weed species for different cropping practices.

    A population dynamics model was developed to

  20. Evolutionary dynamics of cooperation in neutral populations

    Science.gov (United States)

    Szolnoki, Attila; Perc, Matjaž

    2018-01-01

    Cooperation is a difficult proposition in the face of Darwinian selection. Those that defect have an evolutionary advantage over cooperators who should therefore die out. However, spatial structure enables cooperators to survive through the formation of homogeneous clusters, which is the hallmark of network reciprocity. Here we go beyond this traditional setup and study the spatiotemporal dynamics of cooperation in a population of populations. We use the prisoner's dilemma game as the mathematical model and show that considering several populations simultaneously gives rise to fascinating spatiotemporal dynamics and pattern formation. Even the simplest assumption that strategies between different populations are payoff-neutral with one another results in the spontaneous emergence of cyclic dominance, where defectors of one population become prey of cooperators in the other population, and vice versa. Moreover, if social interactions within different populations are characterized by significantly different temptations to defect, we observe that defectors in the population with the largest temptation counterintuitively vanish the fastest, while cooperators that hang on eventually take over the whole available space. Our results reveal that considering the simultaneous presence of different populations significantly expands the complexity of evolutionary dynamics in structured populations, and it allows us to understand the stability of cooperation under adverse conditions that could never be bridged by network reciprocity alone.

  1. Dynamics of Time Delay-Induced Multiple Synchronous Behaviors in Inhibitory Coupled Neurons

    Science.gov (United States)

    Gu, Huaguang; Zhao, Zhiguo

    2015-01-01

    The inhibitory synapse can induce synchronous behaviors different from the anti-phase synchronous behaviors, which have been reported in recent studies. In the present paper, synchronous behaviors are investigated in the motif model composed of reciprocal inhibitory coupled neurons with endogenous bursting and time delay. When coupling strength is weak, synchronous behavior appears at a single interval of time delay within a bursting period. When coupling strength is strong, multiple synchronous behaviors appear at different intervals of time delay within a bursting period. The different bursting patterns of synchronous behaviors, and time delays and coupling strengths that can induce the synchronous bursting patterns can be well interpreted by the dynamics of the endogenous bursting pattern of isolated neuron, which is acquired by the fast-slow dissection method, combined with the inhibitory coupling current. For an isolated neuron, when a negative impulsive current with suitable strength is applied at different phases of the bursting, multiple different bursting patterns can be induced. For a neuron in the motif, the inhibitory coupling current, of which the application time and strength is modulated by time delay and coupling strength, can cause single or multiple synchronous firing patterns like the negative impulsive current when time delay and coupling strength is suitable. The difference compared to the previously reported multiple synchronous behaviors that appear at time delays wider than a period of the endogenous firing is discussed. The results present novel examples of synchronous behaviors in the neuronal network with inhibitory synapses and provide a reasonable explanation. PMID:26394224

  2. Feedback between Population and Evolutionary Dynamics Determines the Fate of Social Microbial Populations

    Science.gov (United States)

    Sanchez, Alvaro; Gore, Jeff

    2013-01-01

    The evolutionary spread of cheater strategies can destabilize populations engaging in social cooperative behaviors, thus demonstrating that evolutionary changes can have profound implications for population dynamics. At the same time, the relative fitness of cooperative traits often depends upon population density, thus leading to the potential for bi-directional coupling between population density and the evolution of a cooperative trait. Despite the potential importance of these eco-evolutionary feedback loops in social species, they have not yet been demonstrated experimentally and their ecological implications are poorly understood. Here, we demonstrate the presence of a strong feedback loop between population dynamics and the evolutionary dynamics of a social microbial gene, SUC2, in laboratory yeast populations whose cooperative growth is mediated by the SUC2 gene. We directly visualize eco-evolutionary trajectories of hundreds of populations over 50–100 generations, allowing us to characterize the phase space describing the interplay of evolution and ecology in this system. Small populations collapse despite continual evolution towards increased cooperative allele frequencies; large populations with a sufficient number of cooperators “spiral” to a stable state of coexistence between cooperator and cheater strategies. The presence of cheaters does not significantly affect the equilibrium population density, but it does reduce the resilience of the population as well as its ability to adapt to a rapidly deteriorating environment. Our results demonstrate the potential ecological importance of coupling between evolutionary dynamics and the population dynamics of cooperatively growing organisms, particularly in microbes. Our study suggests that this interaction may need to be considered in order to explain intraspecific variability in cooperative behaviors, and also that this feedback between evolution and ecology can critically affect the demographic fate

  3. feedback between population and evolutionary dynamics determines the fate of social microbial populations.

    Directory of Open Access Journals (Sweden)

    Alvaro Sanchez

    Full Text Available The evolutionary spread of cheater strategies can destabilize populations engaging in social cooperative behaviors, thus demonstrating that evolutionary changes can have profound implications for population dynamics. At the same time, the relative fitness of cooperative traits often depends upon population density, thus leading to the potential for bi-directional coupling between population density and the evolution of a cooperative trait. Despite the potential importance of these eco-evolutionary feedback loops in social species, they have not yet been demonstrated experimentally and their ecological implications are poorly understood. Here, we demonstrate the presence of a strong feedback loop between population dynamics and the evolutionary dynamics of a social microbial gene, SUC2, in laboratory yeast populations whose cooperative growth is mediated by the SUC2 gene. We directly visualize eco-evolutionary trajectories of hundreds of populations over 50-100 generations, allowing us to characterize the phase space describing the interplay of evolution and ecology in this system. Small populations collapse despite continual evolution towards increased cooperative allele frequencies; large populations with a sufficient number of cooperators "spiral" to a stable state of coexistence between cooperator and cheater strategies. The presence of cheaters does not significantly affect the equilibrium population density, but it does reduce the resilience of the population as well as its ability to adapt to a rapidly deteriorating environment. Our results demonstrate the potential ecological importance of coupling between evolutionary dynamics and the population dynamics of cooperatively growing organisms, particularly in microbes. Our study suggests that this interaction may need to be considered in order to explain intraspecific variability in cooperative behaviors, and also that this feedback between evolution and ecology can critically affect the

  4. Optical recording of neuronal activity with a genetically-encoded calcium indicator in anesthetized and freely moving mice

    Directory of Open Access Journals (Sweden)

    Henry Lütcke

    2010-04-01

    Full Text Available Fluorescent calcium (Ca2+ indicator proteins (FCIPs are promising tools for functional imaging of cellular activity in living animals. However, they have still not reached their full potential for in vivo imaging of neuronal activity due to limitations in expression levels, dynamic range, and sensitivity for reporting action potentials. Here, we report that viral expression of the ratiometric Ca2+ sensor yellow cameleon 3.60 (YC3.60 in pyramidal neurons of mouse barrel cortex enables in vivo measurement of neuronal activity with high dynamic range and sensitivity across multiple spatial scales. By combining juxtacellular recordings and two-photon imaging in vitro and in vivo, we demonstrate that YC3.60 can resolve single action potential (AP-evoked Ca2+ transients and reliably reports bursts of APs with negligible saturation. Spontaneous and whisker-evoked Ca2+ transients were detected in individual apical dendrites and somata as well as in local neuronal populations. Moreover, bulk measurements using wide-field imaging or fiber-optics revealed sensory-evoked YC3.60 signals in large areas of the barrel field. Fiber-optic recordings in particular enabled measurements in awake, freely moving mice and revealed complex Ca2+ dynamics, possibly reflecting different behavior-related brain states. Viral expression of YC3.60 - in combination with various optical techniques - thus opens a multitude of opportunities for functional studies of the neural basis of animal behavior, from dendrites to the levels of local and large-scale neuronal populations.

  5. Synchronization of Coupled FitzHugh-Nagumo Neurons Using Self-Feedback Time Delay

    Science.gov (United States)

    Fan, Denggui; Song, Xinle; Liao, Fucheng

    Many neurological diseases are characterized by abnormally synchronous oscillations of neuronal populations. However, how the neurons can synchronize with each other is still not fully understood, which may have potentially hampered the understanding and diagnosis for these dynamical diseases. In this paper, the self-feedback time delay (SFTD) and adaptive control theory are employed to control the onset of synchronization in the coupled FitzHugh-Nagumo (FHN) neurons. It is found that the larger SFTD can induce the complete synchronization of coupled neuronal system. Further investigation reveals that the reinforcing SFTD can significantly postpone the synchronization onsets. In addition, for the case that synchronization cannot be achieved by adjusting SFTD, the parameter estimation update laws and adaptive controller with respect to SFTD of coupled system are investigated to deduce the sufficient condition for complete synchronization. Simulations are also provided to illustrate the effectiveness of the proposed methods. In particular, we observed the fascinating dynamical synchronization transitions, such as chaotic synchronization and bursting synchronization transitions, as well as the transition from anti-synchronization to complete synchronization.

  6. Predictive coding of dynamical variables in balanced spiking networks.

    Science.gov (United States)

    Boerlin, Martin; Machens, Christian K; Denève, Sophie

    2013-01-01

    Two observations about the cortex have puzzled neuroscientists for a long time. First, neural responses are highly variable. Second, the level of excitation and inhibition received by each neuron is tightly balanced at all times. Here, we demonstrate that both properties are necessary consequences of neural networks that represent information efficiently in their spikes. We illustrate this insight with spiking networks that represent dynamical variables. Our approach is based on two assumptions: We assume that information about dynamical variables can be read out linearly from neural spike trains, and we assume that neurons only fire a spike if that improves the representation of the dynamical variables. Based on these assumptions, we derive a network of leaky integrate-and-fire neurons that is able to implement arbitrary linear dynamical systems. We show that the membrane voltage of the neurons is equivalent to a prediction error about a common population-level signal. Among other things, our approach allows us to construct an integrator network of spiking neurons that is robust against many perturbations. Most importantly, neural variability in our networks cannot be equated to noise. Despite exhibiting the same single unit properties as widely used population code models (e.g. tuning curves, Poisson distributed spike trains), balanced networks are orders of magnitudes more reliable. Our approach suggests that spikes do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly underestimated.

  7. The role of degree distribution in shaping the dynamics in networks of sparsely connected spiking neurons

    Directory of Open Access Journals (Sweden)

    Alex eRoxin

    2011-03-01

    Full Text Available Neuronal network models often assume a fixed probability of connectionbetween neurons. This assumption leads to random networks withbinomial in-degree and out-degree distributions which are relatively narrow. Here I study the effect of broaddegree distributions on network dynamics by interpolating between abinomial and a truncated powerlaw distribution for the in-degree andout-degree independently. This is done both for an inhibitory network(I network as well as for the recurrent excitatory connections in anetwork of excitatory and inhibitory neurons (EI network. In bothcases increasing the width of the in-degree distribution affects theglobal state of the network by driving transitions betweenasynchronous behavior and oscillations. This effect is reproduced ina simplified rate model which includes the heterogeneity in neuronalinput due to the in-degree of cells. On the other hand, broadeningthe out-degree distribution is shown to increase the fraction ofcommon inputs to pairs of neurons. This leads to increases in theamplitude of the cross-correlation (CC of synaptic currents. In thecase of the I network, despite strong oscillatory CCs in the currents, CCs of the membrane potential are low due to filtering and reset effects, leading to very weak CCs of the spikecount. In the asynchronous regime ofthe EI network, broadening the out-degree increases the amplitude ofCCs in the recurrent excitatory currents, while CC of the totalcurrent is essentially unaffected as are pairwise spikingcorrelations. This is due to a dynamic balance between excitatoryand inhibitory synaptic currents. In the oscillatory regime, changesin the out-degree can have a large effect on spiking correlations andeven on the qualitative dynamical state of the network.

  8. An improved ivermectin-activated chloride channel receptor for inhibiting electrical activity in defined neuronal populations

    DEFF Research Database (Denmark)

    Lynagh, Timothy Peter; Lynch, Joseph W

    2010-01-01

    The ability to silence the electrical activity of defined neuronal populations in vivo is dramatically advancing our understanding of brain function. This technology may eventually be useful clinically for treating a variety of neuropathological disorders caused by excessive neuronal activity...... conductance, homomeric expression, and human origin may render the F207A/A288G alpha1 glycine receptor an improved silencing receptor for neuroscientific and clinical purposes. As all known highly ivermectin-sensitive GluClRs contain an endogenous glycine residue at the corresponding location, this residue...

  9. Heterogeneity and convergence of olfactory first-order neurons account for the high speed and sensitivity of second-order neurons.

    Directory of Open Access Journals (Sweden)

    Jean-Pierre Rospars

    2014-12-01

    Full Text Available In the olfactory system of male moths, a specialized subset of neurons detects and processes the main component of the sex pheromone emitted by females. It is composed of several thousand first-order olfactory receptor neurons (ORNs, all expressing the same pheromone receptor, that contact synaptically a few tens of second-order projection neurons (PNs within a single restricted brain area. The functional simplicity of this system makes it a favorable model for studying the factors that contribute to its exquisite sensitivity and speed. Sensory information--primarily the identity and intensity of the stimulus--is encoded as the firing rate of the action potentials, and possibly as the latency of the neuron response. We found that over all their dynamic range, PNs respond with a shorter latency and a higher firing rate than most ORNs. Modelling showed that the increased sensitivity of PNs can be explained by the ORN-to-PN convergent architecture alone, whereas their faster response also requires cell-to-cell heterogeneity of the ORN population. So, far from being detrimental to signal detection, the ORN heterogeneity is exploited by PNs, and results in two different schemes of population coding based either on the response of a few extreme neurons (latency or on the average response of many (firing rate. Moreover, ORN-to-PN transformations are linear for latency and nonlinear for firing rate, suggesting that latency could be involved in concentration-invariant coding of the pheromone blend and that sensitivity at low concentrations is achieved at the expense of precise encoding at high concentrations.

  10. Permanent Genetic Access to Transiently Active Neurons via TRAP: Targeted Recombination in Active Populations

    OpenAIRE

    Guenthner, Casey J.; Miyamichi, Kazunari; Yang, Helen H.; Heller, H. Craig; Luo, Liqun

    2013-01-01

    Targeting genetically encoded tools for neural circuit dissection to relevant cellular populations is a major challenge in neurobiology. We developed a new approach, Targeted Recombination in Active Populations (TRAP), to obtain genetic access to neurons that were activated by defined stimuli. This method utilizes mice in which the tamoxifen-dependent recombinase CreERT2 is expressed in an activity-dependent manner from the loci of the immediate early genes Arc and Fos. Active cells that expr...

  11. Transient optogenetic inactivation of the medial entorhinal cortex biases the active population of hippocampal neurons.

    Science.gov (United States)

    Rueckemann, Jon W; DiMauro, Audrey J; Rangel, Lara M; Han, Xue; Boyden, Edward S; Eichenbaum, Howard

    2016-02-01

    The mechanisms that enable the hippocampal network to express the appropriate spatial representation for a particular circumstance are not well understood. Previous studies suggest that the medial entorhinal cortex (MEC) may have a role in reproducibly selecting the hippocampal representation of an environment. To examine how ongoing MEC activity is continually integrated by the hippocampus, we performed transient unilateral optogenetic inactivations of the MEC while simultaneously recording place cell activity in CA1. Inactivation of the MEC caused a partial remapping in the CA1 population without diminishing the degree of spatial tuning across the active cell assembly. These changes remained stable irrespective of intermittent disruption of MEC input, indicating that while MEC input is integrated over long time scales to bias the active population, there are mechanisms for stabilizing the population of active neurons independent of the MEC. We find that MEC inputs to the hippocampus shape its ongoing activity by biasing the participation of the neurons in the active network, thereby influencing how the hippocampus selectively represents information. © 2015 Wiley Periodicals, Inc.

  12. Parvalbumin+ Neurons and Npas1+ Neurons Are Distinct Neuron Classes in the Mouse External Globus Pallidus.

    Science.gov (United States)

    Hernández, Vivian M; Hegeman, Daniel J; Cui, Qiaoling; Kelver, Daniel A; Fiske, Michael P; Glajch, Kelly E; Pitt, Jason E; Huang, Tina Y; Justice, Nicholas J; Chan, C Savio

    2015-08-26

    Compelling evidence suggests that pathological activity of the external globus pallidus (GPe), a nucleus in the basal ganglia, contributes to the motor symptoms of a variety of movement disorders such as Parkinson's disease. Recent studies have challenged the idea that the GPe comprises a single, homogenous population of neurons that serves as a simple relay in the indirect pathway. However, we still lack a full understanding of the diversity of the neurons that make up the GPe. Specifically, a more precise classification scheme is needed to better describe the fundamental biology and function of different GPe neuron classes. To this end, we generated a novel multicistronic BAC (bacterial artificial chromosome) transgenic mouse line under the regulatory elements of the Npas1 gene. Using a combinatorial transgenic and immunohistochemical approach, we discovered that parvalbumin-expressing neurons and Npas1-expressing neurons in the GPe represent two nonoverlapping cell classes, amounting to 55% and 27% of the total GPe neuron population, respectively. These two genetically identified cell classes projected primarily to the subthalamic nucleus and to the striatum, respectively. Additionally, parvalbumin-expressing neurons and Npas1-expressing neurons were distinct in their autonomous and driven firing characteristics, their expression of intrinsic ion conductances, and their responsiveness to chronic 6-hydroxydopamine lesion. In summary, our data argue that parvalbumin-expressing neurons and Npas1-expressing neurons are two distinct functional classes of GPe neurons. This work revises our understanding of the GPe, and provides the foundation for future studies of its function and dysfunction. Until recently, the heterogeneity of the constituent neurons within the external globus pallidus (GPe) was not fully appreciated. We addressed this knowledge gap by discovering two principal GPe neuron classes, which were identified by their nonoverlapping expression of the

  13. Parvalbumin+ Neurons and Npas1+ Neurons Are Distinct Neuron Classes in the Mouse External Globus Pallidus

    Science.gov (United States)

    Hernández, Vivian M.; Hegeman, Daniel J.; Cui, Qiaoling; Kelver, Daniel A.; Fiske, Michael P.; Glajch, Kelly E.; Pitt, Jason E.; Huang, Tina Y.; Justice, Nicholas J.

    2015-01-01

    Compelling evidence suggests that pathological activity of the external globus pallidus (GPe), a nucleus in the basal ganglia, contributes to the motor symptoms of a variety of movement disorders such as Parkinson's disease. Recent studies have challenged the idea that the GPe comprises a single, homogenous population of neurons that serves as a simple relay in the indirect pathway. However, we still lack a full understanding of the diversity of the neurons that make up the GPe. Specifically, a more precise classification scheme is needed to better describe the fundamental biology and function of different GPe neuron classes. To this end, we generated a novel multicistronic BAC (bacterial artificial chromosome) transgenic mouse line under the regulatory elements of the Npas1 gene. Using a combinatorial transgenic and immunohistochemical approach, we discovered that parvalbumin-expressing neurons and Npas1-expressing neurons in the GPe represent two nonoverlapping cell classes, amounting to 55% and 27% of the total GPe neuron population, respectively. These two genetically identified cell classes projected primarily to the subthalamic nucleus and to the striatum, respectively. Additionally, parvalbumin-expressing neurons and Npas1-expressing neurons were distinct in their autonomous and driven firing characteristics, their expression of intrinsic ion conductances, and their responsiveness to chronic 6-hydroxydopamine lesion. In summary, our data argue that parvalbumin-expressing neurons and Npas1-expressing neurons are two distinct functional classes of GPe neurons. This work revises our understanding of the GPe, and provides the foundation for future studies of its function and dysfunction. SIGNIFICANCE STATEMENT Until recently, the heterogeneity of the constituent neurons within the external globus pallidus (GPe) was not fully appreciated. We addressed this knowledge gap by discovering two principal GPe neuron classes, which were identified by their nonoverlapping

  14. Facial injections of pruritogens and algogens excite partly overlapping populations of primary and second-order trigeminal neurons in mice.

    Science.gov (United States)

    Akiyama, T; Carstens, M Iodi; Carstens, E

    2010-11-01

    Intradermal cheek injection of pruitogens or algogens differentially elicits hindlimb scratching or forelimb wiping, suggesting that these behaviors distinguish between itch and pain. We studied whether pruritogens and algogens excite separate or overlapping populations of primary afferent and second-order trigeminal neurons in mice. Calcium imaging of primary sensory trigeminal ganglion (TG) cells showed that 15.4% responded to histamine, 5.8% to the protease-activated receptor (PAR)-2 agonist, 13.4% to allyl isothiocyanate (AITC), and 36.7% to capsaicin. AITC and/or capsaicin activated the vast majority of histamine- and PAR-2 agonist-sensitive TG cells. A chemical search strategy identified second-order neurons in trigeminal subnucleus caudalis (Vc) responsive to histamine, the PAR-2 agonist, or AITC. A minority of histamine or PAR-2 agonist-responsive Vc neurons responded to the other pruritogen, whereas a large majority of puritogen-responsive Vc neurons responded to capsaicin and/or AITC. A minority of AITC-responsive Vc neurons responded to pruritogens, whereas most responded to capsaicin. These data indicate that most primary and higher-order trigeminal sensory neurons are activated by both pruritic and algesic stimuli, although a minority exhibit selectivity. The results are discussed in terms of population codes for itch and pain that result in distinct behavioral responses of hindlimb scratching and forelimb wiping that are mediated at lumbar and cervical segmental levels, respectively.

  15. Strongly Deterministic Population Dynamics in Closed Microbial Communities

    Directory of Open Access Journals (Sweden)

    Zak Frentz

    2015-10-01

    Full Text Available Biological systems are influenced by random processes at all scales, including molecular, demographic, and behavioral fluctuations, as well as by their interactions with a fluctuating environment. We previously established microbial closed ecosystems (CES as model systems for studying the role of random events and the emergent statistical laws governing population dynamics. Here, we present long-term measurements of population dynamics using replicate digital holographic microscopes that maintain CES under precisely controlled external conditions while automatically measuring abundances of three microbial species via single-cell imaging. With this system, we measure spatiotemporal population dynamics in more than 60 replicate CES over periods of months. In contrast to previous studies, we observe strongly deterministic population dynamics in replicate systems. Furthermore, we show that previously discovered statistical structure in abundance fluctuations across replicate CES is driven by variation in external conditions, such as illumination. In particular, we confirm the existence of stable ecomodes governing the correlations in population abundances of three species. The observation of strongly deterministic dynamics, together with stable structure of correlations in response to external perturbations, points towards a possibility of simple macroscopic laws governing microbial systems despite numerous stochastic events present on microscopic levels.

  16. How Resource Phenology Affects Consumer Population Dynamics.

    Science.gov (United States)

    Bewick, Sharon; Cantrell, R Stephen; Cosner, Chris; Fagan, William F

    2016-02-01

    Climate change drives uneven phenology shifts across taxa, and this can result in changes to the phenological match between interacting species. Shifts in the relative phenology of partner species are well documented, but few studies have addressed the effects of such changes on population dynamics. To explore this, we develop a phenologically explicit model describing consumer-resource interactions. Focusing on scenarios for univoltine insects, we show how changes in resource phenology can be reinterpreted as transformations in the year-to-year recursion relationships defining consumer population dynamics. This perspective provides a straightforward path for interpreting the long-term population consequences of phenology change. Specifically, by relating the outcome of phenological shifts to species traits governing recursion relationships (e.g., consumer fecundity or competitive scenario), we demonstrate how changes in relative phenology can force systems into different dynamical regimes, with major implications for resource management, conservation, and other areas of applied dynamics.

  17. Bayesian Inference and Online Learning in Poisson Neuronal Networks.

    Science.gov (United States)

    Huang, Yanping; Rao, Rajesh P N

    2016-08-01

    Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.

  18. Role of Ih in differentiating the dynamics of the gastric and pyloric neurons in the stomatogastric ganglion of the lobster, Homarus americanus.

    Science.gov (United States)

    Zhu, Lin; Selverston, Allen I; Ayers, Joseph

    2016-06-01

    The hyperpolarization-activated inward cationic current (Ih) is known to regulate the rhythmicity, excitability, and synaptic transmission in heart cells and many types of neurons across a variety of species, including some pyloric and gastric mill neurons in the stomatogastric ganglion (STG) in Cancer borealis and Panulirus interruptus However, little is known about the role of Ih in regulating the gastric mill dynamics and its contribution to the dynamical bifurcation of the gastric mill and pyloric networks. We investigated the role of Ih in the rhythmic activity and cellular excitability of both the gastric mill neurons (medial gastric, gastric mill) and pyloric neurons (pyloric dilator, lateral pyloric) in Homarus americanus Through testing the burst period between 5 and 50 mM CsCl, and elimination of postinhibitory rebound and voltage sag, we found that 30 mM CsCl can sufficiently block Ih in both the pyloric and gastric mill neurons. Our results show that Ih maintains the excitability of both the pyloric and gastric mill neurons. However, Ih regulates slow oscillations of the pyloric and gastric mill neurons differently. Specifically, blocking Ih diminishes the difference between the pyloric and gastric mill burst periods by increasing the pyloric burst period and decreasing the gastric mill burst period. Moreover, the phase-plane analysis shows that blocking Ih causes the trajectory of slow oscillations of the gastric mill neurons to change toward the pyloric sinusoidal-like trajectories. In addition to regulating the pyloric rhythm, we found that Ih is also essential for the gastric mill rhythms and differentially regulates these two dynamics. Copyright © 2016 the American Physiological Society.

  19. Modelling the Dynamics of an Aedes albopictus Population

    Directory of Open Access Journals (Sweden)

    Thomas Anung Basuki

    2010-08-01

    Full Text Available We present a methodology for modelling population dynamics with formal means of computer science. This allows unambiguous description of systems and application of analysis tools such as simulators and model checkers. In particular, the dynamics of a population of Aedes albopictus (a species of mosquito and its modelling with the Stochastic Calculus of Looping Sequences (Stochastic CLS are considered. The use of Stochastic CLS to model population dynamics requires an extension which allows environmental events (such as changes in the temperature and rainfalls to be taken into account. A simulator for the constructed model is developed via translation into the specification language Maude, and used to compare the dynamics obtained from the model with real data.

  20. The expression of Toll-like receptor 4, 7 and co-receptors in neurochemical sub-populations of rat trigeminal ganglion sensory neurons.

