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Sample records for neuronal population dynamics

  1. 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

  2. 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.

  3. 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

  4. 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.

  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. 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.

  7. 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.

  8. 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.

  9. 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...

  10. 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...

  11. Membrane potential dynamics of populations of cortical neurons during auditory streaming

    Science.gov (United States)

    Farley, Brandon J.

    2015-01-01

    How a mixture of acoustic sources is perceptually organized into discrete auditory objects remains unclear. One current hypothesis postulates that perceptual segregation of different sources is related to the spatiotemporal separation of cortical responses induced by each acoustic source or stream. In the present study, the dynamics of subthreshold membrane potential activity were measured across the entire tonotopic axis of the rodent primary auditory cortex during the auditory streaming paradigm using voltage-sensitive dye imaging. Consistent with the proposed hypothesis, we observed enhanced spatiotemporal segregation of cortical responses to alternating tone sequences as their frequency separation or presentation rate was increased, both manipulations known to promote stream segregation. However, across most streaming paradigm conditions tested, a substantial cortical region maintaining a response to both tones coexisted with more peripheral cortical regions responding more selectively to one of them. We propose that these coexisting subthreshold representation types could provide neural substrates to support the flexible switching between the integrated and segregated streaming percepts. PMID:26269558

  12. 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.

  13. 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.

  14. 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

  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. 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

  17. 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.

  18. 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.

  19. 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. .

  20. 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.

  1. 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)

  2. 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.

  3. 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...

  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. 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...

  6. 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

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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

  13. 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

  14. 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.

  15. 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.

  16. 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

  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. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  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. 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

  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. 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

  16. 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.

  17. 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

  18. 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.

  19. 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...

  20. Africa population dynamics

    OpenAIRE

    Akinyoade, A.; Damen, J.C.M.; Dietz, A.J.; Kilama, B.B.; Omme, van, G.

    2014-01-01

    Africa's population has grown extremely rapidly over the last fifty years from 289 million inhabitants in 1961 to more than 1 billion today. This is a growth rate of 350% in just half a century and the number of urban residents has increased even more quickly: from 65 million in 1960 to 460 million today, or from 20% to 46% of the population as a whole. Demographers predict that soon more than 50% of all Africans will be living in cities. The average life expectancy, literacy rates and primar...

  1. Africa population dynamics

    NARCIS (Netherlands)

    Akinyoade, A.; Damen, J.C.M.; Dietz, A.J.; Kilama, B.B.; Omme, van G.

    2014-01-01

    Africa's population has grown extremely rapidly over the last fifty years from 289 million inhabitants in 1961 to more than 1 billion today. This is a growth rate of 350% in just half a century and the number of urban residents has increased even more quickly: from 65 million in 1960 to 460 million

  2. 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.

  3. 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.

  4. 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.

  5. 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...

  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. 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.

  8. 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

  9. 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.

  10. 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.

  11. [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.

  12. 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)

  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. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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...

  4. 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…

  5. 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

  6. 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

  7. 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...

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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

  13. 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.

  14. Nonlinear dynamics of interacting populations

    CERN Document Server

    Bazykin, Alexander D

    1998-01-01

    This book contains a systematic study of ecological communities of two or three interacting populations. Starting from the Lotka-Volterra system, various regulating factors are considered, such as rates of birth and death, predation and competition. The different factors can have a stabilizing or a destabilizing effect on the community, and their interplay leads to increasingly complicated behavior. Studying and understanding this path to greater dynamical complexity of ecological systems constitutes the backbone of this book. On the mathematical side, the tool of choice is the qualitative the

  15. 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.

  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. 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.

  18. 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

  19. 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.

  20. 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

  1. 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

  2. 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

  3. 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.

  4. 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 ).

  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. 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.

  7. 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...

  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. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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

  17. 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.

  18. 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)

  19. 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.

  20. 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

  1. 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.

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. 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

  7. 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

  8. provisional analysis of population dynamics

    Indian Academy of Sciences (India)

    Nicholas Mitchison

    2018-01-11

    Jan 11, 2018 ... Western populations covered by OMIM, or are so mediated to a lesser extent. This we attribute ... tlenecks affected southern Asia: a coalescence analysis of ... included comprehensive survey of previous work (Atkin- son et al.

  9. 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.

  10. 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.

  11. 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…

  12. 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

  13. Population dynamics and rural poverty.

    Science.gov (United States)

    Fong, M S

    1985-01-01

    An overview of the relationship between demographic factors and rural poverty in developing countries is presented. The author examines both the micro- and macro-level perspectives of this relationship and the determinants and consequences of population growth. The author notes the prospects for a rapid increase in the rural labor force and considers its implications for the agricultural production structure and the need for institutional change. Consideration is also given to the continuing demand for high fertility at the family level and the role of infant and child mortality in the poverty cycle. "The paper concludes by drawing attention to the need for developing the mechanism for reconciliation of social and individual optima with respect to family size and population growth." The need for rural development projects that take demographic factors into account is stressed as is the need for effective population programs. (summary in FRE, ITA) excerpt

  14. 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.

  15. 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.

  16. 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.

  17. Bistable dynamics underlying excitability of ion homeostasis in neuron models.

    Directory of Open Access Journals (Sweden)

    Niklas Hübel

    2014-05-01

    Full Text Available When neurons fire action potentials, dissipation of free energy is usually not directly considered, because the change in free energy is often negligible compared to the immense reservoir stored in neural transmembrane ion gradients and the long-term energy requirements are met through chemical energy, i.e., metabolism. However, these gradients can temporarily nearly vanish in neurological diseases, such as migraine and stroke, and in traumatic brain injury from concussions to severe injuries. We study biophysical neuron models based on the Hodgkin-Huxley (HH formalism extended to include time-dependent ion concentrations inside and outside the cell and metabolic energy-driven pumps. We reveal the basic mechanism of a state of free energy-starvation (FES with bifurcation analyses showing that ion dynamics is for a large range of pump rates bistable without contact to an ion bath. This is interpreted as a threshold reduction of a new fundamental mechanism of ionic excitability that causes a long-lasting but transient FES as observed in pathological states. We can in particular conclude that a coupling of extracellular ion concentrations to a large glial-vascular bath can take a role as an inhibitory mechanism crucial in ion homeostasis, while the Na⁺/K⁺ pumps alone are insufficient to recover from FES. Our results provide the missing link between the HH formalism and activator-inhibitor models that have been successfully used for modeling migraine phenotypes, and therefore will allow us to validate the hypothesis that migraine symptoms are explained by disturbed function in ion channel subunits, Na⁺/K⁺ pumps, and other proteins that regulate ion homeostasis.

  18. 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.

  19. 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.

  20. 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...

  1. 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...

  2. 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...

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  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. 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.

  10. 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

  11. Population Dynamics and Air Pollution

    DEFF Research Database (Denmark)

    Flachs, Esben Meulengracht; Sørensen, Jan; Bønløkke, Jacob

    2013-01-01

    Objective. To explore how three different assumptions on demographics affect the health impact of Danish emitted air pollution in Denmark from 2005 to 2030, with health impact modeled from 2005 to 2050. Methods. Modeled air pollution from Danish sources was used as exposure in a newly developed......) a static year 2005 population, (2) morbidity and mortality fixed at the year 2005 level, or (3) an expected development. Results. The health impact of air pollution was estimated at 672,000, 290,000, and 280,000 lost life years depending on demographic assumptions and the corresponding social costs at 430.......4 M€, 317.5 M€, and 261.6 M€ through the modeled years 2005–2050. Conclusion. The modeled health impact of air pollution differed widely with the demographic assumptions, and thus demographics and assumptions on demographics played a key role in making health impact assessments on air pollution....

  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. 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.

  14. 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

  15. 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....

  16. 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.

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. 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 ...

  12. 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.

  13. 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.

  14. 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.

  15. The failure rate dynamics in heterogeneous populations

    International Nuclear Information System (INIS)

    Cha, Ji Hwan; Finkelstein, Maxim

    2013-01-01

    Most populations encountered in real world are heterogeneous. In reliability applications, the mixture (observed) failure rate, obviously, can be considered as a measure of ‘average’ quality in these populations. However, in addition to this average measure, some variability characteristics for failure rates can be very helpful in describing the time-dependent changes in quality of heterogeneous populations. In this paper, we discuss variance and the coefficient of variation of the corresponding random failure rate as variability measures for items in heterogeneous populations. Furthermore, there is often a risk that items of poor quality are selected for important missions. Therefore, along with the ‘average quality’ of a population, more ‘conservative’ quality measures should be also defined and studied. For this purpose, we propose the percentile and the tail-mixture of the failure rates as the corresponding conservative measures. Some illustrative examples are given. -- Highlights: ► This paper provides the insight on the variability measures in heterogeneous populations. ► The conservative quality measures in heterogeneous populations are defined. ► The utility of these measures is illustrated by meaningful examples. ► This paper provides a better understanding of the dynamics in heterogeneous populations

  16. 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

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. 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)

  4. 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.

  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. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. 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.

  13. Calculating evolutionary dynamics in structured populations.

    Directory of Open Access Journals (Sweden)

    Charles G Nathanson

    2009-12-01

    Full Text Available Evolution is shaping the world around us. At the core of every evolutionary process is a population of reproducing individuals. The outcome of an evolutionary process depends on population structure. Here we provide a general formula for calculating evolutionary dynamics in a wide class of structured populations. This class includes the recently introduced "games in phenotype space" and "evolutionary set theory." There can be local interactions for determining the relative fitness of individuals, but we require global updating, which means all individuals compete uniformly for reproduction. We study the competition of two strategies in the context of an evolutionary game and determine which strategy is favored in the limit of weak selection. We derive an intuitive formula for the structure coefficient, sigma, and provide a method for efficient numerical calculation.

  14. 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.

  15. 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.

  16. 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

  17. 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.

  18. 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.

  19. 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...

  20. 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.

  1. 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.

  2. 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

  3. 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.

  4. Dynamics of intrinsic electrophysiological properties in spinal cord neurones

    DEFF Research Database (Denmark)

    Russo, R E; Hounsgaard, J

    1999-01-01

    The spinal cord is engaged in a wide variety of functions including generation of motor acts, coding of sensory information and autonomic control. The intrinsic electrophysiological properties of spinal neurones represent a fundamental building block of the spinal circuits executing these tasks. ....... Specialised, cell specific electrophysiological phenotypes gradually differentiate during development and are continuously adjusted in the adult animal by metabotropic synaptic interactions and activity-dependent plasticity to meet a broad range of functional demands....

  5. 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...

  6. 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.

  7. 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

  8. 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)

  9. Single bumps in a 2-population homogenized neuronal network model

    Science.gov (United States)

    Kolodina, Karina; Oleynik, Anna; Wyller, John

    2018-05-01

    We investigate existence and stability of single bumps in a homogenized 2-population neural field model, when the firing rate functions are given by the Heaviside function. The model is derived by means of the two-scale convergence technique of Nguetseng in the case of periodic microvariation in the connectivity functions. The connectivity functions are periodically modulated in both the synaptic footprint and in the spatial scale. The bump solutions are constructed by using a pinning function technique for the case where the solutions are independent of the local variable. In the weakly modulated case the generic picture consists of two bumps (one narrow and one broad bump) for each admissible set of threshold values for firing. In addition, a new threshold value regime for existence of bumps is detected. Beyond the weakly modulated regime the number of bumps depends sensitively on the degree of heterogeneity. For the latter case we present a configuration consisting of three coexisting bumps. The linear stability of the bumps is studied by means of the spectral properties of a Fredholm integral operator, block diagonalization of this operator and the Fourier decomposition method. In the weakly modulated regime, one of the bumps is unstable for all relative inhibition times, while the other one is stable for small and moderate values of this parameter. The latter bump becomes unstable as the relative inhibition time exceeds a certain threshold. In the case of the three coexisting bumps detected in the regime of finite degree of heterogeneity, we have at least one stable bump (and maximum two stable bumps) for small and moderate values of the relative inhibition time.

  10. Population Model with a Dynamic Food Supply

    Science.gov (United States)

    Dickman, Ronald; da Silva Nascimento, Jonas

    2009-09-01

    We propose a simple population model including the food supply as a dynamic variable. In the model, survival of an organism depends on a certain minimum rate of food consumption; a higher rate of consumption is required for reproduction. We investigate the stationary behavior under steady food input, and the transient behavior of growth and decay when food is present initially but is not replenished. Under a periodic food supply, the system exhibits period-doubling bifurcations and chaos in certain ranges of the reproduction rate. Bifurcations and chaos are favored by a slow reproduction rate and a long period of food-supply oscillation.

  11. Noise-induced effects in population dynamics

    Science.gov (United States)

    Spagnolo, Bernardo; Cirone, Markus; La Barbera, Antonino; de Pasquale, Ferdinando

    2002-03-01

    We investigate the role of noise in the nonlinear relaxation of two ecosystems described by generalized Lotka-Volterra equations in the presence of multiplicative noise. Specifically we study two cases: (i) an ecosystem with two interacting species in the presence of periodic driving; (ii) an ecosystem with a great number of interacting species with random interaction matrix. We analyse the interplay between noise and periodic modulation for case (i) and the role of the noise in the transient dynamics of the ecosystem in the presence of an absorbing barrier in case (ii). We find that the presence of noise is responsible for the generation of temporal oscillations and for the appearance of spatial patterns in the first case. In the other case we obtain the asymptotic behaviour of the time average of the ith population and discuss the effect of the noise on the probability distributions of the population and of the local field.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. Dynamics in the Parameter Space of a Neuron Model

    Science.gov (United States)

    Paulo, C. Rech

    2012-06-01

    Some two-dimensional parameter-space diagrams are numerically obtained by considering the largest Lyapunov exponent for a four-dimensional thirteen-parameter Hindmarsh—Rose neuron model. Several different parameter planes are considered, and it is shown that depending on the combination of parameters, a typical scenario can be preserved: for some choice of two parameters, the parameter plane presents a comb-shaped chaotic region embedded in a large periodic region. It is also shown that there exist regions close to these comb-shaped chaotic regions, separated by the comb teeth, organizing themselves in period-adding bifurcation cascades.

  17. 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

  18. 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.

  19. 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)

  20. 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.

  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. 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.

  3. 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.

  4. 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

  5. Dynamic analysis of a parasite population model

    Science.gov (United States)

    Sibona, G. J.; Condat, C. A.

    2002-03-01

    We study the dynamics of a model that describes the competitive interaction between an invading species (a parasite) and its antibodies in an living being. This model was recently used to examine the dynamical competition between Tripanosoma cruzi and its antibodies during the acute phase of Chagas' disease. Depending on the antibody properties, the model yields three types of outcomes, corresponding, respectively, to healing, chronic disease, and host death. Here, we study the dynamics of the parasite-antibody interaction with the help of simulations, obtaining phase trajectories and phase diagrams for the system. We show that, under certain conditions, the size of the parasite inoculation can be crucial for the infection outcome and that a retardation in the stimulated production of an antibody species may result in the parasite gaining a definitive advantage. We also find a criterion for the relative sizes of the parameters that are required if parasite-generated decoys are indeed to help the invasion. Decoys may also induce a qualitatively different outcome: a limit cycle for the antibody-parasite population phase trajectories.

  6. [Population dynamics and development in the Caribbean].

    Science.gov (United States)

    Boland, B

    1995-12-01

    The impact is examined of socioeconomic factors on Caribbean population dynamics. This work begins by describing the socioeconomic context of the late 1980s and early 1990s, under the influence of the economic changes and crises of the 1980s. The small size, openness, dependency, and lack of diversification of the Caribbean economies have made them vulnerable to external pressures. The Bahamas and Belize had economic growth rates exceeding 5% annually during 1981-90, but most of the countries had low or negative growth. Unemployment, poverty, the structural adjustment measures adopted in the mid-1980s, and declines in social spending exacerbated general economic conditions. In broad terms, the population situation of the Caribbean is marked by diversity of sizes and growth rates. A few countries oriented toward services and tourism had demographic growth rates exceeding 3%, while at least 7 had almost no growth or negative growth. Population growth rates reflected different combinations of natural increase and migration. Crude death rates ranged from around 5/1000 to 11/1000, except in Haiti, and all countries of the region except Haiti had life expectancies of 70 years or higher. Despite fertility decline, the average crude birth rate was still relatively high at 26/1000, and the rate of natural increase was 1.8% annually for the region. Nearly half of the regional population was under 15 or over 65 years old. The body of this work provides greater detail on mortality patterns, variations by sex, infant mortality, causes of death, and implications for policy. The discussion of fertility includes general patterns and trends, age specific fertility rates, contraceptive prevalence, levels of adolescent fertility and age factors in adolescent sexual behavior, characteristics of adolescent unions, contraceptive usage, health and social consequences of adolescent childbearing, and the search for solutions. The final section describes the magnitude and causes of

  7. 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.

  8. 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

  9. 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.

  10. 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

  11. 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.

  12. 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

  13. 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.

  14. 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.

  15. 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.

  16. 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%

  17. 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.

  18. 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.

  19. Dynamics of genome rearrangement in bacterial populations.

    Directory of Open Access Journals (Sweden)

    Aaron E Darling

    2008-07-01

    represent the first characterization of genome arrangement evolution in a bacterial population evolving outside laboratory conditions. Insight into the process of genomic rearrangement may further the understanding of pathogen population dynamics and selection on the architecture of circular bacterial chromosomes.

  20. 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....

  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. Neurobiology: From neurons through nonlinear dynamics to meaning

    Energy Technology Data Exchange (ETDEWEB)

    Freeman, W.J. [Univ. of California, Berkeley, CA (United States)

    1996-12-31

    That branch of semiotics called semantics deals with the relationships between meanings and representations. In my view meanings exist only in brains without representation there. A meaning is the focus of an activity pattern that may occupy the entire available brain. It is constructed by intentional action followed by learning from the consequences of the action. Communication between brains requires reciprocal construction of representations in accordance with meanings, which elicit other meanings in other brains. A representation, as a material object or process, has no meaning in itself. EEG data indicate that neural patterns of meaning occur in trajectories of discrete steps marked by cortical state transitions, served by rapid exchanges of discrete wave packets between interactive cortical domains. These wave packets are made by self-organizing dynamics that control behavior and shape the sensitivities of sensory cortices to the sequellae of actions.

