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Sample records for neuron networks method

  1. Epileptic Neuronal Networks: Methods of Identification and Clinical Relevance

    PubMed Central

    Stefan, Hermann; Lopes da Silva, Fernando H.

    2012-01-01

    The main objective of this paper is to examine evidence for the concept that epileptic activity should be envisaged in terms of functional connectivity and dynamics of neuronal networks. Basic concepts regarding structure and dynamics of neuronal networks are briefly described. Particular attention is given to approaches that are derived, or related, to the concept of causality, as formulated by Granger. Linear and non-linear methodologies aiming at characterizing the dynamics of neuronal networks applied to EEG/MEG and combined EEG/fMRI signals in epilepsy are critically reviewed. The relevance of functional dynamical analysis of neuronal networks with respect to clinical queries in focal cortical dysplasias, temporal lobe epilepsies, and “generalized” epilepsies is emphasized. In the light of the concepts of epileptic neuronal networks, and recent experimental findings, the dichotomic classification in focal and generalized epilepsy is re-evaluated. It is proposed that so-called “generalized epilepsies,” such as absence seizures, are actually fast spreading epilepsies, the onset of which can be tracked down to particular neuronal networks using appropriate network analysis. Finally new approaches to delineate epileptogenic networks are discussed. PMID:23532203

  2. Response functions for electrically coupled neuronal network: a method of local point matching and its applications.

    PubMed

    Yihe, Lu; Timofeeva, Yulia

    2016-06-01

    Neuronal networks connected by electrical synapses, also referred to as gap junctions, are present throughout the entire central nervous system. Many instances of gap-junctional coupling are formed between dendritic arbours of individual cells, and these dendro-dendritic gap junctions are known to play an important role in mediating various brain rhythms in both normal and pathological states. The dynamics of such neuronal networks modelled by passive or quasi-active (resonant) membranes can be described by the Green's function which provides the fundamental input-output relationships of the entire network. One of the methods for calculating this response function is the so-called 'sum-over-trips' framework which enables the construction of the Green's function for an arbitrary network as a convergent infinite series solution. Here we propose an alternative and computationally efficient approach for constructing the Green's functions on dendro-dendritic gap junction-coupled neuronal networks which avoids any infinite terms in the solutions. Instead, the Green's function is constructed from the solution of a system of linear algebraic equations. We apply this new method to a number of systems including a simple single cell model and two-cell neuronal networks. We also demonstrate that the application of this novel approach allows one to reduce a model with complex dendritic formations to an equivalent model with a much simpler morphological structure. PMID:26994016

  3. Micropatterning neuronal networks.

    PubMed

    Hardelauf, Heike; Waide, Sarah; Sisnaiske, Julia; Jacob, Peter; Hausherr, Vanessa; Schöbel, Nicole; Janasek, Dirk; van Thriel, Christoph; West, Jonathan

    2014-07-01

    Spatially organised neuronal networks have wide reaching applications, including fundamental research, toxicology testing, pharmaceutical screening and the realisation of neuronal implant interfaces. Despite the large number of methods catalogued in the literature there remains the need to identify a method that delivers high pattern compliance, long-term stability and is widely accessible to neuroscientists. In this comparative study, aminated (polylysine/polyornithine and aminosilanes) and cytophobic (poly(ethylene glycol) (PEG) and methylated) material contrasts were evaluated. Backfilling plasma stencilled PEGylated substrates with polylysine does not produce good material contrasts, whereas polylysine patterned on methylated substrates becomes mobilised by agents in the cell culture media which results in rapid pattern decay. Aminosilanes, polylysine substitutes, are prone to hydrolysis and the chemistries prove challenging to master. Instead, the stable coupling between polylysine and PLL-g-PEG can be exploited: Microcontact printing polylysine onto a PLL-g-PEG coated glass substrate provides a simple means to produce microstructured networks of primary neurons that have superior pattern compliance during long term (>1 month) culture. PMID:24855658

  4. Numerical methods for solving moment equations in kinetic theory of neuronal network dynamics

    NASA Astrophysics Data System (ADS)

    Rangan, Aaditya V.; Cai, David; Tao, Louis

    2007-02-01

    Recently developed kinetic theory and related closures for neuronal network dynamics have been demonstrated to be a powerful theoretical framework for investigating coarse-grained dynamical properties of neuronal networks. The moment equations arising from the kinetic theory are a system of (1 + 1)-dimensional nonlinear partial differential equations (PDE) on a bounded domain with nonlinear boundary conditions. The PDEs themselves are self-consistently specified by parameters which are functions of the boundary values of the solution. The moment equations can be stiff in space and time. Numerical methods are presented here for efficiently and accurately solving these moment equations. The essential ingredients in our numerical methods include: (i) the system is discretized in time with an implicit Euler method within a spectral deferred correction framework, therefore, the PDEs of the kinetic theory are reduced to a sequence, in time, of boundary value problems (BVPs) with nonlinear boundary conditions; (ii) a set of auxiliary parameters is introduced to recast the original BVP with nonlinear boundary conditions as BVPs with linear boundary conditions - with additional algebraic constraints on the auxiliary parameters; (iii) a careful combination of two Newton's iterates for the nonlinear BVP with linear boundary condition, interlaced with a Newton's iterate for solving the associated algebraic constraints is constructed to achieve quadratic convergence for obtaining the solutions with self-consistent parameters. It is shown that a simple fixed-point iteration can only achieve a linear convergence for the self-consistent parameters. The practicability and efficiency of our numerical methods for solving the moment equations of the kinetic theory are illustrated with numerical examples. It is further demonstrated that the moment equations derived from the kinetic theory of neuronal network dynamics can very well capture the coarse-grained dynamical properties of