    Science.gov (United States)

    Helley, M P; Abate, W; Jackson, S K; Bennett, J H; Thompson, S W N

    2015-12-03

    The recent discovery that mammalian nociceptors express Toll-like receptors (TLRs) has raised the possibility that these cells directly detect and respond to pathogens with implications for either direct nociceptor activation or sensitization. A range of neuronal TLRs have been identified, however a detailed description regarding the distribution of expression of these receptors within sub-populations of sensory neurons is lacking. There is also some debate as to the composition of the TLR4 receptor complex on sensory neurons. Here we use a range of techniques to quantify the expression of TLR4, TLR7 and some associated molecules within neurochemically-identified sub-populations of trigeminal (TG) and dorsal root (DRG) ganglion sensory neurons. We also detail the pattern of expression and co-expression of two isoforms of lysophosphatidylcholine acyltransferase (LPCAT), a phospholipid remodeling enzyme previously shown to be involved in the lipopolysaccharide-dependent TLR4 response in monocytes, within sensory ganglia. Immunohistochemistry shows that both TLR4 and TLR7 preferentially co-localize with transient receptor potential vallinoid 1 (TRPV1) and purinergic receptor P2X ligand-gated ion channel 3 (P2X3), markers of nociceptor populations, within both TG and DRG. A gene expression profile shows that TG sensory neurons express a range of TLR-associated molecules. LPCAT1 is expressed by a proportion of both nociceptors and non-nociceptive neurons. LPCAT2 immunostaining is absent from neuronal profiles within both TG and DRG and is confined to non-neuronal cell types under naïve conditions. Together, our results show that nociceptors express the molecular machinery required to directly respond to pathogenic challenge independently from the innate immune system. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

    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.

  2. Focusing on neuronal cell-type specific mechanisms for brain circuit organization, function and dysfunction

    Institute of Scientific and Technical Information of China (English)

    Lu Li

    2017-01-01

    Mammalian brain circuits consist of dynamically interconnected neurons with characteristic morphology, physiology, connectivity and genetics which are often called neuronal cell types. Neuronal cell types have been considered as building blocks of brain circuits, but knowledge of how neuron types or subtypes connect to and interact with each other to perform neural computation is still lacking. Such mechanistic insights are critical not only to our understanding of normal brain functions, such as perception, motion and cognition, but also to brain disorders including Alzheimer's disease, Schizophrenia and epilepsy, to name a few. Thus it is necessary to carry out systematic and standardized studies on neuronal cell-type specific mechanisms for brain circuit organization and function, which will provide good opportunities to bridge basic and clinical research. Here based on recent technology advancements, we discuss the strategy to target and manipulate specific populations of neuronsin vivo to provide unique insights on how neuron types or subtypes behave, interact, and generate emergent properties in a fully connected brain network. Our approach is highlighted by combining transgenic animal models, targeted electrophysiology and imaging with robotics, thus complete and standardized mapping ofin vivo properties of genetically defined neuron populations can be achieved in transgenic mouse models, which will facilitate the development of novel therapeutic strategies for brain disorders.

  3. Analytical Calculation of Mutual Information between Weakly Coupled Poisson-Spiking Neurons in Models of Dynamically Gated Communication.

    Science.gov (United States)

    Cannon, Jonathan

    2017-01-01

    Mutual information is a commonly used measure of communication between neurons, but little theory exists describing the relationship between mutual information and the parameters of the underlying neuronal interaction. Such a theory could help us understand how specific physiological changes affect the capacity of neurons to synaptically communicate, and, in particular, they could help us characterize the mechanisms by which neuronal dynamics gate the flow of information in the brain. Here we study a pair of linear-nonlinear-Poisson neurons coupled by a weak synapse. We derive an analytical expression describing the mutual information between their spike trains in terms of synapse strength, neuronal activation function, the time course of postsynaptic currents, and the time course of the background input received by the two neurons. This expression allows mutual information calculations that would otherwise be computationally intractable. We use this expression to analytically explore the interaction of excitation, information transmission, and the convexity of the activation function. Then, using this expression to quantify mutual information in simulations, we illustrate the information-gating effects of neural oscillations and oscillatory coherence, which may either increase or decrease the mutual information across the synapse depending on parameters. Finally, we show analytically that our results can quantitatively describe the selection of one information pathway over another when multiple sending neurons project weakly to a single receiving neuron.

  4. Local excitation-inhibition ratio for synfire chain propagation in feed-forward neuronal networks

    Science.gov (United States)

    Guo, Xinmeng; Yu, Haitao; Wang, Jiang; Liu, Jing; Cao, Yibin; Deng, Bin

    2017-09-01

    A leading hypothesis holds that spiking activity propagates along neuronal sub-populations which are connected in a feed-forward manner, and the propagation efficiency would be affected by the dynamics of sub-populations. In this paper, how the interaction between local excitation and inhibition effects on synfire chain propagation in feed-forward network (FFN) is investigated. The simulation results show that there is an appropriate excitation-inhibition (EI) ratio maximizing the performance of synfire chain propagation. The optimal EI ratio can significantly enhance the selectivity of FFN to synchronous signals, which thereby increases the stability to background noise. Moreover, the effect of network topology on synfire chain propagation is also investigated. It is found that synfire chain propagation can be maximized by an optimal interlayer linking probability. We also find that external noise is detrimental to synchrony propagation by inducing spiking jitter. The results presented in this paper may provide insights into the effects of network dynamics on neuronal computations.

  5. Dynamical mean-field theory of noisy spiking neuron ensembles: Application to the Hodgkin-Huxley model

    International Nuclear Information System (INIS)

    Hasegawa, Hideo

    2003-01-01

    A dynamical mean-field approximation (DMA) previously proposed by the present author [H. Hasegawa, Phys. Rev E 67, 041903 (2003)] has been extended to ensembles described by a general noisy spiking neuron model. Ensembles of N-unit neurons, each of which is expressed by coupled K-dimensional differential equations (DEs), are assumed to be subject to spatially correlated white noises. The original KN-dimensional stochastic DEs have been replaced by K(K+2)-dimensional deterministic DEs expressed in terms of means and the second-order moments of local and global variables: the fourth-order contributions are taken into account by the Gaussian decoupling approximation. Our DMA has been applied to an ensemble of Hodgkin-Huxley (HH) neurons (K=4), for which effects of the noise, the coupling strength, and the ensemble size on the response to a single-spike input have been investigated. Numerical results calculated by the DMA theory are in good agreement with those obtained by direct simulations, although the former computation is about a thousand times faster than the latter for a typical HH neuron ensemble with N=100

  6. Distinct populations of GABAergic neurons in mouse rhombomere 1 express but do not require the homeodomain transcription factor PITX2.

    Science.gov (United States)

    Waite, Mindy R; Skaggs, Kaia; Kaviany, Parisa; Skidmore, Jennifer M; Causeret, Frédéric; Martin, James F; Martin, Donna M

    2012-01-01

    Hindbrain rhombomere 1 (r1) is located caudal to the isthmus, a critical organizer region, and rostral to rhombomere 2 in the developing mouse brain. Dorsal r1 gives rise to the cerebellum, locus coeruleus, and several brainstem nuclei, whereas cells from ventral r1 contribute to the trochlear and trigeminal nuclei as well as serotonergic and GABAergic neurons of the dorsal raphe. Recent studies have identified several molecular events controlling dorsal r1 development. In contrast, very little is known about ventral r1 gene expression and the genetic mechanisms regulating its formation. Neurons with distinct neurotransmitter phenotypes have been identified in ventral r1 including GABAergic, serotonergic, and cholinergic neurons. Here we show that PITX2 marks a distinct population of GABAergic neurons in mouse embryonic ventral r1. This population appears to retain its GABAergic identity even in the absence of PITX2. We provide a comprehensive map of markers that places these PITX2-positive GABAergic neurons in a region of r1 that intersects and is potentially in communication with the dorsal raphe. Copyright © 2011 Elsevier Inc. All rights reserved.

  7. Ih equalizes membrane input resistance in a heterogeneous population of fusiform neurons in the dorsal cochlear nucleus.

    Directory of Open Access Journals (Sweden)

    Cesar Celis Ceballos

    2016-10-01

    Full Text Available In a neuronal population, several combinations of its ionic conductances are used to attain a specific firing phenotype. Some neurons present heterogeneity in their firing, generally produced by expression of a specific conductance, but how additional conductances vary along in order to homeostatically regulate membrane excitability is less known. Dorsal cochlear nucleus principal neurons, fusiform neurons, display heterogeneous spontaneous action potential activity and thus represent an appropriate model to study the role of different conductances in establishing firing heterogeneity. Particularly, fusiform neurons are divided into quiet, with no spontaneous firing, or active neurons, presenting spontaneous, regular firing. These modes are determined by the expression levels of an intrinsic membrane conductance, an inwardly rectifying potassium current (IKir. In this work, we tested whether other subthreshold conductances vary homeostatically to maintain membrane excitability constant across the two subtypes. We found that Ih expression covaries specifically with IKir in order to maintain membrane resistance constant. The impact of Ih on membrane resistance is dependent on the level of IKir expression, being much smaller in quiet neurons with bigger IKir, but Ih variations are not relevant for creating the quiet and active phenotypes. Finally, we demonstrate that the individual proportion of each conductance, and not their absolute conductance, is relevant for determining the neuronal firing mode. We conclude that in fusiform neurons the variations of their different subthreshold conductances are limited to specific conductances in order to create firing heterogeneity and maintain membrane homeostasis.

  8. Geometrical Determinants of Neuronal Actin Waves

    OpenAIRE

    Tomba, Caterina; Bra?ni, C?line; Bugnicourt, Ghislain; Cohen, Floriane; Friedrich, Benjamin M.; Gov, Nir S.; Villard, Catherine

    2017-01-01

    Hippocampal neurons produce in their early stages of growth propagative, actin-rich dynamical structures called actin waves. The directional motion of actin waves from the soma to the tip of neuronal extensions has been associated with net forward growth, and ultimately with the specification of neurites into axon and dendrites. Here, geometrical cues are used to control actin wave dynamics by constraining neurons on adhesive stripes of various widths. A key observable, the average time betwe...

  9. Stochastic population dynamics in populations of western terrestrial garter snakes with divergent life histories.

    Science.gov (United States)

    Miller, David A; Clark, William R; Arnold, Stevan J; Bronikowski, Anne M

    2011-08-01

    Comparative evaluations of population dynamics in species with temporal and spatial variation in life-history traits are rare because they require long-term demographic time series from multiple populations. We present such an analysis using demographic data collected during the interval 1978-1996 for six populations of western terrestrial garter snakes (Thamnophis elegans) from two evolutionarily divergent ecotypes. Three replicate populations from a slow-living ecotype, found in mountain meadows of northeastern California, were characterized by individuals that develop slowly, mature late, reproduce infrequently with small reproductive effort, and live longer than individuals of three populations of a fast-living ecotype found at lakeshore locales. We constructed matrix population models for each of the populations based on 8-13 years of data per population and analyzed both deterministic dynamics based on mean annual vital rates and stochastic dynamics incorporating annual variation in vital rates. (1) Contributions of highly variable vital rates to fitness (lambda(s)) were buffered against the negative effects of stochastic variation, and this relationship was consistent with differences between the meadow (M-slow) and lakeshore (L-fast) ecotypes. (2) Annual variation in the proportion of gravid females had the greatest negative effect among all vital rates on lambda(s). The magnitude of variation in the proportion of gravid females and its effect on lambda(s) was greater in M-slow than L-fast populations. (3) Variation in the proportion of gravid females, in turn, depended on annual variation in prey availability, and its effect on lambda(s) was 4 23 times greater in M-slow than L-fast populations. In addition to differences in stochastic dynamics between ecotypes, we also found higher mean mortality rates across all age classes in the L-fast populations. Our results suggest that both deterministic and stochastic selective forces have affected the evolution of

  10. Mature neurons dynamically restrict apoptosis via redundant premitochondrial brakes.

    Science.gov (United States)

    Annis, Ryan P; Swahari, Vijay; Nakamura, Ayumi; Xie, Alison X; Hammond, Scott M; Deshmukh, Mohanish

    2016-12-01

    Apoptotic cell death is critical for the early development of the nervous system, but once the nervous system is established, the apoptotic pathway becomes highly restricted in mature neurons. However, the mechanisms underlying this increased resistance to apoptosis in these mature neurons are not completely understood. We have previously found that members of the miR-29 family of microRNAs (miRNAs) are induced with neuronal maturation and that overexpression of miR-29 was sufficient to restrict apoptosis in neurons. To determine whether endogenous miR-29 alone was responsible for the inhibition of cytochrome c release in mature neurons, we examined the status of the apoptotic pathway in sympathetic neurons deficient for all three miR-29 family members. Unexpectedly, we found that the apoptotic pathway remained largely restricted in miR-29-deficient mature neurons. We therefore probed for additional mechanisms by which mature neurons resist apoptosis. We identify miR-24 as another miRNA that is upregulated in the maturing cerebellum and sympathetic neurons that can act redundantly with miR-29 by targeting a similar repertoire of prodeath BH3-only genes. Overall, our results reveal that mature neurons engage multiple redundant brakes to restrict the apoptotic pathway and ensure their long-term survival. © 2016 Federation of European Biochemical Societies.

  11. Evolutionary dynamics with fluctuating population sizes and strong mutualism

    Science.gov (United States)

    Chotibut, Thiparat; Nelson, David R.

    2015-08-01

    Game theory ideas provide a useful framework for studying evolutionary dynamics in a well-mixed environment. This approach, however, typically enforces a strictly fixed overall population size, deemphasizing natural growth processes. We study a competitive Lotka-Volterra model, with number fluctuations, that accounts for natural population growth and encompasses interaction scenarios typical of evolutionary games. We show that, in an appropriate limit, the model describes standard evolutionary games with both genetic drift and overall population size fluctuations. However, there are also regimes where a varying population size can strongly influence the evolutionary dynamics. We focus on the strong mutualism scenario and demonstrate that standard evolutionary game theory fails to describe our simulation results. We then analytically and numerically determine fixation probabilities as well as mean fixation times using matched asymptotic expansions, taking into account the population size degree of freedom. These results elucidate the interplay between population dynamics and evolutionary dynamics in well-mixed systems.

  12. Evolutionary dynamics with fluctuating population sizes and strong mutualism.

    Science.gov (United States)

    Chotibut, Thiparat; Nelson, David R

    2015-08-01

    Game theory ideas provide a useful framework for studying evolutionary dynamics in a well-mixed environment. This approach, however, typically enforces a strictly fixed overall population size, deemphasizing natural growth processes. We study a competitive Lotka-Volterra model, with number fluctuations, that accounts for natural population growth and encompasses interaction scenarios typical of evolutionary games. We show that, in an appropriate limit, the model describes standard evolutionary games with both genetic drift and overall population size fluctuations. However, there are also regimes where a varying population size can strongly influence the evolutionary dynamics. We focus on the strong mutualism scenario and demonstrate that standard evolutionary game theory fails to describe our simulation results. We then analytically and numerically determine fixation probabilities as well as mean fixation times using matched asymptotic expansions, taking into account the population size degree of freedom. These results elucidate the interplay between population dynamics and evolutionary dynamics in well-mixed systems.

  13. Voltage-Dependent Rhythmogenic Property of Respiratory Pre-Bötzinger Complex Glutamatergic, Dbx1-Derived, and Somatostatin-Expressing Neuron Populations Revealed by Graded Optogenetic Inhibition.

    Science.gov (United States)

    Koizumi, Hidehiko; Mosher, Bryan; Tariq, Mohammad F; Zhang, Ruli; Koshiya, Naohiro; Smith, Jeffrey C

    2016-01-01

    The rhythm of breathing in mammals, originating within the brainstem pre-Bötzinger complex (pre-BötC), is presumed to be generated by glutamatergic neurons, but this has not been directly demonstrated. Additionally, developmental expression of the transcription factor Dbx1 or expression of the neuropeptide somatostatin (Sst), has been proposed as a marker for the rhythmogenic pre-BötC glutamatergic neurons, but it is unknown whether these other two phenotypically defined neuronal populations are functionally equivalent to glutamatergic neurons with regard to rhythm generation. To address these problems, we comparatively investigated, by optogenetic approaches, the roles of pre-BötC glutamatergic, Dbx1-derived, and Sst-expressing neurons in respiratory rhythm generation in neonatal transgenic mouse medullary slices in vitro and also more intact adult perfused brainstem-spinal cord preparations in situ. We established three different triple-transgenic mouse lines with Cre-driven Archaerhodopsin-3 (Arch) expression selectively in glutamatergic, Dbx1-derived, or Sst-expressing neurons for targeted photoinhibition. In each line, we identified subpopulations of rhythmically active, Arch-expressing pre-BötC inspiratory neurons by whole-cell recordings in medullary slice preparations in vitro, and established that Arch-mediated hyperpolarization of these inspiratory neurons was laser power dependent with equal efficacy. By site- and population-specific graded photoinhibition, we then demonstrated that inspiratory frequency was reduced by each population with the same neuronal voltage-dependent frequency control mechanism in each state of the respiratory network examined. We infer that enough of the rhythmogenic pre-BötC glutamatergic neurons also have the Dbx1 and Sst expression phenotypes, and thus all three phenotypes share the same voltage-dependent frequency control property.

  14. A Markov model for the temporal dynamics of balanced random networks of finite size

    Science.gov (United States)

    Lagzi, Fereshteh; Rotter, Stefan

    2014-01-01

    The balanced state of recurrent networks of excitatory and inhibitory spiking neurons is characterized by fluctuations of population activity about an attractive fixed point. Numerical simulations show that these dynamics are essentially nonlinear, and the intrinsic noise (self-generated fluctuations) in networks of finite size is state-dependent. Therefore, stochastic differential equations with additive noise of fixed amplitude cannot provide an adequate description of the stochastic dynamics. The noise model should, rather, result from a self-consistent description of the network dynamics. Here, we consider a two-state Markovian neuron model, where spikes correspond to transitions from the active state to the refractory state. Excitatory and inhibitory input to this neuron affects the transition rates between the two states. The corresponding nonlinear dependencies can be identified directly from numerical simulations of networks of leaky integrate-and-fire neurons, discretized at a time resolution in the sub-millisecond range. Deterministic mean-field equations, and a noise component that depends on the dynamic state of the network, are obtained from this model. The resulting stochastic model reflects the behavior observed in numerical simulations quite well, irrespective of the size of the network. In particular, a strong temporal correlation between the two populations, a hallmark of the balanced state in random recurrent networks, are well represented by our model. Numerical simulations of such networks show that a log-normal distribution of short-term spike counts is a property of balanced random networks with fixed in-degree that has not been considered before, and our model shares this statistical property. Furthermore, the reconstruction of the flow from simulated time series suggests that the mean-field dynamics of finite-size networks are essentially of Wilson-Cowan type. We expect that this novel nonlinear stochastic model of the interaction between

  15. Theoretical Neuroanatomy:Analyzing the Structure, Dynamics,and Function of Neuronal Networks

    Science.gov (United States)

    Seth, Anil K.; Edelman, Gerald M.

    The mammalian brain is an extraordinary object: its networks give rise to our conscious experiences as well as to the generation of adaptive behavior for the organism within its environment. Progress in understanding the structure, dynamics and function of the brain faces many challenges. Biological neural networks change over time, their detailed structure is difficult to elucidate, and they are highly heterogeneous both in their neuronal units and synaptic connections. In facing these challenges, graph-theoretic and information-theoretic approaches have yielded a number of useful insights and promise many more.

  16. Species-specific flight styles of flies are reflected in the response dynamics of a homologue motion sensitive neuron

    Directory of Open Access Journals (Sweden)

    Bart eGeurten

    2012-03-01

    Full Text Available Hoverflies and blowflies have distinctly different flight styles. Yet, both species have been shown to structure their flight behaviour in a way that facilitates extraction of 3D information from the image flow on the retina (optic flow. Neuronal candidates to analyse the optic flow are the tangential cells in the third optical ganglion – the lobula complex. These neurons are directionally selective and integrate the optic flow over large parts of the visual field. Homologue tangential cells in hoverflies and blowflies have a similar morphology. Because blowflies and hoverflies have similar neuronal layout but distinctly different flight behaviours, they are an ideal substrate to pinpoint potential neuronal adaptations to the different flight styles.In this article we describe the relationship between locomotion behaviour and motion vision on three different levels:1.We compare the different flight styles based on the categorisation of flight behaviour into prototypical movements.2.We measure the species specific dynamics of the optic flow under naturalistic flight conditions. We found the translational optic flow of both species to be very different.3.We describe possible adaptations of a homologue motion sensitive neuron. We stimulate this cell in blowflies (Calliphora and hoverflies (Eristalis with naturalistic optic flow generated by both species during free flight. The characterized hoverfly tangential cell responds faster to transient changes in the optic flow than its blowfly homologue. It is discussed whether and how the different dynamical response properties aid optic flow analysis.

  17. Peroxiredoxin distribution in the mouse brain with emphasis on neuronal populations affected in neurodegenerative disorders.

    Science.gov (United States)

    Goemaere, Julie; Knoops, Bernard

    2012-02-01

    Redox changes are observed in neurodegenerative diseases, ranging from increased levels of reactive oxygen/nitrogen species and disturbance of antioxidant systems, to nitro-oxidative damage. By reducing hydrogen peroxide, peroxynitrite, and organic hydroperoxides, peroxiredoxins (Prdxs) represent a major potential protective barrier against nitro-oxidative insults in the brain. While recent works have investigated the putative role of Prdxs in neurodegenerative disorders, less is known about their expression in the healthy brain. Here we used immunohistochemistry to map basal expression of Prdxs throughout C57BL/6 mouse brain. We first confirmed the neuronal localization of Prdx2-5 and the glial expression of Prdx1, Prdx4, and Prdx6. Then we performed an in-depth analysis of neuronal Prdx distribution in the brain. Our results show that Prdx2-5 are widely detected in the different neuronal populations, and especially well expressed in the olfactory bulb, in the cerebral cortex, in pons nuclei, in the red nucleus, in all cranial nerve nuclei, in the cerebellum, and in motor neurons of the spinal cord. In contrast, Prdx expression is very low in the dopaminergic neurons of substantia nigra pars compacta and in the CA1/2 pyramidal cells of hippocampus. This low basal expression may contribute to the vulnerability of these neurons to nitro-oxidative attacks occurring in Parkinson's disease and Alzheimer's disease. In addition, we found that Prdx expression levels are unevenly distributed among neurons of a determined region and that distinct regional patterns of expression are observed between isoforms, reinforcing the hypothesis of the nonredundant function of Prdxs. Copyright © 2011 Wiley-Liss, Inc.

  18. Allee effects on population dynamics in continuous (overlapping) case

    International Nuclear Information System (INIS)

    Merdan, H.; Duman, O.; Akin, O.; Celik, C.

    2009-01-01

    This paper presents the stability analysis of equilibrium points of a continuous population dynamics with delay under the Allee effect which occurs at low population density. The mathematical results and numerical simulations show the stabilizing role of the Allee effects on the stability of the equilibrium point of this population dynamics.

  19. Analysis of Population Dynamics in World Economy

    OpenAIRE

    Martin, Gress

    2011-01-01

    Population dynamics is an important topic in current world economy. The size and growth of population have an impact on economic growth and development of individual countries and vice versa, economic development influences demographic variables in a country. The aim of the article is to analyze historical development of world population, population stock change and relations between population stock change and economic development.

  20. 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.

  1. Rapid evolution leads to differential population dynamics and top-down control in resurrected Daphnia populations.

    Science.gov (United States)

    Goitom, Eyerusalem; Kilsdonk, Laurens J; Brans, Kristien; Jansen, Mieke; Lemmens, Pieter; De Meester, Luc

    2018-01-01

    There is growing evidence of rapid genetic adaptation of natural populations to environmental change, opening the perspective that evolutionary trait change may subsequently impact ecological processes such as population dynamics, community composition, and ecosystem functioning. To study such eco-evolutionary feedbacks in natural populations, however, requires samples across time. Here, we capitalize on a resurrection ecology study that documented rapid and adaptive evolution in a natural population of the water flea Daphnia magna in response to strong changes in predation pressure by fish, and carry out a follow-up mesocosm experiment to test whether the observed genetic changes influence population dynamics and top-down control of phytoplankton. We inoculated populations of the water flea D. magna derived from three time periods of the same natural population known to have genetically adapted to changes in predation pressure in replicate mesocosms and monitored both Daphnia population densities and phytoplankton biomass in the presence and absence of fish. Our results revealed differences in population dynamics and top-down control of algae between mesocosms harboring populations from the time period before, during, and after a peak in fish predation pressure caused by human fish stocking. The differences, however, deviated from our a priori expectations. An S-map approach on time series revealed that the interactions between adults and juveniles strongly impacted the dynamics of populations and their top-down control on algae in the mesocosms, and that the strength of these interactions was modulated by rapid evolution as it occurred in nature. Our study provides an example of an evolutionary response that fundamentally alters the processes structuring population dynamics and impacts ecosystem features.

  2. Amyloid Precursor Proteins Are Dynamically Trafficked and Processed During Neuronal Development

    Directory of Open Access Journals (Sweden)

    Jenna M. Ramaker

    2016-11-01

    Full Text Available Proteolytic processing of the Amyloid Precursor Protein (APP produces beta-amyloid (Aβ peptide fragments that accumulate in Alzheimer’s Disease (AD, but APP may also regulate multiple aspects of neuronal development, albeit via mechanisms that are not well understood. APP is a member of a family of transmembrane glycoproteins expressed by all higher organisms, including two mammalian orthologs (APLP1 and APLP2 that have complicated investigations into the specific activities of APP. By comparison, insects express only a single APP-related protein (APP-Like, or APPL that contains the same protein interaction domains identified in APP. However, unlike its mammalian orthologs, APPL is only expressed by neurons, greatly simplifying an analysis of its functions in vivo. Like APP, APPL is processed by secretases to generate a similar array of extracellular and intracellular cleavage fragments, as well as an Aβ-like fragment that can induce neurotoxic responses in the brain. Exploiting the complementary advantages of two insect models (Drosophila melanogaster and Manduca sexta, we have investigated the regulation of APPL trafficking and processing with respect to different aspects of neuronal development. By comparing the behavior of endogenously expressed APPL with fluorescently tagged versions of APPL and APP, we have shown that some full-length protein is consistently trafficked into the most motile regions of developing neurons both in vitro and in vivo. Concurrently, much of the holoprotein is rapidly processed into N- and C-terminal fragments that undergo bi-directional transport within distinct vesicle populations. Unexpectedly, we also discovered that APPL can be transiently sequestered into an amphisome-like compartment in developing neurons, while manipulations targeting APPL cleavage altered their motile behavior in cultured embryos. These data suggest that multiple mechanisms restrict the bioavailability of the holoprotein to regulate

  3. Transgenic Mouse Lines Subdivide External Segment of the Globus Pallidus (GPe) Neurons and Reveal Distinct GPe Output Pathways

    Science.gov (United States)

    Mastro, Kevin J.; Bouchard, Rachel S.; Holt, Hiromi A. K.