  3. Electrodiffusive model for astrocytic and neuronal ion concentration dynamics.

    Directory of Open Access Journals (Sweden)

    Geir Halnes

    Full Text Available The cable equation is a proper framework for modeling electrical neural signalling that takes place at a timescale at which the ionic concentrations vary little. However, in neural tissue there are also key dynamic processes that occur at longer timescales. For example, endured periods of intense neural signaling may cause the local extracellular K(+-concentration to increase by several millimolars. The clearance of this excess K(+ depends partly on diffusion in the extracellular space, partly on local uptake by astrocytes, and partly on intracellular transport (spatial buffering within astrocytes. These processes, that take place at the time scale of seconds, demand a mathematical description able to account for the spatiotemporal variations in ion concentrations as well as the subsequent effects of these variations on the membrane potential. Here, we present a general electrodiffusive formalism for modeling of ion concentration dynamics in a one-dimensional geometry, including both the intra- and extracellular domains. Based on the Nernst-Planck equations, this formalism ensures that the membrane potential and ion concentrations are in consistency, it ensures global particle/charge conservation and it accounts for diffusion and concentration dependent variations in resistivity. We apply the formalism to a model of astrocytes exchanging ions with the extracellular space. The simulations show that K(+-removal from high-concentration regions is driven by a local depolarization of the astrocyte membrane, which concertedly (i increases the local astrocytic uptake of K(+, (ii suppresses extracellular transport of K(+, (iii increases axial transport of K(+ within astrocytes, and (iv facilitates astrocytic relase of K(+ in regions where the extracellular concentration is low. Together, these mechanisms seem to provide a robust regulatory scheme for shielding the extracellular space from excess K(+.

  4. Passivity analysis of higher order evolutionary dynamics and population games

    KAUST Repository

    Mabrok, Mohamed; Shamma, Jeff S.

    2017-01-01

    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

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  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. 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.

  11. 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.

  12. 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...

  13. 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.

  14. 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

  15. 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.

  16. 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

  17. 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.

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

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    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.

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  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. 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.

  9. 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

  10. 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.

  11. 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.

  12. 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.

  13. Stochastic population dynamics under resource constraints

    Energy Technology Data Exchange (ETDEWEB)

    Gavane, Ajinkya S., E-mail: ajinkyagavane@gmail.com; Nigam, Rahul, E-mail: rahul.nigam@hyderabad.bits-pilani.ac.in [BITS Pilani Hyderabad Campus, Shameerpet, Hyd - 500078 (India)

    2016-06-02

    This paper investigates the population growth of a certain species in which every generation reproduces thrice over a period of predefined time, under certain constraints of resources needed for survival of population. We study the survival period of a species by randomizing the reproduction probabilities within a window at same predefined ages and the resources are being produced by the working force of the population at a variable rate. This randomness in the reproduction rate makes the population growth stochastic in nature and one cannot predict the exact form of evolution. Hence we study the growth by running simulations for such a population and taking an ensemble averaged over 500 to 5000 such simulations as per the need. While the population reproduces in a stochastic manner, we have implemented a constraint on the amount of resources available for the population. This is important to make the simulations more realistic. The rate of resource production then is tuned to find the rate which suits the survival of the species. We also compute the mean life time of the species corresponding to different resource production rate. Study for these outcomes in the parameter space defined by the reproduction probabilities and rate of resource production is carried out.

  14. 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.

  15. 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.

  16. Stochastic dynamics and logistic population growth

    Science.gov (United States)

    Méndez, Vicenç; Assaf, Michael; Campos, Daniel; Horsthemke, Werner

    2015-06-01

    The Verhulst model is probably the best known macroscopic rate equation in population ecology. It depends on two parameters, the intrinsic growth rate and the carrying capacity. These parameters can be estimated for different populations and are related to the reproductive fitness and the competition for limited resources, respectively. We investigate analytically and numerically the simplest possible microscopic scenarios that give rise to the logistic equation in the deterministic mean-field limit. We provide a definition of the two parameters of the Verhulst equation in terms of microscopic parameters. In addition, we derive the conditions for extinction or persistence of the population by employing either the momentum-space spectral theory or the real-space Wentzel-Kramers-Brillouin approximation to determine the probability distribution function and the mean time to extinction of the population. Our analytical results agree well with numerical simulations.

  17. Seasonal population dynamics and energy consumption by ...

    African Journals Online (AJOL)

    Dynamiques saisonnières de population et consommation énergétique par les oiseaux aquatiques d'un petit estuaire tempéré De simples mesures des dynamiques de population et de consommation énergétique peuvent fournir des informations de base sur le rôle des consommateurs au sein des réseaux trophiques, ...

  18. Statistical dynamics of regional populations and economies

    Science.gov (United States)

    Huo, Jie; Wang, Xu-Ming; Hao, Rui; Wang, Peng

    Quantitative analysis of human behavior and social development is becoming a hot spot of some interdisciplinary studies. A statistical analysis on the population and GDP of 150 cities in China from 1990 to 2013 is conducted. The result indicates the cumulative probability distribution of the populations and that of the GDPs obeying the shifted power law, respectively. In order to understand these characteristics, a generalized Langevin equation describing variation of population is proposed, which is based on the correlations between population and GDP as well as the random fluctuations of the related factors. The equation is transformed into the Fokker-Plank equation to express the evolution of population distribution. The general solution demonstrates a transition of the distribution from the normal Gaussian distribution to a shifted power law, which suggests a critical point of time at which the transition takes place. The shifted power law distribution in the supercritical situation is qualitatively in accordance with the practical result. The distribution of the GDPs is derived from the well-known Cobb-Douglas production function. The result presents a change, in supercritical situation, from a shifted power law to the Gaussian distribution. This is a surprising result-the regional GDP distribution of our world will be the Gaussian distribution one day in the future. The discussions based on the changing trend of economic growth suggest it will be true. Therefore, these theoretical attempts may draw a historical picture of our society in the aspects of population and economy.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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

  4. Missing cycles: Effect of climate change on population dynamics

    Indian Academy of Sciences (India)

    population dynamics of the larch budmoth – an insect pest which causes massive defoliation of entire larch forests ... hypothesized that global warming has led to the collapse of the cycles ... When temperatures increase after winter, and the.

  5. A linear model of population dynamics

    Science.gov (United States)

    Lushnikov, A. A.; Kagan, A. I.

    2016-08-01

    The Malthus process of population growth is reformulated in terms of the probability w(n,t) to find exactly n individuals at time t assuming that both the birth and the death rates are linear functions of the population size. The master equation for w(n,t) is solved exactly. It is shown that w(n,t) strongly deviates from the Poisson distribution and is expressed in terms either of Laguerre’s polynomials or a modified Bessel function. The latter expression allows for considerable simplifications of the asymptotic analysis of w(n,t).

  6. 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.

  7. 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.

  8. POPULATION DYNAMICS OF THE WANDERING ALBATROSS ...

    African Journals Online (AJOL)

    Changes in several demographic parameters that appear to be influenced by both environmental and anthropogenic effects are described. From 1994–2001, the proportion of first-time breeders in the population was positively correlated with the maximum ENSO (Niño 3) index, whereas from 1984–2000 the annual survival ...

  9. 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.

  10. 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.

  11. 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

  12. Differential response of olfactory sensory neuron populations to copper ion exposure in zebrafish

    Energy Technology Data Exchange (ETDEWEB)

    Lazzari, Maurizio, E-mail: maurizio.lazzari@unibo.it; Bettini, Simone; Milani, Liliana; Maurizii, Maria Gabriella; Franceschini, Valeria

    2017-02-15

    Highlights: • Copper exposure affects ciliated olfactory receptors more than microvillar cells. • Crypt olfactory sensory neurons are not affected by copper exposure. • Copper exposure induces an increase in the amount of sensory epithelium. - Abstract: The peripheral olfactory system of fish is in direct contact with the external aqueous environment, so dissolved contaminants can easily impair sensory functions and cause neurobehavioral injuries. The olfactory epithelium of fish is arranged in lamellae forming a rosette in the olfactory cavity and contains three main types of olfactory sensory neurons (OSNs): ciliated (cOSNs) and microvillous olfactory sensory neurons (mOSNs), common to all vertebrates, and a third minor group of olfactory neurons, crypt cells, absent in tetrapods. Since copper is a ubiquitously diffusing olfactory toxicant and a spreading contaminant in urban runoff, we investigated the effect of low copper concentration on the three different OSNs in the olfactory epithelium of zebrafish, a model system widely used in biological research. Image analysis was applied for morphometry and quantification of immunohistochemically detected OSNs. Copper exposure resulted in an evident decrease in olfactory epithelium thickness. Moreover, after exposure, the lamellae of the dorsal and ventral halves of the olfactory rosettes showed a different increase in their sensory areas, suggesting a lateral migration of new cells into non-sensory regions. The results of the present study provide clear evidence of a differential response of the three neural cell populations of zebrafish olfactory mucosa after 96 h of exposure to copper ions at the sublethal concentration of 30 μg L{sup −1}. Densitometric values of cONS, immunostained with anti-G {sub αolf}, decreased of about 60% compared to the control. When the fish were transferred to water without copper addition and examined after 3, 10 and 30 days, we observed a partial restoration of anti-G {sub

  13. Differential response of olfactory sensory neuron populations to copper ion exposure in zebrafish

    International Nuclear Information System (INIS)

    Lazzari, Maurizio; Bettini, Simone; Milani, Liliana; Maurizii, Maria Gabriella; Franceschini, Valeria

    2017-01-01

    Highlights: • Copper exposure affects ciliated olfactory receptors more than microvillar cells. • Crypt olfactory sensory neurons are not affected by copper exposure. • Copper exposure induces an increase in the amount of sensory epithelium. - Abstract: The peripheral olfactory system of fish is in direct contact with the external aqueous environment, so dissolved contaminants can easily impair sensory functions and cause neurobehavioral injuries. The olfactory epithelium of fish is arranged in lamellae forming a rosette in the olfactory cavity and contains three main types of olfactory sensory neurons (OSNs): ciliated (cOSNs) and microvillous olfactory sensory neurons (mOSNs), common to all vertebrates, and a third minor group of olfactory neurons, crypt cells, absent in tetrapods. Since copper is a ubiquitously diffusing olfactory toxicant and a spreading contaminant in urban runoff, we investigated the effect of low copper concentration on the three different OSNs in the olfactory epithelium of zebrafish, a model system widely used in biological research. Image analysis was applied for morphometry and quantification of immunohistochemically detected OSNs. Copper exposure resulted in an evident decrease in olfactory epithelium thickness. Moreover, after exposure, the lamellae of the dorsal and ventral halves of the olfactory rosettes showed a different increase in their sensory areas, suggesting a lateral migration of new cells into non-sensory regions. The results of the present study provide clear evidence of a differential response of the three neural cell populations of zebrafish olfactory mucosa after 96 h of exposure to copper ions at the sublethal concentration of 30 μg L"−"1. Densitometric values of cONS, immunostained with anti-G _α_o_l_f, decreased of about 60% compared to the control. When the fish were transferred to water without copper addition and examined after 3, 10 and 30 days, we observed a partial restoration of anti-G _

  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. 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).

  17. 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.

  18. Population dynamics and distribution of the coffee berry borer ...

    African Journals Online (AJOL)

    Population dynamics and distribution of coffee berry borer, Hypothenemus hampei (Ferrari) (Coleoptera: Scolytidae) were studied on Coffea arabica L. in southwestern region of Ethiopia. Thirty coffee trees were sampled at weekly intervals from 2000 to 2001. Findings of this study showed that coffee berry borer population ...

  19. Ruffed grouse population dynamics in the central and southern Appalachians

    Science.gov (United States)

    John M. Giuliano Tirpak; C. Allan Miller; Thomas J. Allen; Steve Bittner; David A. Buehler; John W. Edwards; Craig A. Harper; William K. Igo; Gary W. Norman; M. Seamster; Dean F. Stauffer

    2006-01-01

    Ruffed grouse (Bonasa urnbellus; hereafter grouse) populations in the central and southern Appalachians are in decline. However, limited information on the dynamics of these populations prevents the development of effective management strategies to reverse these trends. We used radiotelemetry data collected on grouse to parameterize 6 models of...

  20. 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.

  1. [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.

  2. 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

  3. 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.

  4. Genetic epidemiology of motor neuron disease-associated variants in the Scottish population.

    Science.gov (United States)

    Black, Holly A; Leighton, Danielle J; Cleary, Elaine M; Rose, Elaine; Stephenson, Laura; Colville, Shuna; Ross, David; Warner, Jon; Porteous, Mary; Gorrie, George H; Swingler, Robert; Goldstein, David; Harms, Matthew B; Connick, Peter; Pal, Suvankar; Aitman, Timothy J; Chandran, Siddharthan

    2017-03-01

    Genetic understanding of motor neuron disease (MND) has evolved greatly in the past 10 years, including the recent identification of association between MND and variants in TBK1 and NEK1. Our aim was to determine the frequency of pathogenic variants in known MND genes and to assess whether variants in TBK1 and NEK1 contribute to the burden of MND in the Scottish population. SOD1, TARDBP, OPTN, TBK1, and NEK1 were sequenced in 441 cases and 400 controls. In addition to 44 cases known to carry a C9orf72 hexanucleotide repeat expansion, we identified 31 cases and 2 controls that carried a loss-of-function or pathogenic variant. Loss-of-function variants were found in TBK1 in 3 cases and no controls and, separately, in NEK1 in 3 cases and no controls. This study provides an accurate description of the genetic epidemiology of MND in Scotland and provides support for the contribution of both TBK1 and NEK1 to MND susceptibility in the Scottish population. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  5. A simple approach to ignoring irrelevant variables by population decoding based on multisensory neurons

    Science.gov (United States)

    Kim, HyungGoo R.; Pitkow, Xaq; Angelaki, Dora E.

    2016-01-01

    Sensory input reflects events that occur in the environment, but multiple events may be confounded in sensory signals. For example, under many natural viewing conditions, retinal image motion reflects some combination of self-motion and movement of objects in the world. To estimate one stimulus event and ignore others, the brain can perform marginalization operations, but the neural bases of these operations are poorly understood. Using computational modeling, we examine how multisensory signals may be processed to estimate the direction of self-motion (i.e., heading) and to marginalize out effects of object motion. Multisensory neurons represent heading based on both visual and vestibular inputs and come in two basic types: “congruent” and “opposite” cells. Congruent cells have matched heading tuning for visual and vestibular cues and have been linked to perceptual benefits of cue integration during heading discrimination. Opposite cells have mismatched visual and vestibular heading preferences and are ill-suited for cue integration. We show that decoding a mixed population of congruent and opposite cells substantially reduces errors in heading estimation caused by object motion. In addition, we present a general formulation of an optimal linear decoding scheme that approximates marginalization and can be implemented biologically by simple reinforcement learning mechanisms. We also show that neural response correlations induced by task-irrelevant variables may greatly exceed intrinsic noise correlations. Overall, our findings suggest a general computational strategy by which neurons with mismatched tuning for two different sensory cues may be decoded to perform marginalization operations that dissociate possible causes of sensory inputs. PMID:27334948

  6. Comparison of classifiers for decoding sensory and cognitive information from prefrontal neuronal populations.

    Directory of Open Access Journals (Sweden)

    Elaine Astrand

    Full Text Available Decoding neuronal information is important in neuroscience, both as a basic means to understand how neuronal activity is related to cerebral function and as a processing stage in driving neuroprosthetic effectors. Here, we compare the readout performance of six commonly used classifiers at decoding two different variables encoded by the spiking activity of the non-human primate frontal eye fields (FEF: the spatial position of a visual cue, and the instructed orientation of the animal's attention. While the first variable is exogenously driven by the environment, the second variable corresponds to the interpretation of the instruction conveyed by the cue; it is endogenously driven and corresponds to the output of internal cognitive operations performed on the visual attributes of the cue. These two variables were decoded using either a regularized optimal linear estimator in its explicit formulation, an optimal linear artificial neural network estimator, a non-linear artificial neural network estimator, a non-linear naïve Bayesian estimator, a non-linear Reservoir recurrent network classifier or a non-linear Support Vector Machine classifier. Our results suggest that endogenous information such as the orientation of attention can be decoded from the FEF with the same accuracy as exogenous visual information. All classifiers did not behave equally in the face of population size and heterogeneity, the available training and testing trials, the subject's behavior and the temporal structure of the variable of interest. In most situations, the regularized optimal linear estimator and the non-linear Support Vector Machine classifiers outperformed the other tested decoders.

  7. 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.

  8. 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

  9. 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, ...

  10. Geography, European colonization, and past population dynamics in Africa

    OpenAIRE

    Vaz Silva, Luis

    2005-01-01

    Past population dynamics in Africa have remained largely elusive due to the lack of demographic data. Researchers are understandably deterred from trying to explain what is not known and African historical population estimates suffer from this lack of interest. In this paper I explain present day African population densities using mostly ecological factors as explanatory variables. I find evidence supporting the view that ecological factors deeply affected precolonial patterns of human settle...

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. Dynamics of epidemics outbreaks in heterogeneous populations

    Science.gov (United States)

    Brockmann, Dirk; Morales-Gallardo, Alejandro; Geisel, Theo

    2007-03-01

    The dynamics of epidemic outbreaks have been investigated in recent years within two alternative theoretical paradigms. The key parameter of mean field type of models such as the SIR model is the basic reproduction number R0, the average number of secondary infections caused by one infected individual. Recently, scale free network models have received much attention as they account for the high variability in the number of social contacts involved. These models predict an infinite basic reproduction number in some cases. We investigate the impact of heterogeneities of contact rates in a generic model for epidemic outbreaks. We present a system in which both the time periods of being infectious and the time periods between transmissions are Poissonian processes. The heterogeneities are introduced by means of strongly variable contact rates. In contrast to scale free network models we observe a finite basic reproduction number and, counterintuitively a smaller overall epidemic outbreak as compared to the homogeneous system. Our study thus reveals that heterogeneities in contact rates do not necessarily facilitate the spread to infectious disease but may well attenuate it.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. 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.

  9. Distinct populations of neurons respond to emotional valence and arousal in the human subthalamic nucleus.

    Science.gov (United States)

    Sieger, Tomáš; Serranová, Tereza; Růžička, Filip; Vostatek, Pavel; Wild, Jiří; Štastná, Daniela; Bonnet, Cecilia; Novák, Daniel; Růžička, Evžen; Urgošík, Dušan; Jech, Robert

    2015-03-10

    Both animal studies and studies using deep brain stimulation in humans have demonstrated the involvement of the subthalamic nucleus (STN) in motivational and emotional processes; however, participation of this nucleus in processing human emotion has not been investigated directly at the single-neuron level. We analyzed the relationship between the neuronal firing from intraoperative microrecordings from the STN during affective picture presentation in patients with Parkinson's disease (PD) and the affective ratings of emotional valence and arousal performed subsequently. We observed that 17% of neurons responded to emotional valence and arousal of visual stimuli according to individual ratings. The activity of some neurons was related to emotional valence, whereas different neurons responded to arousal. In addition, 14% of neurons responded to visual stimuli. Our results suggest the existence of neurons involved in processing or transmission of visual and emotional information in the human STN, and provide evidence of separate processing of the affective dimensions of valence and arousal at the level of single neurons as well.