  5. Spontaneous Calcium Changes in Micro Neuronal Networks

    NASA Astrophysics Data System (ADS)

    Saito, Aki; Moriguchi, Hiroyuki; Iwabuchi, Shin; Goto, Miho; Takayama, Yuzo; Kotani, Kiyoshi; Jimbo, Yasuhiko

    We have developed a practical experimental method to mass-produce and maintain a variation of minimal neuronal networks (“micro neuronal networks”) consisted of a single to several neurons in culture using spray-patterning technique. In this paper, we could maintain the micro-cultures for one month or more by adding conditioned medium and carried out optical recording of spontaneous activity in micro neuronal networks and considered the interactions between them. To determine the interactions between micro neuronal networks, fluorescence changes in several small networks were simultaneously measured using calcium indicator dye fluo-4 AM, and time-series analysis was carried out using surrogate arrangements. By using the spray-patterning method, a large number of cell-adhesive micro regions were formed. Neurons extended neurites along the edge of the cell-adhesive micro regions and form micro neuronal networks. In part of micro regions, some neurite was protruded from the region, and thus micro neuronal networks were connected with synapses. In these networks, a single neuron-induced network activity was observed. On the other hand, even in morphologically non-connected micro neuronal networks, synchronous oscillations between micro neuronal networks were observed. Our micro-patterning methods and results provide the possibility that synchronous activity is occurred between morphologically non-connected neuronal networks. This suggest that the humoral factor is also a important component for network-wide dynamics.

  6. Control of Neuronal Network in Caenorhabditis elegans

    PubMed Central

    Badhwar, Rahul; Bagler, Ganesh

    2015-01-01

    Caenorhabditis elegans, a soil dwelling nematode, is evolutionarily rudimentary and contains only ∼ 300 neurons which are connected to each other via chemical synapses and gap junctions. This structural connectivity can be perceived as nodes and edges of a graph. Controlling complex networked systems (such as nervous system) has been an area of excitement for mankind. Various methods have been developed to identify specific brain regions, which when controlled by external input can lead to achievement of control over the state of the system. But in case of neuronal connectivity network the properties of neurons identified as driver nodes is of much importance because nervous system can produce a variety of states (behaviour of the animal). Hence to gain insight on the type of control achieved in nervous system we implemented the notion of structural control from graph theory to C. elegans neuronal network. We identified ‘driver neurons’ which can provide full control over the network. We studied phenotypic properties of these neurons which are referred to as ‘phenoframe’ as well as the ‘genoframe’ which represents their genetic correlates. We find that the driver neurons are primarily motor neurons located in the ventral nerve cord and contribute to biological reproduction of the animal. Identification of driver neurons and its characterization adds a new dimension in controllability of C. elegans neuronal network. This study suggests the importance of driver neurons and their utility to control the behaviour of the organism. PMID:26413834

  7. A comparison of computational methods for detecting bursts in neuronal spike trains and their application to human stem cell-derived neuronal networks

    PubMed Central

    Charlesworth, Paul; Thomas, Christopher W.; Paulsen, Ole

    2016-01-01

    Accurate identification of bursting activity is an essential element in the characterization of neuronal network activity. Despite this, no one technique for identifying bursts in spike trains has been widely adopted. Instead, many methods have been developed for the analysis of bursting activity, often on an ad hoc basis. Here we provide an unbiased assessment of the effectiveness of eight of these methods at detecting bursts in a range of spike trains. We suggest a list of features that an ideal burst detection technique should possess and use synthetic data to assess each method in regard to these properties. We further employ each of the methods to reanalyze microelectrode array (MEA) recordings from mouse retinal ganglion cells and examine their coherence with bursts detected by a human observer. We show that several common burst detection techniques perform poorly at analyzing spike trains with a variety of properties. We identify four promising burst detection techniques, which are then applied to MEA recordings of networks of human induced pluripotent stem cell-derived neurons and used to describe the ontogeny of bursting activity in these networks over several months of development. We conclude that no current method can provide “perfect” burst detection results across a range of spike trains; however, two burst detection techniques, the MaxInterval and logISI methods, outperform compared with others. We provide recommendations for the robust analysis of bursting activity in experimental recordings using current techniques. PMID:27098024

  8. A comparison of computational methods for detecting bursts in neuronal spike trains and their application to human stem cell-derived neuronal networks.