    2014-01-01

    Cell-type diversity in the brain enables the assembly of complex neural circuits, whose organization and patterns of activity give rise to brain function. However, the identification of distinct neuronal populations within a given brain region is often complicated by a lack of objective criteria to distinguish one neuronal population from another. In the external segment of the globus pallidus (GPe), neuronal populations have been defined using molecular, anatomical, and electrophysiological criteria, but these classification schemes are often not generalizable across preparations and lack consistency even within the same preparation. Here, we present a novel use of existing transgenic mouse lines, Lim homeobox 6 (Lhx6)–Cre and parvalbumin (PV)–Cre, to define genetically distinct cell populations in the GPe that differ molecularly, anatomically, and electrophysiologically. Lhx6–GPe neurons, which do not express PV, are concentrated in the medial portion of the GPe. They have lower spontaneous firing rates, narrower dynamic ranges, and make stronger projections to the striatum and substantia nigra pars compacta compared with PV–GPe neurons. In contrast, PV–GPe neurons are more concentrated in the lateral portions of the GPe. They have narrower action potentials, deeper afterhyperpolarizations, and make stronger projections to the subthalamic nucleus and parafascicular nucleus of the thalamus. These electrophysiological and anatomical differences suggest that Lhx6–GPe and PV–GPe neurons participate in different circuits with the potential to contribute to different aspects of motor function and dysfunction in disease. PMID:24501350

  4. Measure of synchrony in the activity of intrinsic cardiac neurons

    International Nuclear Information System (INIS)

    Longpré, Jean-Philippe; Salavatian, Siamak; Jacquemet, Vincent; Beaumont, Eric; Armour, J Andrew; Ardell, Jeffrey L

    2014-01-01

    Recent multielectrode array recordings in ganglionated plexi of canine atria have opened the way to the study of population dynamics of intrinsic cardiac neurons. These data provide critical insights into the role of local processing that these ganglia play in the regulation of cardiac function. Low firing rates, marked non-stationarity, interplay with the cardiovascular and pulmonary systems and artifacts generated by myocardial activity create new constraints not present in brain recordings for which almost all neuronal analysis techniques have been developed. We adapted and extended the jitter-based synchrony index (SI) to (1) provide a robust and computationally efficient tool for assessing the level and statistical significance of SI between cardiac neurons, (2) estimate the bias on SI resulting from neuronal activity possibly hidden in myocardial artifacts, (3) quantify the synchrony or anti-synchrony between neuronal activity and the phase in the cardiac and respiratory cycles. The method was validated on firing time series from a total of 98 individual neurons identified in 8 dog experiments. SI ranged from −0.14 to 0.66, with 23 pairs of neurons with SI > 0.1. The estimated bias due to artifacts was typically <1%. Strongly cardiovascular- and pulmonary-related neurons (SI > 0.5) were found. Results support the use of jitter-based SI in the context of intrinsic cardiac neurons. (paper)

  5. Coding of information about tactile stimuli by neurones of the cuneate nucleus.

    Science.gov (United States)

    Douglas, P R; Ferrington, D G; Rowe, M

    1978-12-01

    1. The responses of cuneate neurones to controlled tactile stimulation of the foot pads were examined in unanaesthetized, decerebrate cats. The neurones were divided into three functional classes; one sensitive to steady tactile stimuli, and two dynamically sensitive classes which could be readily differentiated by their responsiveness to cutaneous vibration. Each class appeared to receive an exclusive input from only one of the three known groups of tactile receptors associated with the foot pads, namely the Pacinian corpuscles, the Merkel endings and the intradermal, encapsulated endings known as Krause or Meissner corpuscles. 2. Cuneate neurones responsive to steady indentation of the skin displayed approximately linear or sigmoidal stimulus-response relations over indentation ranges up to approximately 1.5--2 mm. Response variability at a fixed stimulus intensity was relatively low and showed little systematic change over the full range of the stimulus-response curves. 3. One class of dynamically sensitive cuneate neurones responded to cutaneous vibration over a range of approximately 5-80 Hz with maximal responsiveness around 30 Hz. The other class, the Pacinian neurones, responded over a range of approximately 80- greater than 600 Hz with maximal responsiveness at 200-400 Hz. The thresholds and combined band width of vibratory sensitivity of these populations were comparable with known subjective thresholds and range of cutaneous vibratory sensibility. 4. Responses of cuneate neurones were phase-locked to the vibratory stimulus suggesting that information about vibration frequency could be coded by the patterns of impulse activity. Quantitative measures indicated that maximal phase-locking occurred in responses to vibration frequencies of 10-50 Hz with a progressive decline at higher frequencies. Above 400 Hz, impulse activity occurred almost randomly throughout the vibratory stimulus cycle and therefore carried little further signal of vibratory frequency

  6. Distinct retrosplenial cortex cell populations and their spike dynamics during ketamine-induced unconscious state.

    Directory of Open Access Journals (Sweden)

    Grace E Fox

    Full Text Available Ketamine is known to induce psychotic-like symptoms, including delirium and visual hallucinations. It also causes neuronal damage and cell death in the retrosplenial cortex (RSC, an area that is thought to be a part of high visual cortical pathways and at least partially responsible for ketamine's psychotomimetic activities. However, the basic physiological properties of RSC cells as well as their response to ketamine in vivo remained largely unexplored. Here, we combine a computational method, the Inter-Spike Interval Classification Analysis (ISICA, and in vivo recordings to uncover and profile excitatory cell subtypes within layers 2&3 and 5&6 of the RSC in mice within both conscious, sleep, and ketamine-induced unconscious states. We demonstrate two distinct excitatory principal cell sub-populations, namely, high-bursting excitatory principal cells and low-bursting excitatory principal cells, within layers 2&3, and show that this classification is robust over the conscious states, namely quiet awake, and natural unconscious sleep periods. Similarly, we provide evidence of high-bursting and low-bursting excitatory principal cell sub-populations within layers 5&6 that remained distinct during quiet awake and sleep states. We further examined how these subtypes are dynamically altered by ketamine. During ketamine-induced unconscious state, these distinct excitatory principal cell subtypes in both layer 2&3 and layer 5&6 exhibited distinct dynamics. We also uncovered different dynamics of local field potential under various brain states in layer 2&3 and layer 5&6. Interestingly, ketamine administration induced high gamma oscillations in layer 2&3 of the RSC, but not layer 5&6. Our results show that excitatory principal cells within RSC layers 2&3 and 5&6 contain multiple physiologically distinct sub-populations, and they are differentially affected by ketamine.

  7. Neuronal hyperexcitability in the ventral posterior thalamus of neuropathic rats: modality selective effects of pregabalin.

    Science.gov (United States)

    Patel, Ryan; Dickenson, Anthony H

    2016-07-01

    Neuropathic pain represents a substantial clinical challenge; understanding the underlying neural mechanisms and back-translation of therapeutics could aid targeting of treatments more effectively. The ventral posterior thalamus (VP) is the major termination site for the spinothalamic tract and relays nociceptive activity to the somatosensory cortex; however, under neuropathic conditions, it is unclear how hyperexcitability of spinal neurons converges onto thalamic relays. This study aimed to identify neural substrates of hypersensitivity and the influence of pregabalin on central processing. In vivo electrophysiology was performed to record from VP wide dynamic range (WDR) and nociceptive-specific (NS) neurons in anesthetized spinal nerve-ligated (SNL), sham-operated, and naive rats. In neuropathic rats, WDR neurons had elevated evoked responses to low- and high-intensity punctate mechanical stimuli, dynamic brushing, and innocuous and noxious cooling, but less so to heat stimulation, of the receptive field. NS neurons in SNL rats also displayed increased responses to noxious punctate mechanical stimulation, dynamic brushing, noxious cooling, and noxious heat. Additionally, WDR, but not NS, neurons in SNL rats exhibited substantially higher rates of spontaneous firing, which may correlate with ongoing pain. The ratio of WDR-to-NS neurons was comparable between SNL and naive/sham groups, suggesting relatively few NS neurons gain sensitivity to low-intensity stimuli leading to a "WDR phenotype." After neuropathy was induced, the proportion of cold-sensitive WDR and NS neurons increased, supporting the suggestion that changes in frequency-dependent firing and population coding underlie cold hypersensitivity. In SNL rats, pregabalin inhibited mechanical and heat responses but not cold-evoked or elevated spontaneous activity. Copyright © 2016 the American Physiological Society.

  8. Markers of pathological excitability derived from principal dynamic modes of hippocampal neurons

    Science.gov (United States)

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

    2012-10-01

    Transformation of principal dynamic modes (PDMs) under epileptogenic conditions was investigated by computing the Volterra kernels in a rodent epilepsy model derived from a mouse whole hippocampal preparation, where epileptogenesis was induced by altering the concentrations of Mg2 + and K+ of the perfusate for different levels of excitability. Both integrating and differentiating PDMs were present in the neuronal dynamics, and both of them increased in absolute magnitude for increased excitability levels. However, the integrating PDMs dominated at all levels of excitability in terms of their relative contributions to the overall response, whereas the dominant frequency responses of the differentiating PDMs were shifted to higher ranges under epileptogenic conditions, from ripple activities (75-200 Hz) to fast ripple activities (200-500 Hz).

  9. Stage-Structured Population Dynamics of AEDES AEGYPTI

    Science.gov (United States)

    Yusoff, Nuraini; Budin, Harun; Ismail, Salemah

    Aedes aegypti is the main vector in the transmission of dengue fever, a vector-borne disease affecting world population living in tropical and sub-tropical countries. Better understanding of the dynamics of its population growth will help in the efforts of controlling the spread of this disease. In looking at the population dynamics of Aedes aegypti, this paper explored the stage-structured modeling of the population growth of the mosquito using the matrix population model. The life cycle of the mosquito was divided into five stages: eggs, larvae, pupae, adult1 and adult2. Developmental rates were obtained for the average Malaysian temperature and these were used in constructing the transition matrix for the matrix model. The model, which was based only on temperature, projected that the population of Aedes aegypti will blow up with time, which is not realistic. For further work, other factors need to be taken into account to obtain a more realistic result.

  10. Cochlear nucleus neuron analysis in individuals with presbycusis.

    Science.gov (United States)

    Hinojosa, Raul; Nelson, Erik G

    2011-12-01

    The aim of this study was to analyze the cochlear nucleus neuron population in individuals with normal hearing and presbycusis. Retrospective study of archival human temporal bone and brain stem tissues. Using strict inclusion criteria, the temporal bones and cochlear nuclei from six normal hearing individuals and four individuals with presbycusis were selected for analysis. The spiral ganglion cell population, the cochlear nucleus neuron population, and the cell body size of the neurons were quantified in these cases. A relationship was not observed between age and the spiral ganglion cell population in the normal hearing group. Presbycusis subjects exhibited a reduced spiral ganglion cell population. The mean cochlear nucleus neuron population was observed to be significantly higher in the presbycusis group (mean ± standard deviation: 114,170 ± 10,570) compared to the normal hearing group (91,470 ± 9,510) (P = .019). This difference was predominantly the result of greater multipolar and granule cell neuron populations. Only the fusiform neuron type exhibited a significantly different mean cell body cross-sectional area between the normal hearing group (242 ± 27) and the presbycusis group (300 ± 37) (P = .033). This investigation is the first time, to our knowledge, that the populations of the eight neuron types in the cochlear nucleus have been quantified in both normal hearing individuals and individuals with presbycusis. The data support the concept that presbycusis is not an effect of aging alone but instead may be a condition that predisposes one to hearing loss with advancing age and is characterized by a congenitally elevated cochlear nucleus neuron population. Copyright © 2011 The American Laryngological, Rhinological, and Otological Society, Inc.

  11. Perturbation analysis of transient population dynamics using matrix projection models

    DEFF Research Database (Denmark)

    Stott, Iain

    2016-01-01

    Non-stable populations exhibit short-term transient dynamics: size, growth and structure that are unlike predicted long-term asymptotic stable, stationary or equilibrium dynamics. Understanding transient dynamics of non-stable populations is important for designing effective population management...... these methods to know exactly what is being measured. Despite a wealth of existing methods, I identify some areas that would benefit from further development....

  12. Stochastic population dynamics of a montane ground-dwelling squirrel.

    Science.gov (United States)

    Hostetler, Jeffrey A; Kneip, Eva; Van Vuren, Dirk H; Oli, Madan K

    2012-01-01

    Understanding the causes and consequences of population fluctuations is a central goal of ecology. We used demographic data from a long-term (1990-2008) study and matrix population models to investigate factors and processes influencing the dynamics and persistence of a golden-mantled ground squirrel (Callospermophilus lateralis) population, inhabiting a dynamic subalpine habitat in Colorado, USA. The overall deterministic population growth rate λ was 0.94±SE 0.05 but it varied widely over time, ranging from 0.45±0.09 in 2006 to 1.50±0.12 in 2003, and was below replacement (λbounce back from low densities and prevented extinction. These results suggest that dynamics and persistence of our study population are determined synergistically by density-dependence, stochastic forces, and immigration.

  13. Kisspeptins modulate the biology of multiple populations of gonadotropin-releasing hormone neurons during embryogenesis and adulthood in zebrafish (Danio rerio).

    Science.gov (United States)

    Zhao, Yali; Lin, Meng-Chin A; Mock, Allan; Yang, Ming; Wayne, Nancy L

    2014-01-01

    Kisspeptin1 (product of the Kiss1 gene) is the key neuropeptide that gates puberty and maintains fertility by regulating the gonadotropin-releasing hormone (GnRH) neuronal system in mammals. Inactivating mutations in Kiss1 and the kisspeptin receptor (GPR54/Kiss1r) are associated with pubertal failure and infertility. Kiss2, a paralogous gene for kiss1, has been recently identified in several vertebrates including zebrafish. Using our transgenic zebrafish model system in which the GnRH3 promoter drives expression of emerald green fluorescent protein, we investigated the effects of kisspeptins on development of the GnRH neuronal system during embryogenesis and on electrical activity during adulthood. Quantitative PCR showed detectable levels of kiss1 and kiss2 mRNA by 1 day post fertilization, increasing throughout embryonic and larval development. Early treatment with Kiss1 or Kiss2 showed that both kisspeptins stimulated proliferation of trigeminal GnRH3 neurons located in the peripheral nervous system. However, only Kiss1, but not Kiss2, stimulated proliferation of terminal nerve and hypothalamic populations of GnRH3 neurons in the central nervous system. Immunohistochemical analysis of synaptic vesicle protein 2 suggested that Kiss1, but not Kiss2, increased synaptic contacts on the cell body and along the terminal nerve-GnRH3 neuronal processes during embryogenesis. In intact brain of adult zebrafish, whole-cell patch clamp recordings of GnRH3 neurons from the preoptic area and hypothalamus revealed opposite effects of Kiss1 and Kiss2 on spontaneous action potential firing frequency and membrane potential. Kiss1 increased spike frequency and depolarized membrane potential, whereas Kiss2 suppressed spike frequency and hyperpolarized membrane potential. We conclude that in zebrafish, Kiss1 is the primary stimulator of GnRH3 neuronal development in the embryo and an activator of stimulating hypophysiotropic neuron activities in the adult, while Kiss2 plays an

  14. The influence of single neuron dynamics and network topology on time delay-induced multiple synchronous behaviors in inhibitory coupled network

    International Nuclear Information System (INIS)

    Zhao, Zhiguo; Gu, Huaguang

    2015-01-01

    Highlights: • Time delay-induced multiple synchronous behaviors was simulated in neuronal networks. • Multiple behaviors appear at time delays shorter than a bursting period of neurons. • The more spikes per burst of bursting, the more synchronous regions of time delay. • From regular to random via small-world networks, synchronous degree becomes weak. • An interpretation of the multiple behaviors and the influence of network are provided. - Abstract: Time delay induced-multiple synchronous behaviors are simulated in neuronal network composed of many inhibitory neurons and appear at different time delays shorter than a period of endogenous bursting of individual neurons. It is different from previous investigations wherein only one of multiple synchronous behaviors appears at time delay shorter than a period of endogenous firing and others appear at time delay longer than the period duration. The bursting patterns of the synchronous behaviors are identified based on the dynamics of an individual neuron stimulated by a signal similar to the inhibitory coupling current, which is applied at the decaying branch of a spike and suitable phase within the quiescent state of the endogenous bursting. If a burst of endogenous bursting contains more spikes, the synchronous behaviors appear at more regions of time delay. As the coupling strength increases, the multiple synchronous behaviors appear in a sequence because the different threshold of coupling current or strength is needed to achieve synchronous behaviors. From regular, to small-world, and to random networks, synchronous degree of the multiple synchronous behaviors becomes weak, and synchronous bursting patterns with lower spikes per burst disappear, which is properly interpreted by the difference of coupling current between neurons induced by different degree and the high threshold of coupling current to achieve synchronization for the absent synchronous bursting patterns. The results of the influence of

  15. Central-marginal population dynamics in species invasions

    Directory of Open Access Journals (Sweden)

    Qinfeng eGuo

    2014-06-01

    Full Text Available The species’ range limits and associated central-marginal (C-M; i.e., from species range center to margin population dynamics continue to draw increasing attention because of their importance for current emerging issues such as biotic invasions and epidemic diseases under global change. Previous studies have mainly focused on species borders and C-M process in natural settings for native species. More recently, growing efforts are devoted to examine the C-M patterns and process for invasive species partly due to their relatively short history, highly dynamic populations, and management implications. Here I examine recent findings and information gaps related to (1 the C-M population dynamics linked to species invasions, and (2 the possible effects of climate change and land use on the C-M patterns and processes. Unlike most native species that are relatively stable (some even having contracting populations or ranges, many invasive species are still spreading fast and form new distribution or abundance centers. Because of the strong nonlinearity of population demographic or vital rates (i.e. birth, death, immigration and emigration across the C-M gradients and the increased complexity of species ranges due to habitat fragmentation, multiple introductions, range-wide C-M comparisons and simulation involving multiple vital rates are needed in the future.

  16. Steady-state dynamics and experience-dependent plasticity of dendritic spines of layer 4/5a pyramidal neurons in somatosensory cortex

    Directory of Open Access Journals (Sweden)

    Amaya Miquelajauregui

    2014-04-01

    Full Text Available The steady state dynamics and experience-dependent plasticity of dendritic spines of layer (L 2/3 and L5B cortical pyramidal neurons have recently been assessed using in vivo two-photon microscopy (Trachtenberg et al., 2002; Zuo et al., 2005; Holtmaat et al., 2006. In contrast, not much is known about spine dynamics in L4/5a neurons, regarded as direct recipients of thalamocortical input (Constantinople and Bruno, 2013. In the adult mouse somatosensory cortex (SCx, the transcription factor Ebf2 is enriched in excitatory neurons of L4/5a, including pyramidal neurons. We assessed the molecular and electrophysiological properties of these neurons as well as the morphology of their apical tufts (Scholl analysis and cortical outputs (optogenetics within the SCx. To test the hypothesis that L4/5a pyramidal neurons play an important role in sensory processing (given their key laminar position; soma depth ~450-480 µm, we successfully labeled them in Ebf2-Cre mice with EGFP by expressing recombinant rAAV vectors in utero. Using longitudinal in vivo two-photon microscopy through a craniotomy (Mostany and Portera-Cailliau, 2008, we repeatedly imaged spines in apical dendritic tufts of L4/5a neurons under basal conditions and after sensory deprivation. Under steady-state conditions in adults, the morphology of the apical tufts and the mean spine density were stable at 0.39 ± 0.05 spines/μm (comparable to L5B, Mostany et al., 2011. Interestingly, spine elimination increases 4-8 days after sensory deprivation, probably due to input loss. This suggests that Ebf2+ L4/5a neurons could be involved in early steps of processing of thalamocortical information.

  17. Versatile Networks of Simulated Spiking Neurons Displaying Winner-Take-All Behavior

    Directory of Open Access Journals (Sweden)

    Yanqing eChen

    2013-03-01

    Full Text Available We describe simulations of large-scale networks of excitatory and inhibitory spiking neurons that can generate dynamically stable winner-take-all (WTA behavior. The network connectivity is a variant of center-surround architecture that we call center-annular-surround (CAS. In this architecture each neuron is excited by nearby neighbors and inhibited by more distant neighbors in an annular-surround region. The neural units of these networks simulate conductance-based spiking neurons that interact via mechanisms susceptible to both short-term synaptic plasticity and STDP. We show that such CAS networks display robust WTA behavior unlike the center-surround networks and other control architectures that we have studied. We find that a large-scale network of spiking neurons with separate populations of excitatory and inhibitory neurons can give rise to smooth maps of sensory input. In addition, we show that a humanoid Brain-Based-Device (BBD under the control of a spiking WTA neural network can learn to reach to target positions in its visual field, thus demonstrating the acquisition of sensorimotor coordination.

  18. Versatile networks of simulated spiking neurons displaying winner-take-all behavior.

    Science.gov (United States)

    Chen, Yanqing; McKinstry, Jeffrey L; Edelman, Gerald M

    2013-01-01

    We describe simulations of large-scale networks of excitatory and inhibitory spiking neurons that can generate dynamically stable winner-take-all (WTA) behavior. The network connectivity is a variant of center-surround architecture that we call center-annular-surround (CAS). In this architecture each neuron is excited by nearby neighbors and inhibited by more distant neighbors in an annular-surround region. The neural units of these networks simulate conductance-based spiking neurons that interact via mechanisms susceptible to both short-term synaptic plasticity and STDP. We show that such CAS networks display robust WTA behavior unlike the center-surround networks and other control architectures that we have studied. We find that a large-scale network of spiking neurons with separate populations of excitatory and inhibitory neurons can give rise to smooth maps of sensory input. In addition, we show that a humanoid brain-based-device (BBD) under the control of a spiking WTA neural network can learn to reach to target positions in its visual field, thus demonstrating the acquisition of sensorimotor coordination.

  19. Targeting Neurotrophins to Specific Populations of Neurons: NGF, BDNF, and NT-3 and Their Relevance for Treatment of Spinal Cord Injury

    Science.gov (United States)

    Keefe, Kathleen M.; Sheikh, Imran S.; Smith, George M.

    2017-01-01

    Neurotrophins are a family of proteins that regulate neuronal survival, synaptic function, and neurotransmitter release, and elicit the plasticity and growth of axons within the adult central and peripheral nervous system. Since the 1950s, these factors have been extensively studied in traumatic injury models. Here we review several members of the classical family of neurotrophins, the receptors they bind to, and their contribution to axonal regeneration and sprouting of sensory and motor pathways after spinal cord injury (SCI). We focus on nerve growth factor (NGF), brain derived neurotrophic factor (BDNF), and neurotrophin-3 (NT-3), and their effects on populations of neurons within diverse spinal tracts. Understanding the cellular targets of neurotrophins and the responsiveness of specific neuronal populations will allow for the most efficient treatment strategies in the injured spinal cord. PMID:28273811

  20. Targeting Neurotrophins to Specific Populations of Neurons: NGF, BDNF, and NT-3 and Their Relevance for Treatment of Spinal Cord Injury.

    Science.gov (United States)

    Keefe, Kathleen M; Sheikh, Imran S; Smith, George M

    2017-03-03

    Neurotrophins are a family of proteins that regulate neuronal survival, synaptic function, and neurotransmitter release, and elicit the plasticity and growth of axons within the adult central and peripheral nervous system. Since the 1950s, these factors have been extensively studied in traumatic injury models. Here we review several members of the classical family of neurotrophins, the receptors they bind to, and their contribution to axonal regeneration and sprouting of sensory and motor pathways after spinal cord injury (SCI). We focus on nerve growth factor (NGF), brain derived neurotrophic factor (BDNF), and neurotrophin-3 (NT-3), and their effects on populations of neurons within diverse spinal tracts. Understanding the cellular targets of neurotrophins and the responsiveness of specific neuronal populations will allow for the most efficient treatment strategies in the injured spinal cord.

  1. Volumetric Two-photon Imaging of Neurons Using Stereoscopy (vTwINS)

    Science.gov (United States)

    Song, Alexander; Charles, Adam S.; Koay, Sue Ann; Gauthier, Jeff L.; Thiberge, Stephan Y.; Pillow, Jonathan W.; Tank, David W.

    2017-01-01

    Two-photon laser scanning microscopy of calcium dynamics using fluorescent indicators is a widely used imaging method for large scale recording of neural activity in vivo. Here we introduce volumetric Two-photon Imaging of Neurons using Stereoscopy (vTwINS), a volumetric calcium imaging method that employs an elongated, V-shaped point spread function to image a 3D brain volume. Single neurons project to spatially displaced “image pairs” in the resulting 2D image, and the separation distance between images is proportional to depth in the volume. To demix the fluorescence time series of individual neurons, we introduce a novel orthogonal matching pursuit algorithm that also infers source locations within the 3D volume. We illustrate vTwINS by imaging neural population activity in mouse primary visual cortex and hippocampus. Our results demonstrate that vTwINS provides an effective method for volumetric two-photon calcium imaging that increases the number of neurons recorded while maintaining a high frame-rate. PMID:28319111

  2. Preprotachykinin A is expressed by a distinct population of excitatory neurons in the mouse superficial spinal dorsal horn including cells that respond to noxious and pruritic stimuli.

    Science.gov (United States)

    Gutierrez-Mecinas, Maria; Bell, Andrew M; Marin, Alina; Taylor, Rebecca; Boyle, Kieran A; Furuta, Takahiro; Watanabe, Masahiko; Polgár, Erika; Todd, Andrew J

    2017-03-01

    The superficial dorsal horn, which is the main target for nociceptive and pruritoceptive primary afferents, contains a high density of excitatory interneurons. Our understanding of their roles in somatosensory processing has been restricted by the difficulty of distinguishing functional populations among these cells. We recently defined 3 nonoverlapping populations among the excitatory neurons, based on the expression of neurotensin, neurokinin B, and gastrin-releasing peptide. Here we identify and characterise another population: neurons that express the tachykinin peptide substance P. We show with immunocytochemistry that its precursor protein (preprotachykinin A, PPTA) can be detected in ∼14% of lamina I-II neurons, and these are concentrated in the outer part of lamina II. Over 80% of the PPTA-positive cells lack the transcription factor Pax2 (which determines an inhibitory phenotype), and these account for ∼15% of the excitatory neurons in this region. They are different from the neurotensin, neurokinin B, or gastrin-releasing peptide neurons, although many of them contain somatostatin, which is widely expressed among superficial dorsal horn excitatory interneurons. We show that many of these cells respond to noxious thermal and mechanical stimuli and to intradermal injection of pruritogens. Finally, we demonstrate that these cells can also be identified in a knock-in Cre mouse line (Tac1), although our findings suggest that there is an additional population of neurons that transiently express PPTA. This population of substance P-expressing excitatory neurons is likely to play an important role in the transmission of signals that are perceived as pain and itch.