  10. An age-structured population balance model for microbial dynamics

    Directory of Open Access Journals (Sweden)

    Duarte M.V.E.

    2003-01-01

    Full Text Available This work presents an age-structured population balance model (ASPBM for a bioprocess in a continuous stirred-tank fermentor. It relates the macroscopic properties and dynamic behavior of biomass to the operational parameters and microscopic properties of cells. Population dynamics is governed by two time- and age-dependent density functions for living and dead cells, accounting for the influence of substrate and dissolved oxygen concentrations on cell division, aging and death processes. The ASPBM described biomass and substrate oscillations in aerobic continuous cultures as experimentally observed. It is noteworthy that a small data set consisting of nonsegregated measurements was sufficient to adjust a complex segregated mathematical model.

  11. 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...

  12. Network evolution induced by the dynamical rules of two populations

    International Nuclear Information System (INIS)

    Platini, Thierry; Zia, R K P

    2010-01-01

    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 (N a and N b ) and preferred degree (κ a and κ b 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 (k bb ) and (k ab ) presents three time regimes and a non-monotonic behavior well captured by our theory. Surprisingly, when the population sizes are equal N a = N b , the ratio of the restricted degree θ 0 = (k ab )/(k bb ) appears to be an integer in the asymptotic limits of the three time regimes. For early times (defined by t 1 = κ b ) the total number of links presents a linear evolution, where the two populations are indistinguishable and where θ 0 = 1. Interestingly, in the intermediate time regime (defined for t 1 2 ∝κ a and for which θ 0 = 5), the system reaches a transient stationary state, where the number of contacts among 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 θ 0 = 3

  13. Social Information Links Individual Behavior to Population and Community Dynamics.

    Science.gov (United States)

    Gil, Michael A; Hein, Andrew M; Spiegel, Orr; Baskett, Marissa L; Sih, Andrew

    2018-05-07

    When individual animals make decisions, they routinely use information produced intentionally or unintentionally by other individuals. Despite its prevalence and established fitness consequences, the effects of such social information on ecological dynamics remain poorly understood. Here, we synthesize results from ecology, evolutionary biology, and animal behavior to show how the use of social information can profoundly influence the dynamics of populations and communities. We combine recent theoretical and empirical results and introduce simple population models to illustrate how social information use can drive positive density-dependent growth of populations and communities (Allee effects). Furthermore, social information can shift the nature and strength of species interactions, change the outcome of competition, and potentially increase extinction risk in harvested populations and communities. Copyright © 2018 Elsevier Ltd. All rights reserved.

  14. 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...

  15. From migration to settlement: the pathways, migration modes and dynamics of neurons in the developing brain

    Science.gov (United States)

    HATANAKA, Yumiko; ZHU, Yan; TORIGOE, Makio; KITA, Yoshiaki; MURAKAMI, Fujio

    2016-01-01

    Neuronal migration is crucial for the construction of the nervous system. To reach their correct destination, migrating neurons choose pathways using physical substrates and chemical cues of either diffusible or non-diffusible nature. Migrating neurons extend a leading and a trailing process. The leading process, which extends in the direction of migration, determines navigation, in particular when a neuron changes its direction of migration. While most neurons simply migrate radially, certain neurons switch their mode of migration between radial and tangential, with the latter allowing migration to destinations far from the neurons’ site of generation. Consequently, neurons with distinct origins are intermingled, which results in intricate neuronal architectures and connectivities and provides an important basis for higher brain function. The trailing process, in contrast, contributes to the late stage of development by turning into the axon, thus contributing to the formation of neuronal circuits. PMID:26755396

  16. A general modeling framework for describing spatially structured population dynamics

    Science.gov (United States)

    Sample, Christine; Fryxell, John; Bieri, Joanna; Federico, Paula; Earl, Julia; Wiederholt, Ruscena; Mattsson, Brady; Flockhart, Tyler; Nicol, Sam; Diffendorfer, James E.; Thogmartin, Wayne E.; Erickson, Richard A.; Norris, D. Ryan

    2017-01-01

    Variation in movement across time and space fundamentally shapes the abundance and distribution of populations. Although a variety of approaches model structured population dynamics, they are limited to specific types of spatially structured populations and lack a unifying framework. Here, we propose a unified network-based framework sufficiently novel in its flexibility to capture a wide variety of spatiotemporal processes including metapopulations and a range of migratory patterns. It can accommodate different kinds of age structures, forms of population growth, dispersal, nomadism and migration, and alternative life-history strategies. Our objective was to link three general elements common to all spatially structured populations (space, time and movement) under a single mathematical framework. To do this, we adopt a network modeling approach. The spatial structure of a population is represented by a weighted and directed network. Each node and each edge has a set of attributes which vary through time. The dynamics of our network-based population is modeled with discrete time steps. Using both theoretical and real-world examples, we show how common elements recur across species with disparate movement strategies and how they can be combined under a unified mathematical framework. We illustrate how metapopulations, various migratory patterns, and nomadism can be represented with this modeling approach. We also apply our network-based framework to four organisms spanning a wide range of life histories, movement patterns, and carrying capacities. General computer code to implement our framework is provided, which can be applied to almost any spatially structured population. This framework contributes to our theoretical understanding of population dynamics and has practical management applications, including understanding the impact of perturbations on population size, distribution, and movement patterns. By working within a common framework, there is less chance

  17. 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.

  18. SIR dynamics in structured populations with heterogeneous connectivity

    OpenAIRE

    Volz, Erik

    2005-01-01

    Most epidemic models assume equal mixing among members of a population. An alternative approach is to model a population as random network in which individuals may have heterogeneous connectivity. This paper builds on previous research by describing the exact dynamical behavior of epidemics as they occur in random networks. A system of nonlinear differential equations is presented which describes the behavior of epidemics spreading through random networks with arbitrary degree distributions. ...

  19. 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.

  20. 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.

  1. Adaptation in Coding by Large Populations of Neurons in the Retina

    Science.gov (United States)

    Ioffe, Mark L.

    A comprehensive theory of neural computation requires an understanding of the statistical properties of the neural population code. The focus of this work is the experimental study and theoretical analysis of the statistical properties of neural activity in the tiger salamander retina. This is an accessible yet complex system, for which we control the visual input and record from a substantial portion--greater than a half--of the ganglion cell population generating the spiking output. Our experiments probe adaptation of the retina to visual statistics: a central feature of sensory systems which have to adjust their limited dynamic range to a far larger space of possible inputs. In Chapter 1 we place our work in context with a brief overview of the relevant background. In Chapter 2 we describe the experimental methodology of recording from 100+ ganglion cells in the tiger salamander retina. In Chapter 3 we first present the measurements of adaptation of individual cells to changes in stimulation statistics and then investigate whether pairwise correlations in fluctuations of ganglion cell activity change across different stimulation conditions. We then transition to a study of the population-level probability distribution of the retinal response captured with maximum-entropy models. Convergence of the model inference is presented in Chapter 4. In Chapter 5 we first test the empirical presence of a phase transition in such models fitting the retinal response to different experimental conditions, and then proceed to develop other characterizations which are sensitive to complexity in the interaction matrix. This includes an analysis of the dynamics of sampling at finite temperature, which demonstrates a range of subtle attractor-like properties in the energy landscape. These are largely conserved when ambient illumination is varied 1000-fold, a result not necessarily apparent from the measured low-order statistics of the distribution. Our results form a consistent

  2. 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

  3. Population dynamics of light-limited phytoplankton : Microcosm experiments

    NARCIS (Netherlands)

    Huisman, Jef

    This paper investigates the extent to which the predictions of an elementary model for light-limited growth are matched by laboratory experiments with light-limited phytoplankton. The model and experiments link the population dynamics of phytoplankton species with changes in the light gradient

  4. Distribution and population dynamics of Rhizobium sp. introduced into soil

    NARCIS (Netherlands)

    Postma, J.

    1989-01-01

    In this thesis the population dynamics of bacteria introduced into soil was studied. In the introduction, the existence of microhabitats favourable for the survival of indigenous bacteria is discussed. Knowledge about the distribution of introduced bacteria over

  5. seasonal population dynamics of rodents of mount chilalo, arsi ...

    African Journals Online (AJOL)

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    ABSTRACT: A study on seasonal population dynamics of rodents was carried out on Mount. Chilalo from .... vegetation growth, availability of food and water, and ... vegetation (3,300–4,200 masl) (Alemayehu. Mengistu, 1975; APEDO and ABRDP, 2004). The mountain is one of the Afrotropical biodiversity hotspots areas.

  6. Population dynamics of soil microbes and diversity of Bacillus ...

    African Journals Online (AJOL)

    ONOS

    2010-01-25

    Jan 25, 2010 ... Population dynamics of soil microbes and diversity of ... 25.78, 25.78, 86.26, 24.73, 68.0, 26.8 and 26.8 kDa proteins and equivalent to Cyt, Cry5 and Cry2 toxins ..... Molecular weight (kDa) of protein fractions of the BT isolates.

  7. Population dynamics of the invasive fish, Gambusia affinis , in ...

    African Journals Online (AJOL)

    Repeated-measures ANOVA analyses on the catch per unit effort (CPUE) of G. affinis between sampling events and dams revealed significant differences in population dynamics among dams, although an overall trend of rapid increase followed by plateau in summer, with a rapid decline in winter was seen in most dams.

  8. Individual based model of slug population and spatial dynamics

    NARCIS (Netherlands)

    Choi, Y.H.; Bohan, D.A.; Potting, R.P.J.; Semenov, M.A.; Glen, D.M.

    2006-01-01

    The slug, Deroceras reticulatum, is one of the most important pests of agricultural and horticultural crops in UK and Europe. In this paper, a spatially explicit individual based model (IbM) is developed to study the dynamics of a population of D. reticulatum. The IbM establishes a virtual field

  9. 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.

  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 and armed violence in Colombia, 1985-2010].

    Science.gov (United States)

    Salaya, Hernán Eduardo; Rodríguez, Jesús

    2014-09-01

    Describe changes in the population structure of Colombia's municipalities in relation to internal displacement in response to armed violence. A descriptive ecological study was carried out. Secondary sources were consulted, taken from the Consolidated Registry of Displaced Population and from the National Administrative Department of Statistics, to calculate expulsion and reception rates for population displaced by violence from 2002 to 2010. Based on these rates, four groups were created of municipalities in the extreme quartile for each rate during the entire period, which were classified as high expulsion, low expulsion, high reception, and low reception. Subsequently, population pyramids and structure indicators were constructed for each group of municipalities for two comparative reference years (1985 and 2010). Municipalities with high expulsion or reception rates experienced a slower epidemiological transition, with lower mean ages and aging indices. The high expulsion group had the least regression, based on the Sundbärg index. In the high reception group, the masculinity ratio decreased the most, especially among the economically active population, and it had the highest population growth. Population dynamics in Colombia have been affected by armed violence and changes in these dynamics are not uniform across the country, leading to important social, economic, and cultural consequences. This study is useful for decision-making and public policy making.

  12. Bridging the Timescales of Single-Cell and Population Dynamics

    Science.gov (United States)

    Jafarpour, Farshid; Wright, Charles S.; Gudjonson, Herman; Riebling, Jedidiah; Dawson, Emma; Lo, Klevin; Fiebig, Aretha; Crosson, Sean; Dinner, Aaron R.; Iyer-Biswas, Srividya

    2018-04-01

    How are granular details of stochastic growth and division of individual cells reflected in smooth deterministic growth of population numbers? We provide an integrated, multiscale perspective of microbial growth dynamics by formulating a data-validated theoretical framework that accounts for observables at both single-cell and population scales. We derive exact analytical complete time-dependent solutions to cell-age distributions and population growth rates as functionals of the underlying interdivision time distributions, for symmetric and asymmetric cell division. These results provide insights into the surprising implications of stochastic single-cell dynamics for population growth. Using our results for asymmetric division, we deduce the time to transition from the reproductively quiescent (swarmer) to the replication-competent (stalked) stage of the Caulobacter crescentus life cycle. Remarkably, population numbers can spontaneously oscillate with time. We elucidate the physics leading to these population oscillations. For C. crescentus cells, we show that a simple measurement of the population growth rate, for a given growth condition, is sufficient to characterize the condition-specific cellular unit of time and, thus, yields the mean (single-cell) growth and division timescales, fluctuations in cell division times, the cell-age distribution, and the quiescence timescale.

  13. Nonequilibrium population dynamics of phenotype conversion of cancer cells.

    Directory of Open Access Journals (Sweden)

    Joseph Xu Zhou

    Full Text Available Tumorigenesis is a dynamic biological process that involves distinct cancer cell subpopulations proliferating at different rates and interconverting between them. In this paper we proposed a mathematical framework of population dynamics that considers both distinctive growth rates and intercellular transitions between cancer cell populations. Our mathematical framework showed that both growth and transition influence the ratio of cancer cell subpopulations but the latter is more significant. We derived the condition that different cancer cell types can maintain distinctive subpopulations and we also explain why there always exists a stable fixed ratio after cell sorting based on putative surface markers. The cell fraction ratio can be shifted by changing either the growth rates of the subpopulations (Darwinism selection or by environment-instructed transitions (Lamarckism induction. This insight can help us to understand the dynamics of the heterogeneity of cancer cells and lead us to new strategies to overcome cancer drug resistance.

  14. 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.

  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. Rethinking the logistic approach for population dynamics of mutualistic interactions.

    Science.gov (United States)

    García-Algarra, Javier; Galeano, Javier; Pastor, Juan Manuel; Iriondo, José María; Ramasco, José J

    2014-12-21

    Mutualistic communities have an internal structure that makes them resilient to external perturbations. Late research has focused on their stability and the topology of the relations between the different organisms to explain the reasons of the system robustness. Much less attention has been invested in analyzing the systems dynamics. The main population models in use are modifications of the r-K formulation of logistic equation with additional terms to account for the benefits produced by the interspecific interactions. These models have shortcomings as the so-called r-K formulation diverges under some conditions. In this work, we introduce a model for population dynamics under mutualism that preserves the original logistic formulation. It is mathematically simpler than the widely used type II models, although it shows similar complexity in terms of fixed points and stability of the dynamics. We perform an analytical stability analysis and numerical simulations to study the model behavior in general interaction scenarios including tests of the resilience of its dynamics under external perturbations. Despite its simplicity, our results indicate that the model dynamics shows an important richness that can be used to gain further insights in the dynamics of mutualistic communities. Copyright © 2014 Elsevier Ltd. All rights reserved.

  17. Dynamics and Synchronization of Noise Perturbed Ensembles of Periodically Activated neuron Cells

    DEFF Research Database (Denmark)

    Belykh, V. N.; Pankratova, Evgeniya; Mosekilde, Erik

    2008-01-01

    The role of noise for a single neuron and for an ensemble of mutually coupled neurons is investigated. For a single element we show that an increase in noise intensity in the regime of irregular. ring enhances the coherence of the neuronal response. For this regime of spiking a study...... based on the connection graph stability method and through numerical simulation....

  18. 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.

  19. 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.

  20. Complexity and multifractality of neuronal noise in mouse and human hippocampal epileptiform dynamics

    Science.gov (United States)

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

    2012-10-01

    Fractal methods offer an invaluable means of investigating turbulent nonlinearity in non-stationary biomedical recordings from the brain. Here, we investigate properties of complexity (i.e. the correlation dimension, maximum Lyapunov exponent, 1/fγ noise and approximate entropy) and multifractality in background neuronal noise-like activity underlying epileptiform transitions recorded at the intracellular and local network scales from two in vitro models: the whole-intact mouse hippocampus and lesional human hippocampal slices. Our results show evidence for reduced dynamical complexity and multifractal signal features following transition to the ictal epileptiform state. These findings suggest that pathological breakdown in multifractal complexity coincides with loss of signal variability or heterogeneity, consistent with an unhealthy ictal state that is far from the equilibrium of turbulent yet healthy fractal dynamics in the brain. Thus, it appears that background noise-like activity successfully captures complex and multifractal signal features that may, at least in part, be used to classify and identify brain state transitions in the healthy and epileptic brain, offering potential promise for therapeutic neuromodulatory strategies for afflicted patients suffering from epilepsy and other related neurological disorders. This paper is based on chapter 5 of Serletis (2010 PhD Dissertation Department of Physiology, Institute of Biomaterials and Biomedical Engineering, University of Toronto).

  1. Theta rhythm-like bidirectional cycling dynamics of living neuronal networks in vitro.

    Science.gov (United States)

    Gladkov, Arseniy; Grinchuk, Oleg; Pigareva, Yana; Mukhina, Irina; Kazantsev, Victor; Pimashkin, Alexey

    2018-01-01

    The phenomena of synchronization, rhythmogenesis and coherence observed in brain networks are believed to be a dynamic substrate for cognitive functions such as learning and memory. However, researchers are still debating whether the rhythmic activity emerges from the network morphology that developed during neurogenesis or as a result of neuronal dynamics achieved under certain conditions. In the present study, we observed self-organized spiking activity that converged to long, complex and rhythmically repeated superbursts in neural networks formed by mature hippocampal cultures with a high cellular density. The superburst lasted for tens of seconds and consisted of hundreds of short (50-100 ms) small bursts with a high spiking rate of 139.0 ± 78.6 Hz that is associated with high-frequency oscillations in the hippocampus. In turn, the bursting frequency represents a theta rhythm (11.2 ± 1.5 Hz). The distribution of spikes within the bursts was non-random, representing a set of well-defined spatio-temporal base patterns or motifs. The long superburst was classified into two types. Each type was associated with a unique direction of spike propagation and, hence, was encoded by a binary sequence with random switching between the two "functional" states. The precisely structured bidirectional rhythmic activity that developed in self-organizing cultured networks was quite similar to the activity observed in the in vivo experiments.