    PubMed

    Cotterill, Ellese; Charlesworth, Paul; Thomas, Christopher W; Paulsen, Ole; Eglen, Stephen J

    2016-08-01

    Accurate identification of bursting activity is an essential element in the characterization of neuronal network activity. Despite this, no one technique for identifying bursts in spike trains has been widely adopted. Instead, many methods have been developed for the analysis of bursting activity, often on an ad hoc basis. Here we provide an unbiased assessment of the effectiveness of eight of these methods at detecting bursts in a range of spike trains. We suggest a list of features that an ideal burst detection technique should possess and use synthetic data to assess each method in regard to these properties. We further employ each of the methods to reanalyze microelectrode array (MEA) recordings from mouse retinal ganglion cells and examine their coherence with bursts detected by a human observer. We show that several common burst detection techniques perform poorly at analyzing spike trains with a variety of properties. We identify four promising burst detection techniques, which are then applied to MEA recordings of networks of human induced pluripotent stem cell-derived neurons and used to describe the ontogeny of bursting activity in these networks over several months of development. We conclude that no current method can provide "perfect" burst detection results across a range of spike trains; however, two burst detection techniques, the MaxInterval and logISI methods, outperform compared with others. We provide recommendations for the robust analysis of bursting activity in experimental recordings using current techniques. PMID:27098024

  9. Experiments on clustered neuronal networks

    NASA Astrophysics Data System (ADS)

    Teller, S.; Soriano, J.

    2013-01-01

    Neuronal cultures show a rich repertoire of spontaneous activity. However, the mechanisms that relate a particular network architecture with a specific dynamic behavior are still not well understood. In order to investigate the dependence of neuronal network dynamics on architecture we study spontaneous activity in networks formed by interconnected aggregates of neurons (clustered neuronal networks). In the experiments we monitor the spontaneous activity using calcium fluorescence imaging. Network's firing is characterized by bursts of activity, in which the clusters fire sequentially in a short time window, remaining silent until the next bursting episode. We also investigate perturbations on the connectivity of the network. We mainly focus in physical damage. In some cases we observe important changes in the collective activity of the network, while in other cases some dynamic motifs are preserved, hinting at the existence of dynamic robustness.

  10. Simulating synchronization in neuronal networks

    NASA Astrophysics Data System (ADS)

    Fink, Christian G.

    2016-06-01

    We discuss several techniques used in simulating neuronal networks by exploring how a network's connectivity structure affects its propensity for synchronous spiking. Network connectivity is generated using the Watts-Strogatz small-world algorithm, and two key measures of network structure are described. These measures quantify structural characteristics that influence collective neuronal spiking, which is simulated using the leaky integrate-and-fire model. Simulations show that adding a small number of random connections to an otherwise lattice-like connectivity structure leads to a dramatic increase in neuronal synchronization.

  11. Nanometric resolution magnetic resonance imaging methods for mapping functional activity in neuronal networks

    PubMed Central

    Boretti, Albert; Castelletto, Stefania

    2016-01-01

    This contribution highlights and compares some recent achievements in the use of k-space and real space imaging (scanning probe and wide-filed microscope techniques), when applied to a luminescent color center in diamond, known as nitrogen vacancy (NV) center. These techniques combined with the optically detected magnetic resonance of NV, provide a unique platform to achieve nanometric magnetic resonance imaging (MRI) resolution of nearby nuclear spins (known as nanoMRI), and nanometric NV real space localization. • Atomic size optically detectable spin probe. • High magnetic field sensitivity and nanometric resolution. • Non-invasive mapping of functional activity in neuronal networks. PMID:27144128

  12. Nanometric resolution magnetic resonance imaging methods for mapping functional activity in neuronal networks.

    PubMed

    Boretti, Albert; Castelletto, Stefania

    2016-01-01

    This contribution highlights and compares some recent achievements in the use of k-space and real space imaging (scanning probe and wide-filed microscope techniques), when applied to a luminescent color center in diamond, known as nitrogen vacancy (NV) center. These techniques combined with the optically detected magnetic resonance of NV, provide a unique platform to achieve nanometric magnetic resonance imaging (MRI) resolution of nearby nuclear spins (known as nanoMRI), and nanometric NV real space localization. •Atomic size optically detectable spin probe.•High magnetic field sensitivity and nanometric resolution.•Non-invasive mapping of functional activity in neuronal networks. PMID:27144128

  13. Network synchronization in hippocampal neurons.

    PubMed

    Penn, Yaron; Segal, Menahem; Moses, Elisha

    2016-03-22

    Oscillatory activity is widespread in dynamic neuronal networks. The main paradigm for the origin of periodicity consists of specialized pacemaking elements that synchronize and drive the rest of the network; however, other models exist. Here, we studied the spontaneous emergence of synchronized periodic bursting in a network of cultured dissociated neurons from rat hippocampus and cortex. Surprisingly, about 60% of all active neurons were self-sustained oscillators when disconnected, each with its own natural frequency. The individual neuron's tendency to oscillate and the corresponding oscillation frequency are controlled by its excitability. The single neuron intrinsic oscillations were blocked by riluzole, and are thus dependent on persistent sodium leak currents. Upon a gradual retrieval of connectivity, the synchrony evolves: Loose synchrony appears already at weak connectivity, with the oscillators converging to one common oscillation frequency, yet shifted in phase across the population. Further strengthening of the connectivity causes a reduction in the mean phase shifts until zero-lag is achieved, manifested by synchronous periodic network bursts. Interestingly, the frequency of network bursting matches the average of the intrinsic frequencies. Overall, the network behaves like other universal systems, where order emerges spontaneously by entrainment of independent rhythmic units. Although simplified with respect to circuitry in the brain, our results attribute a basic functional role for intrinsic single neuron excitability mechanisms in driving the network's activity and dynamics, contributing to our understanding of developing neural circuits. PMID:26961000

  14. Automatic neuron segmentation and neural network analysis method for phase contrast microscopy images

    PubMed Central

    Pang, Jincheng; Özkucur, Nurdan; Ren, Michael; Kaplan, David L.; Levin, Michael; Miller, Eric L.