  3. Nonlinear Relaxation in Population Dynamics

    Science.gov (United States)

    Cirone, Markus A.; de Pasquale, Ferdinando; Spagnolo, Bernardo

    We analyze the nonlinear relaxation of a complex ecosystem composed of many interacting species. The ecological system is described by generalized Lotka-Volterra equations with a multiplicative noise. The transient dynamics is studied in the framework of the mean field theory and with random interaction between the species. We focus on the statistical properties of the asymptotic behaviour of the time integral of the ith population and on the distribution of the population and of the local field.

  4. Nonlinear development of the populations of neurons expressing c-Fos under sustained electrical intracochlear stimulation in the rat auditory brainstem.

    Science.gov (United States)

    Rosskothen-Kuhl, Nicole; Illing, Robert-Benjamin

    2010-08-06

    The immediate-early-gene c-fos is among the first genes to be expressed following sensory-invoked neuronal activity. Its gene product c-Fos forms the limiting monomer of the heterodimeric activator protein-1 transcription factor that triggers various genes involved in neuroplastic remodeling. This study investigated the pattern of c-Fos expression in anteroventral (AVCN) and dorsal cochlear nucleus (DCN) and central inferior colliculus (CIC) after 45 min, 73 min, 2 h, 3:15 h and 5 h of unilateral electrical intracochlear stimulation (EIS) at 50 Hz in anaesthetized rats. Following EIS, tonotopic c-Fos expression was observed for each stimulation time in ipsilateral AVCN, DCN bilaterally, and contralateral CIC. By counting c-Fos positive nuclei, we discovered temporal non-linearities in the size of the respective population of c-Fos expressing neurons. In all regions investigated, the populations significantly increased from 73 min to 2 h but decreased towards 3:15 h. In AVCN, the number rose again by 5 h of EIS. Remarkably, the same was noted for neurons with large nuclei in deep DCN. In both regions, the population of responsive neurons shifted spatially: In central AVCN, the density of c-Fos positive cells increased significantly from 2 to 5h with medial and lateral regions remaining unchanged. In DCN, the density of large c-Fos positive nuclei fell in the upper and rose in the deep layers from 45 min to 5h of EIS. In conclusion, spatiotemporally varying recruitments of neuronal subpopulations into cellular networks responding to specific patterns of sensory activity take place in the auditory brainstem. Copyright 2010 Elsevier B.V. All rights reserved.

  5. Timing control by redundant inhibitory neuronal circuits

    International Nuclear Information System (INIS)

    Tristan, I.; Rulkov, N. F.; Huerta, R.; Rabinovich, M.

    2014-01-01

    Rhythms and timing control of sequential activity in the brain is fundamental to cognition and behavior. Although experimental and theoretical studies support the understanding that neuronal circuits are intrinsically capable of generating different time intervals, the dynamical origin of the phenomenon of functionally dependent timing control is still unclear. Here, we consider a new mechanism that is related to the multi-neuronal cooperative dynamics in inhibitory brain motifs consisting of a few clusters. It is shown that redundancy and diversity of neurons within each cluster enhances the sensitivity of the timing control with the level of neuronal excitation of the whole network. The generality of the mechanism is shown to work on two different neuronal models: a conductance-based model and a map-based model

  6. Timing control by redundant inhibitory neuronal circuits

    Energy Technology Data Exchange (ETDEWEB)

    Tristan, I., E-mail: itristan@ucsd.edu; Rulkov, N. F.; Huerta, R.; Rabinovich, M. [BioCircuits Institute, University of California, San Diego, La Jolla, California 92093-0402 (United States)

    2014-03-15

    Rhythms and timing control of sequential activity in the brain is fundamental to cognition and behavior. Although experimental and theoretical studies support the understanding that neuronal circuits are intrinsically capable of generating different time intervals, the dynamical origin of the phenomenon of functionally dependent timing control is still unclear. Here, we consider a new mechanism that is related to the multi-neuronal cooperative dynamics in inhibitory brain motifs consisting of a few clusters. It is shown that redundancy and diversity of neurons within each cluster enhances the sensitivity of the timing control with the level of neuronal excitation of the whole network. The generality of the mechanism is shown to work on two different neuronal models: a conductance-based model and a map-based model.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

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

    2016-01-01

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

  9. Subthreshold membrane potential oscillations in inferior olive neurons are dynamically regulated by P/Q- and T-type calcium channels: a study in mutant mice.

    Science.gov (United States)

    Choi, Soonwook; Yu, Eunah; Kim, Daesoo; Urbano, Francisco J; Makarenko, Vladimir; Shin, Hee-Sup; Llinás, Rodolfo R

    2010-08-15

    The role of P/Q- and T-type calcium channels in the rhythmic oscillatory behaviour of inferior olive (IO) neurons was investigated in mutant mice. Mice lacking either the CaV2.1 gene of the pore-forming alpha1A subunit for P/Q-type calcium channel, or the CaV3.1 gene of the pore-forming alpha1G subunit for T-type calcium channel were used. In vitro intracellular recording from IO neurons reveals that the amplitude and frequency of sinusoidal subthreshold oscillations (SSTOs) were reduced in the CaV2.1-/- mice. In the CaV3.1-/- mice, IO neurons also showed altered patterns of SSTOs and the probability of SSTO generation was significantly lower (15%, 5 of 34 neurons) than that of wild-type (78%, 31 of 40 neurons) or CaV2.1-/- mice (73%, 22 of 30 neurons). In addition, the low-threshold calcium spike and the sustained endogenous oscillation following rebound potentials were absent in IO neurons from CaV3.1-/- mice. Moreover, the phase-reset dynamics of oscillatory properties of single neurons and neuronal clusters in IO were remarkably altered in both CaV2.1-/- and CaV3.1-/- mice. These results suggest that both alpha1A P/Q- and alpha1G T-type calcium channels are required for the dynamic control of neuronal oscillations in the IO. These findings were supported by results from a mathematical IO neuronal model that incorporated T and P/Q channel kinetics.

  10. Voltage-Dependent Rhythmogenic Property of Respiratory Pre-Bötzinger Complex Glutamatergic, Dbx1-Derived, and Somatostatin-Expressing Neuron Populations Revealed by Graded Optogenetic Inhibition123

    Science.gov (United States)

    Koizumi, Hidehiko; Mosher, Bryan; Tariq, Mohammad F.; Zhang, Ruli

    2016-01-01

    Abstract The rhythm of breathing in mammals, originating within the brainstem pre-Bötzinger complex (pre-BötC), is presumed to be generated by glutamatergic neurons, but this has not been directly demonstrated. Additionally, developmental expression of the transcription factor Dbx1 or expression of the neuropeptide somatostatin (Sst), has been proposed as a marker for the rhythmogenic pre-BötC glutamatergic neurons, but it is unknown whether these other two phenotypically defined neuronal populations are functionally equivalent to glutamatergic neurons with regard to rhythm generation. To address these problems, we comparatively investigated, by optogenetic approaches, the roles of pre-BötC glutamatergic, Dbx1-derived, and Sst-expressing neurons in respiratory rhythm generation in neonatal transgenic mouse medullary slices in vitro and also more intact adult perfused brainstem-spinal cord preparations in situ. We established three different triple-transgenic mouse lines with Cre-driven Archaerhodopsin-3 (Arch) expression selectively in glutamatergic, Dbx1-derived, or Sst-expressing neurons for targeted photoinhibition. In each line, we identified subpopulations of rhythmically active, Arch-expressing pre-BötC inspiratory neurons by whole-cell recordings in medullary slice preparations in vitro, and established that Arch-mediated hyperpolarization of these inspiratory neurons was laser power dependent with equal efficacy. By site- and population-specific graded photoinhibition, we then demonstrated that inspiratory frequency was reduced by each population with the same neuronal voltage-dependent frequency control mechanism in each state of the respiratory network examined. We infer that enough of the rhythmogenic pre-BötC glutamatergic neurons also have the Dbx1 and Sst expression phenotypes, and thus all three phenotypes share the same voltage-dependent frequency control property. PMID:27275007

  11. Dynamic Regulation of Delta-Opioid Receptor in Rat Trigeminal Ganglion Neurons by Lipopolysaccharide-induced Acute Pulpitis.

    Science.gov (United States)

    Huang, Jin; Lv, Yiheng; Fu, Yunjie; Ren, Lili; Wang, Pan; Liu, Baozhu; Huang, Keqiang; Bi, Jing

    2015-12-01

    Delta-opioid receptor (DOR) and its endogenous ligands distribute in trigeminal system and play a very important role in modulating peripheral inflammatory pain. DOR activation can trigger p44/42 mitogen-activated protein kinase (ERK1/2) and Akt signaling pathways, which participate in anti-inflammatory and neuroprotective effects. In this study, our purpose was to determine the dynamic changes of DOR in trigeminal ganglion (TG) neurons during the process of acute dental pulp inflammation and elucidate its possible mechanism. Forty rats were used to generate lipopolysaccharide-induced acute pulpitis animal models at 6, 12, and 24 hours and sham-operated groups. Acute pulpitis was confirmed by hematoxylin-eosin staining, and TG neuron activation was determined by anti-c-Fos immunohistochemistry. DOR protein and gene expression in TG was investigated by immunohistochemistry, Western blotting, and real-time polymerase chain reaction, and DOR expression in trigeminal nerves and dental pulp was also determined by immunohistochemistry. To further investigate the mechanism of DOR modulating acute inflammation, the change of pErk1/2 and pAkt in TG was examined by immunohistochemistry. Lipopolysaccharide could successfully induce acute pulpitis and activated TG neurons. Acute pulpitis could dynamically increase DOR protein and gene expression at 6, 12, and 24 hours in TG, and DOR dimerization was significantly increased at 12 and 24 hours. Acute pulpitis also induced the dynamic change of DOR protein in trigeminal nerve and dental pulp. Furthermore, ERK1/2 and Akt signaling pathways were inhibited in TG after acute pulpitis. Increased DOR expression and dimerization may play important roles in peripheral acute inflammatory pain. Copyright © 2015 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

    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

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

    Directory of Open Access Journals (Sweden)

    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.

  14. Population and evolutionary dynamics in spatially structured seasonally varying environments.

    Science.gov (United States)

    Reid, Jane M; Travis, Justin M J; Daunt, Francis; Burthe, Sarah J; Wanless, Sarah; Dytham, Calvin

    2018-03-25

    Increasingly imperative objectives in ecology are to understand and forecast population dynamic and evolutionary responses to seasonal environmental variation and change. Such population and evolutionary dynamics result from immediate and lagged responses of all key life-history traits, and resulting demographic rates that affect population growth rate, to seasonal environmental conditions and population density. However, existing population dynamic and eco-evolutionary theory and models have not yet fully encompassed within-individual and among-individual variation, covariation, structure and heterogeneity, and ongoing evolution, in a critical life-history trait that allows individuals to respond to seasonal environmental conditions: seasonal migration. Meanwhile, empirical studies aided by new animal-tracking technologies are increasingly demonstrating substantial within-population variation in the occurrence and form of migration versus year-round residence, generating diverse forms of 'partial migration' spanning diverse species, habitats and spatial scales. Such partially migratory systems form a continuum between the extreme scenarios of full migration and full year-round residence, and are commonplace in nature. Here, we first review basic scenarios of partial migration and associated models designed to identify conditions that facilitate the maintenance of migratory polymorphism. We highlight that such models have been fundamental to the development of partial migration theory, but are spatially and demographically simplistic compared to the rich bodies of population dynamic theory and models that consider spatially structured populations with dispersal but no migration, or consider populations experiencing strong seasonality and full obligate migration. Second, to provide an overarching conceptual framework for spatio-temporal population dynamics, we define a 'partially migratory meta-population' system as a spatially structured set of locations that can

  15. Targeting Neurotrophins to Specific Populations of Neurons: NGF, BDNF, and NT-3 and Their Relevance for Treatment of Spinal Cord Injury

    Directory of Open Access Journals (Sweden)

    Kathleen M. Keefe

    2017-03-01

    Full Text Available Neurotrophins are a family of proteins that regulate neuronal survival, synaptic function, and neurotransmitter release, and elicit the plasticity and growth of axons within the adult central and peripheral nervous system. Since the 1950s, these factors have been extensively studied in traumatic injury models. Here we review several members of the classical family of neurotrophins, the receptors they bind to, and their contribution to axonal regeneration and sprouting of sensory and motor pathways after spinal cord injury (SCI. We focus on nerve growth factor (NGF, brain derived neurotrophic factor (BDNF, and neurotrophin-3 (NT-3, and their effects on populations of neurons within diverse spinal tracts. Understanding the cellular targets of neurotrophins and the responsiveness of specific neuronal populations will allow for the most efficient treatment strategies in the injured spinal cord.

  16. Near-field electromagnetic holography for high-resolution analysis of network interactions in neuronal tissue.

    Science.gov (United States)

    Kjeldsen, Henrik D; Kaiser, Marcus; Whittington, Miles A

    2015-09-30

    Brain function is dependent upon the concerted, dynamical interactions between a great many neurons distributed over many cortical subregions. Current methods of quantifying such interactions are limited by consideration only of single direct or indirect measures of a subsample of all neuronal population activity. Here we present a new derivation of the electromagnetic analogy to near-field acoustic holography allowing high-resolution, vectored estimates of interactions between sources of electromagnetic activity that significantly improves this situation. In vitro voltage potential recordings were used to estimate pseudo-electromagnetic energy flow vector fields, current and energy source densities and energy dissipation in reconstruction planes at depth into the neural tissue parallel to the recording plane of the microelectrode array. The properties of the reconstructed near-field estimate allowed both the utilization of super-resolution techniques to increase the imaging resolution beyond that of the microelectrode array, and facilitated a novel approach to estimating causal relationships between activity in neocortical subregions. The holographic nature of the reconstruction method allowed significantly better estimation of the fine spatiotemporal detail of neuronal population activity, compared with interpolation alone, beyond the spatial resolution of the electrode arrays used. Pseudo-energy flow vector mapping was possible with high temporal precision, allowing a near-realtime estimate of causal interaction dynamics. Basic near-field electromagnetic holography provides a powerful means to increase spatial resolution from electrode array data with careful choice of spatial filters and distance to reconstruction plane. More detailed approaches may provide the ability to volumetrically reconstruct activity patterns on neuronal tissue, but the ability to extract vectored data with the method presented already permits the study of dynamic causal interactions

  17. Temporally coordinated spiking activity of human induced pluripotent stem cell-derived neurons co-cultured with astrocytes.

    Science.gov (United States)

    Kayama, Tasuku; Suzuki, Ikuro; Odawara, Aoi; Sasaki, Takuya; Ikegaya, Yuji

    2018-01-01

    In culture conditions, human induced-pluripotent stem cells (hiPSC)-derived neurons form synaptic connections with other cells and establish neuronal networks, which are expected to be an in vitro model system for drug discovery screening and toxicity testing. While early studies demonstrated effects of co-culture of hiPSC-derived neurons with astroglial cells on survival and maturation of hiPSC-derived neurons, the population spiking patterns of such hiPSC-derived neurons have not been fully characterized. In this study, we analyzed temporal spiking patterns of hiPSC-derived neurons recorded by a multi-electrode array system. We discovered that specific sets of hiPSC-derived neurons co-cultured with astrocytes showed more frequent and highly coherent non-random synchronized spike trains and more dynamic changes in overall spike patterns over time. These temporally coordinated spiking patterns are physiological signs of organized circuits of hiPSC-derived neurons and suggest benefits of co-culture of hiPSC-derived neurons with astrocytes. Copyright © 2017 Elsevier Inc. All rights reserved.

  18. Cutaneous TRPM8-expressing sensory afferents are a small population of neurons with unique firing properties.

    Science.gov (United States)

    Jankowski, Michael P; Rau, Kristofer K; Koerber, H Richard

    2017-04-01

    It has been well documented that the transient receptor potential melastatin 8 (TRPM8) receptor is involved in environmental cold detection. The role that this receptor plays in nociception however, has been somewhat controversial since conflicting reports have shown different neurochemical identities and responsiveness of TRPM8 neurons. In order to functionally characterize cutaneous TRMP8 fibers, we used two ex vivo somatosensory recording preparations to functionally characterize TRPM8 neurons that innervate the hairy skin in mice genetically engineered to express GFP from the TRPM8 locus. We found several types of cold-sensitive neurons that innervate the hairy skin of the mouse but the TRPM8-expressing neurons were found to be of two specific populations that responded with rapid firing to cool temperatures. The first group was mechanically insensitive but the other did respond to high threshold mechanical deformation of the skin. None of these fibers were found to contain calcitonin gene-related peptide, transient receptor potential vanilloid type 1 or bind isolectin B4. These results taken together with other reports suggest that TRPM8 containing sensory neurons are environmental cooling detectors that may be nociceptive or non-nociceptive depending on the sensitivity of individual fibers to different combinations of stimulus modalities. © 2017 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society.

  19. Effects of an invasive plant on population dynamics in toads.

    Science.gov (United States)

    Greenberg, Daniel A; Green, David M

    2013-10-01

    When populations decline in response to unfavorable environmental change, the dynamics of their population growth shift. In populations that normally exhibit high levels of variation in recruitment and abundance, as do many amphibians, declines may be difficult to identify from natural fluctuations in abundance. However, the onset of declines may be evident from changes in population growth rate in sufficiently long time series of population data. With data from 23 years of study of a population of Fowler's toad (Anaxyrus [ = Bufo] fowleri) at Long Point, Ontario (1989-2011), we sought to identify such a shift in dynamics. We tested for trends in abundance to detect a change point in population dynamics and then tested among competing population models to identify associated intrinsic and extrinsic factors. The most informative models of population growth included terms for toad abundance and the extent of an invasive marsh plant, the common reed (Phragmites australis), throughout the toads' marshland breeding areas. Our results showed density-dependent growth in the toad population from 1989 through 2002. After 2002, however, we found progressive population decline in the toads associated with the spread of common reeds and consequent loss of toad breeding habitat. This resulted in reduced recruitment and population growth despite the lack of significant loss of adult habitat. Our results underscore the value of using long-term time series to identify shifts in population dynamics coincident with the advent of population decline. © 2013 Society for Conservation Biology.

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

    International Nuclear Information System (INIS)

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

    2016-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Qin, Qing; Wang, Jiang; Yu, Haitao; Deng, Bin, E-mail: dengbin@tju.edu.cn; Chan, Wai-lok [School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China)

    2016-06-15

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

  2. Acquisition, extinction, and recall of opiate reward memory are signaled by dynamic neuronal activity patterns in the prefrontal cortex.

    Science.gov (United States)

    Sun, Ninglei; Chi, Ning; Lauzon, Nicole; Bishop, Stephanie; Tan, Huibing; Laviolette, Steven R

    2011-12-01

    The medial prefrontal cortex (mPFC) comprises an important component in the neural circuitry underlying drug-related associative learning and memory processing. Neuronal activation within mPFC circuits is correlated with the recall of opiate-related drug-taking experiences in both humans and other animals. Using an unbiased associative place conditioning procedure, we recorded mPFC neuronal populations during the acquisition, recall, and extinction phases of morphine-related associative learning and memory. Our analyses revealed that mPFC neurons show increased activity both in terms of tonic and phasic activity patterns during the acquisition phase of opiate reward-related memory and demonstrate stimulus-locked associative activity changes in real time, during the recall of opiate reward memories. Interestingly, mPFC neuronal populations demonstrated divergent patterns of bursting activity during the acquisition versus recall phases of newly acquired opiate reward memory, versus the extinction of these memories, with strongly increased bursting during the recall of an extinction memory and no associative bursting during the recall of a newly acquired opiate reward memory. Our results demonstrate that neurons within the mPFC are involved in both the acquisition, recall, and extinction of opiate-related reward memories, showing unique patterns of tonic and phasic activity patterns during these separate components of the opiate-related reward learning and memory recall.

  3. Kisspeptins modulate the biology of multiple populations of gonadotropin-releasing hormone neurons during embryogenesis and adulthood in zebrafish (Danio rerio.

    Directory of Open Access Journals (Sweden)

    Yali Zhao

    Full Text Available Kisspeptin1 (product of the Kiss1 gene is the key neuropeptide that gates puberty and maintains fertility by regulating the gonadotropin-releasing hormone (GnRH neuronal system in mammals. Inactivating mutations in Kiss1 and the kisspeptin receptor (GPR54/Kiss1r are associated with pubertal failure and infertility. Kiss2, a paralogous gene for kiss1, has been recently identified in several vertebrates including zebrafish. Using our transgenic zebrafish model system in which the GnRH3 promoter drives expression of emerald green fluorescent protein, we investigated the effects of kisspeptins on development of the GnRH neuronal system during embryogenesis and on electrical activity during adulthood. Quantitative PCR showed detectable levels of kiss1 and kiss2 mRNA by 1 day post fertilization, increasing throughout embryonic and larval development. Early treatment with Kiss1 or Kiss2 showed that both kisspeptins stimulated proliferation of trigeminal GnRH3 neurons located in the peripheral nervous system. However, only Kiss1, but not Kiss2, stimulated proliferation of terminal nerve and hypothalamic populations of GnRH3 neurons in the central nervous system. Immunohistochemical analysis of synaptic vesicle protein 2 suggested that Kiss1, but not Kiss2, increased synaptic contacts on the cell body and along the terminal nerve-GnRH3 neuronal processes during embryogenesis. In intact brain of adult zebrafish, whole-cell patch clamp recordings of GnRH3 neurons from the preoptic area and hypothalamus revealed opposite effects of Kiss1 and Kiss2 on spontaneous action potential firing frequency and membrane potential. Kiss1 increased spike frequency and depolarized membrane potential, whereas Kiss2 suppressed spike frequency and hyperpolarized membrane potential. We conclude that in zebrafish, Kiss1 is the primary stimulator of GnRH3 neuronal development in the embryo and an activator of stimulating hypophysiotropic neuron activities in the adult, while

  4. Dynamical community structure of populations evolving on genotype networks

    International Nuclear Information System (INIS)

    Capitán, José A.; Aguirre, Jacobo; Manrubia, Susanna

    2015-01-01

    Neutral evolutionary dynamics of replicators occurs on large and heterogeneous networks of genotypes. These networks, formed by all genotypes that yield the same phenotype, have a complex architecture that conditions the molecular composition of populations and their movements on genome spaces. Here we consider as an example the case of populations evolving on RNA secondary structure neutral networks and study the community structure of the network revealed through dynamical properties of the population at equilibrium and during adaptive transients. We unveil a rich hierarchical community structure that, eventually, can be traced back to the non-trivial relationship between RNA secondary structure and sequence composition. We demonstrate that usual measures of modularity that only take into account the static, topological structure of networks, cannot identify the community structure disclosed by population dynamics

  5. Evolutionary Dynamics and Diversity in Microbial Populations

    Science.gov (United States)

    Thompson, Joel; Fisher, Daniel

    2013-03-01

    Diseases such as flu and cancer adapt at an astonishing rate. In large part, viruses and cancers are so difficult to prevent because they are continually evolving. Controlling such ``evolutionary diseases'' requires a better understanding of the underlying evolutionary dynamics. It is conventionally assumed that adaptive mutations are rare and therefore will occur and sweep through the population in succession. Recent experiments using modern sequencing technologies have illuminated the many ways in which real population sequence data does not conform to the predictions of conventional theory. We consider a very simple model of asexual evolution and perform simulations in a range of parameters thought to be relevant for microbes and cancer. Simulation results reveal complex evolutionary dynamics typified by competition between lineages with different sets of adaptive mutations. This dynamical process leads to a distribution of mutant gene frequencies different than expected under the conventional assumption that adaptive mutations are rare. Simulated gene frequencies share several conspicuous features with data collected from laboratory-evolved yeast and the worldwide population of influenza.

  6. Dynamics of a physiologically structured population in a time-varying environment

    DEFF Research Database (Denmark)

    Heilmann, Irene Louise Torpe; Starke, Jens; Andersen, Ken Haste

    2016-01-01

    Physiologically structured population models have become a valuable tool to model the dynamics of populations. In a stationary environment such models can exhibit equilibrium solutions as well as periodic solutions. However, for many organisms the environment is not stationary, but varies more...... or less regularly. In order to understand the interaction between an external environmental forcing and the internal dynamics in a population, we examine the response of a physiologically structured population model to a periodic variation in the food resource. We explore the addition of forcing in two...... cases: (A) where the population dynamics is in equilibrium in a stationary environment, and (B) where the population dynamics exhibits a periodic solution in a stationary environment. When forcing is applied in case A, the solutions are mainly periodic. In case B the forcing signal interacts...

  7. Complex dynamics of a delayed discrete neural network of two nonidentical neurons.

    Science.gov (United States)

    Chen, Yuanlong; Huang, Tingwen; Huang, Yu

    2014-03-01

    In this paper, we discover that a delayed discrete Hopfield neural network of two nonidentical neurons with self-connections and no self-connections can demonstrate chaotic behaviors. To this end, we first transform the model, by a novel way, into an equivalent system which has some interesting properties. Then, we identify the chaotic invariant set for this system and show that the dynamics of this system within this set is topologically conjugate to the dynamics of the full shift map with two symbols. This confirms chaos in the sense of Devaney. Our main results generalize the relevant results of Huang and Zou [J. Nonlinear Sci. 15, 291-303 (2005)], Kaslik and Balint [J. Nonlinear Sci. 18, 415-432 (2008)] and Chen et al. [Sci. China Math. 56(9), 1869-1878 (2013)]. We also give some numeric simulations to verify our theoretical results.

  8. Population dynamics of minimally cognitive individuals. Part 2: Dynamics of time-dependent knowledge

    Energy Technology Data Exchange (ETDEWEB)

    Schmieder, R.W.

    1995-07-01

    The dynamical principle for a population of interacting individuals with mutual pairwise knowledge, presented by the author in a previous paper for the case of constant knowledge, is extended to include the possibility that the knowledge is time-dependent. Several mechanisms are presented by which the mutual knowledge, represented by a matrix K, can be altered, leading to dynamical equations for K(t). The author presents various examples of the transient and long time asymptotic behavior of K(t) for populations of relatively isolated individuals interacting infrequently in local binary collisions. Among the effects observed in the numerical experiments are knowledge diffusion, learning transients, and fluctuating equilibria. This approach will be most appropriate to small populations of complex individuals such as simple animals, robots, computer networks, agent-mediated traffic, simple ecosystems, and games. Evidence of metastable states and intermittent switching leads them to envision a spectroscopy associated with such transitions that is independent of the specific physical individuals and the population. Such spectra may serve as good lumped descriptors of the collective emergent behavior of large classes of populations in which mutual knowledge is an important part of the dynamics.