  2. Theta rhythm-like bidirectional cycling dynamics of living neuronal networks in vitro.

    Directory of Open Access Journals (Sweden)

    Arseniy Gladkov

    Full Text Available The phenomena of synchronization, rhythmogenesis and coherence observed in brain networks are believed to be a dynamic substrate for cognitive functions such as learning and memory. However, researchers are still debating whether the rhythmic activity emerges from the network morphology that developed during neurogenesis or as a result of neuronal dynamics achieved under certain conditions. In the present study, we observed self-organized spiking activity that converged to long, complex and rhythmically repeated superbursts in neural networks formed by mature hippocampal cultures with a high cellular density. The superburst lasted for tens of seconds and consisted of hundreds of short (50-100 ms small bursts with a high spiking rate of 139.0 ± 78.6 Hz that is associated with high-frequency oscillations in the hippocampus. In turn, the bursting frequency represents a theta rhythm (11.2 ± 1.5 Hz. The distribution of spikes within the bursts was non-random, representing a set of well-defined spatio-temporal base patterns or motifs. The long superburst was classified into two types. Each type was associated with a unique direction of spike propagation and, hence, was encoded by a binary sequence with random switching between the two "functional" states. The precisely structured bidirectional rhythmic activity that developed in self-organizing cultured networks was quite similar to the activity observed in the in vivo experiments.

  3. Vagal stimulation targets select populations of intrinsic cardiac neurons to control neurally induced atrial fibrillation.

    Science.gov (United States)

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

    2016-11-01

    Mediastinal nerve stimulation (MNS) reproducibly evokes atrial fibrillation (AF) by excessive and heterogeneous activation of intrinsic cardiac (IC) neurons. This study evaluated whether preemptive vagus nerve stimulation (VNS) impacts MNS-induced evoked changes in IC neural network activity to thereby alter susceptibility to AF. IC neuronal activity in the right atrial ganglionated plexus was directly recorded in anesthetized canines (n = 8) using a linear microelectrode array concomitant with right atrial electrical activity in response to: 1) epicardial touch or great vessel occlusion vs. 2) stellate or vagal stimulation. From these stressors, post hoc analysis (based on the Skellam distribution) defined IC neurons so recorded as afferent, efferent, or convergent (afferent and efferent inputs) local circuit neurons (LCN). The capacity of right-sided MNS to modify IC activity in the induction of AF was determined before and after preemptive right (RCV)- vs. left (LCV)-sided VNS (15 Hz, 500 μs; 1.2× bradycardia threshold). Neuronal (n = 89) activity at baseline (0.11 ± 0.29 Hz) increased during MNS-induced AF (0.51 ± 1.30 Hz; P neuronal synchrony increased during neurally induced AF, a local neural network response mitigated by preemptive VNS. These antiarrhythmic effects persisted post-VNS for, on average, 26 min. In conclusion, VNS preferentially targets convergent LCNs and their interactive coherence to mitigate the potential for neurally induced AF. The antiarrhythmic properties imposed by VNS exhibit memory. Copyright © 2016 the American Physiological Society.

  4. Dynamic changes in GABAA receptors on basal forebrain cholinergic neurons following sleep deprivation and recovery

    Directory of Open Access Journals (Sweden)

    Jones Barbara E

    2007-02-01

    Full Text Available Abstract Background The basal forebrain (BF cholinergic neurons play an important role in cortical activation and arousal and are active in association with cortical activation of waking and inactive in association with cortical slow wave activity of sleep. In view of findings that GABAA receptors (Rs and inhibitory transmission undergo dynamic changes as a function of prior activity, we investigated whether the GABAARs on cholinergic cells might undergo such changes as a function of their prior activity during waking vs. sleep. Results In the brains of rats under sleep control (SC, sleep deprivation (SD or sleep recovery (SR conditions in the 3 hours prior to sacrifice, we examined immunofluorescent staining for β2–3 subunit GABAARs on choline acetyltransferase (ChAT immunopositive (+ cells in the magnocellular BF. In sections also stained for c-Fos, β2–3 GABAARs were present on ChAT+ neurons which expressed c-Fos in the SD group alone and were variable or undetectable on other ChAT+ cells across groups. In dual-immunostained sections, the luminance of β2–3 GABAARs over the membrane of ChAT+ cells was found to vary significantly across conditions and to be significantly higher in SD than SC or SR groups. Conclusion We conclude that membrane GABAARs increase on cholinergic cells as a result of activity during sustained waking and reciprocally decrease as a result of inactivity during sleep. These changes in membrane GABAARs would be associated with increased GABA-mediated inhibition of cholinergic cells following prolonged waking and diminished inhibition following sleep and could thus reflect a homeostatic process regulating cholinergic cell activity and thereby indirectly cortical activity across the sleep-waking cycle.

  5. An age structured model for obesity prevalence dynamics in populations

    Directory of Open Access Journals (Sweden)

    Gilberto González Parra

    2010-08-01

    Full Text Available Objective. Modeling the correlation of the development of obesity in a population with age and time and predict the dynamics of the correlation of the development of obesity in a population with age and time under different scenarios in Valencia (Spain. Materials and methods. An age structured mathematical model is used to describe the future dynamics of obesity prevalence for different ages in human population with excess weight. Simulation of the model with parameters estimated using the Health Survey of the Region of Valencia 2000 (4.319 interviews and Health Survey of the Region of Valencia 2005 (4.012 interviews. The model considers only overweight and obese populations since these subpopulations are the most relevant on obesity health concern. Results. The model allows predicting and studying the prevalence of obesity for each age. Results showed an increasing trend of obesity in the following years in well accordance with the trend observed in several countries. Conclusions. Based on the numerical simulations it is possible to conclude that the age structured mathematical model is suitable to forecast the obesity epidemic in each age group in different countries. Additionally, this type of models may be applied to study other characteristics of other populations such animal populations.

  6. Modeling structured population dynamics using data from unmarked individuals

    Science.gov (United States)

    Grant, Evan H. Campbell; Zipkin, Elise; Thorson, James T.; See, Kevin; Lynch, Heather J.; Kanno, Yoichiro; Chandler, Richard; Letcher, Benjamin H.; Royle, J. Andrew

    2014-01-01

    The study of population dynamics requires unbiased, precise estimates of abundance and vital rates that account for the demographic structure inherent in all wildlife and plant populations. Traditionally, these estimates have only been available through approaches that rely on intensive mark–recapture data. We extended recently developed N-mixture models to demonstrate how demographic parameters and abundance can be estimated for structured populations using only stage-structured count data. Our modeling framework can be used to make reliable inferences on abundance as well as recruitment, immigration, stage-specific survival, and detection rates during sampling. We present a range of simulations to illustrate the data requirements, including the number of years and locations necessary for accurate and precise parameter estimates. We apply our modeling framework to a population of northern dusky salamanders (Desmognathus fuscus) in the mid-Atlantic region (USA) and find that the population is unexpectedly declining. Our approach represents a valuable advance in the estimation of population dynamics using multistate data from unmarked individuals and should additionally be useful in the development of integrated models that combine data from intensive (e.g., mark–recapture) and extensive (e.g., counts) data sources.

  7. 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…

  8. 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.

  9. 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.

  10. Efficient characterisation of large deviations using population dynamics

    Science.gov (United States)

    Brewer, Tobias; Clark, Stephen R.; Bradford, Russell; Jack, Robert L.

    2018-05-01

    We consider population dynamics as implemented by the cloning algorithm for analysis of large deviations of time-averaged quantities. We use the simple symmetric exclusion process with periodic boundary conditions as a prototypical example and investigate the convergence of the results with respect to the algorithmic parameters, focussing on the dynamical phase transition between homogeneous and inhomogeneous states, where convergence is relatively difficult to achieve. We discuss how the performance of the algorithm can be optimised, and how it can be efficiently exploited on parallel computing platforms.

  11. Changes in Population Dynamics in Mutualistic versus Pathogenic Viruses

    Directory of Open Access Journals (Sweden)

    Marilyn J. Roossinck

    2011-01-01

    Full Text Available Although generally regarded as pathogens, viruses can also be mutualists. A number of examples of extreme mutualism (i.e., symbiogenesis have been well studied. Other examples of mutualism are less common, but this is likely because viruses have rarely been thought of as having any beneficial effects on their hosts. The effect of mutualism on the population dynamics of viruses is a topic that has not been addressed experimentally. However, the potential for understanding mutualism and how a virus might become a mutualist may be elucidated by understanding these dynamics.

  12. Midbrain and forebrain patterning delivers immunocytochemically and functionally similar populations of neuropeptide Y containing GABAergic neurons.

    Science.gov (United States)

    Khaira, S K; Nefzger, C M; Beh, S J; Pouton, C W; Haynes, J M

    2011-09-01

    Neurons differentiated in vitro from embryonic stem cells (ESCs) have the potential to serve both as models of disease states and in drug discovery programs. In this study, we use sonic hedgehog (SHH) and fibroblast growth factor 8 (FGF-8) to enrich for forebrain and midbrain phenotypes from mouse ESCs. We then investigate, using Ca(2+) imaging and [(3)H]-GABA release studies, whether the GABAergic neurons produced exhibit distinct functional phenotypes. At day 24 of differentiation, reverse transcriptase-PCR showed the presence of both forebrain (Bf-1, Hesx1, Pgc-1α, Six3) and midbrain (GATA2, GATA3) selective mRNA markers in developing forebrain-enriched cultures. All markers were present in midbrain cultures except for Bf-1 and Pgc-1α. Irrespective of culture conditions all GABA immunoreactive neurons were also immunoreactive to neuropeptide Y (NPY) antibodies. Forebrain and midbrain GABAergic neurons responded to ATP (1 mM), L-glutamate (30 μM), noradrenaline (30 μM), acetylcholine (30 μM) and dopamine (30 μM), with similar elevations of intracellular Ca(2+)([Ca(2+)](i)). The presence of GABA(A) and GABA(B) antagonists, bicuculline (30 μM) and CGP55845 (1 μM), increased the elevation of [Ca(2+)](i) in response to dopamine (30 μM) in midbrain, but not forebrain GABAergic neurons. All agonists, except dopamine, elicited similar [(3)H]-GABA release from forebrain and midbrain cultures. Dopamine (30 μM) did not stimulate significant [(3)H]-GABA release in midbrain cultures, although it was effective in forebrain cultures. This study shows that differentiating neurons toward a midbrain fate restricts the expression of forebrain markers. Forebrain differentiation results in the expression of forebrain and midbrain markers. All GABA(+) neurons contain NPY, and show similar agonist-induced elevations of [Ca(2+)](i) and [(3)H]-GABA release. This study indicates that the pharmacological phenotype of these particular neurons may be independent of the addition of

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

    Science.gov (United States)

    Tanaka, Takuma; Aoyagi, Toshio; Kaneko, Takeshi

    2012-10-01

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

  14. A Knockin Reporter Allows Purification and Characterization of mDA Neurons from Heterogeneous Populations

    Directory of Open Access Journals (Sweden)

    Ninuo Xia

    2017-03-01

    Full Text Available Generation of midbrain dopaminergic (mDA neurons from human pluripotent stem cells provides a platform for inquiry into basic and translational studies of Parkinson’s disease (PD. However, heterogeneity in differentiation in vitro makes it difficult to identify mDA neurons in culture or in vivo following transplantation. Here, we report the generation of a human embryonic stem cell (hESC line with a tyrosine hydroxylase (TH-RFP (red fluorescent protein reporter. We validated that RFP faithfully mimicked TH expression during differentiation. Use of this TH-RFP reporter cell line enabled purification of mDA-like neurons from heterogeneous cultures with subsequent characterization of neuron transcriptional and epigenetic programs (global binding profiles of H3K27ac, H3K4me1, and 5-hydroxymethylcytosine [5hmC] at four different stages of development. We anticipate that the tools and data described here will contribute to the development of mDA neurons for applications in disease modeling and/or drug screening and cell replacement therapies for PD.

  15. A Knockin Reporter Allows Purification and Characterization of mDA Neurons from Heterogeneous Populations.

    Science.gov (United States)

    Xia, Ninuo; Fang, Fang; Zhang, Pengbo; Cui, Jun; Tep-Cullison, Chhavy; Hamerley, Tim; Lee, Hyun Joo; Palmer, Theo; Bothner, Brian; Lee, Jin Hyung; Pera, Renee Reijo

    2017-03-07

    Generation of midbrain dopaminergic (mDA) neurons from human pluripotent stem cells provides a platform for inquiry into basic and translational studies of Parkinson's disease (PD). However, heterogeneity in differentiation in vitro makes it difficult to identify mDA neurons in culture or in vivo following transplantation. Here, we report the generation of a human embryonic stem cell (hESC) line with a tyrosine hydroxylase (TH)-RFP (red fluorescent protein) reporter. We validated that RFP faithfully mimicked TH expression during differentiation. Use of this TH-RFP reporter cell line enabled purification of mDA-like neurons from heterogeneous cultures with subsequent characterization of neuron transcriptional and epigenetic programs (global binding profiles of H3K27ac, H3K4me1, and 5-hydroxymethylcytosine [5hmC]) at four different stages of development. We anticipate that the tools and data described here will contribute to the development of mDA neurons for applications in disease modeling and/or drug screening and cell replacement therapies for PD. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  16. Dynamics of a population of oscillatory and excitable elements.

    Science.gov (United States)

    O'Keeffe, Kevin P; Strogatz, Steven H

    2016-06-01

    We analyze a variant of a model proposed by Kuramoto, Shinomoto, and Sakaguchi for a large population of coupled oscillatory and excitable elements. Using the Ott-Antonsen ansatz, we reduce the behavior of the population to a two-dimensional dynamical system with three parameters. We present the stability diagram and calculate several of its bifurcation curves analytically, for both excitatory and inhibitory coupling. Our main result is that when the coupling function is broad, the system can display bistability between steady states of constant high and low activity, whereas when the coupling function is narrow and inhibitory, one of the states in the bistable regime can show persistent pulsations in activity.

  17. Structured population dynamics: continuous size and discontinuous stage structures.

    Science.gov (United States)

    Buffoni, Giuseppe; Pasquali, Sara

    2007-04-01

    A nonlinear stochastic model for the dynamics of a population with either a continuous size structure or a discontinuous stage structure is formulated in the Eulerian formalism. It takes into account dispersion effects due to stochastic variability of the development process of the individuals. The discrete equations of the numerical approximation are derived, and an analysis of the existence and stability of the equilibrium states is performed. An application to a copepod population is illustrated; numerical results of Eulerian and Lagrangian models are compared.

  18. Vagal stimulation targets select populations of intrinsic cardiac neurons to control neurally induced atrial fibrillation

    Science.gov (United States)

    Salavatian, Siamak; Beaumont, Eric; Longpré, Jean-Philippe; Armour, J. Andrew; Vinet, Alain; Jacquemet, Vincent; Shivkumar, Kalyanam

    2016-01-01

    Mediastinal nerve stimulation (MNS) reproducibly evokes atrial fibrillation (AF) by excessive and heterogeneous activation of intrinsic cardiac (IC) neurons. This study evaluated whether preemptive vagus nerve stimulation (VNS) impacts MNS-induced evoked changes in IC neural network activity to thereby alter susceptibility to AF. IC neuronal activity in the right atrial ganglionated plexus was directly recorded in anesthetized canines (n = 8) using a linear microelectrode array concomitant with right atrial electrical activity in response to: 1) epicardial touch or great vessel occlusion vs. 2) stellate or vagal stimulation. From these stressors, post hoc analysis (based on the Skellam distribution) defined IC neurons so recorded as afferent, efferent, or convergent (afferent and efferent inputs) local circuit neurons (LCN). The capacity of right-sided MNS to modify IC activity in the induction of AF was determined before and after preemptive right (RCV)- vs. left (LCV)-sided VNS (15 Hz, 500 μs; 1.2× bradycardia threshold). Neuronal (n = 89) activity at baseline (0.11 ± 0.29 Hz) increased during MNS-induced AF (0.51 ± 1.30 Hz; P < 0.001). Convergent LCNs were preferentially activated by MNS. Preemptive RCV reduced MNS-induced changes in LCN activity (by 70%) while mitigating MNS-induced AF (by 75%). Preemptive LCV reduced LCN activity by 60% while mitigating AF potential by 40%. IC neuronal synchrony increased during neurally induced AF, a local neural network response mitigated by preemptive VNS. These antiarrhythmic effects persisted post-VNS for, on average, 26 min. In conclusion, VNS preferentially targets convergent LCNs and their interactive coherence to mitigate the potential for neurally induced AF. The antiarrhythmic properties imposed by VNS exhibit memory. PMID:27591222

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

    Science.gov (United States)

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

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

  20. 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.

  1. Biology as population dynamics: heuristics for transmission risk.

    Science.gov (United States)

    Keebler, Daniel; Walwyn, David; Welte, Alex

    2013-02-01

    Population-type models, accounting for phenomena such as population lifetimes, mixing patterns, recruitment patterns, genetic evolution and environmental conditions, can be usefully applied to the biology of HIV infection and viral replication. A simple dynamic model can explore the effect of a vaccine-like stimulus on the mortality and infectiousness, which formally looks like fertility, of invading virions; the mortality of freshly infected cells; and the availability of target cells, all of which impact on the probability of infection. Variations on this model could capture the importance of the timing and duration of different key events in viral transmission, and hence be applied to questions of mucosal immunology. The dynamical insights and assumptions of such models are compatible with the continuum of between- and within-individual risks in sexual violence and may be helpful in making sense of the sparse data available on the association between HIV transmission and sexual violence. © 2012 John Wiley & Sons A/S.

  2. Effect of temperature on the population dynamics of Aedes aegypti

    Science.gov (United States)

    Yusoff, Nuraini; Tokachil, Mohd Najir

    2015-10-01

    Aedes aegypti is one of the main vectors in the transmission of dengue fever. Its abundance may cause the spread of the disease to be more intense. In the study of its biological life cycle, temperature was found to increase the development rate of each stage of this species and thus, accelerate the process of the development from egg to adult. In this paper, a Lefkovitch matrix model will be used to study the stage-structured population dynamics of Aedes aegypti. In constructing the transition matrix, temperature will be taken into account. As a case study, temperature recorded at the Subang Meteorological Station for year 2006 until 2010 will be used. Population dynamics of Aedes aegypti at maximum, average and minimum temperature for each year will be simulated and compared. It is expected that the higher the temperature, the faster the mosquito will breed. The result will be compared to the number of dengue fever incidences to see their relationship.