    2015-01-01

    Phase Contrast Microscopy (PCM) is an important tool for the long term study of living cells. Unlike fluorescence methods which suffer from photobleaching of fluorophore or dye molecules, PCM image contrast is generated by the natural variations in optical index of refraction. Unfortunately, the same physical principles which allow for these studies give rise to complex artifacts in the raw PCM imagery. Of particular interest in this paper are neuron images where these image imperfections manifest in very different ways for the two structures of specific interest: cell bodies (somas) and dendrites. To address these challenges, we introduce a novel parametric image model using the level set framework and an associated variational approach which simultaneously restores and segments this class of images. Using this technique as the basis for an automated image analysis pipeline, results for both the synthetic and real images validate and demonstrate the advantages of our approach. PMID:26601004

  15. Stages of neuronal network formation

    NASA Astrophysics Data System (ADS)

    Woiterski, Lydia; Claudepierre, Thomas; Luxenhofer, Robert; Jordan, Rainer; Käs, Josef A.

    2013-02-01

    Graph theoretical approaches have become a powerful tool for investigating the architecture and dynamics of complex networks. The topology of network graphs revealed small-world properties for very different real systems among these neuronal networks. In this study, we observed the early development of mouse retinal ganglion cell (RGC) networks in vitro using time-lapse video microscopy. By means of a time-resolved graph theoretical analysis of the connectivity, shortest path length and the edge length, we were able to discover the different stages during the network formation. Starting from single cells, at the first stage neurons connected to each other ending up in a network with maximum complexity. In the further course, we observed a simplification of the network which manifested in a change of relevant network parameters such as the minimization of the path length. Moreover, we found that RGC networks self-organized as small-world networks at both stages; however, the optimization occurred only in the second stage.

  16. Parallel Network Simulations with NEURON

    PubMed Central

    Migliore, M.; Cannia, C.; Lytton, W.W; Markram, Henry; Hines, M. L.

    2009-01-01

    The NEURON simulation environment has been extended to support parallel network simulations. Each processor integrates the equations for its subnet over an interval equal to the minimum (interprocessor) presynaptic spike generation to postsynaptic spike delivery connection delay. The performance of three published network models with very different spike patterns exhibits superlinear speedup on Beowulf clusters and demonstrates that spike communication overhead is often less than the benefit of an increased fraction of the entire problem fitting into high speed cache. On the EPFL IBM Blue Gene, almost linear speedup was obtained up to 100 processors. Increasing one model from 500 to 40,000 realistic cells exhibited almost linear speedup on 2000 processors, with an integration time of 9.8 seconds and communication time of 1.3 seconds. The potential for speed-ups of several orders of magnitude makes practical the running of large network simulations that could otherwise not be explored. PMID:16732488

  17. Shaping Neuronal Network Activity by Presynaptic Mechanisms

    PubMed Central

    Ashery, Uri

    2015-01-01

    Neuronal microcircuits generate oscillatory activity, which has been linked to basic functions such as sleep, learning and sensorimotor gating. Although synaptic release processes are well known for their ability to shape the interaction between neurons in microcircuits, most computational models do not simulate the synaptic transmission process directly and hence cannot explain how changes in synaptic parameters alter neuronal network activity. In this paper, we present a novel neuronal network model that incorporates presynaptic release mechanisms, such as vesicle pool dynamics and calcium-dependent release probability, to model the spontaneous activity of neuronal networks. The model, which is based on modified leaky integrate-and-fire neurons, generates spontaneous network activity patterns, which are similar to experimental data and robust under changes in the model's primary gain parameters such as excitatory postsynaptic potential and connectivity ratio. Furthermore, it reliably recreates experimental findings and provides mechanistic explanations for data obtained from microelectrode array recordings, such as network burst termination and the effects of pharmacological and genetic manipulations. The model demonstrates how elevated asynchronous release, but not spontaneous release, synchronizes neuronal network activity and reveals that asynchronous release enhances utilization of the recycling vesicle pool to induce the network effect. The model further predicts a positive correlation between vesicle priming at the single-neuron level and burst frequency at the network level; this prediction is supported by experimental findings. Thus, the model is utilized to reveal how synaptic release processes at the neuronal level govern activity patterns and synchronization at the network level. PMID:26372048

  18. Neuronal Networks on Nanocellulose Scaffolds.

    PubMed

    Jonsson, Malin; Brackmann, Christian; Puchades, Maja; Brattås, Karoline; Ewing, Andrew; Gatenholm, Paul; Enejder, Annika