  9. BlastNeuron for Automated Comparison, Retrieval and Clustering of 3D Neuron Morphologies.

    Science.gov (United States)

    Wan, Yinan; Long, Fuhui; Qu, Lei; Xiao, Hang; Hawrylycz, Michael; Myers, Eugene W; Peng, Hanchuan

    2015-10-01

    Characterizing the identity and types of neurons in the brain, as well as their associated function, requires a means of quantifying and comparing 3D neuron morphology. Presently, neuron comparison methods are based on statistics from neuronal morphology such as size and number of branches, which are not fully suitable for detecting local similarities and differences in the detailed structure. We developed BlastNeuron to compare neurons in terms of their global appearance, detailed arborization patterns, and topological similarity. BlastNeuron first compares and clusters 3D neuron reconstructions based on global morphology features and moment invariants, independent of their orientations, sizes, level of reconstruction and other variations. Subsequently, BlastNeuron performs local alignment between any pair of retrieved neurons via a tree-topology driven dynamic programming method. A 3D correspondence map can thus be generated at the resolution of single reconstruction nodes. We applied BlastNeuron to three datasets: (1) 10,000+ neuron reconstructions from a public morphology database, (2) 681 newly and manually reconstructed neurons, and (3) neurons reconstructions produced using several independent reconstruction methods. Our approach was able to accurately and efficiently retrieve morphologically and functionally similar neuron structures from large morphology database, identify the local common structures, and find clusters of neurons that share similarities in both morphology and molecular profiles.

  10. An individual-based model of Zebrafish population dynamics accounting for energy dynamics

    DEFF Research Database (Denmark)

    Beaudouin, Remy; Goussen, Benoit; Piccini, Benjamin

    2015-01-01

    Developing population dynamics models for zebrafish is crucial in order to extrapolate from toxicity data measured at the organism level to biological levels relevant to support and enhance ecological risk assessment. To achieve this, a dynamic energy budget for individual zebrafish (DEB model...

  11. Population dynamics of active and total ciliate populations in arable soil amended with wheat

    DEFF Research Database (Denmark)

    Ekelund, F.; Frederiksen, Helle B.; Ronn, R.

    2002-01-01

    of the population may be encysted. The factors governing the dynamics of active and encysted cells in the soil are not well understood. Our objective was to determine the dynamics of active and encysted populations of ciliates during the decomposition of freshly added organic material. We monitored, in soil...... microcosms, the active and total populations of ciliates, their potential prey (bacteria and small protozoa), their potential competitors (amoebae, flagellates, and nematodes), and their potential predators (nematodes). We sampled with short time intervals (2 to 6 days) and generated a data set, suitable...

  12. Hungry Neurons: Metabolic Insights on Seizure Dynamics

    Directory of Open Access Journals (Sweden)

    Paolo Bazzigaluppi

    2017-10-01

    Full Text Available Epilepsy afflicts up to 1.6% of the population and the mechanisms underlying the appearance of seizures are still not understood. In past years, many efforts have been spent trying to understand the mechanisms underlying the excessive and synchronous firing of neurons. Traditionally, attention was pointed towards synaptic (dysfunction and extracellular ionic species (dysregulation. Recently, novel clinical and preclinical studies explored the role of brain metabolism (i.e., glucose utilization of seizures pathophysiology revealing (in most cases reduced metabolism in the inter-ictal period and increased metabolism in the seconds preceding and during the appearance of seizures. In the present review, we summarize the clinical and preclinical observations showing metabolic dysregulation during epileptogenesis, seizure initiation, and termination, and in the inter-ictal period. Recent preclinical studies have shown that 2-Deoxyglucose (2-DG, a glycolysis blocker is a novel therapeutic approach to reduce seizures. Furthermore, we present initial evidence for the effectiveness of 2-DG in arresting 4-Aminopyridine induced neocortical seizures in vivo in the mouse.

  13. Tracking the Fear Memory Engram: Discrete Populations of Neurons within Amygdala, Hypothalamus, and Lateral Septum Are Specifically Activated by Auditory Fear Conditioning

    Science.gov (United States)

    Butler, Christopher W.; Wilson, Yvette M.; Gunnersen, Jenny M.; Murphy, Mark

    2015-01-01

    Memory formation is thought to occur via enhanced synaptic connectivity between populations of neurons in the brain. However, it has been difficult to localize and identify the neurons that are directly involved in the formation of any specific memory. We have previously used "fos-tau-lacZ" ("FTL") transgenic mice to identify…

  14. Estimating Traveler Populations at Airport and Cruise Terminals for Population Distribution and Dynamics

    Energy Technology Data Exchange (ETDEWEB)

    Jochem, Warren C [ORNL; Sims, Kelly M [ORNL; Bright, Eddie A [ORNL; Urban, Marie L [ORNL; Rose, Amy N [ORNL; Coleman, Phil R [ORNL; Bhaduri, Budhendra L [ORNL

    2013-01-01

    In recent years, uses of high-resolution population distribution databases are increasing steadily for environmental, socioeconomic, public health, and disaster-related research and operations. With the development of daytime population distribution, temporal resolution of such databases has been improved. However, the lack of incorporation of transitional population, namely business and leisure travelers, leaves a significant population unaccounted for within the critical infrastructure networks, such as at transportation hubs. This paper presents two general methodologies for estimating passenger populations in airport and cruise port terminals at a high temporal resolution which can be incorporated into existing population distribution models. The methodologies are geographically scalable and are based on, and demonstrate how, two different transportation hubs with disparate temporal population dynamics can be modeled utilizing publicly available databases including novel data sources of flight activity from the Internet which are updated in near-real time. The airport population estimation model shows great potential for rapid implementation for a large collection of airports on a national scale, and the results suggest reasonable accuracy in the estimated passenger traffic. By incorporating population dynamics at high temporal resolutions into population distribution models, we hope to improve the estimates of populations exposed to or at risk to disasters, thereby improving emergency planning and response, and leading to more informed policy decisions.

  15. Two-photon imaging and analysis of neural network dynamics

    International Nuclear Information System (INIS)

    Luetcke, Henry; Helmchen, Fritjof

    2011-01-01

    The glow of a starry night sky, the smell of a freshly brewed cup of coffee or the sound of ocean waves breaking on the beach are representations of the physical world that have been created by the dynamic interactions of thousands of neurons in our brains. How the brain mediates perceptions, creates thoughts, stores memories and initiates actions remains one of the most profound puzzles in biology, if not all of science. A key to a mechanistic understanding of how the nervous system works is the ability to measure and analyze the dynamics of neuronal networks in the living organism in the context of sensory stimulation and behavior. Dynamic brain properties have been fairly well characterized on the microscopic level of individual neurons and on the macroscopic level of whole brain areas largely with the help of various electrophysiological techniques. However, our understanding of the mesoscopic level comprising local populations of hundreds to thousands of neurons (so-called 'microcircuits') remains comparably poor. Predominantly, this has been due to the technical difficulties involved in recording from large networks of neurons with single-cell spatial resolution and near-millisecond temporal resolution in the brain of living animals. In recent years, two-photon microscopy has emerged as a technique which meets many of these requirements and thus has become the method of choice for the interrogation of local neural circuits. Here, we review the state-of-research in the field of two-photon imaging of neuronal populations, covering the topics of microscope technology, suitable fluorescent indicator dyes, staining techniques, and in particular analysis techniques for extracting relevant information from the fluorescence data. We expect that functional analysis of neural networks using two-photon imaging will help to decipher fundamental operational principles of neural microcircuits.

  16. Two-photon imaging and analysis of neural network dynamics

    Science.gov (United States)

    Lütcke, Henry; Helmchen, Fritjof

    2011-08-01

    The glow of a starry night sky, the smell of a freshly brewed cup of coffee or the sound of ocean waves breaking on the beach are representations of the physical world that have been created by the dynamic interactions of thousands of neurons in our brains. How the brain mediates perceptions, creates thoughts, stores memories and initiates actions remains one of the most profound puzzles in biology, if not all of science. A key to a mechanistic understanding of how the nervous system works is the ability to measure and analyze the dynamics of neuronal networks in the living organism in the context of sensory stimulation and behavior. Dynamic brain properties have been fairly well characterized on the microscopic level of individual neurons and on the macroscopic level of whole brain areas largely with the help of various electrophysiological techniques. However, our understanding of the mesoscopic level comprising local populations of hundreds to thousands of neurons (so-called 'microcircuits') remains comparably poor. Predominantly, this has been due to the technical difficulties involved in recording from large networks of neurons with single-cell spatial resolution and near-millisecond temporal resolution in the brain of living animals. In recent years, two-photon microscopy has emerged as a technique which meets many of these requirements and thus has become the method of choice for the interrogation of local neural circuits. Here, we review the state-of-research in the field of two-photon imaging of neuronal populations, covering the topics of microscope technology, suitable fluorescent indicator dyes, staining techniques, and in particular analysis techniques for extracting relevant information from the fluorescence data. We expect that functional analysis of neural networks using two-photon imaging will help to decipher fundamental operational principles of neural microcircuits.

  17. Two-photon imaging and analysis of neural network dynamics

    Energy Technology Data Exchange (ETDEWEB)

    Luetcke, Henry; Helmchen, Fritjof [Brain Research Institute, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich (Switzerland)

    2011-08-15

    The glow of a starry night sky, the smell of a freshly brewed cup of coffee or the sound of ocean waves breaking on the beach are representations of the physical world that have been created by the dynamic interactions of thousands of neurons in our brains. How the brain mediates perceptions, creates thoughts, stores memories and initiates actions remains one of the most profound puzzles in biology, if not all of science. A key to a mechanistic understanding of how the nervous system works is the ability to measure and analyze the dynamics of neuronal networks in the living organism in the context of sensory stimulation and behavior. Dynamic brain properties have been fairly well characterized on the microscopic level of individual neurons and on the macroscopic level of whole brain areas largely with the help of various electrophysiological techniques. However, our understanding of the mesoscopic level comprising local populations of hundreds to thousands of neurons (so-called 'microcircuits') remains comparably poor. Predominantly, this has been due to the technical difficulties involved in recording from large networks of neurons with single-cell spatial resolution and near-millisecond temporal resolution in the brain of living animals. In recent years, two-photon microscopy has emerged as a technique which meets many of these requirements and thus has become the method of choice for the interrogation of local neural circuits. Here, we review the state-of-research in the field of two-photon imaging of neuronal populations, covering the topics of microscope technology, suitable fluorescent indicator dyes, staining techniques, and in particular analysis techniques for extracting relevant information from the fluorescence data. We expect that functional analysis of neural networks using two-photon imaging will help to decipher fundamental operational principles of neural microcircuits.

  18. DYNAMICS OF Cercospora zeina POPULATIONS IN MAIZE-BASED ...

    African Journals Online (AJOL)

    ACSS

    DYNAMICS OFCercospora zeina POPULATIONS IN MAIZE-BASED AGRO- ..... Population differentiation of Cercospora zeina in three districts of Uganda based on analysis of molecular variance ..... interactions: The example of the Erysiphe.

  19. Peripheral chemoreceptors tune inspiratory drive via tonic expiratory neuron hubs in the medullary ventral respiratory column network.

    Science.gov (United States)

    Segers, L S; Nuding, S C; Ott, M M; Dean, J B; Bolser, D C; O'Connor, R; Morris, K F; Lindsey, B G

    2015-01-01

    Models of brain stem ventral respiratory column (VRC) circuits typically emphasize populations of neurons, each active during a particular phase of the respiratory cycle. We have proposed that "tonic" pericolumnar expiratory (t-E) neurons tune breathing during baroreceptor-evoked reductions and central chemoreceptor-evoked enhancements of inspiratory (I) drive. The aims of this study were to further characterize the coordinated activity of t-E neurons and test the hypothesis that peripheral chemoreceptors also modulate drive via inhibition of t-E neurons and disinhibition of their inspiratory neuron targets. Spike trains of 828 VRC neurons were acquired by multielectrode arrays along with phrenic nerve signals from 22 decerebrate, vagotomized, neuromuscularly blocked, artificially ventilated adult cats. Forty-eight of 191 t-E neurons fired synchronously with another t-E neuron as indicated by cross-correlogram central peaks; 32 of the 39 synchronous pairs were elements of groups with mutual pairwise correlations. Gravitational clustering identified fluctuations in t-E neuron synchrony. A network model supported the prediction that inhibitory populations with spike synchrony reduce target neuron firing probabilities, resulting in offset or central correlogram troughs. In five animals, stimulation of carotid chemoreceptors evoked changes in the firing rates of 179 of 240 neurons. Thirty-two neuron pairs had correlogram troughs consistent with convergent and divergent t-E inhibition of I cells and disinhibitory enhancement of drive. Four of 10 t-E neurons that responded to sequential stimulation of peripheral and central chemoreceptors triggered 25 cross-correlograms with offset features. The results support the hypothesis that multiple afferent systems dynamically tune inspiratory drive in part via coordinated t-E neurons. Copyright © 2015 the American Physiological Society.

  20. Descending Command Neurons in the Brainstem that Halt Locomotion

    DEFF Research Database (Denmark)

    Bouvier, Julien; Caggiano, Vittorio; Leiras, Roberto

    2015-01-01

    identifiable brainstem populations to a potential locomotor stop signal, we used developmental genetics and considered a discrete neuronal population in the reticular formation: the V2a neurons. We find that those neurons constitute a major excitatory pathway to locomotor areas of the ventral spinal cord....... Selective activation of V2a neurons of the rostral medulla stops ongoing locomotor activity, owing to an inhibition of premotor locomotor networks in the spinal cord. Moreover, inactivation of such neurons decreases spontaneous stopping in vivo. Therefore, the V2a "stop neurons" represent a glutamatergic...

  1. Efficient transmission of subthreshold signals in complex networks of spiking neurons.

    Science.gov (United States)

    Torres, Joaquin J; Elices, Irene; Marro, J

    2015-01-01

    We investigate the efficient transmission and processing of weak, subthreshold signals in a realistic neural medium in the presence of different levels of the underlying noise. Assuming Hebbian weights for maximal synaptic conductances--that naturally balances the network with excitatory and inhibitory synapses--and considering short-term synaptic plasticity affecting such conductances, we found different dynamic phases in the system. This includes a memory phase where population of neurons remain synchronized, an oscillatory phase where transitions between different synchronized populations of neurons appears and an asynchronous or noisy phase. When a weak stimulus input is applied to each neuron, increasing the level of noise in the medium we found an efficient transmission of such stimuli around the transition and critical points separating different phases for well-defined different levels of stochasticity in the system. We proved that this intriguing phenomenon is quite robust, as it occurs in different situations including several types of synaptic plasticity, different type and number of stored patterns and diverse network topologies, namely, diluted networks and complex topologies such as scale-free and small-world networks. We conclude that the robustness of the phenomenon in different realistic scenarios, including spiking neurons, short-term synaptic plasticity and complex networks topologies, make very likely that it could also occur in actual neural systems as recent psycho-physical experiments suggest.

  2. Efficient transmission of subthreshold signals in complex networks of spiking neurons.

    Directory of Open Access Journals (Sweden)

    Joaquin J Torres

    Full Text Available We investigate the efficient transmission and processing of weak, subthreshold signals in a realistic neural medium in the presence of different levels of the underlying noise. Assuming Hebbian weights for maximal synaptic conductances--that naturally balances the network with excitatory and inhibitory synapses--and considering short-term synaptic plasticity affecting such conductances, we found different dynamic phases in the system. This includes a memory phase where population of neurons remain synchronized, an oscillatory phase where transitions between different synchronized populations of neurons appears and an asynchronous or noisy phase. When a weak stimulus input is applied to each neuron, increasing the level of noise in the medium we found an efficient transmission of such stimuli around the transition and critical points separating different phases for well-defined different levels of stochasticity in the system. We proved that this intriguing phenomenon is quite robust, as it occurs in different situations including several types of synaptic plasticity, different type and number of stored patterns and diverse network topologies, namely, diluted networks and complex topologies such as scale-free and small-world networks. We conclude that the robustness of the phenomenon in different realistic scenarios, including spiking neurons, short-term synaptic plasticity and complex networks topologies, make very likely that it could also occur in actual neural systems as recent psycho-physical experiments suggest.

  3. Gene Expression and the Diversity of Identified Neurons

    OpenAIRE

    Buck, L.; Stein, R.; Palazzolo, M.; Anderson, D. J.; Axel, R.

    1983-01-01

    Nervous systems consist of diverse populations of neurons that are anatomically and functionally distinct. The diversity of neurons and the precision with which they are interconnected suggest that specific genes or sets of genes are activated in some neurons but not expressed in others. Experimentally, this problem may be considered at two levels. First, what is the total number of genes expressed in the brain, and how are they distributed among the different populations of neurons? Second, ...

  4. A possible role of the non-GAT1 GABA transporters in transfer of GABA from GABAergic to glutamatergic neurons in mouse cerebellar neuronal cultures

    DEFF Research Database (Denmark)

    Suñol, C; Babot, Z; Cristòfol, R

    2010-01-01

    Cultures of dissociated cerebellum from 7-day-old mice were used to investigate the mechanism involved in synthesis and cellular redistribution of GABA in these cultures consisting primarily of glutamatergic granule neurons and a smaller population of GABAergic Golgi and stellate neurons......3 transporters. Only a small population of cells were immuno-stained for GAD while many cells exhibited VGlut-1 like immuno-reactivity which, however, never co-localized with GAD positive neurons. This likely reflects the small number of GABAergic neurons compared to the glutamatergic granule......M concentrations (95%). Essentially all neurons showed GABA like immunostaining albeit with differences in intensity. The results indicate that GABA which is synthesized in a small population of GAD-positive neurons is redistributed to essentially all neurons including the glutamatergic granule cells. GAT1...

  5. The Neuronal Transition Probability (NTP) Model for the Dynamic Progression of Non-REM Sleep EEG: The Role of the Suprachiasmatic Nucleus

    CERN Document Server

    Merica, H

    2011-01-01

    Little attention has gone into linking to its neuronal substrates the dynamic structure of non-rapid-eye-movement (NREM) sleep, defined as the pattern of time-course power in all frequency bands across an entire episode. Using the spectral power time-courses in the sleep electroencephalogram (EEG), we showed in the typical first episode, several moves towards-and-away from deep sleep, each having an identical pattern linking the major frequency bands beta, sigma and delta. The neuronal transition probability model (NTP) - in fitting the data well - successfully explained the pattern as resulting from stochastic transitions of the firing-rates of the thalamically-projecting brainstem-activating neurons, alternating between two steady dynamic-states (towards-and-away from deep sleep) each initiated by a so-far unidentified flip-flop. The aims here are to identify this flip-flop and to demonstrate that the model fits well all NREM episodes, not just the first. Using published data on suprachiasmatic nucleus (SCN...

  6. Distinct Temporal Coordination of Spontaneous Population Activity between Basal Forebrain and Auditory Cortex

    Directory of Open Access Journals (Sweden)

    Josue G. Yague

    2017-09-01

    Full Text Available The basal forebrain (BF has long been implicated in attention, learning and memory, and recent studies have established a causal relationship between artificial BF activation and arousal. However, neural ensemble dynamics in the BF still remains unclear. Here, recording neural population activity in the BF and comparing it with simultaneously recorded cortical population under both anesthetized and unanesthetized conditions, we investigate the difference in the structure of spontaneous population activity between the BF and the auditory cortex (AC in mice. The AC neuronal population show a skewed spike rate distribution, a higher proportion of short (≤80 ms inter-spike intervals (ISIs and a rich repertoire of rhythmic firing across frequencies. Although the distribution of spontaneous firing rate in the BF is also skewed, a proportion of short ISIs can be explained by a Poisson model at short time scales (≤20 ms and spike count correlations are lower compared to AC cells, with optogenetically identified cholinergic cell pairs showing exceptionally higher correlations. Furthermore, a smaller fraction of BF neurons shows spike-field entrainment across frequencies: a subset of BF neurons fire rhythmically at slow (≤6 Hz frequencies, with varied phase preferences to ongoing field potentials, in contrast to a consistent phase preference of AC populations. Firing of these slow rhythmic BF cells is correlated to a greater degree than other rhythmic BF cell pairs. Overall, the fundamental difference in the structure of population activity between the AC and BF is their temporal coordination, in particular their operational timescales. These results suggest that BF neurons slowly modulate downstream populations whereas cortical circuits transmit signals on multiple timescales. Thus, the characterization of the neural ensemble dynamics in the BF provides further insight into the neural mechanisms, by which brain states are regulated.

  7. POPULATION DYNAMICS OF PSEUDO-NITZSCHIA SPECIES ...

    African Journals Online (AJOL)

    nb

    current study aimed at assessing the population dynamics of Pseudo-nitzschia ... and to the developing aquaculture industry ... B. Hotel. Pangani Island. Bongoyo Island. Mbudya Island. Msasani Bay ... Salinity values did not show clear trends.

  8. A phase plane analysis of neuron-astrocyte interactions.

    Science.gov (United States)

    Amiri, Mahmood; Montaseri, Ghazal; Bahrami, Fariba

    2013-08-01

    Intensive experimental studies have shown that astrocytes are active partners in modulation of synaptic transmission. In the present research, we study neuron-astrocyte signaling using a biologically inspired model of one neuron synapsing one astrocyte. In this model, the firing dynamics of the neuron is described by the Morris-Lecar model and the Ca(2+) dynamics of a single astrocyte explained by a functional model introduced by Postnov and colleagues. Using the coupled neuron-astrocyte model and based on the results of the phase plane analyses, it is demonstrated that the astrocyte is able to activate the silent neuron or change the neuron spiking frequency through bidirectional communication. This suggests that astrocyte feedback signaling is capable of modulating spike transmission frequency by changing neuron spiking frequency. This effect is described by a saddle-node on invariant circle bifurcation in the coupled neuron-astrocyte model. In this way, our results suggest that the neuron-astrocyte crosstalk has a fundamental role in producing diverse neuronal activities and therefore enhances the information processing capabilities of the brain. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

  9. Unidirectional synchronization of Hodgkin-Huxley neurons

    Energy Technology Data Exchange (ETDEWEB)

    Cornejo-Perez, Octavio [Division de Matematicas Aplicadas y Sistemas, Computacionales, IPICYT, Apdo. Postal 3-74 Tangamanga, 78231 San Luis Potosi (Mexico)]. E-mail: octavio@ipicyt.edu.mx; Femat, Ricardo [Division de Matematicas Aplicadas y Sistemas, Computacionales, IPICYT, Apdo. Postal 3-74 Tangamanga, 78231 San Luis Potosi (Mexico)]. E-mail: rfemat@ipicyt.edu.mx

    2005-07-01

    Synchronization dynamics of two noiseless Hodgkin-Huxley (HH) neurons under the action of feedback control is studied. The spiking patterns of the action potentials evoked by periodic external modulations attain synchronization states under the feedback action. Numerical simulations for the synchronization dynamics of regular-irregular desynchronized spiking sequences are displayed. The results are discussed in context of generalized synchronization. It is also shown that the HH neurons can be synchronized in face of unmeasured states.

  10. The finite state projection approach to analyze dynamics of heterogeneous populations

    Science.gov (United States)

    Johnson, Rob; Munsky, Brian

    2017-06-01

    Population modeling aims to capture and predict the dynamics of cell populations in constant or fluctuating environments. At the elementary level, population growth proceeds through sequential divisions of individual cells. Due to stochastic effects, populations of cells are inherently heterogeneous in phenotype, and some phenotypic variables have an effect on division or survival rates, as can be seen in partial drug resistance. Therefore, when modeling population dynamics where the control of growth and division is phenotype dependent, the corresponding model must take account of the underlying cellular heterogeneity. The finite state projection (FSP) approach has often been used to analyze the statistics of independent cells. Here, we extend the FSP analysis to explore the coupling of cell dynamics and biomolecule dynamics within a population. This extension allows a general framework with which to model the state occupations of a heterogeneous, isogenic population of dividing and expiring cells. The method is demonstrated with a simple model of cell-cycle progression, which we use to explore possible dynamics of drug resistance phenotypes in dividing cells. We use this method to show how stochastic single-cell behaviors affect population level efficacy of drug treatments, and we illustrate how slight modifications to treatment regimens may have dramatic effects on drug efficacy.

  11. Critical dynamics in population vaccinating behavior.

    Science.gov (United States)

    Pananos, A Demetri; Bury, Thomas M; Wang, Clara; Schonfeld, Justin; Mohanty, Sharada P; Nyhan, Brendan; Salathé, Marcel; Bauch, Chris T

    2017-12-26

    Vaccine refusal can lead to renewed outbreaks of previously eliminated diseases and even delay global eradication. Vaccinating decisions exemplify a complex, coupled system where vaccinating behavior and disease dynamics influence one another. Such systems often exhibit critical phenomena-special dynamics close to a tipping point leading to a new dynamical regime. For instance, critical slowing down (declining rate of recovery from small perturbations) may emerge as a tipping point is approached. Here, we collected and geocoded tweets about measles-mumps-rubella vaccine and classified their sentiment using machine-learning algorithms. We also extracted data on measles-related Google searches. We find critical slowing down in the data at the level of California and the United States in the years before and after the 2014-2015 Disneyland, California measles outbreak. Critical slowing down starts growing appreciably several years before the Disneyland outbreak as vaccine uptake declines and the population approaches the tipping point. However, due to the adaptive nature of coupled behavior-disease systems, the population responds to the outbreak by moving away from the tipping point, causing "critical speeding up" whereby resilience to perturbations increases. A mathematical model of measles transmission and vaccine sentiment predicts the same qualitative patterns in the neighborhood of a tipping point to greatly reduced vaccine uptake and large epidemics. These results support the hypothesis that population vaccinating behavior near the disease elimination threshold is a critical phenomenon. Developing new analytical tools to detect these patterns in digital social data might help us identify populations at heightened risk of widespread vaccine refusal. Copyright © 2017 the Author(s). Published by PNAS.

  12. Short-term memory in olfactory network dynamics

    Science.gov (United States)

    Stopfer, Mark; Laurent, Gilles

    1999-12-01

    Neural assemblies in a number of animal species display self-organized, synchronized oscillations in response to sensory stimuli in a variety of brain areas.. In the olfactory system of insects, odour-evoked oscillatory synchronization of antennal lobe projection neurons (PNs) is superimposed on slower and stimulus-specific temporal activity patterns. Hence, each odour activates a specific and dynamic projection neuron assembly whose evolution during a stimulus is locked to the oscillation clock. Here we examine, using locusts, the changes in population dynamics of projection-neuron assemblies over repeated odour stimulations, as would occur when an animal first encounters and then repeatedly samples an odour for identification or localization. We find that the responses of these assemblies rapidly decrease in intensity, while they show a marked increase in spike time precision and inter-neuronal oscillatory coherence. Once established, this enhanced precision in the representation endures for several minutes. This change is stimulus-specific, and depends on events within the antennal lobe circuits, independent of olfactory receptor adaptation: it may thus constitute a form of sensory memory. Our results suggest that this progressive change in olfactory network dynamics serves to converge, over repeated odour samplings, on a more precise and readily classifiable odour representation, using relational information contained across neural assemblies.