  3. Interacting trophic forcing and the population dynamics of herring

    DEFF Research Database (Denmark)

    Lindegren, Martin; Ostman, Orjan; Gardmark, Anna

    2011-01-01

    -up nor top-down, but rather through multiple external and internal drivers. While in many studies single drivers have been identified, potential synergies of multiple factors, as well as their relative importance in regulating population dynamics of small pelagic fish, is a largely unresolved issue....... Using a statistical, age-structured modeling approach, we demonstrate the relative importance and influence of bottom-up (e.g., climate, zooplankton availability) and top-down (i.e., fishing and predation) factors on the population dynamics of Bothnian Sea herring (Clupea harengus) throughout its life...... cycle. Our results indicate significant bottom-up effects of zooplankton and interspecific competition from sprat (Sprattus sprattus), particularly on younger age classes of herring. Although top-down forcing through fishing and predation by grey seals (Halichoerus grypus) and Atlantic cod (Gadus morhua...

  4. Integrating population dynamics into mapping human exposure to seismic hazard

    Directory of Open Access Journals (Sweden)

    S. Freire

    2012-11-01

    Full Text Available Disaster risk is not fully characterized without taking into account vulnerability and population exposure. Assessment of earthquake risk in urban areas would benefit from considering the variation of population distribution at more detailed spatial and temporal scales, and from a more explicit integration of this improved demographic data with existing seismic hazard maps. In the present work, "intelligent" dasymetric mapping is used to model population dynamics at high spatial resolution in order to benefit the analysis of spatio-temporal exposure to earthquake hazard in a metropolitan area. These night- and daytime-specific population densities are then classified and combined with seismic intensity levels to derive new spatially-explicit four-class-composite maps of human exposure. The presented approach enables a more thorough assessment of population exposure to earthquake hazard. Results show that there are significantly more people potentially at risk in the daytime period, demonstrating the shifting nature of population exposure in the daily cycle and the need to move beyond conventional residence-based demographic data sources to improve risk analyses. The proposed fine-scale maps of human exposure to seismic intensity are mainly aimed at benefiting visualization and communication of earthquake risk, but can be valuable in all phases of the disaster management process where knowledge of population densities is relevant for decision-making.

  5. [Population dynamics of oligosporous actinomycetes in Chernozem soil].

    Science.gov (United States)

    Zenova, G M; Mikhaĭlova, N V; Zviagintsev, D G

    2000-01-01

    Investigation of the dynamics of an oligosporous actinomycete population in chernozem soil in the course of succession induced by soil wetting allowed us to reveal the time intervals and conditions optimal for the isolation of particular oligosporous actinomycetes. Saccharopolysporas and microbisporas proved to be best isolated in the early and late stages of succession, whereas actinomycetes of the subgroup Actinomadura and saccharomonosporas could be best isolated in the early and intermediate stages of succession.

  6. Scaling up population dynamic processes in a ladybird–aphid

    Czech Academy of Sciences Publication Activity Database

    Houdková, Kateřina; Kindlmann, Pavel

    2006-01-01

    Roč. 48, - (2006), s. 323-332 ISSN 1438-3896 R&D Projects: GA ČR(CZ) GEDIV/06/E013; GA MŠk(CZ) LC06073; GA AV ČR(CZ) IAA6087301; GA ČR(CZ) GD206/03/H034 Keywords : Aphids * Egg window * Ladybirds * Metapopulation * Model * Population dynamics Subject RIV: EH - Ecology, Behaviour Impact factor: 1.534, year: 2006

  7. 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.

  8. Building the bridge between animal movement and population dynamics.

    Science.gov (United States)

    Morales, Juan M; Moorcroft, Paul R; Matthiopoulos, Jason; Frair, Jacqueline L; Kie, John G; Powell, Roger A; Merrill, Evelyn H; Haydon, Daniel T

    2010-07-27

    While the mechanistic links between animal movement and population dynamics are ecologically obvious, it is much less clear when knowledge of animal movement is a prerequisite for understanding and predicting population dynamics. GPS and other technologies enable detailed tracking of animal location concurrently with acquisition of landscape data and information on individual physiology. These tools can be used to refine our understanding of the mechanistic links between behaviour and individual condition through 'spatially informed' movement models where time allocation to different behaviours affects individual survival and reproduction. For some species, socially informed models that address the movements and average fitness of differently sized groups and how they are affected by fission-fusion processes at relevant temporal scales are required. Furthermore, as most animals revisit some places and avoid others based on their previous experiences, we foresee the incorporation of long-term memory and intention in movement models. The way animals move has important consequences for the degree of mixing that we expect to find both within a population and between individuals of different species. The mixing rate dictates the level of detail required by models to capture the influence of heterogeneity and the dynamics of intra- and interspecific interaction.

  9. Border Collision Bifurcations in a Generalized Model of Population Dynamics

    Directory of Open Access Journals (Sweden)

    Lilia M. Ladino

    2016-01-01

    Full Text Available We analyze the dynamics of a generalized discrete time population model of a two-stage species with recruitment and capture. This generalization, which is inspired by other approaches and real data that one can find in literature, consists in considering no restriction for the value of the two key parameters appearing in the model, that is, the natural death rate and the mortality rate due to fishing activity. In the more general case the feasibility of the system has been preserved by posing opportune formulas for the piecewise map defining the model. The resulting two-dimensional nonlinear map is not smooth, though continuous, as its definition changes as any border is crossed in the phase plane. Hence, techniques from the mathematical theory of piecewise smooth dynamical systems must be applied to show that, due to the existence of borders, abrupt changes in the dynamic behavior of population sizes and multistability emerge. The main novelty of the present contribution with respect to the previous ones is that, while using real data, richer dynamics are produced, such as fluctuations and multistability. Such new evidences are of great interest in biology since new strategies to preserve the survival of the species can be suggested.

  10. Hybrid Cubature Kalman filtering for identifying nonlinear models from sampled recording: Estimation of neuronal dynamics.

    Science.gov (United States)

    Madi, Mahmoud K; Karameh, Fadi N

    2017-01-01

    Kalman filtering methods have long been regarded as efficient adaptive Bayesian techniques for estimating hidden states in models of linear dynamical systems under Gaussian uncertainty. Recent advents of the Cubature Kalman filter (CKF) have extended this efficient estimation property to nonlinear systems, and also to hybrid nonlinear problems where by the processes are continuous and the observations are discrete (continuous-discrete CD-CKF). Employing CKF techniques, therefore, carries high promise for modeling many biological phenomena where the underlying processes exhibit inherently nonlinear, continuous, and noisy dynamics and the associated measurements are uncertain and time-sampled. This paper investigates the performance of cubature filtering (CKF and CD-CKF) in two flagship problems arising in the field of neuroscience upon relating brain functionality to aggregate neurophysiological recordings: (i) estimation of the firing dynamics and the neural circuit model parameters from electric potentials (EP) observations, and (ii) estimation of the hemodynamic model parameters and the underlying neural drive from BOLD (fMRI) signals. First, in simulated neural circuit models, estimation accuracy was investigated under varying levels of observation noise (SNR), process noise structures, and observation sampling intervals (dt). When compared to the CKF, the CD-CKF consistently exhibited better accuracy for a given SNR, sharp accuracy increase with higher SNR, and persistent error reduction with smaller dt. Remarkably, CD-CKF accuracy shows only a mild deterioration for non-Gaussian process noise, specifically with Poisson noise, a commonly assumed form of background fluctuations in neuronal systems. Second, in simulated hemodynamic models, parametric estimates were consistently improved under CD-CKF. Critically, time-localization of the underlying neural drive, a determinant factor in fMRI-based functional connectivity studies, was significantly more accurate

  11. Hybrid Cubature Kalman filtering for identifying nonlinear models from sampled recording: Estimation of neuronal dynamics

    Science.gov (United States)

    2017-01-01

    Kalman filtering methods have long been regarded as efficient adaptive Bayesian techniques for estimating hidden states in models of linear dynamical systems under Gaussian uncertainty. Recent advents of the Cubature Kalman filter (CKF) have extended this efficient estimation property to nonlinear systems, and also to hybrid nonlinear problems where by the processes are continuous and the observations are discrete (continuous-discrete CD-CKF). Employing CKF techniques, therefore, carries high promise for modeling many biological phenomena where the underlying processes exhibit inherently nonlinear, continuous, and noisy dynamics and the associated measurements are uncertain and time-sampled. This paper investigates the performance of cubature filtering (CKF and CD-CKF) in two flagship problems arising in the field of neuroscience upon relating brain functionality to aggregate neurophysiological recordings: (i) estimation of the firing dynamics and the neural circuit model parameters from electric potentials (EP) observations, and (ii) estimation of the hemodynamic model parameters and the underlying neural drive from BOLD (fMRI) signals. First, in simulated neural circuit models, estimation accuracy was investigated under varying levels of observation noise (SNR), process noise structures, and observation sampling intervals (dt). When compared to the CKF, the CD-CKF consistently exhibited better accuracy for a given SNR, sharp accuracy increase with higher SNR, and persistent error reduction with smaller dt. Remarkably, CD-CKF accuracy shows only a mild deterioration for non-Gaussian process noise, specifically with Poisson noise, a commonly assumed form of background fluctuations in neuronal systems. Second, in simulated hemodynamic models, parametric estimates were consistently improved under CD-CKF. Critically, time-localization of the underlying neural drive, a determinant factor in fMRI-based functional connectivity studies, was significantly more accurate

  12. Multiscale Molecular Dynamics Simulations of Beta-Amyloid Interactions with Neurons

    Science.gov (United States)

    Qiu, Liming; Vaughn, Mark; Cheng, Kelvin

    2012-10-01

    Early events of human beta-amyloid protein interactions with cholesterol-containing membranes are critical to understanding the pathogenesis of Alzheimer's disease (AD) and to exploring new therapeutic interventions of AD. Atomistic molecular dynamics (AMD) simulations have been extensively used to study the protein-lipid interaction at high atomic resolutions. However, traditional MD simulations are not efficient in sampling the phase space of complex lipid/protein systems with rugged free energy landscapes. Meanwhile, coarse-grained MD (CGD) simulations are efficient in the phase space sampling but suffered from low spatial resolutions and from the fact that the energy landscapes are not identical to those of the AMD. Here, a multiscale approach was employed to simulate the protein-lipid interactions of beta-amyloid upon its release from proteolysis residing in the neuronal membranes. We utilized a forward (AMD to CGD) and reverse (CGD-AMD) strategy to explore new transmembrane and surface protein configuration and evaluate the stabilization mechanisms by measuring the residue-specific protein-lipid or protein conformations. The detailed molecular interactions revealed in this multiscale MD approach will provide new insights into understanding the early molecular events leading to the pathogenesis of AD.

  13. COMPUTER DYNAMICS SIMULATION OF DRUG DEPENDENCE THROUGH ARTIFICIAL NEURONAL NETWORK: PEDAGOGICAL AND CLINICAL IMPLICATIONS

    Directory of Open Access Journals (Sweden)

    G. SANTOS

    2008-05-01

    Full Text Available To develop and to evaluate the efficiency of a software able to simulate a virtual patient at different stages of addition was the main goal and challenge of this work. We developed the software in Borland™ Delphi  5®  programming language. Techniques of artificial intelligence, neuronal networks and expert systems, were responsible for modeling the neurobiological structures and mechanisms of the interaction with the drugs used. Dynamical simulation and  hypermedia were designed to increase the software’s interactivity which was able to show graphical information from virtual instrumentation and from realistic functional magnetic resonance imaging display. Early, the program was designed to be used by undergraduate students to improve their neurophysiologic learn, based not only in an interaction of membrane receptors with drugs, but in such a large behavioral simulation. The experimental manipulation of the software was accomplished by: i creating a virtual patient from a normal mood to a behavioral addiction, increasing gradatively: alcohol, opiate or cocaine doses. ii designing an approach to treat the patient, to get total or partial remission of behavioral disorder by combining psychopharmacology and psychotherapy. Integration of dynamic simulation with hypermedia and artificial intelligence has been able to point out behavioral details as tolerance, sensitization and level of addiction to drugs of abuse and so on, turned into a potentially useful tool in the development of teaching activities on several ways, such as education as well clinical skills, in which it could assist patients, families and health care to improve and test their knowledge and skills about different faces supported by drugs dependency. Those features are currently under investigation.

  14. Spatiotemporal intracellular dynamics of neurotrophin and its receptors. Implications for neurotrophin signaling and neuronal function.

    Science.gov (United States)

    Bronfman, F C; Lazo, O M; Flores, C; Escudero, C A

    2014-01-01

    Neurons possess a polarized morphology specialized to contribute to neuronal networks, and this morphology imposes an important challenge for neuronal signaling and communication. The physiology of the network is regulated by neurotrophic factors that are secreted in an activity-dependent manner modulating neuronal connectivity. Neurotrophins are a well-known family of neurotrophic factors that, together with their cognate receptors, the Trks and the p75 neurotrophin receptor, regulate neuronal plasticity and survival and determine the neuronal phenotype in healthy and regenerating neurons. Is it now becoming clear that neurotrophin signaling and vesicular transport are coordinated to modify neuronal function because disturbances of vesicular transport mechanisms lead to disturbed neurotrophin signaling and to diseases of the nervous system. This chapter summarizes our current understanding of how the regulated secretion of neurotrophin, the distribution of neurotrophin receptors in different locations of neurons, and the intracellular transport of neurotrophin-induced signaling in distal processes are achieved to allow coordinated neurotrophin signaling in the cell body and axons.

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

    DEFF Research Database (Denmark)

    Ryge, Jesper; Westerdahl, Ann Charlotte; Alstøm, Preben

    2008-01-01

    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 p...

  16. Homogenization techniques for population dynamics in strongly heterogeneous landscapes.

    Science.gov (United States)

    Yurk, Brian P; Cobbold, Christina A

    2018-12-01

    An important problem in spatial ecology is to understand how population-scale patterns emerge from individual-level birth, death, and movement processes. These processes, which depend on local landscape characteristics, vary spatially and may exhibit sharp transitions through behavioural responses to habitat edges, leading to discontinuous population densities. Such systems can be modelled using reaction-diffusion equations with interface conditions that capture local behaviour at patch boundaries. In this work we develop a novel homogenization technique to approximate the large-scale dynamics of the system. We illustrate our approach, which also generalizes to multiple species, with an example of logistic growth within a periodic environment. We find that population persistence and the large-scale population carrying capacity is influenced by patch residence times that depend on patch preference, as well as movement rates in adjacent patches. The forms of the homogenized coefficients yield key theoretical insights into how large-scale dynamics arise from the small-scale features.

  17. Drivers of waterfowl population dynamics: from teal to swans

    Science.gov (United States)

    Koons, David N.; Gunnarsson, Gunnar; Schmutz, Joel A.; Rotella, Jay J.

    2014-01-01

    Waterfowl are among the best studied and most extensively monitored species in the world. Given their global importance for sport and subsistence hunting, viewing and ecosystem functioning, great effort has been devoted since the middle part of the 20th century to understanding both the environmental and demographic mechanisms that influence waterfowl population and community dynamics. Here we use comparative approaches to summarise and contrast our understanding ofwaterfowl population dynamics across species as short-lived as the teal Anas discors and A.crecca to those such as the swans Cygnus sp. which have long life-spans. Specifically, we focus on population responses to vital rate perturbations across life history strategies, discuss bottom-up and top-down responses of waterfowlpopulations to global change, and summarise our current understanding of density dependence across waterfowl species. We close by identifying research needs and highlight ways to overcome the challenges of sustainably managing waterfowl populations in the 21st century.

  18. 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)

  19. Linking animal population dynamics to alterations in foraging behaviour

    DEFF Research Database (Denmark)

    Nabe-Nielsen, Jacob; Sibly, Richard; Tougaard, Jakob

    Background/Question/Methods The survival of animal populations is strongly influenced by the individuals’ ability to forage efficiently, yet there are few studies of how populations respond when disturbances cause animals to deviate from their natural foraging behavior. Animals that respond...... that are increasingly exposed to noise from ships, wind turbines, etc. In the present study we investigate how the dynamics of the harbor porpoise population (Phocoena phocoena) in the inner Danish waters is influenced by disturbances using an agent- based simulation model. In the model animal movement, and hence...... the animals’ ability to forage efficiently and to sustain their energy intake, is influenced by noise emitted from wind turbines and ships. The energy levels in turn affect their survival. The fine-scale movements of the simulated animals was governed by a spatial memory, which allowed the model to produce...

  20. Studies on population dynamic of diamondback moth in the field

    International Nuclear Information System (INIS)

    Malakrong, A.; Limohpasmanee, W.; Keawchoung, P.; Kodcharint, P.

    1994-01-01

    The population dynamic of diamondback moth larva in the field was studied at Khao Khor High-land Agricultural Research Station during August-October 1993 and February-April 1994. The distribution patterns of diamondback moth larva was clumped when population was low and would change to be random when population was high. The maximun and minimum number of diamondback moth in the field were 71,203 and 2,732 larva/rai during March and September. Temperature, rainfall and age of cabbage were slightly relative with number of larva (r=-0.2891, p=0.30; r=-0.2816, p=0.31 and r=0.2931, p=0.29 respectively) but relative humidity has no effect on number of larva

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. Population Dynamics of Early Human Migration in Britain.

    Directory of Open Access Journals (Sweden)

    Mayank N Vahia

    Full Text Available Early human migration is largely determined by geography and human needs. These are both deterministic parameters when small populations move into unoccupied areas where conflicts and large group dynamics are not important. The early period of human migration into the British Isles provides such a laboratory which, because of its relative geographical isolation, may allow some insights into the complex dynamics of early human migration and interaction.We developed a simulation code based on human affinity to habitable land, as defined by availability of water sources, altitude, and flatness of land, in choosing the path of migration. Movement of people on the British island over the prehistoric period from their initial entry points was simulated on the basis of data from the megalithic period. Topographical and hydro-shed data from satellite databases was used to define habitability, based on distance from water bodies, flatness of the terrain, and altitude above sea level. We simulated population movement based on assumptions of affinity for more habitable places, with the rate of movement tempered by existing populations. We compared results of our computer simulations with genetic data and show that our simulation can predict fairly accurately the points of contacts between different migratory paths. Such comparison also provides more detailed information about the path of peoples' movement over ~2000 years before the present era.We demonstrate an accurate method to simulate prehistoric movements of people based upon current topographical satellite data. Our findings are validated by recently-available genetic data. Our method may prove useful in determining early human population dynamics even when no genetic information is available.

  6. Environmental influence on population dynamics of the bivalve Anomalocardia brasiliana

    Science.gov (United States)

    Corte, Guilherme Nascimento; Coleman, Ross A.; Amaral, A. Cecília Z.