    2015-11-01

    Proliferation, integration, and neurite extension of PC12 cells, a widely used culture model for cholinergic neurons, were studied in nanocellulose scaffolds biosynthesized by Gluconacetobacter xylinus to allow a three-dimensional (3D) extension of neurites better mimicking neuronal networks in tissue. The interaction with control scaffolds was compared with cationized nanocellulose (trimethyl ammonium betahydroxy propyl [TMAHP] cellulose) to investigate the impact of surface charges on the cell interaction mechanisms. Furthermore, coatings with extracellular matrix proteins (collagen, fibronectin, and laminin) were investigated to determine the importance of integrin-mediated cell attachment. Cell proliferation was evaluated by a cellular proliferation assay, while cell integration and neurite propagation were studied by simultaneous label-free Coherent anti-Stokes Raman Scattering and second harmonic generation microscopy, providing 3D images of PC12 cells and arrangement of nanocellulose fibrils, respectively. Cell attachment and proliferation were enhanced by TMAHP modification, but not by protein coating. Protein coating instead promoted active interaction between the cells and the scaffold, hence lateral cell migration and integration. Irrespective of surface modification, deepest cell integration measured was one to two cell layers, whereas neurites have a capacity to integrate deeper than the cell bodies in the scaffold due to their fine dimensions and amoeba-like migration pattern. Neurites with lengths of >50 μm were observed, successfully connecting individual cells and cell clusters. In conclusion, TMAHP-modified nanocellulose scaffolds promote initial cellular scaffold adhesion, which combined with additional cell-scaffold treatments enables further formation of 3D neuronal networks. PMID:26398224

  19. Solving Constraint Satisfaction Problems with Networks of Spiking Neurons.

    PubMed

    Jonke, Zeno; Habenschuss, Stefan; Maass, Wolfgang

    2016-01-01

    Network of neurons in the brain apply-unlike processors in our current generation of computer hardware-an event-based processing strategy, where short pulses (spikes) are emitted sparsely by neurons to signal the occurrence of an event at a particular point in time. Such spike-based computations promise to be substantially more power-efficient than traditional clocked processing schemes. However, it turns out to be surprisingly difficult to design networks of spiking neurons that can solve difficult computational problems on the level of single spikes, rather than rates of spikes. We present here a new method for designing networks of spiking neurons via an energy function. Furthermore, we show how the energy function of a network of stochastically firing neurons can be shaped in a transparent manner by composing the networks of simple stereotypical network motifs. We show that this design approach enables networks of spiking neurons to produce approximate solutions to difficult (NP-hard) constraint satisfaction problems from the domains of planning/optimization and verification/logical inference. The resulting networks employ noise as a computational resource. Nevertheless, the timing of spikes plays an essential role in their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines) and Gibbs sampling. PMID:27065785

  20. Solving Constraint Satisfaction Problems with Networks of Spiking Neurons

    PubMed Central

    Jonke, Zeno; Habenschuss, Stefan; Maass, Wolfgang

    2016-01-01

    Network of neurons in the brain apply—unlike processors in our current generation of computer hardware—an event-based processing strategy, where short pulses (spikes) are emitted sparsely by neurons to signal the occurrence of an event at a particular point in time. Such spike-based computations promise to be substantially more power-efficient than traditional clocked processing schemes. However, it turns out to be surprisingly difficult to design networks of spiking neurons that can solve difficult computational problems on the level of single spikes, rather than rates of spikes. We present here a new method for designing networks of spiking neurons via an energy function. Furthermore, we show how the energy function of a network of stochastically firing neurons can be shaped in a transparent manner by composing the networks of simple stereotypical network motifs. We show that this design approach enables networks of spiking neurons to produce approximate solutions to difficult (NP-hard) constraint satisfaction problems from the domains of planning/optimization and verification/logical inference. The resulting networks employ noise as a computational resource. Nevertheless, the timing of spikes plays an essential role in their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines) and Gibbs sampling. PMID:27065785

  1. Robust Multiobjective Controllability of Complex Neuronal Networks.

    PubMed

    Tang, Yang; Gao, Huijun; Du, Wei; Lu, Jianquan; Vasilakos, Athanasios V; Kurths, Jurgen

    2016-01-01

    This paper addresses robust multiobjective identification of driver nodes in the neuronal network of a cat's brain, in which uncertainties in determination of driver nodes and control gains are considered. A framework for robust multiobjective controllability is proposed by introducing interval uncertainties and optimization algorithms. By appropriate definitions of robust multiobjective controllability, a robust nondominated sorting adaptive differential evolution (NSJaDE) is presented by means of the nondominated sorting mechanism and the adaptive differential evolution (JaDE). The simulation experimental results illustrate the satisfactory performance of NSJaDE for robust multiobjective controllability, in comparison with six statistical methods and two multiobjective evolutionary algorithms (MOEAs): nondominated sorting genetic algorithms II (NSGA-II) and nondominated sorting composite differential evolution. It is revealed that the existence of uncertainties in choosing driver nodes and designing control gains heavily affects the controllability of neuronal networks. We also unveil that driver nodes play a more drastic role than control gains in robust controllability. The developed NSJaDE and obtained results will shed light on the understanding of robustness in controlling realistic complex networks such as transportation networks, power grid networks, biological networks, etc. PMID:26441452