  13. A SAGE-based screen for genes expressed in sub-populations of neurons in the mouse dorsal root ganglion

    Directory of Open Access Journals (Sweden)

    Garces Alain

    2007-11-01

    Full Text Available Abstract Background The different sensory modalities temperature, pain, touch and muscle proprioception are carried by somatosensory neurons of the dorsal root ganglia. Study of this system is hampered by the lack of molecular markers for many of these neuronal sub-types. In order to detect genes expressed in sub-populations of somatosensory neurons, gene profiling was carried out on wild-type and TrkA mutant neonatal dorsal root ganglia (DRG using SAGE (serial analysis of gene expression methodology. Thermo-nociceptors constitute up to 80 % of the neurons in the DRG. In TrkA mutant DRGs, the nociceptor sub-class of sensory neurons is lost due to absence of nerve growth factor survival signaling through its receptor TrkA. Thus, comparison of wild-type and TrkA mutants allows the identification of transcripts preferentially expressed in the nociceptor or mechano-proprioceptor subclasses, respectively. Results Our comparison revealed 240 genes differentially expressed between the two tissues (P Conclusion We have identified and characterized the detailed expression patterns of three genes in the developing DRG, placing them in the context of the known major neuronal sub-types defined by molecular markers. Further analysis of differentially expressed genes in this tissue promises to extend our knowledge of the molecular diversity of different cell types and forms the basis for understanding their particular functional specificities.

  14. Complex dynamics of a delayed discrete neural network of two nonidentical neurons

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Yuanlong [Mathematics Department, GuangDong University of Finance, Guangzhou 510521 (China); Huang, Tingwen [Mathematics Department, Texas A and M University at Qatar, P. O. Box 23874, Doha (Qatar); Huang, Yu, E-mail: stshyu@mail.sysu.edu.cn [Mathematics Department, Sun Yat-Sen University, Guangzhou 510275, People' s Republic China (China)

    2014-03-15

    In this paper, we discover that a delayed discrete Hopfield neural network of two nonidentical neurons with self-connections and no self-connections can demonstrate chaotic behaviors. To this end, we first transform the model, by a novel way, into an equivalent system which has some interesting properties. Then, we identify the chaotic invariant set for this system and show that the dynamics of this system within this set is topologically conjugate to the dynamics of the full shift map with two symbols. This confirms chaos in the sense of Devaney. Our main results generalize the relevant results of Huang and Zou [J. Nonlinear Sci. 15, 291–303 (2005)], Kaslik and Balint [J. Nonlinear Sci. 18, 415–432 (2008)] and Chen et al. [Sci. China Math. 56(9), 1869–1878 (2013)]. We also give some numeric simulations to verify our theoretical results.

  15. Complex dynamics of a delayed discrete neural network of two nonidentical neurons

    International Nuclear Information System (INIS)

    Chen, Yuanlong; Huang, Tingwen; Huang, Yu

    2014-01-01

    In this paper, we discover that a delayed discrete Hopfield neural network of two nonidentical neurons with self-connections and no self-connections can demonstrate chaotic behaviors. To this end, we first transform the model, by a novel way, into an equivalent system which has some interesting properties. Then, we identify the chaotic invariant set for this system and show that the dynamics of this system within this set is topologically conjugate to the dynamics of the full shift map with two symbols. This confirms chaos in the sense of Devaney. Our main results generalize the relevant results of Huang and Zou [J. Nonlinear Sci. 15, 291–303 (2005)], Kaslik and Balint [J. Nonlinear Sci. 18, 415–432 (2008)] and Chen et al. [Sci. China Math. 56(9), 1869–1878 (2013)]. We also give some numeric simulations to verify our theoretical results

  16. The impact of rapid evolution on population dynamics in the wild: experimental test of eco-evolutionary dynamics.

    Science.gov (United States)

    Turcotte, Martin M; Reznick, David N; Hare, J Daniel

    2011-11-01

    Rapid evolution challenges the assumption that evolution is too slow to impact short-term ecological dynamics. This insight motivates the study of 'Eco-Evolutionary Dynamics' or how evolution and ecological processes reciprocally interact on short time scales. We tested how rapid evolution impacts concurrent population dynamics using an aphid (Myzus persicae) and an undomesticated host (Hirschfeldia incana) in replicated wild populations. We manipulated evolvability by creating non-evolving (single clone) and potentially evolving (two-clone) aphid populations that contained genetic variation in intrinsic growth rate. We observed significant evolution in two-clone populations whether or not they were exposed to predators and competitors. Evolving populations grew up to 42% faster and attained up to 67% higher density, compared with non-evolving control populations but only in treatments exposed to competitors and predators. Increased density also correlates with relative fitness of competing clones suggesting a full eco-evolutionary dynamic cycle defined as reciprocal interactions between evolution and density. © 2011 Blackwell Publishing Ltd/CNRS.

  17. Intratelencephalic corticostriatal neurons equally excite striatonigral and striatopallidal neurons and their discharge activity is selectively reduced in experimental parkinsonism

    OpenAIRE

    Ballion, B. (B.); Mallet, N. (Nicolas); Bezard, E. (E.); Lanciego, J.L. (José Luis); Gonon, F. (Francois)

    2008-01-01

    Striatonigral and striatopallidal neurons form distinct populations of striatal projection neurons. Their discharge activity is imbalanced after dopaminergic degeneration in Parkinson's disease. Striatal projection neurons receive massive cortical excitatory inputs from bilateral intratelencephalic (IT) neurons projecting to both the ipsilateral and contralateral striatum and from collateral axons of ipsilateral neurons that send their main axon through the pyramidal tract (PT). Previous anat...

  18. Neuronal integration of dynamic sources: Bayesian learning and Bayesian inference.

    Science.gov (United States)

    Siegelmann, Hava T; Holzman, Lars E

    2010-09-01

    One of the brain's most basic functions is integrating sensory data from diverse sources. This ability causes us to question whether the neural system is computationally capable of intelligently integrating data, not only when sources have known, fixed relative dependencies but also when it must determine such relative weightings based on dynamic conditions, and then use these learned weightings to accurately infer information about the world. We suggest that the brain is, in fact, fully capable of computing this parallel task in a single network and describe a neural inspired circuit with this property. Our implementation suggests the possibility that evidence learning requires a more complex organization of the network than was previously assumed, where neurons have different specialties, whose emergence brings the desired adaptivity seen in human online inference.

  19. Mechanisms for multiple activity modes of VTA dopamine neurons

    Directory of Open Access Journals (Sweden)

    Andrew eOster

    2015-07-01

    Full Text Available Midbrain ventral segmental area (VTA dopaminergic neurons send numerous projections to cortical and sub-cortical areas, and diffusely release dopamine (DA to their targets. DA neurons display a range of activity modes that vary in frequency and degree of burst firing. Importantly, DA neuronal bursting is associated with a significantly greater degree of DA release than an equivalent tonic activity pattern. Here, we introduce a single compartmental, conductance-based computational model for DA cell activity that captures the behavior of DA neuronal dynamics and examine the multiple factors that underlie DA firing modes: the strength of the SK conductance, the amount of drive, and GABA inhibition. Our results suggest that neurons with low SK conductance fire in a fast firing mode, are correlated with burst firing, and require higher levels of applied current before undergoing depolarization block. We go on to consider the role of GABAergic inhibition on an ensemble of dynamical classes of DA neurons and find that strong GABA inhibition suppresses burst firing. Our studies suggest differences in the distribution of the SK conductance and GABA inhibition levels may indicate subclasses of DA neurons within the VTA. We further identify, that by considering alternate potassium dynamics, the dynamics display burst patterns that terminate via depolarization block, akin to those observed in vivo in VTA DA neurons and in substantia nigra pars compacta DA cell preparations under apamin application. In addition, we consider the generation of transient burst firing events that are NMDA-initiated or elicited by a sudden decrease of GABA inhibition, that is, disinhibition.

  20. Activation of different neural precursor populations in the adult hippocampus: does this lead to new neurons with discrete functions?

    Science.gov (United States)

    Jhaveri, Dhanisha J; Taylor, Chanel J; Bartlett, Perry F

    2012-07-01

    Resident populations of stem and precursor cells drive the production of new neurons in the adult hippocampus. Recent discoveries have highlighted that a large proportion of these precursor cells are in fact quiescent and can be activated by distinct neuronal activity under both normal physiological and pathological conditions. As growing evidence indicates that newborn neurons play a critical role in cognitive functions such as learning and memory and in mood regulation, it is paramount that we obtain a better understanding of how the reservoirs of stem and precursor cells are maintained and activated. In this review, we critically examine the roles of key molecular mechanisms that have been shown to regulate hippocampal precursor cells, especially their activation. We believe that understanding the mechanistic details of the activity-driven regulation of precursor cells will equip us with the ability to develop tailored strategies to trigger the generation of new neurons, thereby improving the functional outcomes in various neurological and psychiatric conditions. Copyright © 2012 Wiley Periodicals, Inc.

  1. Coding of time-dependent stimuli in homogeneous and heterogeneous neural populations.

    Science.gov (United States)

    Beiran, Manuel; Kruscha, Alexandra; Benda, Jan; Lindner, Benjamin

    2018-04-01

    We compare the information transmission of a time-dependent signal by two types of uncoupled neuron populations that differ in their sources of variability: i) a homogeneous population whose units receive independent noise and ii) a deterministic heterogeneous population, where each unit exhibits a different baseline firing rate ('disorder'). Our criterion for making both sources of variability quantitatively comparable is that the interspike-interval distributions are identical for both systems. Numerical simulations using leaky integrate-and-fire neurons unveil that a non-zero amount of both noise or disorder maximizes the encoding efficiency of the homogeneous and heterogeneous system, respectively, as a particular case of suprathreshold stochastic resonance. Our findings thus illustrate that heterogeneity can render similarly profitable effects for neuronal populations as dynamic noise. The optimal noise/disorder depends on the system size and the properties of the stimulus such as its intensity or cutoff frequency. We find that weak stimuli are better encoded by a noiseless heterogeneous population, whereas for strong stimuli a homogeneous population outperforms an equivalent heterogeneous system up to a moderate noise level. Furthermore, we derive analytical expressions of the coherence function for the cases of very strong noise and of vanishing intrinsic noise or heterogeneity, which predict the existence of an optimal noise intensity. Our results show that, depending on the type of signal, noise as well as heterogeneity can enhance the encoding performance of neuronal populations.

  2. Delay differential systems for tick population dynamics.

    Science.gov (United States)

    Fan, Guihong; Thieme, Horst R; Zhu, Huaiping

    2015-11-01

    Ticks play a critical role as vectors in the transmission and spread of Lyme disease, an emerging infectious disease which can cause severe illness in humans or animals. To understand the transmission dynamics of Lyme disease and other tick-borne diseases, it is necessary to investigate the population dynamics of ticks. Here, we formulate a system of delay differential equations which models the stage structure of the tick population. Temperature can alter the length of time delays in each developmental stage, and so the time delays can vary geographically (and seasonally which we do not consider). We define the basic reproduction number [Formula: see text] of stage structured tick populations. The tick population is uniformly persistent if [Formula: see text] and dies out if [Formula: see text]. We present sufficient conditions under which the unique positive equilibrium point is globally asymptotically stable. In general, the positive equilibrium can be unstable and the system show oscillatory behavior. These oscillations are primarily due to negative feedback within the tick system, but can be enhanced by the time delays of the different developmental stages.

  3. Understanding Neuronal Mechanisms of Epilepsy ...

    Indian Academy of Sciences (India)

    Admin

    α subunit of Rat Brain type IIA Voltage Gated Sodium Channel and geneticin selection ..... scaling the mother wavelet. Scale = 1/ .... through dynamic clamp. Dynamic Clamp ... It has been shown that like in vivo neurons, cortical networks in.

  4. Imaging auditory representations of song and syllables in populations of sensorimotor neurons essential to vocal communication.

    Science.gov (United States)

    Peh, Wendy Y X; Roberts, Todd F; Mooney, Richard

    2015-04-08

    Vocal communication depends on the coordinated activity of sensorimotor neurons important to vocal perception and production. How vocalizations are represented by spatiotemporal activity patterns in these neuronal populations remains poorly understood. Here we combined intracellular recordings and two-photon calcium imaging in anesthetized adult zebra finches (Taeniopygia guttata) to examine how learned birdsong and its component syllables are represented in identified projection neurons (PNs) within HVC, a sensorimotor region important for song perception and production. These experiments show that neighboring HVC PNs can respond at markedly different times to song playback and that different syllables activate spatially intermingled PNs within a local (~100 μm) region of HVC. Moreover, noise correlations were stronger between PNs that responded most strongly to the same syllable and were spatially graded within and between classes of PNs. These findings support a model in which syllabic and temporal features of song are represented by spatially intermingled PNs functionally organized into cell- and syllable-type networks within local spatial scales in HVC. Copyright © 2015 the authors 0270-6474/15/355589-17$15.00/0.

  5. Modeling the population dynamics of Pacific yew.

    Science.gov (United States)

    Richard T. Busing; Thomas A. Spies

    1995-01-01

    A study of Pacific yew (Taxus brevifolia Nutt.) population dynamics in the mountains of western Oregon and Washington was based on a combination of long-term population data and computer modeling. Rates of growth and mortality were low in mature and old-growth forest stands. Diameter growth at breast height ranged from 0 to 3 centimeters per decade...

  6. Stochastic population dynamic models as probability networks

    Science.gov (United States)

    M.E. and D.C. Lee. Borsuk

    2009-01-01

    The dynamics of a population and its response to environmental change depend on the balance of birth, death and age-at-maturity, and there have been many attempts to mathematically model populations based on these characteristics. Historically, most of these models were deterministic, meaning that the results were strictly determined by the equations of the model and...

  7. Life-long stability of neurons: a century of research on neurogenesis, neuronal death and neuron quantification in adult CNS.

    Science.gov (United States)

    Turlejski, Kris; Djavadian, Ruzanna

    2002-01-01

    In this chapter we provide an extensive review of 100 years of research on the stability of neurons in the mammalian brain, with special emphasis on humans. Although Cajal formulated the Neuronal Doctrine, he was wrong in his beliefs that adult neurogenesis did not occur and adult neurons are dying throughout life. These two beliefs became accepted "common knowledge" and have shaped much of neuroscience research and provided much of the basis for clinical treatment of age-related brain diseases. In this review, we consider adult neurogenesis from a historical and evolutionary perspective. It is concluded, that while adult neurogenesis is a factor in the dynamics of the dentate gyrus and olfactory bulb, it is probably not a major factor during the life-span in most brain areas. Likewise, the acceptance of neuronal death as an explanation for normal age-related senility is challenged with evidence collected over the last fifty years. Much of the problem in changing this common belief of dying neurons was the inadequacies of neuronal counting methods. In this review we discuss in detail implications of recent improvements in neuronal quantification. We conclude: First, age-related neuronal atrophy is the major factor in functional deterioration of existing neurons and could be slowed down, or even reversed by various pharmacological interventions. Second, in most cases neuronal degeneration during aging is a pathology that in principle may be avoided. Third, loss of myelin and of the white matter is more frequent and important than the limited neuronal death in normal aging.

  8. Mechanisms of Winner-Take-All and Group Selection in Neuronal Spiking Networks.

    Science.gov (United States)

    Chen, Yanqing

    2017-01-01

    A major function of central nervous systems is to discriminate different categories or types of sensory input. Neuronal networks accomplish such tasks by learning different sensory maps at several stages of neural hierarchy, such that different neurons fire selectively to reflect different internal or external patterns and states. The exact mechanisms of such map formation processes in the brain are not completely understood. Here we study the mechanism by which a simple recurrent/reentrant neuronal network accomplish group selection and discrimination to different inputs in order to generate sensory maps. We describe the conditions and mechanism of transition from a rhythmic epileptic state (in which all neurons fire synchronized and indiscriminately to any input) to a winner-take-all state in which only a subset of neurons fire for a specific input. We prove an analytic condition under which a stable bump solution and a winner-take-all state can emerge from the local recurrent excitation-inhibition interactions in a three-layer spiking network with distinct excitatory and inhibitory populations, and demonstrate the importance of surround inhibitory connection topology on the stability of dynamic patterns in spiking neural network.

  9. Estimating spatio-temporal dynamics of size-structured populations

    DEFF Research Database (Denmark)

    Kristensen, Kasper; Thygesen, Uffe Høgsbro; Andersen, Ken Haste

    2014-01-01

    with simple stock dynamics, to estimate simultaneously how size distributions and spatial distributions develop in time. We demonstrate the method for a cod population sampled by trawl surveys. Particular attention is paid to correlation between size classes within each trawl haul due to clustering...... of individuals with similar size. The model estimates growth, mortality and reproduction, after which any aspect of size-structure, spatio-temporal population dynamics, as well as the sampling process can be probed. This is illustrated by two applications: 1) tracking the spatial movements of a single cohort...

  10. Dynamic stereotypic responses of Basal Ganglia neurons to subthalamic nucleus high-frequency stimulation in the parkinsonian primate.

    Science.gov (United States)

    Moran, Anan; Stein, Edward; Tischler, Hadass; Belelovsky, Katya; Bar-Gad, Izhar

    2011-01-01

    Deep brain stimulation (DBS) in the subthalamic nucleus (STN) is a well-established therapy for patients with severe Parkinson's disease (PD); however, its mechanism of action is still unclear. In this study we explored static and dynamic activation patterns in the basal ganglia (BG) during high-frequency macro-stimulation of the STN. Extracellular multi-electrode recordings were performed in primates rendered parkinsonian using 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine. Recordings were preformed simultaneously in the STN and the globus pallidus externus and internus. Single units were recorded preceding and during the stimulation. During the stimulation, STN mean firing rate dropped significantly, while pallidal mean firing rates did not change significantly. The vast majority of neurons across all three nuclei displayed stimulation driven modulations, which were stereotypic within each nucleus but differed across nuclei. The predominant response pattern of STN neurons was somatic inhibition. However, most pallidal neurons demonstrated synaptic activation patterns. A minority of neurons across all nuclei displayed axonal activation. Temporal dynamics were observed in the response to stimulation over the first 10 seconds in the STN and over the first 30 seconds in the pallidum. In both pallidal segments, the synaptic activation response patterns underwent delay and decay of the magnitude of the peak response due to short term synaptic depression. We suggest that during STN macro-stimulation the STN goes through a functional ablation as its upper bound on information transmission drops significantly. This notion is further supported by the evident dissociation between the stimulation driven pre-synaptic STN somatic inhibition and the post-synaptic axonal activation of its downstream targets. Thus, BG output maintains its firing rate while losing the deleterious effect of the STN. This may be a part of the mechanism leading to the beneficial effect of DBS in PD.

  11. Dynamic stereotypic responses of basal ganglia neurons to subthalamic nucleus high frequency stimulation in the parkinsonian primate

    Directory of Open Access Journals (Sweden)

    Anan eMoran

    2011-04-01

    Full Text Available Deep brain stimulation in the subthalamic nucleus (STN is a well-established therapy for patients with severe Parkinson‟s disease (PD; however, its mechanism of action is still unclear. In this study we explored static and dynamic activation patterns in the basal ganglia during high frequency macro-stimulation of the STN. Extracellular multi-electrode recordings were performed in primates rendered parkinsonian using 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine. Recordings were preformed simultaneously in the STN and the globus pallidus externus and internus. Single units were recorded preceding and during the stimulation. During the stimulation, STN mean firing rate dropped significantly, while pallidal mean firing rates did not change significantly. The vast majority of neurons across all three nuclei displayed stimulation driven modulations, which were stereotypic within each nucleus but differed across nuclei. The predominant response pattern of STN neurons was somatic inhibition. However, most pallidal neurons demonstrated synaptic activation patterns. A minority of neurons across all nuclei displayed axonal activation. Temporal dynamics were observed in the response to stimulation over the first 10 seconds in the STN and over the first 30 seconds in the pallidum. In both pallidal segments, the synaptic activation response patterns underwent delay and decay of the magnitude of the peak response due to short term synaptic depression. We suggest that during STN macro stimulation the STN goes through a functional ablation as its upper bound on information transmission drops significantly. This notion is further supported by the evident dissociation between the stimulation driven pre-synaptic STN somatic inhibition and the post-synaptic axonal activation of its downstream targets. Thus, basal ganglia output maintains its firing rate while losing the deleterious effect of the STN. This may be a part of the mechanism leading to the beneficial

  12. Computer simulation of population dynamics inside the urban environment

    Science.gov (United States)

    Andreev, A. S.; Inovenkov, I. N.; Echkina, E. Yu.; Nefedov, V. V.; Ponomarenko, L. S.; Tikhomirov, V. V.

    2017-12-01

    In this paper using a mathematical model of the so-called “space-dynamic” approach we investigate the problem of development and temporal dynamics of different urban population groups. For simplicity we consider an interaction of only two population groups inside a single urban area with axial symmetry. This problem can be described qualitatively by a system of two non-stationary nonlinear differential equations of the diffusion type with boundary conditions of the third type. The results of numerical simulations show that with a suitable choice of the diffusion coefficients and interaction functions between different population groups we can receive different scenarios of population dynamics: from complete displacement of one population group by another (originally more “aggressive”) to the “peaceful” situation of co-existence of them together.

  13. Long-term estradiol-17β administration changes the population of paracervical ganglion neurons supplying the ovary in adult gilts.

    Science.gov (United States)

    Jana, Barbara; Palus, Katarzyna; Czarzasta, Joanna; Całka, Jarosław

    2013-07-01

    The aim of this study was to determine the influence of estradiol-17β (E(2)) overdose on the number and distribution of ovarian parasympathetic neurons in the paracervical ganglion (PCG) in adult pigs. To identify the neurons innervating gonads on day 3 of the estrous cycle, the ovaries of both the control and experimental gilts were injected with retrograde neuronal tracer Fast Blue. From next day to the expected day 20 of the second studied cycle, experimental gilts were injected with E(2), while control gilts received oil. The PCG were then collected and processed for double-labeling immunofluorescence. Injections of E(2) increased the E(2) level in the peripheral blood approximately four- to fivefold and reduced the following in the PCG: the total number of Fast Blue-positive neurons; the number of perikarya in the lateral part of the PCG; the numbers of vesicular acetylcholine transporter (VAChT)(+)/somatostatin(+), VAChT(+)/vasoactive intestinal polypeptide (VIP)(+), VAChT(+)/neuronal isoform of nitric oxide synthase(+), VAChT(+)/VIP(-), VAChT(+)/dopamine β-hydroxylase (DβH)(-), VAChT(-)/VIP(-), and VAChT(-)/DβH(-) perikarya; and the total number of perikarya expressing estrogen receptors (ERs) subtype α and/or β. In summary, long-term E(2) treatment of adult gilts downregulates the population of both cholinergic and ERs expressing the PCG ovary-projecting neurons. Our results suggest that elevated E(2) levels occurring during pathological states may regulate gonadal function(s) by affecting ovary-supplying neurons.

  14. Dendritic slow dynamics enables localized cortical activity to switch between mobile and immobile modes with noisy background input.

    Directory of Open Access Journals (Sweden)

    Hiroki Kurashige

    Full Text Available Mounting lines of evidence suggest the significant computational ability of a single neuron empowered by active dendritic dynamics. This motivates us to study what functionality can be acquired by a network of such neurons. The present paper studies how such rich single-neuron dendritic dynamics affects the network dynamics, a question which has scarcely been specifically studied to date. We simulate neurons with active dendrites networked locally like cortical pyramidal neurons, and find that naturally arising localized activity--called a bump--can be in two distinct modes, mobile or immobile. The mode can be switched back and forth by transient input to the cortical network. Interestingly, this functionality arises only if each neuron is equipped with the observed slow dendritic dynamics and with in vivo-like noisy background input. If the bump activity is considered to indicate a point of attention in the sensory areas or to indicate a representation of memory in the storage areas of the cortex, this would imply that the flexible mode switching would be of great potential use for the brain as an information processing device. We derive these conclusions using a natural extension of the conventional field model, which is defined by combining two distinct fields, one representing the somatic population and the other representing the dendritic population. With this tool, we analyze the spatial distribution of the degree of after-spike adaptation and explain how we can understand the presence of the two distinct modes and switching between the modes. We also discuss the possible functional impact of this mode-switching ability.

  15. The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code

    Directory of Open Access Journals (Sweden)

    Susanne Kunkel

    2017-06-01

    Full Text Available NEST is a simulator for spiking neuronal networks that commits to a general purpose approach: It allows for high flexibility in the design of network models, and its applications range from small-scale simulations on laptops to brain-scale simulations on supercomputers. Hence, developers need to test their code for various use cases and ensure that changes to code do not impair scalability. However, running a full set of benchmarks on a supercomputer takes up precious compute-time resources and can entail long queuing times. Here, we present the NEST dry-run mode, which enables comprehensive dynamic code analysis without requiring access to high-performance computing facilities. A dry-run simulation is carried out by a single process, which performs all simulation steps except communication as if it was part of a parallel environment with many processes. We show that measurements of memory usage and runtime of neuronal network simulations closely match the corresponding dry-run data. Furthermore, we demonstrate the successful application of the dry-run mode in the areas of profiling and performance modeling.

  16. 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.

  17. 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

  18. How structure determines correlations in neuronal networks.

    Directory of Open Access Journals (Sweden)

    Volker Pernice

    2011-05-01

    Full Text Available Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, we face an intricate interplay between the topology of the network and the dynamics of the system, which is generally very hard to disentangle. A dynamical feature that has been subject of intense research in various fields are correlations between the noisy activity of nodes in a network. We consider a class of systems, where discrete signals are sent along the links of the network. Such systems are of particular relevance in neuroscience, because they provide models for networks of neurons that use action potentials for communication. We study correlations in dynamic networks with arbitrary topology, assuming linear pulse coupling. With our novel approach, we are able to understand in detail how specific structural motifs affect pairwise correlations. Based on a power series decomposition of the covariance matrix, we describe the conditions under which very indirect interactions will have a pronounced effect on correlations and population dynamics. In random networks, we find that indirect interactions may lead to a broad distribution of activation levels with low average but highly variable correlations. This phenomenon is even more pronounced in networks with distance dependent connectivity. In contrast, networks with highly connected hubs or patchy connections often exhibit strong average correlations. Our results are particularly relevant in view of new experimental techniques that enable the parallel recording of spiking activity from a large number of neurons, an appropriate interpretation of which is hampered by the currently limited understanding of structure-dynamics relations in complex networks.