    2017-03-01

    Understanding how species respond to the environment in terms of population attributes (e.g. abundance, growth, mortality, fecundity, and productivity) is essential to protect ecologically and economically important species. Nevertheless, responses of macrobenthic populations to environmental features are overlooked due to the need of consecutive samplings and time-consuming measurements. We examined the population dynamics of the filter-feeding bivalve Anomalocardia brasiliana on a tidal flat over the course of one year to investigate the hypothesis that, as accepted for macrobenthic communities, populations inhabiting environments with low hydrodynamic conditions such as tidal flat should have higher attributes than populations inhabiting more energetic habitats (i.e. areas more influenced by wave energy such as reflective and intermediate beaches). This would be expected because the harsh conditions of more energetic habitats force organisms to divert more energy towards maintenance, resulting in lower population attributes. We found that A. brasiliana showed moderate growth and secondary production at the study area. Moreover the recruitment period was restricted to a few months. A comparison with previous studies showed that, contrary to expected, A. brasiliana populations from areas with low hydrodynamic conditions have lower abundance, growth, recruitment and turnover rate. It is likely that morphodynamic characteristics recorded in these environments, such as larger periods of air exposure and lower water circulation, may affect food conditions for filter-feeding species and increase competition. In addition, these characteristics may negatively affect macrobenthic species by enhancing eutrophication processes and anoxia. Overall, our results suggest that models accepted and applied at the macrobenthic community level might not be directly extended to A. brasiliana populations.

  7. [Dynamics of the dominance of identified cardioregulatory neurons in the snail Achatina fulica] .

    Science.gov (United States)

    Zhuravlev, V L; Bugaĭ, V V; Safronova, T A

    2000-08-01

    9 cardioregulating neurones belonging to 5 different functional groups were studied in visceral and right parietal ganglia of the Giant African snail Achatina fulica. The neuronal network included multimodal and multifunctional cells exerting short- or long-lasting chronoionotropic effects on the cardiac electro- and mechanograms. Mechanisms of the differences in the cardioregulating effectiveness of these groups were discussed.

  8. 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.

  9. 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

  10. 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.

  11. 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

  12. Alternating event processes during lifetimes: population dynamics and statistical inference.

    Science.gov (United States)

    Shinohara, Russell T; Sun, Yifei; Wang, Mei-Cheng

    2018-01-01

    In the literature studying recurrent event data, a large amount of work has been focused on univariate recurrent event processes where the occurrence of each event is treated as a single point in time. There are many applications, however, in which univariate recurrent events are insufficient to characterize the feature of the process because patients experience nontrivial durations associated with each event. This results in an alternating event process where the disease status of a patient alternates between exacerbations and remissions. In this paper, we consider the dynamics of a chronic disease and its associated exacerbation-remission process over two time scales: calendar time and time-since-onset. In particular, over calendar time, we explore population dynamics and the relationship between incidence, prevalence and duration for such alternating event processes. We provide nonparametric estimation techniques for characteristic quantities of the process. In some settings, exacerbation processes are observed from an onset time until death; to account for the relationship between the survival and alternating event processes, nonparametric approaches are developed for estimating exacerbation process over lifetime. By understanding the population dynamics and within-process structure, the paper provide a new and general way to study alternating event processes.

  13. Stochastic population dynamics in spatially extended predator-prey systems

    Science.gov (United States)

    Dobramysl, Ulrich; Mobilia, Mauro; Pleimling, Michel; Täuber, Uwe C.

    2018-02-01

    Spatially extended population dynamics models that incorporate demographic noise serve as case studies for the crucial role of fluctuations and correlations in biological systems. Numerical and analytic tools from non-equilibrium statistical physics capture the stochastic kinetics of these complex interacting many-particle systems beyond rate equation approximations. Including spatial structure and stochastic noise in models for predator-prey competition invalidates the neutral Lotka-Volterra population cycles. Stochastic models yield long-lived erratic oscillations stemming from a resonant amplification mechanism. Spatially extended predator-prey systems display noise-stabilized activity fronts that generate persistent correlations. Fluctuation-induced renormalizations of the oscillation parameters can be analyzed perturbatively via a Doi-Peliti field theory mapping of the master equation; related tools allow detailed characterization of extinction pathways. The critical steady-state and non-equilibrium relaxation dynamics at the predator extinction threshold are governed by the directed percolation universality class. Spatial predation rate variability results in more localized clusters, enhancing both competing species’ population densities. Affixing variable interaction rates to individual particles and allowing for trait inheritance subject to mutations induces fast evolutionary dynamics for the rate distributions. Stochastic spatial variants of three-species competition with ‘rock-paper-scissors’ interactions metaphorically describe cyclic dominance. These models illustrate intimate connections between population dynamics and evolutionary game theory, underscore the role of fluctuations to drive populations toward extinction, and demonstrate how space can support species diversity. Two-dimensional cyclic three-species May-Leonard models are characterized by the emergence of spiraling patterns whose properties are elucidated by a mapping onto a complex

  14. Population Dynamics of the Mediterranean Fruit Fly in Montenegro

    Directory of Open Access Journals (Sweden)

    Sanja Radonjić

    2013-01-01

    Full Text Available Population dynamics of the Mediterranean fruit fly was studied along Montenegro seacoast. Tephri traps baited with 3 component female-biased attractants were used in 11 different localities to monitor the fruit fly population in commercial citrus orchards, mixed-fruit orchards, and in backyards. From 2008–2010, the earliest captures were recorded no earlier than July. In 2011, the first adult fly was detected in mid-June. Low captures rates were recorded in July and August (below 0.5 flies per trap per day; FTD and peaked from mid-September to the end of October of each year. Our results indicate fluctuation of fly per trap per day depending on dates of inspection and locality, with significant differences in the adult population density. A maximum population was always reached in the area of Budva-Herceg Novi with an FTD of 66.5, 89.5, 71.63, and 24.64 (from 2008–2011 respectively. Fly activity lasts from mid-June/early-July to end December, with distinct seasonal variation in the population.

  15. Population dynamics of Ascaris suum in trickle-infected pigs.

    Science.gov (United States)

    Nejsum, Peter; Thamsborg, Stig M; Petersen, Heidi H; Kringel, Helene; Fredholm, Merete; Roepstorff, Allan

    2009-10-01

    The population dynamics of Ascaris suum was studied by long-term exposure of pigs to infective eggs. The pigs were experimentally inoculated with 25 A. suum eggs/kg/day, and 7, 8, and 8 pigs were necropsied at weeks 4, 8, and 14 postinoculation (PI), respectively. Despite the fact that the pigs were continuously reinfected, dramatic reductions in numbers of liver lesions (white spots) and migrating lung larvae were observed as a function of time. However, even at the end of the study, a few larvae were able to complete migration, but these larvae seemed unable to mature in the small intestine. Thus, the adult worm population seemed to consist of worms from the first part of the exposure period. The noticeable decrease in number of white spots suggests that the level of exposure is not reflected in the number of white spots in the late phase of a continuous infection. The serum levels of A. suum L3-specific IgG1 and IgA were significantly elevated by week 4 PI, after which the antibody levels declined. The population dynamics and parasite regulating mechanisms are discussed for A. suum in pigs as well as for the closely related species A. lumbricoides in humans.

  16. Periodic matrix models for seasonal dynamics of structured populations with application to a seabird population.

    Science.gov (United States)

    Cushing, J M; Henson, Shandelle M

    2018-02-03

    For structured populations with an annual breeding season, life-stage interactions and behavioral tactics may occur on a faster time scale than that of population dynamics. Motivated by recent field studies of the effect of rising sea surface temperature (SST) on within-breeding-season behaviors in colonial seabirds, we formulate and analyze a general class of discrete-time matrix models designed to account for changes in behavioral tactics within the breeding season and their dynamic consequences at the population level across breeding seasons. As a specific example, we focus on egg cannibalism and the daily reproductive synchrony observed in seabirds. Using the model, we investigate circumstances under which these life history tactics can be beneficial or non-beneficial at the population level in light of the expected continued rise in SST. Using bifurcation theoretic techniques, we study the nature of non-extinction, seasonal cycles as a function of environmental resource availability as they are created upon destabilization of the extinction state. Of particular interest are backward bifurcations in that they typically create strong Allee effects in population models which, in turn, lead to the benefit of possible (initial condition dependent) survival in adverse environments. We find that positive density effects (component Allee effects) due to increased adult survival from cannibalism and the propensity of females to synchronize daily egg laying can produce a strong Allee effect due to a backward bifurcation.

  17. Immunotoxic destruction of distinct catecholaminergic neuron populations disrupts the reproductive response to glucoprivation in female rats.

    Science.gov (United States)

    I'Anson, Helen; Sundling, Lois A; Roland, Shannon M; Ritter, Sue

    2003-10-01

    We tested the hypothesis that hindbrain catecholamine (norepinephrine or epinephrine) neurons, in addition to their essential role in glucoprivic feeding, are responsible for suppressing estrous cycles during chronic glucoprivation. Normally cycling female rats were given bilateral injections of the retrogradely transported ribosomal toxin, saporin, conjugated to monoclonal dopamine beta-hydroxylase antibody (DSAP) into the paraventricular nucleus (PVN) of the hypothalamus to selectively destroy norepinephrine and epinephrine neurons projecting to the PVN. Controls were injected with unconjugated saporin. After recovery, we assessed the lesion effects on estrous cyclicity under basal conditions and found that DSAP did not alter estrous cycle length. Subsequently, we examined effects of chronic 2-deoxy-d-glucose-induced glucoprivation on cycle length. After two normal 4- to 5-d cycles, rats were injected with 2-deoxy-d-glucose (200 mg/kg every 6 h for 72 h) beginning 24 h after detection of estrus. Chronic glucoprivation increased cycle length in seven of eight unconjugated saporin rats but in only one of eight DSAP rats. Immunohistochemical results confirmed loss of dopamine beta-hydroxylase immunoreactivity in PVN. Thus, hindbrain catecholamine neurons with projections to the PVN are required for inhibition of reproductive function during chronic glucose deficit but are not required for normal estrous cyclicity when metabolic fuels are in abundance.

  18. 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.

  19. Effects of Peanut-Tobacco Rotations on Population Dynamics of Meloidogyne arenaria in Mixed Race Populations

    OpenAIRE

    Hirunsalee, Anan; Barker, K. R.; Beute, M. K.

    1995-01-01

    A 3-year microplot study was initiated to characterize the population dynamics, reproduction potential, and survivorship of single or mixed populations of Meloidogyne arenaria race 1 (Ma1) and race 2 (Ma2), as affected by crop rotations of peanut 'Florigiant' and M. incognita races 1 and 3-resistant 'McNair 373' and susceptible 'Coker 371-Gold' tobacco. Infection, reproduction, and root damage by Ma2 on peanut and by Ma1 on resistant tobacco were limited in the first year. Infection, reproduc...

  20. Exploiting Fast-Variables to Understand Population Dynamics and Evolution

    Science.gov (United States)

    Constable, George W. A.; McKane, Alan J.

    2017-11-01

    We describe a continuous-time modelling framework for biological population dynamics that accounts for demographic noise. In the spirit of the methodology used by statistical physicists, transitions between the states of the system are caused by individual events while the dynamics are described in terms of the time-evolution of a probability density function. In general, the application of the diffusion approximation still leaves a description that is quite complex. However, in many biological applications one or more of the processes happen slowly relative to the system's other processes, and the dynamics can be approximated as occurring within a slow low-dimensional subspace. We review these time-scale separation arguments and analyse the more simple stochastic dynamics that result in a number of cases. We stress that it is important to retain the demographic noise derived in this way, and emphasise this point by showing that it can alter the direction of selection compared to the prediction made from an analysis of the corresponding deterministic model.

  1. Cooperation guided by the coexistence of imitation dynamics and aspiration dynamics in structured populations

    Science.gov (United States)

    Xu, Kuangyi; Li, Kun; Cong, Rui; Wang, Long

    2017-02-01

    In the framework of the evolutionary game theory, two fundamentally different mechanisms, the imitation process and the aspiration-driven dynamics, can be adopted by players to update their strategies. In the former case, individuals imitate the strategy of a more successful peer, while in the latter case individuals change their strategies based on a comparison of payoffs they collect in the game to their own aspiration levels. Here we explore how cooperation evolves for the coexistence of these two dynamics. Intriguingly, cooperation reaches its lowest level when a certain moderate fraction of individuals pick aspiration-level-driven rule while the others choose pairwise comparison rule. Furthermore, when individuals can adjust their update rules besides their strategies, either imitation dynamics or aspiration-driven dynamics will finally take over the entire population, and the stationary cooperation level is determined by the outcome of competition between these two dynamics. We find that appropriate synergetic effects and moderate aspiration level boost the fixation probability of aspiration-driven dynamics most effectively. Our work may be helpful in understanding the cooperative behavior induced by the coexistence of imitation dynamics and aspiration dynamics in the society.

  2. Population Dynamics of Patients with Bacterial Resistance in Hospital Environment

    Directory of Open Access Journals (Sweden)

    Leilei Qu

    2016-01-01

    Full Text Available During the past decades, the increase of antibiotic resistance has become a major concern worldwide. The researchers found that superbugs with new type of resistance genes (NDM-1 have two aspects of transmission characteristics; the first is that the antibiotic resistance genes can horizontally transfer among bacteria, and the other is that the superbugs can spread between humans through direct contact. Based on these two transmission mechanisms, we study the dynamics of population in hospital environment where superbugs exist. In this paper, we build three mathematic models to illustrate the dynamics of patients with bacterial resistance in hospital environment. The models are analyzed using stability theory of differential equations. Positive equilibrium points of the system are investigated and their stability analysis is carried out. Moreover, the numerical simulation of the proposed model is also performed which supports the theoretical findings.

  3. Dynamic population gratings in rare-earth-doped optical fibres

    Energy Technology Data Exchange (ETDEWEB)

    Stepanov, Serguei [Optics Department, CICESE, km.107 carr. Tijuana-Ensenada, Ensenada, 22860, BC (Mexico)], E-mail: steps@cicese.mx

    2008-11-21

    Dynamic Bragg gratings can be recorded in rare-earth (e.g. Er, Yb) doped optical fibres by two counter-propagating mutually coherent laser waves via local saturation of the fibre optical absorption or gain (in optically pumped fibres). Typical recording cw light power needed for efficient grating formation is of sub-mW-mW scale which results in characteristic recording/erasure times of 10-0.1 ms. This review paper discusses fundamental aspects of the population grating formation, their basic properties, relating wave-mixing processes and also considers different applications of these dynamic gratings in single-frequency fibre lasers, tunable filters, optical fibre sensors and adaptive interferometry.

  4. Dynamic population gratings in rare-earth-doped optical fibres

    International Nuclear Information System (INIS)

    Stepanov, Serguei

    2008-01-01

    Dynamic Bragg gratings can be recorded in rare-earth (e.g. Er, Yb) doped optical fibres by two counter-propagating mutually coherent laser waves via local saturation of the fibre optical absorption or gain (in optically pumped fibres). Typical recording cw light power needed for efficient grating formation is of sub-mW-mW scale which results in characteristic recording/erasure times of 10-0.1 ms. This review paper discusses fundamental aspects of the population grating formation, their basic properties, relating wave-mixing processes and also considers different applications of these dynamic gratings in single-frequency fibre lasers, tunable filters, optical fibre sensors and adaptive interferometry.

  5. 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.

  6. 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

  7. Do farming practices influence population dynamics of rodents?

    DEFF Research Database (Denmark)

    Massawe, A W; Rwamugira, W; Leirs, Herwig

    2007-01-01

    A capture-mark-recapture study was conducted in crop fields in Morogoro, Tanzania, to investigate how the population dynamics of multimammate field rats, Mastomys natalensis, was influenced by the commonly practised land preparation methods and cropping systems. Two land preparation methods (trac...... practices. In maize fields in Tanzania, the crop is most susceptible to damage by M. natalensis in the first 2 weeks after planting, and therefore, lower densities of rodents will result into lower crop damage in tractor ploughed fields....

  8. Optimal growth entails risky localization in population dynamics

    Science.gov (United States)

    Gueudré, Thomas; Martin, David G.

    2018-03-01

    Essential to each other, growth and exploration are jointly observed in alive and inanimate entities, such as animals, cells or goods. But how the environment's structural and temporal properties weights in this balance remains elusive. We analyze a model of stochastic growth with time correlations and diffusive dynamics that sheds light on the way populations grow and spread over general networks. This model suggests natural explanations of empirical facts in econo-physics or ecology, such as the risk-return trade-off and the Zipf law. We conclude that optimal growth leads to a localized population distribution, but such risky position can be mitigated through the space geometry. These results have broad applicability and are subsequently illustrated over an empirical study of financial data.

  9. Fast stochastic algorithm for simulating evolutionary population dynamics

    Science.gov (United States)

    Tsimring, Lev; Hasty, Jeff; Mather, William

    2012-02-01

    Evolution and co-evolution of ecological communities are stochastic processes often characterized by vastly different rates of reproduction and mutation and a coexistence of very large and very small sub-populations of co-evolving species. This creates serious difficulties for accurate statistical modeling of evolutionary dynamics. In this talk, we introduce a new exact algorithm for fast fully stochastic simulations of birth/death/mutation processes. It produces a significant speedup compared to the direct stochastic simulation algorithm in a typical case when the total population size is large and the mutation rates are much smaller than birth/death rates. We illustrate the performance of the algorithm on several representative examples: evolution on a smooth fitness landscape, NK model, and stochastic predator-prey system.

  10. Outward migration may alter population dynamics and income inequality

    Science.gov (United States)

    Shayegh, Soheil

    2017-11-01

    Climate change impacts may drive affected populations to migrate. However, migration decisions in response to climate change could have broader effects on population dynamics in affected regions. Here, I model the effect of climate change on fertility rates, income inequality, and human capital accumulation in developing countries, focusing on the instrumental role of migration as a key adaptation mechanism. In particular, I investigate how climate-induced migration in developing countries will affect those who do not migrate. I find that holding all else constant, climate change raises the return on acquiring skills, because skilled individuals have greater migration opportunities than unskilled individuals. In response to this change in incentives, parents may choose to invest more in education and have fewer children. This may ultimately reduce local income inequality, partially offsetting some of the damages of climate change for low-income individuals who do not migrate.