  2. Macroscopic Description for Networks of Spiking Neurons

    NASA Astrophysics Data System (ADS)

    Montbrió, Ernest; Pazó, Diego; Roxin, Alex

    2015-04-01

    A major goal of neuroscience, statistical physics, and nonlinear dynamics is to understand how brain function arises from the collective dynamics of networks of spiking neurons. This challenge has been chiefly addressed through large-scale numerical simulations. Alternatively, researchers have formulated mean-field theories to gain insight into macroscopic states of large neuronal networks in terms of the collective firing activity of the neurons, or the firing rate. However, these theories have not succeeded in establishing an exact correspondence between the firing rate of the network and the underlying microscopic state of the spiking neurons. This has largely constrained the range of applicability of such macroscopic descriptions, particularly when trying to describe neuronal synchronization. Here, we provide the derivation of a set of exact macroscopic equations for a network of spiking neurons. Our results reveal that the spike generation mechanism of individual neurons introduces an effective coupling between two biophysically relevant macroscopic quantities, the firing rate and the mean membrane potential, which together govern the evolution of the neuronal network. The resulting equations exactly describe all possible macroscopic dynamical states of the network, including states of synchronous spiking activity. Finally, we show that the firing-rate description is related, via a conformal map, to a low-dimensional description in terms of the Kuramoto order parameter, called Ott-Antonsen theory. We anticipate that our results will be an important tool in investigating how large networks of spiking neurons self-organize in time to process and encode information in the brain.

  3. Adaptive Neurons For Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Tawel, Raoul

    1990-01-01

    Training time decreases dramatically. In improved mathematical model of neural-network processor, temperature of neurons (in addition to connection strengths, also called weights, of synapses) varied during supervised-learning phase of operation according to mathematical formalism and not heuristic rule. Evidence that biological neural networks also process information at neuronal level.

  4. Vehicle dynamic analysis using neuronal network algorithms

    NASA Astrophysics Data System (ADS)

    Oloeriu, Florin; Mocian, Oana

    2014-06-01

    Theoretical developments of certain engineering areas, the emergence of new investigation tools, which are better and more precise and their implementation on-board the everyday vehicles, all these represent main influence factors that impact the theoretical and experimental study of vehicle's dynamic behavior. Once the implementation of these new technologies onto the vehicle's construction had been achieved, it had led to more and more complex systems. Some of the most important, such as the electronic control of engine, transmission, suspension, steering, braking and traction had a positive impact onto the vehicle's dynamic behavior. The existence of CPU on-board vehicles allows data acquisition and storage and it leads to a more accurate and better experimental and theoretical study of vehicle dynamics. It uses the information offered directly by the already on-board built-in elements of electronic control systems. The technical literature that studies vehicle dynamics is entirely focused onto parametric analysis. This kind of approach adopts two simplifying assumptions. Functional parameters obey certain distribution laws, which are known in classical statistics theory. The second assumption states that the mathematical models are previously known and have coefficients that are not time-dependent. Both the mentioned assumptions are not confirmed in real situations: the functional parameters do not follow any known statistical repartition laws and the mathematical laws aren't previously known and contain families of parameters and are mostly time-dependent. The purpose of the paper is to present a more accurate analysis methodology that can be applied when studying vehicle's dynamic behavior. A method that provides the setting of non-parametrical mathematical models for vehicle's dynamic behavior is relying on neuronal networks. This method contains coefficients that are time-dependent. Neuronal networks are mostly used in various types' system controls, thus

  5. Inferring Single Neuron Properties in Conductance Based Balanced Networks

    PubMed Central

    Pool, Román Rossi; Mato, Germán

    2011-01-01

    Balanced states in large networks are a usual hypothesis for explaining the variability of neural activity in cortical systems. In this regime the statistics of the inputs is characterized by static and dynamic fluctuations. The dynamic fluctuations have a Gaussian distribution. Such statistics allows to use reverse correlation methods, by recording synaptic inputs and the spike trains of ongoing spontaneous activity without any additional input. By using this method, properties of the single neuron dynamics that are masked by the balanced state can be quantified. To show the feasibility of this approach we apply it to large networks of conductance based neurons. The networks are classified as Type I or Type II according to the bifurcations which neurons of the different populations undergo near the firing onset. We also analyze mixed networks, in which each population has a mixture of different neuronal types. We determine under which conditions the intrinsic noise generated by the network can be used to apply reverse correlation methods. We find that under realistic conditions we can ascertain with low error the types of neurons present in the network. We also find that data from neurons with similar firing rates can be combined to perform covariance analysis. We compare the results of these methods (that do not requite any external input) to the standard procedure (that requires the injection of Gaussian noise into a single neuron). We find a good agreement between the two procedures. PMID:22016730

  6. Slow waves in mutually inhibitory neuronal networks

    NASA Astrophysics Data System (ADS)

    Jalics, Jozsi

    2004-05-01

    A variety of experimental and modeling studies have been performed to investigate wave propagation in networks of thalamic neurons and their relationship to spindle sleep rhythms. It is believed that spindle oscillations result from the reciprocal interaction between thalamocortical (TC) and thalamic reticular (RE) neurons. We consider a network of TC and RE cells reduced to a one-layer network model and represented by a system of singularly perturbed integral-differential equations. Geometric singular perturbation methods are used to prove the existence of a locally unique slow wave pulse that propagates along the network. By seeking a slow pulse solution, we reformulate the problem to finding a heteroclinic orbit in a 3D system of ODEs with two additional constraints on the location of the orbit at two distinct points in time. In proving the persistence of the singular heteroclinic orbit, difficulties arising from the solution passing near points where normal hyperbolicity is lost on a 2D critical manifold are overcome by employing results by Wechselberger [Singularly perturbed folds and canards in R3, Thesis, TU-Wien, 1998].