  19. Data Driven Approach for High Resolution Population Distribution and Dynamics Models

    Energy Technology Data Exchange (ETDEWEB)

    Bhaduri, Budhendra L [ORNL; Bright, Eddie A [ORNL; Rose, Amy N [ORNL; Liu, Cheng [ORNL; Urban, Marie L [ORNL; Stewart, Robert N [ORNL

    2014-01-01

    High resolution population distribution data are vital for successfully addressing critical issues ranging from energy and socio-environmental research to public health to human security. Commonly available population data from Census is constrained both in space and time and does not capture population dynamics as functions of space and time. This imposes a significant limitation on the fidelity of event-based simulation models with sensitive space-time resolution. This paper describes ongoing development of high-resolution population distribution and dynamics models, at Oak Ridge National Laboratory, through spatial data integration and modeling with behavioral or activity-based mobility datasets for representing temporal dynamics of population. The model is resolved at 1 km resolution globally and describes the U.S. population for nighttime and daytime at 90m. Integration of such population data provides the opportunity to develop simulations and applications in critical infrastructure management from local to global scales.

  20. Analyzing dendritic growth in a population of immature neurons in the adult dentate gyrus using laminar quantification of disjointed dendrites

    Directory of Open Access Journals (Sweden)

    Shira eRosenzweig

    2011-03-01

    Full Text Available In the dentate gyrus of the hippocampus, new granule neurons are continuously produced throughout adult life. A prerequisite for the successful synaptic integration of these neurons is the sprouting and extension of dendrites into the molecular layer of the dentate gyrus. Thus, studies aimed at investigating the developmental stages of adult neurogenesis often use dendritic growth as an important indicator of neuronal health and maturity. Based on the known topography of the dentate gyrus, characterized by distinct laminar arrangement of granule neurons and their extensions, we have developed a new method for analysis of dendritic growth in immature adult-born granule neurons. The method is comprised of laminar quantification of cell bodies, primary, secondary and tertiary dendrites separately and independently from each other. In contrast to most existing methods, laminar quantification of dendrites does not require the use of exogenous markers and does not involve arbitrary selection of individual neurons. The new method relies on immonuhistochemical detection of endogenous markers such as doublecortin to perform a comprehensive analysis of a sub-population of immature neurons. Disjointed, orphan dendrites that often appear in the thin histological sections are taken into account. Using several experimental groups of rats and mice, we demonstrate here the suitable techniques for quantifying neurons and dendrites, and explain how the ratios between the quantified values can be used in a comparative analysis to indicate variations in dendritic growth and complexity.

  1. Neuronal Activation After Prolonged Immobilization: Do the Same or Different Neurons Respond to a Novel Stressor?

    Science.gov (United States)

    Marín-Blasco, Ignacio; Muñoz-Abellán, Cristina; Andero, Raül; Nadal, Roser; Armario, Antonio

    2018-04-01

    Despite extensive research on the impact of emotional stressors on brain function using immediate-early genes (e.g., c-fos), there are still important questions that remain unanswered such as the reason for the progressive decline of c-fos expression in response to prolonged stress and the neuronal populations activated by different stressors. This study tackles these 2 questions by evaluating c-fos expression in response to 2 different emotional stressors applied sequentially, and performing a fluorescent double labeling of c-Fos protein and c-fos mRNA on stress-related brain areas. Results were complemented with the assessment of the hypothalamic-pituitary-adrenal axis activation. We showed that the progressive decline of c-fos expression could be related to 2 differing mechanisms involving either transcriptional repression or changes in stimulatory inputs. Moreover, the neuronal populations that respond to the different stressors appear to be predominantly separated in high-level processing areas (e.g., medial prefrontal cortex). However, in low-hierarchy areas (e.g., paraventricular nucleus of the hypothalamus) neuronal populations appear to respond unspecifically. The data suggest that the distinct physiological and behavioral consequences of emotional stressors, and their implication in the development of psychopathologies, are likely to be closely associated with neuronal populations specifically activated by each stressor.

  2. Direct projections from hypothalamic orexin neurons to brainstem cardiac vagal neurons.

    Science.gov (United States)

    Dergacheva, Olga; Yamanaka, Akihiro; Schwartz, Alan R; Polotsky, Vsevolod Y; Mendelowitz, David

    2016-12-17

    Orexin neurons are known to augment the sympathetic control of cardiovascular function, however the role of orexin neurons in parasympathetic cardiac regulation remains unclear. To test the hypothesis that orexin neurons contribute to parasympathetic control we selectively expressed channelrhodopsin-2 (ChR2) in orexin neurons in orexin-Cre transgenic rats and examined postsynaptic currents in cardiac vagal neurons (CVNs) in the dorsal motor nucleus of the vagus (DMV). Simultaneous photostimulation and recording in ChR2-expressing orexin neurons in the lateral hypothalamus resulted in reliable action potential firing as well as large whole-cell currents suggesting a strong expression of ChR2 and reliable optogenetic excitation. Photostimulation of ChR2-expressing fibers in the DMV elicited short-latency (ranging from 3.2ms to 8.5ms) postsynaptic currents in 16 out of 44 CVNs tested. These responses were heterogeneous and included excitatory glutamatergic (63%) and inhibitory GABAergic (37%) postsynaptic currents. The results from this study suggest different sub-population of orexin neurons may exert diverse influences on brainstem CVNs and therefore may play distinct functional roles in parasympathetic control of the heart. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

  3. Different Populations of Human Locus Ceruleus Neurons Contain Heavy Metals or Hyperphosphorylated Tau: Implications for Amyloid-β and Tau Pathology in Alzheimer's Disease.

    Science.gov (United States)

    Pamphlett, Roger; Kum Jew, Stephen

    2015-01-01

    A marked loss of locus ceruleus (LC) neurons is a striking pathological feature of Alzheimer's disease (AD). LC neurons are particularly prone to taking up circulating toxicants such as heavy metals, and hyperphosphorylated tau (tau(HYP)) appears early in these neurons. In an attempt to find out if both heavy metals and tau(HYP) could be damaging LC neurons, we looked in the LC neurons of 21 sporadic AD patients and 43 non-demented controls for the heavy metals mercury, bismuth, and silver using autometallography, and for tau(HYP) using AT8 immunostaining. Heavy metals or tau(HYP) were usually seen in separate LC neurons, and rarely co-existed within the same neuron. The number of heavy metal-containing LC neurons did not correlate with the number containing tau(HYP). Heavy metals therefore appear to occupy a mostly different population of LC neurons to those containing tau(HYP), indicating that the LC in AD is vulnerable to two different assaults. Reduced brain noradrenaline from LC damage is linked to amyloid-β deposition, and tau(HYP) in the LC may seed neurofibrillary tangles in other neurons. A model is described, incorporating the present findings, that proposes that the LC plays a part in both the amyloid-β and tau pathologies of AD.

  4. Dynamic neural networking as a basis for plasticity in the control of heart rate.

    Science.gov (United States)

    Kember, G; Armour, J A; Zamir, M

    2013-01-21

    A model is proposed in which the relationship between individual neurons within a neural network is dynamically changing to the effect of providing a measure of "plasticity" in the control of heart rate. The neural network on which the model is based consists of three populations of neurons residing in the central nervous system, the intrathoracic extracardiac nervous system, and the intrinsic cardiac nervous system. This hierarchy of neural centers is used to challenge the classical view that the control of heart rate, a key clinical index, resides entirely in central neuronal command (spinal cord, medulla oblongata, and higher centers). Our results indicate that dynamic networking allows for the possibility of an interplay among the three populations of neurons to the effect of altering the order of control of heart rate among them. This interplay among the three levels of control allows for different neural pathways for the control of heart rate to emerge under different blood flow demands or disease conditions and, as such, it has significant clinical implications because current understanding and treatment of heart rate anomalies are based largely on a single level of control and on neurons acting in unison as a single entity rather than individually within a (plastically) interconnected network. Copyright © 2012 Elsevier Ltd. All rights reserved.

  5. Population Dynamics and Cost-Benefit Analysis. An Attempt to Relate Population Dynamics via Lifetime Reproductive Success to Short-Term Decisions

    NARCIS (Netherlands)

    Tinbergen, J.M.; Balen, J.H. van; Drent, P.J.; Cavé, A.J.; Mertens, J.A.L.; Boer-Hazewinkel, J. den

    1987-01-01

    1. The aim of this article is to explore whether cost-benefit analysis of behaviour may help to understand the population dynamics of a species. The Great Tit is taken as an example. 2. The lifetime reproductive success in different populations of Great Tits amounts from 0.7 (Hoge Veluwe, Wytham) to

  6. Differential Activation of Fast-Spiking and Regular-Firing Neuron Populations During Movement and Reward in the Dorsal Medial Frontal Cortex

    Science.gov (United States)

    Insel, Nathan; Barnes, Carol A.

    2015-01-01

    The medial prefrontal cortex is thought to be important for guiding behavior according to an animal's expectations. Efforts to decode the region have focused not only on the question of what information it computes, but also how distinct circuit components become engaged during behavior. We find that the activity of regular-firing, putative projection neurons contains rich information about behavioral context and firing fields cluster around reward sites, while activity among putative inhibitory and fast-spiking neurons is most associated with movement and accompanying sensory stimulation. These dissociations were observed even between adjacent neurons with apparently reciprocal, inhibitory–excitatory connections. A smaller population of projection neurons with burst-firing patterns did not show clustered firing fields around rewards; these neurons, although heterogeneous, were generally less selective for behavioral context than regular-firing cells. The data suggest a network that tracks an animal's behavioral situation while, at the same time, regulating excitation levels to emphasize high valued positions. In this scenario, the function of fast-spiking inhibitory neurons is to constrain network output relative to incoming sensory flow. This scheme could serve as a bridge between abstract sensorimotor information and single-dimensional codes for value, providing a neural framework to generate expectations from behavioral state. PMID:24700585

  7. Identifying Chaotic FitzHugh–Nagumo Neurons Using Compressive Sensing

    Directory of Open Access Journals (Sweden)

    Ri-Qi Su

    2014-07-01

    Full Text Available We develop a completely data-driven approach to reconstructing coupled neuronal networks that contain a small subset of chaotic neurons. Such chaotic elements can be the result of parameter shift in their individual dynamical systems and may lead to abnormal functions of the network. To accurately identify the chaotic neurons may thus be necessary and important, for example, applying appropriate controls to bring the network to a normal state. However, due to couplings among the nodes, the measured time series, even from non-chaotic neurons, would appear random, rendering inapplicable traditional nonlinear time-series analysis, such as the delay-coordinate embedding method, which yields information about the global dynamics of the entire network. Our method is based on compressive sensing. In particular, we demonstrate that identifying chaotic elements can be formulated as a general problem of reconstructing the nodal dynamical systems, network connections and all coupling functions, as well as their weights. The working and efficiency of the method are illustrated by using networks of non-identical FitzHugh–Nagumo neurons with randomly-distributed coupling weights.

  8. Observability and synchronization of neuron models

    Science.gov (United States)

    Aguirre, Luis A.; Portes, Leonardo L.; Letellier, Christophe

    2017-10-01

    Observability is the property that enables recovering the state of a dynamical system from a reduced number of measured variables. In high-dimensional systems, it is therefore important to make sure that the variable recorded to perform the analysis conveys good observability of the system dynamics. The observability of a network of neuron models depends nontrivially on the observability of the node dynamics and on the topology of the network. The aim of this paper is twofold. First, to perform a study of observability using four well-known neuron models by computing three different observability coefficients. This not only clarifies observability properties of the models but also shows the limitations of applicability of each type of coefficients in the context of such models. Second, to study the emergence of phase synchronization in networks composed of neuron models. This is done performing multivariate singular spectrum analysis which, to the best of the authors' knowledge, has not been used in the context of networks of neuron models. It is shown that it is possible to detect phase synchronization: (i) without having to measure all the state variables, but only one (that provides greatest observability) from each node and (ii) without having to estimate the phase.

  9. Particle algorithms for population dynamics in flows

    International Nuclear Information System (INIS)

    Perlekar, Prasad; Toschi, Federico; Benzi, Roberto; Pigolotti, Simone

    2011-01-01

    We present and discuss particle based algorithms to numerically study the dynamics of population subjected to an advecting flow condition. We discuss few possible variants of the algorithms and compare them in a model compressible flow. A comparison against appropriate versions of the continuum stochastic Fisher equation (sFKPP) is also presented and discussed. The algorithms can be used to study populations genetics in fluid environments.

  10. Comparison of the dynamics of neural interactions in integrate-and-fire networks with current-based and conductance-based synapses

    Directory of Open Access Journals (Sweden)

    Stefano eCavallari

    2014-03-01

    Full Text Available Models of networks of Leaky Integrate-and-Fire neurons (LIF are a widely used tool for theoretical investigations of brain function. These models have been used both with current- and conductance-based synapses. However, the differences in the dynamics expressed by these two approaches have been so far mainly studied at the single neuron level. To investigate how these synaptic models affect network activity, we compared the single-neuron and neural population dynamics of conductance-based networks (COBN and current-based networks (CUBN of LIF neurons. These networks were endowed with sparse excitatory and inhibitory recurrent connections, and were tested in conditions including both low- and high-conductance states. We developed a novel procedure to obtain comparable networks by properly tuning the synaptic parameters not shared by the models. The so defined comparable networks displayed an excellent and robust match of first order statistics (average single neuron firing rates and average frequency spectrum of network activity. However, these comparable networks showed profound differences in the second order statistics of neural population interactions and in the modulation of these properties by external inputs. The correlation between inhibitory and excitatory synaptic currents and the cross-neuron correlation between synaptic inputs, membrane potentials and spike trains were stronger and more stimulus-sensitive in the COBN. Because of these properties, the spike train correlation carried more information about the strength of the input in the COBN, although the firing rates were equally informative in both network models. Moreover, COBN showed stronger neuronal population synchronization in the gamma band, and their spectral information about the network input was higher and spread over a broader range of frequencies. These results suggest that second order properties of network dynamics depend strongly on the choice of synaptic model.

  11. Population dynamics of rural Ethiopia.

    Science.gov (United States)

    Bariabagar, H

    1978-01-01

    2 rounds of the national sample surveys, conducted by the central statistical office of Ethiopia during 1964-1967 and 1969-1971, provide the only comprehensive demographic data for the country and are the basis for this discussion of rural Ethiopia's population dynamics. The population of Ethiopia is predominantly rural. Agglomerations of 2000 and over inhabitants constitute about 14% of the population, and this indicates that Ethiopia has a low level of urbanization. In rural Ethiopia, international migration was negligent in the 1970's and the age structure can be assumed to be the results of past trends of fertility and mortality conditions. The reported crude birthrate (38.2), crude death rate (12.3) and infant mortality rate (90) of rural Ethiopia fall short of the averages for African countries. Prospects of population growth of rural Ethiopia would be immense. At the rate of natural increase of between 2.4 and 3.0% per annum, the population would double in 24-29 years. Regarding population issues, the programs of the National Democratic Revolution of Ethiopia faces the following main challenging problems: 1) carrying out national population censuses in order to obtain basic information for socialist planning; 2) minimizing or curtailing the existing high urban growth rates; 3) reducing rapidly growing population; and 5) mobilizing Ethiopian women to participate in the social, economic and political life of the country in order to create favorable conditions for future fertility reduction.

  12. Song tutoring in presinging zebra finch juveniles biases a small population of higher-order song-selective neurons toward the tutor song.

    Science.gov (United States)

    Adret, Patrice; Meliza, C Daniel; Margoliash, Daniel

    2012-10-01

    We explored physiological changes correlated with song tutoring by recording the responses of caudal nidopallium neurons of zebra finches aged P21-P24 (days post hatching) to a broad spectrum of natural and synthetic stimuli. Those birds raised with their fathers tended to show behavioral evidence of song memorization but not of singing; thus auditory responses were not confounded by the birds' own vocalizations. In study 1, 37 of 158 neurons (23%) in 17 of 22 tutored and untutored birds were selective for only 1 of 10 stimuli comprising broadband signals, early juvenile songs and calls, female calls, and adult songs. Approximately 30% of the selective neurons (12/37 neurons in 9 birds) were selective for adult conspecific songs. All these were found in the song system nuclei HVC and paraHVC. Of 122 neurons (17 birds) in tutored birds, all of the conspecific song-selective neurons (8 neurons in 6 birds) were selective for the adult tutor song; none was selective for unfamiliar song. In study 2 with a different sampling strategy, we found that 11 of 12 song-selective neurons in 6 of 7 birds preferred the tutor song; none preferred unfamiliar or familiar conspecific songs. Most of these neurons were found in caudal lateral nidopallium (NCL) below HVC. Thus by the time a bird begins to sing, there are small numbers of tutor song-selective neurons distributed in several forebrain regions. We hypothesize that a small population of higher-order auditory neurons is innately selective for complex features of behaviorally relevant stimuli and these responses are modified by specific perceptual/social experience during development.

  13. Correlated conductance parameters in leech heart motor neurons contribute to motor pattern formation.

    Science.gov (United States)

    Lamb, Damon G; Calabrese, Ronald L

    2013-01-01

    Neurons can have widely differing intrinsic membrane properties, in particular the density of specific conductances, but how these contribute to characteristic neuronal activity or pattern formation is not well understood. To explore the relationship between conductances, and in particular how they influence the activity of motor neurons in the well characterized leech heartbeat system, we developed a new multi-compartmental Hodgkin-Huxley style leech heart motor neuron model. To do so, we evolved a population of model instances, which differed in the density of specific conductances, capable of achieving specific output activity targets given an associated input pattern. We then examined the sensitivity of measures of output activity to conductances and how the model instances responded to hyperpolarizing current injections. We found that the strengths of many conductances, including those with differing dynamics, had strong partial correlations and that these relationships appeared to be linked by their influence on heart motor neuron activity. Conductances that had positive correlations opposed one another and had the opposite effects on activity metrics when perturbed whereas conductances that had negative correlations could compensate for one another and had similar effects on activity metrics.

  14. Nested synchrony – a novel cross-scale interaction among neuronal oscillations

    Directory of Open Access Journals (Sweden)

    Simo eMonto

    2012-09-01

    Full Text Available Neuronal interactions form the basis for our brain function, and oscillations and synchrony are the principal candidates for mediating them in the cortical networks. Phase synchrony, where oscillatory neuronal ensembles directly synchronize their phases, enables precise integration between separated brain regions. However, it is unclear how neuronal interactions are dynamically coordinated in space and over time. Cross-scale effects have been proposed to be responsible for linking levels of processing hierarchy and to regulate neuronal dynamics. Most notably, nested oscillations, where the phase of a neuronal oscillation modulates the amplitude of a faster one, may locally integrate neuronal activities in distinct frequency bands. Yet, hierarchical control of inter-areal synchrony could provide a more comprehensive view to the dynamical structure of oscillatory interdependencies in the human brain.In this study, the notion of nested oscillations is extended to a cross-frequency and inter-areal model of oscillatory interactions. In this model, the phase of a slower oscillation modulates inter-areal synchrony in a higher frequency band. This would allow cross-scale integration of global interactions and, thus, offers a mechanism for binding distributed neuronal activities.We show that inter-areal phase synchrony can be modulated by the phase of a slower neuronal oscillation using magnetoencephalography. This effect is the most pronounced at frequencies below 35 Hz. Importantly, changes in oscillation amplitudes did not explain the findings. We expect that the novel cross-frequency interaction could offer new ways to understand the flexible but accurate dynamic organization of ongoing neuronal oscillations and synchrony.

  15. Coupling population dynamics with earth system models: the POPEM model.

    Science.gov (United States)

    Navarro, Andrés; Moreno, Raúl; Jiménez-Alcázar, Alfonso; Tapiador, Francisco J

    2017-09-16

    Precise modeling of CO 2 emissions is important for environmental research. This paper presents a new model of human population dynamics that can be embedded into ESMs (Earth System Models) to improve climate modeling. Through a system dynamics approach, we develop a cohort-component model that successfully simulates historical population dynamics with fine spatial resolution (about 1°×1°). The population projections are used to improve the estimates of CO 2 emissions, thus transcending the bulk approach of existing models and allowing more realistic non-linear effects to feature in the simulations. The module, dubbed POPEM (from Population Parameterization for Earth Models), is compared with current emission inventories and validated against UN aggregated data. Finally, it is shown that the module can be used to advance toward fully coupling the social and natural components of the Earth system, an emerging research path for environmental science and pollution research.

  16. Interleukin-1 beta activates specific populations of enteric neurons and enteric glia in the guinea pig ileum and colon

    NARCIS (Netherlands)

    Tjwa, ETTL; Bradley, JM; Keenan, CM; Kroese, ABA; Sharkey, KA

    2003-01-01

    Fos expression was used to assess whether the proinflammatory cytokine interleukin-1beta (IL-1beta) activated specific, chemically coded neuronal populations in isolated preparations of guinea pig ileum and colon. Whether the effects of IL-1beta were mediated through a prostaglandin pathway and

  17. Essential roles of mitochondrial depolarization in neuron loss through microglial activation and attraction toward neurons.

    Science.gov (United States)

    Nam, Min-Kyung; Shin, Hyun-Ah; Han, Ji-Hye; Park, Dae-Wook; Rhim, Hyangshuk

    2013-04-10

    As life spans increased, neurodegenerative disorders that affect aging populations have also increased. Progressive neuronal loss in specific brain regions is the most common cause of neurodegenerative disease; however, key determinants mediating neuron loss are not fully understood. Using a model of mitochondrial membrane potential (ΔΨm) loss, we found only 25% cell loss in SH-SY5Y (SH) neuronal mono-cultures, but interestingly, 85% neuronal loss occurred when neurons were co-cultured with BV2 microglia. SH neurons overexpressing uncoupling protein 2 exhibited an increase in neuron-microglia interactions, which represent an early step in microglial phagocytosis of neurons. This result indicates that ΔΨm loss in SH neurons is an important contributor to recruitment of BV2 microglia. Notably, we show that ΔΨm loss in BV2 microglia plays a crucial role in microglial activation and phagocytosis of damaged SH neurons. Thus, our study demonstrates that ΔΨm loss in both neurons and microglia is a critical determinant of neuron loss. These findings also offer new insights into neuroimmunological and bioenergetical aspects of neurodegenerative disease. Copyright © 2013 Elsevier B.V. All rights reserved.

  18. Dynamics of Population on the Verge of Extinction

    OpenAIRE

    Oborny, B.; Meszena, G.; Szabo, G.

    2005-01-01

    Theoretical considerations suggest that extinction in dispersal-limited populations is necessarily a threshold-like process that is analogous to a critical phase transition in physics. We use this analogy to find robust, common features in the dynamics of extinctions, and suggest early warning signals which may indicate that a population is endangered. As the critical threshold of extinction is approached, the population spontaneously fragments into discrete subpopulations and, consequently, ...

  19. Stochastic population dynamics of a montane ground-dwelling squirrel.

    Directory of Open Access Journals (Sweden)

    Jeffrey A Hostetler

    Full Text Available Understanding the causes and consequences of population fluctuations is a central goal of ecology. We used demographic data from a long-term (1990-2008 study and matrix population models to investigate factors and processes influencing the dynamics and persistence of a golden-mantled ground squirrel (Callospermophilus lateralis population, inhabiting a dynamic subalpine habitat in Colorado, USA. The overall deterministic population growth rate λ was 0.94±SE 0.05 but it varied widely over time, ranging from 0.45±0.09 in 2006 to 1.50±0.12 in 2003, and was below replacement (λ<1 for 9 out of 18 years. The stochastic population growth rate λ(s was 0.92, suggesting a declining population; however, the 95% CI on λ(s included 1.0 (0.52-1.60. Stochastic elasticity analysis showed that survival of adult females, followed by survival of juvenile females and litter size, were potentially the most influential vital rates; analysis of life table response experiments revealed that the same three life history variables made the largest contributions to year-to year changes in λ. Population viability analysis revealed that, when the influences of density dependence and immigration were not considered, the population had a high (close to 1.0 in 50 years probability of extinction. However, probability of extinction declined to as low as zero when density dependence and immigration were considered. Destabilizing effects of stochastic forces were counteracted by regulating effects of density dependence and rescue effects of immigration, which allowed our study population to bounce back from low densities and prevented extinction. These results suggest that dynamics and persistence of our study population are determined synergistically by density-dependence, stochastic forces, and immigration.

  20. [The dynamics of heath indicators of population of industrial town].

    Science.gov (United States)

    Kalinkin, D E; Karpov, A B; Takhauov, R M; Samoĭlova, Iu A

    2013-01-01

    The article presents the results of analysis of dynamics of health indicators of population of industrial town (medical demographic indicators, disability, morbidity of social hygienically important diseases) during 1970-2010. The classified administrative territorial municipality of Seversk constructed near the Siberian chemical industrial center, the internationally first-rate complex of nuclear industry enterprises was used as a research base. It is demonstrated that dynamics of health indicators of studied population had such negative tendencies as rapid population ageing, population loss due to decrease of natality and increase of mortality (population of able-bodied age included), prevalence of cardio-vascular diseases, malignant neoplasms and external causes, chronization of diseases. The established tendencies are to be considered in management decision making targeted to support and promote population health in industrial towns.

  1. A Theoretical Approach to Understanding Population Dynamics with Seasonal Developmental Durations

    Science.gov (United States)

    Lou, Yijun; Zhao, Xiao-Qiang

    2017-04-01

    There is a growing body of biological investigations to understand impacts of seasonally changing environmental conditions on population dynamics in various research fields such as single population growth and disease transmission. On the other side, understanding the population dynamics subject to seasonally changing weather conditions plays a fundamental role in predicting the trends of population patterns and disease transmission risks under the scenarios of climate change. With the host-macroparasite interaction as a motivating example, we propose a synthesized approach for investigating the population dynamics subject to seasonal environmental variations from theoretical point of view, where the model development, basic reproduction ratio formulation and computation, and rigorous mathematical analysis are involved. The resultant model with periodic delay presents a novel term related to the rate of change of the developmental duration, bringing new challenges to dynamics analysis. By investigating a periodic semiflow on a suitably chosen phase space, the global dynamics of a threshold type is established: all solutions either go to zero when basic reproduction ratio is less than one, or stabilize at a positive periodic state when the reproduction ratio is greater than one. The synthesized approach developed here is applicable to broader contexts of investigating biological systems with seasonal developmental durations.

  2. Collective excitability in a mesoscopic neuronal model of epileptic activity

    Science.gov (United States)

    Jedynak, Maciej; Pons, Antonio J.; Garcia-Ojalvo, Jordi

    2018-01-01

    At the mesoscopic scale, the brain can be understood as a collection of interacting neuronal oscillators, but the extent to which its sustained activity is due to coupling among brain areas is still unclear. Here we address this issue in a simplified situation by examining the effect of coupling between two cortical columns described via Jansen-Rit neural mass models. Our results show that coupling between the two neuronal populations gives rise to stochastic initiations of sustained collective activity, which can be interpreted as epileptic events. For large enough coupling strengths, termination of these events results mainly from the emergence of synchronization between the columns, and thus it is controlled by coupling instead of noise. Stochastic triggering and noise-independent durations are characteristic of excitable dynamics, and thus we interpret our results in terms of collective excitability.