  11. State-dependent neutral delay equations from population dynamics.

    Science.gov (United States)

    Barbarossa, M V; Hadeler, K P; Kuttler, C

    2014-10-01

    A novel class of state-dependent delay equations is derived from the balance laws of age-structured population dynamics, assuming that birth rates and death rates, as functions of age, are piece-wise constant and that the length of the juvenile phase depends on the total adult population size. The resulting class of equations includes also neutral delay equations. All these equations are very different from the standard delay equations with state-dependent delay since the balance laws require non-linear correction factors. These equations can be written as systems for two variables consisting of an ordinary differential equation (ODE) and a generalized shift, a form suitable for numerical calculations. It is shown that the neutral equation (and the corresponding ODE--shift system) is a limiting case of a system of two standard delay equations.

  12. 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.

  13. 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

  14. 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.

  15. Effects of Peanut-Tobacco Rotations on Population Dynamics of Meloidogyne arenaria in Mixed Race Populations.

    Science.gov (United States)

    Hirunsalee, A; Barker, K R; Beute, M K

    1995-06-01

    A 3-year microplot study was initiated to characterize the population dynamics, reproduction potential, and survivorship of single or mixed populations of Meloidogyne arenaria race 1 (Ma1) and race 2 (Ma2), as affected by crop rotations of peanut 'Florigiant' and M. incognita races 1 and 3-resistant 'McNair 373' and susceptible 'Coker 371-Gold' tobacco. Infection, reproduction, and root damage by Ma2 on peanut and by Ma1 on resistant tobacco were limited in the first year. Infection, reproduction, and root-damage potentials on susceptible tobacco were similar for Ma1 and Ma2. In the mixed (1:1) population, Ma1 was dominant on peanut and Ma2 was dominant on both tobacco cultivars. Crop rotation affected the population dynamics of different nematode races. For years 2 and 3, the low numbers of Ma1 and Ma2 from a previous-year poor host increased rapidly on suitable hosts. Ma1 had greater reproduction factors ([RF] = population density at harvest/population density at preplandng) than did Ma2 and Ma1 + Ma2 in second-year peanut plots following first-year resistant tobacco, and in third-year peanut plots following second-year tobacco. In mixed infestations, Ma1 predominated over Ma2 in previous-year peanut plots, whereas Ma2 predominated over Ma1 in previous-year tobacco plots. Moderate damage on resistant tobacco was induced by Ma1 in the second year. In the third year, moderate damage on peanut was associated with 'Ma2' from previous-year peanut plots. The resistant tobacco supported sufficient reproduction of Ma1 over 2 years to effect moderate damage and yield suppression to peanut in year 3.

  16. Application of System Dynamics Methodology in Population Analysis

    Directory of Open Access Journals (Sweden)

    August Turina

    2009-09-01

    Full Text Available The goal of this work is to present the application of system dynamics and system thinking, as well as the advantages and possible defects of this analytic approach, in order to improve the analysis of complex systems such as population and, thereby, to monitor more effectively the underlying causes of migrations. This methodology has long been present in interdisciplinary scientific circles, but its scientific contribution has not been sufficiently applied in analysis practice in Croatia. Namely, the major part of system analysis is focused on detailed complexity rather than on dynamic complexity. Generally, the science of complexity deals with emergence, innovation, learning and adaptation. Complexity is viewed according to the number of system components, or through a number of combinations that must be continually analyzed in order to understand and consequently provide adequate decisions. Simulations containing thousands of variables and complex arrays of details distract overall attention from the basic cause patterns and key inter-relations emerging and prevailing within an analyzed population. Systems thinking offers a holistic and integral perspective for observation of the world.

  17. Population dynamics of Trichuris suis in trickle-infected pigs.

    Science.gov (United States)

    Nejsum, P; Thamsborg, S M; Petersen, H H; Kringel, H; Fredholm, M; Roepstorff, A

    2009-05-01

    The population dynamics of Trichuris suis in pigs was studied during long-term experimental infections. Twenty-three 10-week-old pigs were inoculated with 5 T. suis eggs/kg/day. Seven, 8, and 8 pigs were necropsied at weeks 4, 8, and 14 post-start of infection (p.i.), respectively. The median numbers of worms in the colon were 538 (min-max: 277-618), 332 (14-1140) and 0 (0-4) at 4, 8, and 14 weeks p.i. respectively, suggesting an increased aggregation of the worms with time and acquisition of nearly sterile immunity. The serum levels of T. suis specific antibodies (IgG1, IgG2 and IgA) peaked at week 8 p.i. By week 14 p.i. the IgG2 and IgA antibody levels remained significantly elevated above the level of week 0. The population dynamics of T. suis trickle infections in pigs is discussed with focus on interpretation of diagnostic and epidemiological data of pigs, the use of pigs as a model for human Trichuris trichiura infections and the novel approach of using T. suis eggs in the treatment of patients with inflammatory bowel disease.

  18. A small population of hypothalamic neurons govern fertility: the critical role of VAX1 in GnRH neuron development and fertility maintenance.

    Science.gov (United States)

    Hoffmann, Hanne M; Mellon, Pamela L

    2016-01-01

    Fertility depends on the correct maturation and function of approximately 800 gonadotropin-releasing hormone (GnRH) neurons in the brain. GnRH neurons are at the apex of the hypothalamic-pituitary-gonadal axis that regulates fertility. In adulthood, GnRH neurons are scattered throughout the anterior hypothalamic area and project to the median eminence, where GnRH is released into the portal vasculature to stimulate release of luteinizing hormone (LH) and follicle-stimulating hormone (FSH) from the pituitary. LH and FSH then regulate gonadal steroidogenesis and gametogenesis. Absence of GnRH neurons or inappropriate GnRH release leads to infertility. Despite the critical role of GnRH neurons in fertility, we still have a limited understanding of the genes responsible for proper GnRH neuron development and function in adulthood. GnRH neurons originate in the olfactory placode then migrate into the brain. Homeodomain transcription factors expressed within GnRH neurons or along their migratory path are candidate genes for inherited infertility. Using a combined in vitro and in vivo approach, we have identified Ventral Anterior Homeobox 1 ( Vax1 ) as a novel homeodomain transcription factor responsible for GnRH neuron maturation and fertility. GnRH neuron counts in Vax1 knock-out embryos revealed Vax1 to be required for the presence of GnRH-expressing cells at embryonic day 17.5 (E17.5), but not at E13.5. To localize the effects of Vax1 on fertility, we generated Vax1 flox mice and crossed them with Gnrh cre mice to specifically delete Vax1 within GnRH neurons. GnRH staining in Vax1 flox/flox :GnRH cre mice show a total absence of GnRH expression in the adult. We performed lineage tracing in Vax1 flox/flox :GnRH cre :RosaLacZ mice which proved GnRH neurons to be alive, but incapable of expressing GnRH. The absence of GnRH leads to delayed puberty, hypogonadism and complete infertility in both sexes. Finally, using the immortalized model GnRH neuron cell lines, GN11 and

  19. 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

  20. 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.

  1. Modeling population dynamics of mitochondria in mammalian cells

    Science.gov (United States)

    Kornick, Kellianne; Das, Moumita

    Mitochondria are organelles located inside eukaryotic cells and are essential for several key cellular processes such as energy (ATP) production, cell signaling, differentiation, and apoptosis. All organisms are believed to have low levels of variation in mitochondrial DNA (mtDNA), and alterations in mtDNA are connected to a range of human health conditions, including epilepsy, heart failure, Parkinsons disease, diabetes, and multiple sclerosis. Therefore, understanding how changes in mtDNA accumulate over time and are correlated to changes in mitochondrial function and cell properties can have a profound impact on our understanding of cell physiology and the origins of some diseases. Motivated by this, we develop and study a mathematical model to determine which cellular parameters have the largest impact on mtDNA population dynamics. The model consists of coupled ODEs to describe subpopulations of healthy and dysfunctional mitochondria subject to mitochondrial fission, fusion, autophagy, and mutation. We study the time evolution and stability of each sub-population under specific selection biases and pressures by tuning specific terms in our model. Our results may provide insights into how sub-populations of mitochondria survive and evolve under different selection pressures. This work was supported by a Grant from the Moore Foundation.

  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. 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.

  4. Morphological changes in different populations of bladder afferent neurons detected by herpes simplex virus (HSV) vectors with cell-type-specific promoters in mice with spinal cord injury.

    Science.gov (United States)

    Shimizu, Nobutaka; Doyal, Mark F; Goins, William F; Kadekawa, Katsumi; Wada, Naoki; Kanai, Anthony J; de Groat, William C; Hirayama, Akihide; Uemura, Hirotsugu; Glorioso, Joseph C; Yoshimura, Naoki

    2017-11-19

    Functional and morphological changes in C-fiber bladder afferent pathways are reportedly involved in neurogenic detrusor overactivity (NDO) after spinal cord injury (SCI). This study examined the morphological changes in different populations of bladder afferent neurons after SCI using replication-defective herpes simplex virus (HSV) vectors encoding the mCherry reporter driven by neuronal cell-type-specific promoters. Spinal intact (SI) and SCI mice were injected into the bladder wall with HSV mCherry vectors driven by the cytomegalovirus (CMV) promoter, CGRP promoter, TRPV1 promoter or neurofilament 200 (NF200) promoter. Two weeks after vector inoculation into the bladder wall, L1 and L6 dorsal root ganglia (DRG) were removed bilaterally for immunofluorescent staining using anti-mCherry antibody. The number of CMV promoter vector-labeled neurons was not altered after SCI. The number of CGRP and TRPV1 promoter vector-labeled neurons was significantly increased whereas the number of NF200 vector-labeled neurons was decreased in L6 DRG after SCI. The median size of CGRP promoter-labeled C-fiber neurons was increased from 247.0 in SI mice to 271.3μm 2 in SCI mice whereas the median cell size of TRPV1 promoter vector-labeled neurons was decreased from 245.2 in SI mice to 216.5μm 2 in SCI mice. CGRP and TRPV1 mRNA levels of laser-captured bladder afferent neurons labeled with Fast Blue were significantly increased in SCI mice compared to SI mice. Thus, using a novel HSV vector-mediated neuronal labeling technique, we found that SCI induces expansion of the CGRP- and TRPV1-expressing C-fiber cell population, which could contribute to C-fiber afferent hyperexcitability and NDO after SCI. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

  5. 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.

  6. Hippocampal neuron populations are reduced in vervet monkeys with fetal alcohol exposure

    DEFF Research Database (Denmark)

    Burke, Mark W; Ptito, Maurice; Ervin, Frank R

    2015-01-01

    of pregnancy. Here, we report significant numerical reductions in the principal hippocampal neurons of fetal alcohol-exposed (FAE) offspring, as compared to age-matched, similarly housed conspecifics with isocaloric sucrose exposure. These deficits, particularly marked in CA1 and CA3, are present neonatally......Prenatal exposure to beverage alcohol is a major cause of mild mental retardation and developmental delay. In nonendangered alcohol-preferring vervet monkeys, we modeled the most common nondysmorphic form of fetal alcohol syndrome disorder with voluntary drinking during the third trimester...... and persist through infancy (5 months) and juvenile (2 years) stages. Although the volumes of hippocampal subdivisions in FAE animals are not atypical at birth, by age 2, they are only 65-70% of those estimated in age-matched controls. These data suggest that moderate, naturalistic alcohol consumption during...

  7. Plasmodium vivax Population Structure and Transmission Dynamics in Sabah Malaysia

    Science.gov (United States)

    Abdullah, Noor Rain; Barber, Bridget E.; William, Timothy; Norahmad, Nor Azrina; Satsu, Umi Rubiah; Muniandy, Prem Kumar; Ismail, Zakiah; Grigg, Matthew J.; Jelip, Jenarun; Piera, Kim; von Seidlein, Lorenz; Yeo, Tsin W.; Anstey, Nicholas M.; Price, Ric N.; Auburn, Sarah

    2013-01-01

    Despite significant progress in the control of malaria in Malaysia, the complex transmission dynamics of P. vivax continue to challenge national efforts to achieve elimination. To assess the impact of ongoing interventions on P. vivax transmission dynamics in Sabah, we genotyped 9 short tandem repeat markers in a total of 97 isolates (8 recurrences) from across Sabah, with a focus on two districts, Kota Marudu (KM, n = 24) and Kota Kinabalu (KK, n = 21), over a 2 year period. STRUCTURE analysis on the Sabah-wide dataset demonstrated multiple sub-populations. Significant differentiation (F ST  = 0.243) was observed between KM and KK, located just 130 Km apart. Consistent with low endemic transmission, infection complexity was modest in both KM (mean MOI  = 1.38) and KK (mean MOI  = 1.19). However, population diversity remained moderate (H E  = 0.583 in KM and H E  = 0.667 in KK). Temporal trends revealed clonal expansions reflecting epidemic transmission dynamics. The haplotypes of these isolates declined in frequency over time, but persisted at low frequency throughout the study duration. A diverse array of low frequency isolates were detected in both KM and KK, some likely reflecting remnants of previous expansions. In accordance with clonal expansions, high levels of Linkage Disequilibrium (I A S >0.5 [P<0.0001] in KK and KM) declined sharply when identical haplotypes were represented once (I A S  = 0.07 [P = 0.0076] in KM, and I A S = -0.003 [P = 0.606] in KK). All 8 recurrences, likely to be relapses, were homologous to the prior infection. These recurrences may promote the persistence of parasite lineages, sustaining local diversity. In summary, Sabah's shrinking P. vivax population appears to have rendered this low endemic setting vulnerable to epidemic expansions. Migration may play an important role in the introduction of new parasite strains leading to epidemic expansions, with important implications for malaria

  8. 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.

  9. 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.

  10. THE TOPOLOGICAL AND DYNAMIC CHARACTERISTICS OF NEURONIC ENSEMBLES IN THE BRAIN AS PERCOLATING FRACTAL SETS

    Directory of Open Access Journals (Sweden)

    Sergey L’vovich Molchatsky

    2017-10-01

    Full Text Available The objective of the research was to determine of neuronic ensembles in the brain. The research was based that neuronic ensembles of a brain are considered as the percolating clusters. In the basic part of the study the main concern was determination of the following parameters: fractal dimension on a passing threshold df; for geodetic lines on a fractal dθ and for trajectories of particles in a turbulence field dw. In the same part of a research the index of a compendency (θ of neuronic ensembles of animals and the human brain is defined. As well as it was supposed has a negative value θ 1. Numerical calculations with use of results of computer analysis frontal section images of a hypothalamus of a brain of animals and human are shown, that the considered objects can be ranked to the special class of fractal objects. Such class of objects is called asymptotically arcwise connected.

  11. Anti-correlated cortical networks arise from spontaneous neuronal dynamics at slow timescales.

    Science.gov (United States)

    Kodama, Nathan X; Feng, Tianyi; Ullett, James J; Chiel, Hillel J; Sivakumar, Siddharth S; Galán, Roberto F

    2018-01-12

    In the highly interconnected architectures of the cerebral cortex, recurrent intracortical loops disproportionately outnumber thalamo-cortical inputs. These networks are also capable of generating neuronal activity without feedforward sensory drive. It is unknown, however, what spatiotemporal patterns may be solely attributed to intrinsic connections of the local cortical network. Using high-density microelectrode arrays, here we show that in the isolated, primary somatosensory cortex of mice, neuronal firing fluctuates on timescales from milliseconds to tens of seconds. Slower firing fluctuations reveal two spatially distinct neuronal ensembles, which correspond to superficial and deeper layers. These ensembles are anti-correlated: when one fires more, the other fires less and vice versa. This interplay is clearest at timescales of several seconds and is therefore consistent with shifts between active sensing and anticipatory behavioral states in mice.

  12. 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.

  13. Subtype-Specific Genes that Characterize Subpopulations of Callosal Projection Neurons in Mouse Identify Molecularly Homologous Populations in Macaque Cortex.

    Science.gov (United States)

    Fame, Ryann M; Dehay, Colette; Kennedy, Henry; Macklis, Jeffrey D

    2017-03-01

    Callosal projection neurons (CPN) interconnect the neocortical hemispheres via the corpus callosum and are implicated in associative integration of multimodal information. CPN have undergone differential evolutionary elaboration, leading to increased diversity of cortical neurons-and more extensive and varied connections in neocortical gray and white matter-in primates compared with rodents. In mouse, distinct sets of genes are enriched in discrete subpopulations of CPN, indicating the molecular diversity of rodent CPN. Elements of rodent CPN functional and organizational diversity might thus be present in the further elaborated primate cortex. We address the hypothesis that genes controlling mouse CPN subtype diversity might reflect molecular patterns shared among mammals that arose prior to the divergence of rodents and primates. We find that, while early expression of the examined CPN-enriched genes, and postmigratory expression of these CPN-enriched genes in deep layers are highly conserved (e.g., Ptn, Nnmt, Cited2, Dkk3), in contrast, the examined genes expressed by superficial layer CPN show more variable levels of conservation (e.g., EphA3, Chn2). These results suggest that there has been evolutionarily differential retraction and elaboration of superficial layer CPN subpopulations between mouse and macaque, with independent derivation of novel populations in primates. Together, these data inform future studies regarding CPN subpopulations that are unique to primates and rodents, and indicate putative evolutionary relationships. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  14. Mean-field games with logistic population dynamics

    KAUST Repository

    Gomes, Diogo A.

    2013-12-01

    In its standard form, a mean-field game can be defined by coupled system of equations, a Hamilton-Jacobi equation for the value function of agents and a Fokker-Planck equation for the density of agents. Traditionally, the latter equation is adjoint to the linearization of the former. Since the Fokker-Planck equation models a population dynamic, we introduce natural features such as seeding and birth, and nonlinear death rates. In this paper we analyze a stationary meanfield game in one dimension, illustrating various techniques to obtain regularity of solutions in this class of systems. In particular we consider a logistic-type model for birth and death of the agents which is natural in problems where crowding affects the death rate of the agents. The introduction of these new terms requires a number of new ideas to obtain wellposedness. In a forthcoming publication we will address higher dimensional models. ©2013 IEEE.

  15. Mean-field games with logistic population dynamics

    KAUST Repository

    Gomes, Diogo A.; De Lima Ribeiro, Ricardo

    2013-01-01

    In its standard form, a mean-field game can be defined by coupled system of equations, a Hamilton-Jacobi equation for the value function of agents and a Fokker-Planck equation for the density of agents. Traditionally, the latter equation is adjoint to the linearization of the former. Since the Fokker-Planck equation models a population dynamic, we introduce natural features such as seeding and birth, and nonlinear death rates. In this paper we analyze a stationary meanfield game in one dimension, illustrating various techniques to obtain regularity of solutions in this class of systems. In particular we consider a logistic-type model for birth and death of the agents which is natural in problems where crowding affects the death rate of the agents. The introduction of these new terms requires a number of new ideas to obtain wellposedness. In a forthcoming publication we will address higher dimensional models. ©2013 IEEE.