  7. Stochastic resonance in mammalian neuronal networks

    SciTech Connect

    Gluckman, B.J.; So, P.; Netoff, T.I.; Spano, M.L.; Schiff, S.J. |

    1998-09-01

    We present stochastic resonance observed in the dynamics of neuronal networks from mammalian brain. Both sinusoidal signals and random noise were superimposed into an applied electric field. As the amplitude of the noise component was increased, an optimization (increase then decrease) in the signal-to-noise ratio of the network response to the sinusoidal signal was observed. The relationship between the measures used to characterize the dynamics is discussed. Finally, a computational model of these neuronal networks that includes the neuronal interactions with the electric field is presented to illustrate the physics behind the essential features of the experiment. {copyright} {ital 1998 American Institute of Physics.}

  8. Cultured neuronal networks as environmental biosensors.

    PubMed

    O'Shaughnessy, Thomas J; Gray, Samuel A; Pancrazio, Joseph J

    2004-01-01

    Contamination of water by toxins, either intentionally or unintentionally, is a growing concern for both military and civilian agencies and thus there is a need for systems capable of monitoring a wide range of natural and industrial toxicants. The EILATox-Oregon Workshop held in September 2002 provided an opportunity to test the capabilities of a prototype neuronal network-based biosensor with unknown contaminants in water samples. The biosensor is a portable device capable of recording the action potential activity from a network of mammalian neurons grown on glass microelectrode arrays. Changes in the action potential fi ring rate across the network are monitored to determine exposure to toxicants. A series of three neuronal networks derived from mice was used to test seven unknown samples. Two of these unknowns later were revealed to be blanks, to which the neuronal networks did not respond. Of the five remaining unknowns, a significant change in network activity was detected for four of the compounds at concentrations below a lethal level for humans: mercuric chloride, sodium arsenite, phosdrin and chlordimeform. These compounds--two heavy metals, an organophosphate and an insecticide--demonstrate the breadth of detection possible with neuronal networks. The results generated at the workshop show the promise of the neuronal network biosensor as an environmental detector but there is still considerable effort needed to produce a device suitable for routine environmental threat monitoring. PMID:15478174

  9. Oscillatorylike behavior in feedforward neuronal networks

    NASA Astrophysics Data System (ADS)

    Payeur, Alexandre; Maler, Leonard; Longtin, André

    2015-07-01

    We demonstrate how rhythmic activity can arise in neural networks from feedforward rather than recurrent circuitry and, in so doing, we provide a mechanism capable of explaining the temporal decorrelation of γ -band oscillations. We compare the spiking activity of a delayed recurrent network of inhibitory neurons with that of a feedforward network with the same neural properties and axonal delays. Paradoxically, these very different connectivities can yield very similar spike-train statistics in response to correlated input. This happens when neurons are noisy and axonal delays are short. A Taylor expansion of the feedback network's susceptibility—or frequency-dependent gain function—can then be stopped at first order to a good approximation, thus matching the feedforward net's susceptibility. The feedback network is known to display oscillations; these oscillations imply that the spiking activity of the population is felt by all neurons within the network, leading to direct spike correlations in a given neuron. On the other hand, in the output layer of the feedforward net, the interaction between the external drive and the delayed feedforward projection of this drive by the input layer causes indirect spike correlations: spikes fired by a given output layer neuron are correlated only through the activity of the input layer neurons. High noise and short delays partially bridge the gap between these two types of correlation, yielding similar spike-train statistics for both networks. This similarity is even stronger when the delay is distributed, as confirmed by linear response theory.

  10. Oscillatorylike behavior in feedforward neuronal networks.

    PubMed

    Payeur, Alexandre; Maler, Leonard; Longtin, André

    2015-07-01

    We demonstrate how rhythmic activity can arise in neural networks from feedforward rather than recurrent circuitry and, in so doing, we provide a mechanism capable of explaining the temporal decorrelation of γ-band oscillations. We compare the spiking activity of a delayed recurrent network of inhibitory neurons with that of a feedforward network with the same neural properties and axonal delays. Paradoxically, these very different connectivities can yield very similar spike-train statistics in response to correlated input. This happens when neurons are noisy and axonal delays are short. A Taylor expansion of the feedback network's susceptibility-or frequency-dependent gain function-can then be stopped at first order to a good approximation, thus matching the feedforward net's susceptibility. The feedback network is known to display oscillations; these oscillations imply that the spiking activity of the population is felt by all neurons within the network, leading to direct spike correlations in a given neuron. On the other hand, in the output layer of the feedforward net, the interaction between the external drive and the delayed feedforward projection of this drive by the input layer causes indirect spike correlations: spikes fired by a given output layer neuron are correlated only through the activity of the input layer neurons. High noise and short delays partially bridge the gap between these two types of correlation, yielding similar spike-train statistics for both networks. This similarity is even stronger when the delay is distributed, as confirmed by linear response theory. PMID:26274199

  11. Maximum hyperchaos in chaotic nonmonotonic neuronal networks

    NASA Astrophysics Data System (ADS)

    Shuai, J. W.; Chen, Z. X.; Liu, R. T.; Wu, B. X.