  3. Investigating sub-spine actin dynamics in rat hippocampal neurons with super-resolution optical imaging.

    Directory of Open Access Journals (Sweden)

    Vedakumar Tatavarty

    Full Text Available Morphological changes in dendritic spines represent an important mechanism for synaptic plasticity which is postulated to underlie the vital cognitive phenomena of learning and memory. These morphological changes are driven by the dynamic actin cytoskeleton that is present in dendritic spines. The study of actin dynamics in these spines traditionally has been hindered by the small size of the spine. In this study, we utilize a photo-activation localization microscopy (PALM-based single-molecule tracking technique to analyze F-actin movements with approximately 30-nm resolution in cultured hippocampal neurons. We were able to observe the kinematic (physical motion of actin filaments, i.e., retrograde flow and kinetic (F-actin turn-over dynamics of F-actin at the single-filament level in dendritic spines. We found that F-actin in dendritic spines exhibits highly heterogeneous kinematic dynamics at the individual filament level, with simultaneous actin flows in both retrograde and anterograde directions. At the ensemble level, movements of filaments integrate into a net retrograde flow of approximately 138 nm/min. These results suggest a weakly polarized F-actin network that consists of mostly short filaments in dendritic spines.

  4. Investigating sub-spine actin dynamics in rat hippocampal neurons with super-resolution optical imaging.

    Science.gov (United States)

    Tatavarty, Vedakumar; Kim, Eun-Ji; Rodionov, Vladimir; Yu, Ji

    2009-11-09

    Morphological changes in dendritic spines represent an important mechanism for synaptic plasticity which is postulated to underlie the vital cognitive phenomena of learning and memory. These morphological changes are driven by the dynamic actin cytoskeleton that is present in dendritic spines. The study of actin dynamics in these spines traditionally has been hindered by the small size of the spine. In this study, we utilize a photo-activation localization microscopy (PALM)-based single-molecule tracking technique to analyze F-actin movements with approximately 30-nm resolution in cultured hippocampal neurons. We were able to observe the kinematic (physical motion of actin filaments, i.e., retrograde flow) and kinetic (F-actin turn-over) dynamics of F-actin at the single-filament level in dendritic spines. We found that F-actin in dendritic spines exhibits highly heterogeneous kinematic dynamics at the individual filament level, with simultaneous actin flows in both retrograde and anterograde directions. At the ensemble level, movements of filaments integrate into a net retrograde flow of approximately 138 nm/min. These results suggest a weakly polarized F-actin network that consists of mostly short filaments in dendritic spines.

  5. Network evolution induced by the dynamical rules of two populations

    Science.gov (United States)

    Platini, Thierry; Zia, R. K. P.

    2010-10-01

    We study the dynamical properties of a finite dynamical network composed of two interacting populations, namely extrovert (a) and introvert (b). In our model, each group is characterized by its size (Na and Nb) and preferred degree (κa and \\kappa_b\\ll \\kappa_a ). The network dynamics is governed by the competing microscopic rules of each population that consist of the creation and destruction of links. Starting from an unconnected network, we give a detailed analysis of the mean field approach which is compared to Monte Carlo simulation data. The time evolution of the restricted degrees langkbbrang and langkabrang presents three time regimes and a non-monotonic behavior well captured by our theory. Surprisingly, when the population sizes are equal Na = Nb, the ratio of the restricted degree θ0 = langkabrang/langkbbrang appears to be an integer in the asymptotic limits of the three time regimes. For early times (defined by t introverts remains constant while the number of connections increases linearly in the extrovert population. Finally, due to the competing dynamics, the network presents a frustrated stationary state characterized by a ratio θ0 = 3.

  6. Bounds on the dynamics of sink populations with noisy immigration.

    Science.gov (United States)

    Eager, Eric Alan; Guiver, Chris; Hodgson, Dave; Rebarber, Richard; Stott, Iain; Townley, Stuart

    2014-03-01

    Sink populations are doomed to decline to extinction in the absence of immigration. The dynamics of sink populations are not easily modelled using the standard framework of per capita rates of immigration, because numbers of immigrants are determined by extrinsic sources (for example, source populations, or population managers). Here we appeal to a systems and control framework to place upper and lower bounds on both the transient and future dynamics of sink populations that are subject to noisy immigration. Immigration has a number of interpretations and can fit a wide variety of models found in the literature. We apply the results to case studies derived from published models for Chinook salmon (Oncorhynchus tshawytscha) and blowout penstemon (Penstemon haydenii). Copyright © 2013 Elsevier Inc. All rights reserved.

  7. Population dynamics model for plasmid bearing and plasmid lacking ...

    African Journals Online (AJOL)

    Streptokinase production in bioreactor is well associated to cell population dynamics. It is an established fact that two types of cell populations are found to emerge from the initial pool of recombinant cell population. This phenomenon leads to an undesired loss in yield of the product. Primary metabolites, like acetic acid etc ...

  8. Neurochemical differences between target-specific populations of rat dorsal raphe projection neurons.

    Science.gov (United States)

    Prouty, Eric W; Chandler, Daniel J; Waterhouse, Barry D

    2017-11-15

    Serotonin (5-HT)-containing neurons in the dorsal raphe (DR) nucleus project throughout the forebrain and are implicated in many physiological processes and neuropsychiatric disorders. Diversity among these neurons has been characterized in terms of their neurochemistry and anatomical organization, but a clear sense of whether these attributes align with specific brain functions or terminal fields is lacking. DR 5-HT neurons can co-express additional neuroactive substances, increasing the potential for individualized regulation of target circuits. The goal of this study was to link DR neurons to a specific functional role by characterizing cells according to both their neurotransmitter expression and efferent connectivity; specifically, cells projecting to the medial prefrontal cortex (mPFC), a region implicated in cognition, emotion, and responses to stress. Following retrograde tracer injection, brainstem sections from Sprague-Dawley rats were immunohistochemically stained for markers of serotonin, glutamate, GABA, and nitric oxide (NO). 98% of the mPFC-projecting serotonergic neurons co-expressed the marker for glutamate, while the markers for NO and GABA were observed in 60% and less than 1% of those neurons, respectively. To identify potential target-specific differences in co-transmitter expression, we also characterized DR neurons projecting to a visual sensory structure, the lateral geniculate nucleus (LGN). The proportion of serotonergic neurons co-expressing NO was greater amongst cells targeting the mPFC vs LGN (60% vs 22%). The established role of 5-HT in affective disorders and the emerging role of NO in stress signaling suggest that the impact of 5-HT/NO co-localization in DR neurons that regulate mPFC circuit function may be clinically relevant. Copyright © 2017 Elsevier B.V. All rights reserved.

  9. Dynamics of action potential backpropagation in basal dendrites of prefrontal cortical pyramidal neurons.

    Science.gov (United States)

    Zhou, Wen-Liang; Yan, Ping; Wuskell, Joseph P; Loew, Leslie M; Antic, Srdjan D

    2008-02-01

    Basal dendrites of neocortical pyramidal neurons are relatively short and directly attached to the cell body. This allows electrical signals arising in basal dendrites to strongly influence the neuronal output. Likewise, somatic action potentials (APs) should readily propagate back into the basilar dendritic tree to influence synaptic plasticity. Two recent studies, however, determined that sodium APs are severely attenuated in basal dendrites of cortical pyramidal cells, so that they completely fail in distal dendritic segments. Here we used the latest improvements in the voltage-sensitive dye imaging technique (Zhou et al., 2007) to study AP backpropagation in basal dendrites of layer 5 pyramidal neurons of the rat prefrontal cortex. With a signal-to-noise ratio of > 15 and minimal temporal averaging (only four sweeps) we were able to sample AP waveforms from the very last segments of individual dendritic branches (dendritic tips). We found that in short- (< 150 microm) and medium (150-200 microm in length)-range basal dendrites APs backpropagated with modest changes in AP half-width or AP rise-time. The lack of substantial changes in AP shape and dynamics of rise is inconsistent with the AP-failure model. The lack of substantial amplitude boosting of the third AP in the high-frequency burst also suggests that in short- and medium-range basal dendrites backpropagating APs were not severely attenuated. Our results show that the AP-failure concept does not apply in all basal dendrites of the rat prefrontal cortex. The majority of synaptic contacts in the basilar dendritic tree actually received significant AP-associated electrical and calcium transients.

  10. Corticothalamic Synaptic Noise as a Mechanism for Selective Attention in Thalamic Neurons

    Science.gov (United States)

    Béhuret, Sébastien; Deleuze, Charlotte; Bal, Thierry

    2015-01-01

    A reason why the thalamus is more than a passive gateway for sensory signals is that two-third of the synapses of thalamocortical neurons are directly or indirectly related to the activity of corticothalamic axons. While the responses of thalamocortical neurons evoked by sensory stimuli are well characterized, with ON- and OFF-center receptive field structures, the prevalence of synaptic noise resulting from neocortical feedback in intracellularly recorded thalamocortical neurons in vivo has attracted little attention. However, in vitro and modeling experiments point to its critical role for the integration of sensory signals. Here we combine our recent findings in a unified framework suggesting the hypothesis that corticothalamic synaptic activity is adapted to modulate the transfer efficiency of thalamocortical neurons during selective attention at three different levels: First, on ionic channels by interacting with intrinsic membrane properties, second at the neuron level by impacting on the input-output gain, and third even more effectively at the cell assembly level by boosting the information transfer of sensory features encoded in thalamic subnetworks. This top-down population control is achieved by tuning the correlations in subthreshold membrane potential fluctuations and is adapted to modulate the transfer of sensory features encoded by assemblies of thalamocortical relay neurons. We thus propose that cortically-controlled (de-)correlation of subthreshold noise is an efficient and swift dynamic mechanism for selective attention in the thalamus. PMID:26733818

  11. Corticothalamic Synaptic Noise as a Mechanism for Selective Attention in Thalamic Neurons.

    Science.gov (United States)

    Béhuret, Sébastien; Deleuze, Charlotte; Bal, Thierry

    2015-01-01

    A reason why the thalamus is more than a passive gateway for sensory signals is that two-third of the synapses of thalamocortical neurons are directly or indirectly related to the activity of corticothalamic axons. While the responses of thalamocortical neurons evoked by sensory stimuli are well characterized, with ON- and OFF-center receptive field structures, the prevalence of synaptic noise resulting from neocortical feedback in intracellularly recorded thalamocortical neurons in vivo has attracted little attention. However, in vitro and modeling experiments point to its critical role for the integration of sensory signals. Here we combine our recent findings in a unified framework suggesting the hypothesis that corticothalamic synaptic activity is adapted to modulate the transfer efficiency of thalamocortical neurons during selective attention at three different levels: First, on ionic channels by interacting with intrinsic membrane properties, second at the neuron level by impacting on the input-output gain, and third even more effectively at the cell assembly level by boosting the information transfer of sensory features encoded in thalamic subnetworks. This top-down population control is achieved by tuning the correlations in subthreshold membrane potential fluctuations and is adapted to modulate the transfer of sensory features encoded by assemblies of thalamocortical relay neurons. We thus propose that cortically-controlled (de-)correlation of subthreshold noise is an efficient and swift dynamic mechanism for selective attention in the thalamus.

  12. Corticothalamic Synaptic Noise as a Mechanism for Selective Attention in Thalamic Neurons

    Directory of Open Access Journals (Sweden)

    Sébastien eBéhuret

    2015-12-01

    Full Text Available A reason why the thalamus is more than a passive gateway for sensory signals is that two-third of the synapses of thalamocortical neurons are directly or indirectly related to the activity of corticothalamic axons. While the responses of thalamocortical neurons evoked by sensory stimuli are well characterized, with ON- and OFF-center receptive field structures, the prevalence of synaptic noise resulting from neocortical feedback in intracellularly recorded thalamocortical neurons in vivo has attracted little attention. However, in vitro and modeling experiments point to its critical role for the integration of sensory signals. Here we combine our recent findings in a unified framework suggesting the hypothesis that corticothalamic synaptic activity is adapted to modulate the transfer efficiency of thalamocortical neurons during selective attention at three different levels: First, on ionic channels by interacting with intrinsic membrane properties, second at the neuron level by impacting on the input-output gain, and third even more effectively at the cell assembly level by boosting the information transfer of sensory features encoded in thalamic subnetworks. This top-down population control is achieved by tuning the correlations in subthreshold membrane potential fluctuations and is adapted to modulate the transfer of sensory features encoded by assemblies of thalamocortical relay neurons. We thus propose that cortically-controlled (de-correlation of subthreshold noise is an efficient and swift dynamic mechanism for selective attention in the thalamus.

  13. Passivity analysis of higher order evolutionary dynamics and population games

    KAUST Repository

    Mabrok, Mohamed

    2017-01-05

    Evolutionary dynamics describe how the population composition changes in response to the fitness levels, resulting in a closed-loop feedback system. Recent work established a connection between passivity theory and certain classes of population games, namely so-called “stable games”. In particular, it was shown that a combination of stable games and (an analogue of) passive evolutionary dynamics results in stable convergence to Nash equilibrium. This paper considers the converse question of necessary conditions for evolutionary dynamics to exhibit stable behaviors for all generalized stable games. Using methods from robust control analysis, we show that if an evolutionary dynamic does not satisfy a passivity property, then it is possible to construct a generalized stable game that results in instability. The results are illustrated on selected evolutionary dynamics with particular attention to replicator dynamics, which are also shown to be lossless, a special class of passive systems.

  14. Population dynamics of Pseudo-nitzschia species ...

    African Journals Online (AJOL)

    The genus Pseudo-nitzschia is a chain-forming diatom comprising about 30 species some of which are known to produce domoic acid (DA) that causes amnesic shellfish poisoning (ASP). The current study aimed at assessing the population dynamics of Pseudo-nitzschia in the near shore waters of Dar es Salaam. Samples ...

  15. Effects of demographic structure on key properties of stochastic density-independent population dynamics.

    Science.gov (United States)

    Vindenes, Yngvild; Sæther, Bernt-Erik; Engen, Steinar

    2012-12-01

    The development of stochastic demography has largely been based on age structured populations, although other types of demographic structure, especially permanent and dynamic heterogeneity, are likely common in natural populations. The combination of stochasticity and demographic structure is a challenge for analyses of population dynamics and extinction risk, because the population structure will fluctuate around the stable structure and the population size shows transient fluctuations. However, by using a diffusion approximation for the total reproductive value, density-independent dynamics of structured populations can be described with only three population parameters: the expected population growth rate, the environmental variance and the demographic variance. These parameters depend on population structure via the state-specific vital rates and transition rates. Once they are found, the diffusion approximation represents a substantial reduction in model complexity. Here, we review and compare the key population parameters across a wide range of demographic structure, from the case of no structure to the most general case of dynamic heterogeneity, and for both discrete and continuous types. We focus on the demographic variance, but also show how environmental stochasticity can be included. This study brings together results from recent models, each considering a specific type of population structure, and places them in a general framework for structured populations. Comparison across different types of demographic structure reveals that the reproductive value is an essential concept for understanding how population structure affects stochastic dynamics and extinction risk. Copyright © 2011 Elsevier Inc. All rights reserved.

  16. Glia co-culture with neurons in microfluidic platforms promotes the formation and stabilization of synaptic contacts.

    Science.gov (United States)

    Shi, Mingjian; Majumdar, Devi; Gao, Yandong; Brewer, Bryson M; Goodwin, Cody R; McLean, John A; Li, Deyu; Webb, Donna J

    2013-08-07

    Two novel microfluidic cell culture schemes, a vertically-layered set-up and a four chamber set-up, were developed for co-culturing central nervous system (CNS) neurons and glia. The cell chambers in these devices were separated by pressure-enabled valve barriers, which permitted us to control communication between the two cell types. The unique design of these devices facilitated the co-culture of glia with neurons in close proximity (∼50-100 μm), differential transfection of neuronal populations, and dynamic visualization of neuronal interactions, such as the development of synapses. With these co-culture devices, initial synaptic contact between neurons transfected with different fluorescent markers, such as green fluorescent protein (GFP) and mCherry-synaptophysin, was imaged using high-resolution fluorescence microscopy. The presence of glial cells had a profound influence on synapses by increasing the number and stability of synaptic contacts. Interestingly, as determined by liquid chromatography-ion mobility-mass spectrometry, neuron-glia co-cultures produced elevated levels of soluble factors compared to that secreted by individual neuron or glia cultures, suggesting a potential mechanism by which neuron-glia interactions could modulate synaptic function. Collectively, these results show that communication between neurons and glia is critical for the formation and stability of synapses and point to the importance of developing neuron-glia co-culture systems such as the microfluidic platforms described in this study.

  17. Efficient computation in networks of spiking neurons: simulations and theory

    International Nuclear Information System (INIS)

    Natschlaeger, T.

    1999-01-01

    One of the most prominent features of biological neural systems is that individual neurons communicate via short electrical pulses, the so called action potentials or spikes. In this thesis we investigate possible mechanisms which can in principle explain how complex computations in spiking neural networks (SNN) can be performed very fast, i.e. within a few 10 milliseconds. Some of these models are based on the assumption that relevant information is encoded by the timing of individual spikes (temporal coding). We will also discuss a model which is based on a population code and still is able to perform fast complex computations. In their natural environment biological neural systems have to process signals with a rich temporal structure. Hence it is an interesting question how neural systems process time series. In this context we explore possible links between biophysical characteristics of single neurons (refractory behavior, connectivity, time course of postsynaptic potentials) and synapses (unreliability, dynamics) on the one hand and possible computations on times series on the other hand. Furthermore we describe a general model of computation that exploits dynamic synapses. This model provides a general framework for understanding how neural systems process time-varying signals. (author)

  18. Aspiration dynamics of multi-player games in finite populations.

    Science.gov (United States)

    Du, Jinming; Wu, Bin; Altrock, Philipp M; Wang, Long

    2014-05-06

    On studying strategy update rules in the framework of evolutionary game theory, one can differentiate between imitation processes and aspiration-driven dynamics. In the former case, individuals imitate the strategy of a more successful peer. In the latter case, individuals adjust their strategies based on a comparison of their pay-offs from the evolutionary game to a value they aspire, called the level of aspiration. Unlike imitation processes of pairwise comparison, aspiration-driven updates do not require additional information about the strategic environment and can thus be interpreted as being more spontaneous. Recent work has mainly focused on understanding how aspiration dynamics alter the evolutionary outcome in structured populations. However, the baseline case for understanding strategy selection is the well-mixed population case, which is still lacking sufficient understanding. We explore how aspiration-driven strategy-update dynamics under imperfect rationality influence the average abundance of a strategy in multi-player evolutionary games with two strategies. We analytically derive a condition under which a strategy is more abundant than the other in the weak selection limiting case. This approach has a long-standing history in evolutionary games and is mostly applied for its mathematical approachability. Hence, we also explore strong selection numerically, which shows that our weak selection condition is a robust predictor of the average abundance of a strategy. The condition turns out to differ from that of a wide class of imitation dynamics, as long as the game is not dyadic. Therefore, a strategy favoured under imitation dynamics can be disfavoured under aspiration dynamics. This does not require any population structure, and thus highlights the intrinsic difference between imitation and aspiration dynamics.

  19. On the population dynamics of the malaria vector

    International Nuclear Information System (INIS)

    Ngwa, G.A.

    2005-10-01

    A deterministic differential equation model for the population dynamics of the human malaria vector is derived and studied. Conditions for the existence and stability of a non-zero steady state vector population density are derived. These reveal that a threshold parameter, the vectorial basic reproduction number, exist and the vector can establish itself in the community if and only if this parameter exceeds unity. When a non-zero steady state population density exists, it can be stable but it can also be driven to instability via a Hopf Bifurcation to periodic solutions, as a parameter is varied in parameter space. By considering a special case, an asymptotic perturbation analysis is used to derive the amplitude of the oscillating solutions for the full non-linear system. The present modelling exercise and results show that it is possible to study the population dynamics of disease vectors, and hence oscillatory behaviour as it is often observed in most indirectly transmitted infectious diseases of humans, without recourse to external seasonal forcing. (author)

  20. Neuro-Compatible Metabolic Glycan Labeling of Primary Hippocampal Neurons in Noncontact, Sandwich-Type Neuron-Astrocyte Coculture.

    Science.gov (United States)

    Choi, Ji Yu; Park, Matthew; Cho, Hyeoncheol; Kim, Mi-Hee; Kang, Kyungtae; Choi, Insung S

    2017-12-20

    Glycans are intimately involved in several facets of neuronal development and neuropathology. However, the metabolic labeling of surface glycans in primary neurons is a difficult task because of the neurotoxicity of unnatural monosaccharides that are used as a metabolic precursor, hindering the progress of metabolic engineering in neuron-related fields. Therefore, in this paper, we report a neurosupportive, neuron-astrocyte coculture system that neutralizes the neurotoxic effects of unnatural monosaccharides, allowing for the long-term observation and characterization of glycans in primary neurons in vitro. Polysialic acids in neurons are selectively imaged, via the metabolic labeling of sialoglycans with peracetylated N-azidoacetyl-d-mannosamine (Ac 4 ManNAz), for up to 21 DIV. Two-color labeling shows that neuronal activities, such as neurite outgrowth and recycling of membrane components, are highly dynamic and change over time during development. In addition, the insertion sites of membrane components are suggested to not be random, but be predominantly localized in developing neurites. This work provides a new research platform and also suggests advanced 3D systems for metabolic-labeling studies of glycans in primary neurons.

  1. Olig2 and Hes regulatory dynamics during motor neuron differentiation revealed by single cell transcriptomics.

    Directory of Open Access Journals (Sweden)

    Andreas Sagner

    2018-02-01

    Full Text Available During tissue development, multipotent progenitors differentiate into specific cell types in characteristic spatial and temporal patterns. We addressed the mechanism linking progenitor identity and differentiation rate in the neural tube, where motor neuron (MN progenitors differentiate more rapidly than other progenitors. Using single cell transcriptomics, we defined the transcriptional changes associated with the transition of neural progenitors into MNs. Reconstruction of gene expression dynamics from these data indicate a pivotal role for the MN determinant Olig2 just prior to MN differentiation. Olig2 represses expression of the Notch signaling pathway effectors Hes1 and Hes5. Olig2 repression of Hes5 appears to be direct, via a conserved regulatory element within the Hes5 locus that restricts expression from MN progenitors. These findings reveal a tight coupling between the regulatory networks that control patterning and neuronal differentiation and demonstrate how Olig2 acts as the developmental pacemaker coordinating the spatial and temporal pattern of MN generation.

  2. Dynamic Information Encoding With Dynamic Synapses in Neural Adaptation

    Science.gov (United States)

    Li, Luozheng; Mi, Yuanyuan; Zhang, Wenhao; Wang, Da-Hui; Wu, Si

    2018-01-01

    Adaptation refers to the general phenomenon that the neural system dynamically adjusts its response property according to the statistics of external inputs. In response to an invariant stimulation, neuronal firing rates first increase dramatically and then decrease gradually to a low level close to the background activity. This prompts a question: during the adaptation, how does the neural system encode the repeated stimulation with attenuated firing rates? It has been suggested that the neural system may employ a dynamical encoding strategy during the adaptation, the information of stimulus is mainly encoded by the strong independent spiking of neurons at the early stage of the adaptation; while the weak but synchronized activity of neurons encodes the stimulus information at the later stage of the adaptation. The previous study demonstrated that short-term facilitation (STF) of electrical synapses, which increases the synchronization between neurons, can provide a mechanism to realize dynamical encoding. In the present study, we further explore whether short-term plasticity (STP) of chemical synapses, an interaction form more common than electrical synapse in the cortex, can support dynamical encoding. We build a large-size network with chemical synapses between neurons. Notably, facilitation of chemical synapses only enhances pair-wise correlations between neurons mildly, but its effect on increasing synchronization of the network can be significant, and hence it can serve as a mechanism to convey the stimulus information. To read-out the stimulus information, we consider that a downstream neuron receives balanced excitatory and inhibitory inputs from the network, so that the downstream neuron only responds to synchronized firings of the network. Therefore, the response of the downstream neuron indicates the presence of the repeated stimulation. Overall, our study demonstrates that STP of chemical synapse can serve as a mechanism to realize dynamical neural

  3. High population variability and source-sink dynamics in a solitary bee species.

    Science.gov (United States)

    Franzén, Markus; Nilsson, Sven G

    2013-06-01

    Although solitary bees are considered to play key roles in ecosystem functions, surprisingly few studies have explored their population dynamics. We investigated the population dynamics of a rare, declining, solitary bee (Andrena humilis) in a landscape of 80 km2 in southern Sweden from 2003 to 2011. Only one population was persistent throughout all years studied; most likely this population supplied the surrounding landscape with 11 smaller, temporary local populations. Despite stable pollen availability, the size of the persistent population fluctuated dramatically in a two-year cycle over the nine years, with 490-1230 nests in odd-numbered years and 21-48 nests in even-numbered years. These fluctuations were not significantly related to climatic variables or pollen availability. Nineteen colonization and 14 extinction events were recorded. Occupancy decreased with distance from the persistent population and increased with increasing resource (pollen) availability. There were significant positive correlations between the size of the persistent population and patch occupancy and colonization. Colonizations were generally more common in patches closer to the persistent population, whereas extinctions were independent of distance from the persistent population. Our results highlight the complex population dynamics that exist for this solitary bee species, which could be due to source-sink dynamics, a prolonged diapause, or can represent a bet-hedging strategy to avoid natural enemies and survive in small habitat patches. If large fluctuations in solitary bee populations prove to be widespread, it will have important implications for interpreting ecological relationships, bee conservation, and pollination.

  4. An FPGA-based silicon neuronal network with selectable excitability silicon neurons

    Directory of Open Access Journals (Sweden)

    Jing eLi

    2012-12-01

    Full Text Available This paper presents a digital silicon neuronal network which simulates the nerve system in creatures and has the ability to execute intelligent tasks, such as associative memory. Two essential elements, the mathematical-structure-based digital spiking silicon neuron (DSSN and the transmitter release based silicon synapse, allow the network to show rich dynamic behaviors and are computationally efficient for hardware implementation. We adopt mixed pipeline and parallel structure and shift operations to design a sufficient large and complex network without excessive hardware resource cost. The network with $256$ full-connected neurons is built on a Digilent Atlys board equipped with a Xilinx Spartan-6 LX45 FPGA. Besides, a memory control block and USB control block are designed to accomplish the task of data communication between the network and the host PC. This paper also describes the mechanism of associative memory performed in the silicon