  16. Richards-like two species population dynamics model.

    Science.gov (United States)

    Ribeiro, Fabiano; Cabella, Brenno Caetano Troca; Martinez, Alexandre Souto

    2014-12-01

    The two-species population dynamics model is the simplest paradigm of inter- and intra-species interaction. Here, we present a generalized Lotka-Volterra model with intraspecific competition, which retrieves as particular cases, some well-known models. The generalization parameter is related to the species habitat dimensionality and their interaction range. Contrary to standard models, the species coupling parameters are general, not restricted to non-negative values. Therefore, they may represent different ecological regimes, which are derived from the asymptotic solution stability analysis and are represented in a phase diagram. In this diagram, we have identified a forbidden region in the mutualism regime, and a survival/extinction transition with dependence on initial conditions for the competition regime. Also, we shed light on two types of predation and competition: weak, if there are species coexistence, or strong, if at least one species is extinguished.

  17. Artificial bee colony algorithm with dynamic multi-population

    Science.gov (United States)

    Zhang, Ming; Ji, Zhicheng; Wang, Yan

    2017-07-01

    To improve the convergence rate and make a balance between the global search and local turning abilities, this paper proposes a decentralized form of artificial bee colony (ABC) algorithm with dynamic multi-populations by means of fuzzy C-means (FCM) clustering. Each subpopulation periodically enlarges with the same size during the search process, and the overlapping individuals among different subareas work for delivering information acting as exploring the search space with diffusion of solutions. Moreover, a Gaussian-based search equation with redefined local attractor is proposed to further accelerate the diffusion of the best solution and guide the search towards potential areas. Experimental results on a set of benchmarks demonstrate the competitive performance of our proposed approach.

  18. On the stochastic approach to marine population dynamics

    Directory of Open Access Journals (Sweden)

    Eduardo Ferrandis

    2007-03-01

    Full Text Available The purpose of this article is to deepen and structure the statistical basis of marine population dynamics. The starting point is the correspondence between the concepts of mortality, survival and lifetime distribution. This is the kernel of the possibilities that survival analysis techniques offer to marine population dynamics. A rigorous definition of survival and mortality based on their properties and their probabilistic versions is briefly presented. Some well established models for lifetime distribution, which generalise the usual simple exponential distribution, might be used with their corresponding survivals and mortalities. A critical review of some published models is also made, including original models proposed in the way opened by Caddy (1991 and Sparholt (1990, which allow for a continuously decreasing natural mortality. Considering these elements, the pure death process dealt with in the literature is used as a theoretical basis for the evolution of a marine cohort. The elaboration of this process is based on Chiang´s study of the probability distribution of the life table (Chiang, 1960 and provides specific structured models for stock evolution as a Markovian process. These models may introduce new ideas in the line of thinking developed by Gudmundsson (1987 and Sampson (1990 in order to model the evolution of a marine cohort by stochastic processes. The suitable approximation of these processes by means of Gaussian processes may allow theoretical and computational multivariate Gaussian analysis to be applied to the probabilistic treatment of fisheries issues. As a consequence, the necessary catch equation appears as a stochastic integral with respect to the mentioned Markovian process of the stock. The solution of this equation is available when the mortalities are proportional, hence the use of the proportional hazards model (Cox, 1959. The assumption of these proportional mortalities leads naturally to the construction of a

  19. 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.

  20. Dynamical evolution of a fictitious population of binary Neptune Trojans

    Science.gov (United States)

    Brunini, Adrián

    2018-03-01

    We present numerical simulations of the evolution of a synthetic population of Binary Neptune Trojans, under the influence of the solar perturbations and tidal friction (the so-called Kozai cycles and tidal friction evolution). Our model includes the dynamical influence of the four giant planets on the heliocentric orbit of the binary centre of mass. In this paper, we explore the evolution of initially tight binaries around the Neptune L4 Lagrange point. We found that the variation of the heliocentric orbital elements due to the libration around the Lagrange point introduces significant changes in the orbital evolution of the binaries. Collisional processes would not play a significant role in the dynamical evolution of Neptune Trojans. After 4.5 × 109 yr of evolution, ˜50 per cent of the synthetic systems end up separated as single objects, most of them with slow diurnal rotation rate. The final orbital distribution of the surviving binary systems is statistically similar to the one found for Kuiper Belt Binaries when collisional evolution is not included in the model. Systems composed by a primary and a small satellite are more fragile than the ones composed by components of similar sizes.

  1. Evolutionary game dynamics in a growing structured population

    Energy Technology Data Exchange (ETDEWEB)

    Poncela, Julia; Gomez-Gardenes, Jesus; Moreno, Yamir [Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, E-50009 Zaragoza (Spain); Traulsen, Arne [Emmy-Noether Group for Evolutionary Dynamics, Department of Evolutionary Ecology, Max Planck Institute for Evolutionary Biology, August-Thienemann-Strasse 2, 24306 Ploen (Germany)], E-mail: traulsen@evolbio.mpg.de

    2009-08-15

    We discuss a model for evolutionary game dynamics in a growing, network-structured population. In our model, new players can either make connections to random preexisting players or preferentially attach to those that have been successful in the past. The latter depends on the dynamics of strategies in the game, which we implement following the so-called Fermi rule such that the limits of weak and strong strategy selection can be explored. Our framework allows to address general evolutionary games. With only two parameters describing the preferential attachment and the intensity of selection, we describe a wide range of network structures and evolutionary scenarios. Our results show that even for moderate payoff preferential attachment, over represented hubs arise. Interestingly, we find that while the networks are growing, high levels of cooperation are attained, but the same network structure does not promote cooperation as a static network. Therefore, the mechanism of payoff preferential attachment is different to those usually invoked to explain the promotion of cooperation in static, already-grown networks.

  2. Evolutionary game dynamics in a growing structured population

    International Nuclear Information System (INIS)

    Poncela, Julia; Gomez-Gardenes, Jesus; Moreno, Yamir; Traulsen, Arne

    2009-01-01

    We discuss a model for evolutionary game dynamics in a growing, network-structured population. In our model, new players can either make connections to random preexisting players or preferentially attach to those that have been successful in the past. The latter depends on the dynamics of strategies in the game, which we implement following the so-called Fermi rule such that the limits of weak and strong strategy selection can be explored. Our framework allows to address general evolutionary games. With only two parameters describing the preferential attachment and the intensity of selection, we describe a wide range of network structures and evolutionary scenarios. Our results show that even for moderate payoff preferential attachment, over represented hubs arise. Interestingly, we find that while the networks are growing, high levels of cooperation are attained, but the same network structure does not promote cooperation as a static network. Therefore, the mechanism of payoff preferential attachment is different to those usually invoked to explain the promotion of cooperation in static, already-grown networks.

  3. Dynamic Neuron-Glia Interactions in an Oscillatory Network Controlling Behavioral Plasticity in the Weakly Electric Fish, Apteronotus leptorhynchus.

    Science.gov (United States)

    Zupanc, Günther K H

    2017-01-01

    The involvement of glial cells in the regulation of physiological functions is being increasingly recognized, yet their role in plasticity of neural oscillators has remained largely elusive. An excellent model system to address the latter function is the pacemaker nucleus of the weakly electric fish, Apteronotus leptorhynchus . This brainstem oscillator drives the fish's electric organ discharge in a one-to-one fashion, with median frequencies of 880 Hz in males and 740 Hz in females. Morphometric analysis of the pacemaker nucleus has shown that astrocytes outnumber mature neurons seven-fold, and oscillator neurons even 200-fold. A similar dominance of astrocytes occurs among the adult-born cells that differentiate into glia and neurons. The astrocytes form a dense meshwork of cells interconnected by gap junctions. The degree of association of astrocytic fibers with the neural oscillator cells, and the gap-junction coupling between individual astrocytes, exhibit a sexual dimorphism, which parallels the sexual dimorphisms in the output frequency of the pacemaker nucleus, and ultimately in the electric organ discharge of the fish. It is hypothesized that the dynamics in astroglial structure mediate differences in the capacity to buffer potassium, which increases during the generation of action potentials. These differences, in turn, affect the excitability of the neural oscillator cells, and thus the output frequency of the pacemaker nucleus. Comparison of the pacemaker nucleus with other brain oscillators suggests that modulation of the output activity is one of the chief functions of the interaction of glia with the neural oscillator cells.

  4. Evolutionary Dynamics of Collective Action in Structured Populations

    Science.gov (United States)

    Santos, Marta Daniela de Almeida

    The pervasiveness of cooperation in Nature is not easily explained. If evolution is characterized by competition and survival of the fittest, why should selfish individuals cooperate with each other? Evolutionary Game Theory (EGT) provides a suitable mathematical framework to study this problem, central to many areas of science. Conventionally, interactions between individuals are modeled in terms of one-shot, symmetric 2-Person Dilemmas of Cooperation, but many real-life situations involve decisions within groups with more than 2 individuals, which are best-dealt in the framework of N-Person games. In this Thesis, we investigate the evolutionary dynamics of two paradigmatic collective social dilemmas - the N-Person Prisoner's Dilemma (NPD) and the N-Person Snowdrift Game (NSG) on structured populations, modeled by networks with diverse topological properties. Cooperative strategies are just one example of the many traits that can be transmitted on social networks. Several recent studies based on empirical evidence from a medical database have suggested the existence of a 3 degrees of influence rule, according to which not only our "friends", but also our friends' friends, and our friends' friends' friends, have a non-trivial influence on our decisions. We investigate the degree of peer influence that emerges from the spread of cooperative strategies, opinions and diseases on populations with distinct underlying networks of contacts. Our results show that networks naturally entangle individuals into interactions of many-body nature and that for each network class considered different processes lead to identical degrees of influence. None

  5. Population dynamics of Vibrio fischeri during infection of Euprymna scolopes.

    Science.gov (United States)

    McCann, Jessica; Stabb, Eric V; Millikan, Deborah S; Ruby, Edward G

    2003-10-01

    The luminous bacterium Vibrio fischeri colonizes a specialized light-emitting organ within its squid host, Euprymna scolopes. Newly hatched juvenile squid must acquire their symbiont from ambient seawater, where the bacteria are present at low concentrations. To understand the population dynamics of V. fischeri during colonization more fully, we used mini-Tn7 transposons to mark bacteria with antibiotic resistance so that the growth of their progeny could be monitored. When grown in culture, there was no detectable metabolic burden on V. fischeri cells carrying the transposon, which inserts in single copy in a specific intergenic region of the V. fischeri genome. Strains marked with mini-Tn7 also appeared to be equivalent to the wild type in their ability to infect and multiply within the host during coinoculation experiments. Studies of the early stages of colonization suggested that only a few bacteria became associated with symbiotic tissue when animals were exposed for a discrete period (3 h) to an inoculum of V. fischeri cells equivalent to natural population levels; nevertheless, all these hosts became infected. When three differentially marked strains of V. fischeri were coincubated with juvenile squid, the number of strains recovered from an individual symbiotic organ was directly dependent on the size of the inoculum. Further, these results indicated that, when exposed to low numbers of V. fischeri, the host may become colonized by only one or a few bacterial cells, suggesting that symbiotic infection is highly efficient.

  6. Short and Long-Term Attentional Firing Rates Can Be Explained by ST-Neuron Dynamics

    Directory of Open Access Journals (Sweden)

    Oscar J. Avella Gonzalez

    2018-03-01

    Full Text Available Attention modulates neural selectivity and optimizes the allocation of cortical resources during visual tasks. A large number of experimental studies in primates and humans provide ample evidence. As an underlying principle of visual attention, some theoretical models suggested the existence of a gain element that enhances contrast of the attended stimuli. In contrast, the Selective Tuning model of attention (ST proposes an attentional mechanism based on suppression of irrelevant signals. In this paper, we present an updated characterization of the ST-neuron proposed by the Selective Tuning model, and suggest that the inclusion of adaptation currents (Ih to ST-neurons may explain the temporal profiles of the firing rates recorded in single V4 cells during attentional tasks. Furthermore, using the model we show that the interaction between stimulus-selectivity of a neuron and attention shapes the profile of the firing rate, and is enough to explain its fast modulation and other discontinuities observed, when the neuron responds to a sudden switch of stimulus, or when one stimulus is added to another during a visual task.

  7. Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET.

    Science.gov (United States)

    Dutta, Sangya; Kumar, Vinay; Shukla, Aditya; Mohapatra, Nihar R; Ganguly, Udayan

    2017-08-15

    Neuro-biology inspired Spiking Neural Network (SNN) enables efficient learning and recognition tasks. To achieve a large scale network akin to biology, a power and area efficient electronic neuron is essential. Earlier, we had demonstrated an LIF neuron by a novel 4-terminal impact ionization based n+/p/n+ with an extended gate (gated-INPN) device by physics simulation. Excellent improvement in area and power compared to conventional analog circuit implementations was observed. In this paper, we propose and experimentally demonstrate a compact conventional 3-terminal partially depleted (PD) SOI- MOSFET (100 nm gate length) to replace the 4-terminal gated-INPN device. Impact ionization (II) induced floating body effect in SOI-MOSFET is used to capture LIF neuron behavior to demonstrate spiking frequency dependence on input. MHz operation enables attractive hardware acceleration compared to biology. Overall, conventional PD-SOI-CMOS technology enables very-large-scale-integration (VLSI) which is essential for biology scale (~10 11 neuron based) large neural networks.

  8. The Mirror Neuron System and Observational Learning: Implications for the Effectiveness of Dynamic Visualizations

    Science.gov (United States)

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

    2009-01-01

    Learning by observing and imitating others has long been recognized as constituting a powerful learning strategy for humans. Recent findings from neuroscience research, more specifically on the mirror neuron system, begin to provide insight into the neural bases of learning by observation and imitation. These findings are discussed here, along…

  9. Advanced models of neural networks nonlinear dynamics and stochasticity in biological neurons

    CERN Document Server

    Rigatos, Gerasimos G

    2015-01-01

    This book provides a complete study on neural structures exhibiting nonlinear and stochastic dynamics, elaborating on neural dynamics by introducing advanced models of neural networks. It overviews the main findings in the modelling of neural dynamics in terms of electrical circuits and examines their stability properties with the use of dynamical systems theory. It is suitable for researchers and postgraduate students engaged with neural networks and dynamical systems theory.

  10. Effects of constant immigration on the dynamics and persistence of stable and unstable Drosophila populations

    Science.gov (United States)

    Dey, Snigdhadip; Joshi, Amitabh

    2013-01-01

    Constant immigration can stabilize population size fluctuations but its effects on extinction remain unexplored. We show that constant immigration significantly reduced extinction in fruitfly populations with relatively stable or unstable dynamics. In unstable populations with oscillations of amplitude around 1.5 times the mean population size, persistence and constancy were unrelated. Low immigration enhanced persistence without affecting constancy whereas high immigration increased constancy without enhancing persistence. In relatively stable populations with erratic fluctuations of amplitude close to the mean population size, both low and high immigration enhanced persistence. In these populations, the amplitude of fluctuations relative to mean population size went down due to immigration, and their dynamics were altered to low-period cycles. The effects of immigration on the population size distribution and intrinsic dynamics of stable versus unstable populations differed considerably, suggesting that the mechanisms by which immigration reduced extinction risk depended on underlying dynamics in complex ways. PMID:23470546

  11. 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

  12. Population dynamics of caribou herds in southwestern Alaska

    Directory of Open Access Journals (Sweden)

    Patrick Valkenburg

    2003-04-01

    Full Text Available The five naturally occurring and one transplanted caribou (Rangifer tarandus granti herd in southwestern Alaska composed about 20% of Alaska's caribou population in 2001. All five of the naturally occurring herds fluctuated considerably in size between the late 1800s and 2001 and for some herds the data provide an indication of long-term periodic (40-50 year fluctuations. At the present time, the Unimak (UCH and Southern Alaska Peninsula (SAP are recovering from population declines, the Northern Alaska Peninsula Herd (NAP appears to be nearing the end of a protracted decline, and the Mulchatna Herd (MCH appears to now be declining after 20 years of rapid growth. The remaining naturally occurring herd (Kilbuck has virtually disappeared. Nutrition had a significant effect on the size of 4-month-old and 10-month-old calves in the NAP and the Nushagak Peninsula Herd (NPCH and probably also on population growth in at least 4 (SAP, NAP, NPCH, and MCH of the six caribou herds in southwestern Alaska. Predation does not appear to be sufficient to keep caribou herds in southwestern Alaska from expanding, probably because rabies is endemic in red foxes (Vulpes vulpes and is periodically transferred to wolves (Canis lupus and other canids. However, we found evidence that pneumonia and hoof rot may result in significant mortality of caribou in southwestern Alaska, whereas there is no evidence that disease is important in the dynamics of Interior herds. Cooperative conservation programs, such as the Kilbuck Caribou Management Plan, can be successful in restraining traditional harvest and promoting growth in caribou herds. In southwestern Alaska we also found evidence that small caribou herds can be swamped and assimilated by large herds, and fidelity to traditional calving areas can be lost.

  13. Modelling multi-pulse population dynamics from ultrafast spectroscopy.

    Directory of Open Access Journals (Sweden)

    Luuk J G W van Wilderen

    2011-03-01

    Full Text Available Current advanced laser, optics and electronics technology allows sensitive recording of molecular dynamics, from single resonance to multi-colour and multi-pulse experiments. Extracting the occurring (bio- physical relevant pathways via global analysis of experimental data requires a systematic investigation of connectivity schemes. Here we present a Matlab-based toolbox for this purpose. The toolbox has a graphical user interface which facilitates the application of different reaction models to the data to generate the coupled differential equations. Any time-dependent dataset can be analysed to extract time-independent correlations of the observables by using gradient or direct search methods. Specific capabilities (i.e. chirp and instrument response function for the analysis of ultrafast pump-probe spectroscopic data are included. The inclusion of an extra pulse that interacts with a transient phase can help to disentangle complex interdependent pathways. The modelling of pathways is therefore extended by new theory (which is included in the toolbox that describes the finite bleach (orientation effect of single and multiple intense polarised femtosecond pulses on an ensemble of randomly oriented particles in the presence of population decay. For instance, the generally assumed flat-top multimode beam profile is adapted to a more realistic Gaussian shape, exposing the need for several corrections for accurate anisotropy measurements. In addition, the (selective excitation (photoselection and anisotropy of populations that interact with single or multiple intense polarised laser pulses is demonstrated as function of power density and beam profile. Using example values of real world experiments it is calculated to what extent this effectively orie