    1997-07-01

    Hyperchaos in chaotic nonmonotonic neuronal networks is discussed with computer simulations. Maximum chaos with all Lyapunov exponents positive is found not only in the present dissipative model with weak coupling connections between neurons, but also with some strong-coupling connections. Although the model presented is a noninvertible map, the information dimension of simple chaos still yields a good approximation to the Lyapunov dimension.

  12. Somatostatin-expressing neurons in cortical networks.

    PubMed

    Urban-Ciecko, Joanna; Barth, Alison L

    2016-07-01

    Somatostatin-expressing GABAergic neurons constitute a major class of inhibitory neurons in the mammalian cortex and are characterized by dense wiring into the local network and high basal firing activity that persists in the absence of synaptic input. This firing provides both GABA type A receptor (GABAAR)- and GABABR-mediated inhibition that operates at fast and slow timescales. The activity of somatostatin-expressing neurons is regulated by brain state, during learning and in rewarded behaviour. Here, we review recent advances in our understanding of how this class of cells can control network activity, with specific reference to how this is constrained by their anatomical and electrophysiological properties. PMID:27225074

  13. Associative memory in phasing neuron networks

    SciTech Connect

    Nair, Niketh S; Bochove, Erik J.; Braiman, Yehuda

    2014-01-01

    We studied pattern formation in a network of coupled Hindmarsh-Rose model neurons and introduced a new model for associative memory retrieval using networks of Kuramoto oscillators. Hindmarsh-Rose Neural Networks can exhibit a rich set of collective dynamics that can be controlled by their connectivity. Specifically, we showed an instance of Hebb's rule where spiking was correlated with network topology. Based on this, we presented a simple model of associative memory in coupled phase oscillators.

  14. Reducing Neuronal Networks to Discrete Dynamics

    PubMed Central

    Terman, David; Ahn, Sungwoo; Wang, Xueying; Just, Winfried

    2008-01-01

    We consider a general class of purely inhibitory and excitatory-inhibitory neuronal networks, with a general class of network architectures, and characterize the complex firing patterns that emerge. Our strategy for studying these networks is to first reduce them to a discrete model. In the discrete model, each neuron is represented as a finite number of states and there are rules for how a neuron transitions from one state to another. In this paper, we rigorously demonstrate that the continuous neuronal model can be reduced to the discrete model if the intrinsic and synaptic properties of the cells are chosen appropriately. In a companion paper [1], we analyze the discrete model. PMID:18443649

  15. Sloppiness in Spontaneously Active Neuronal Networks

    PubMed Central

    Panas, Dagmara; Amin, Hayder; Maccione, Alessandro; Muthmann, Oliver; van Rossum, Mark; Berdondini, Luca

    2015-01-01

    Various plasticity mechanisms, including experience-dependent, spontaneous, as well as homeostatic ones, continuously remodel neural circuits. Yet, despite fluctuations in the properties of single neurons and synapses, the behavior and function of neuronal assemblies are generally found to be very stable over time. This raises the important question of how plasticity is coordinated across the network. To address this, we investigated the stability of network activity in cultured rat hippocampal neurons recorded with high-density multielectrode arrays over several days. We used parametric models to characterize multineuron activity patterns and analyzed their sensitivity to changes. We found that the models exhibited sloppiness, a property where the model behavior is insensitive to changes in many parameter combinations, but very sensitive to a few. The activity of neurons with sloppy parameters showed faster and larger fluctuations than the activity of a small subset of neurons associated with sensitive parameters. Furthermore, parameter sensitivity was highly correlated with firing rates. Finally, we tested our observations from cell cultures on an in vivo recording from monkey visual cortex and we confirm that spontaneous cortical activity also shows hallmarks of sloppy behavior and firing rate dependence. Our findings suggest that a small subnetwork of highly active and stable neurons supports group stability, and that this endows neuronal networks with the flexibility to continuously remodel without compromising stability and function. PMID:26041916

  16. Structural Properties of the Caenorhabditis elegans Neuronal Network

    PubMed Central

    Varshney, Lav R.; Chen, Beth L.; Paniagua, Eric; Hall, David H.; Chklovskii, Dmitri B.

    2011-01-01

    Despite recent interest in reconstructing neuronal networks, complete wiring diagrams on the level of individual synapses remain scarce and the insights into function they can provide remain unclear. Even for Caenorhabditis elegans, whose neuronal network is relatively small and stereotypical from animal to animal, published wiring diagrams are neither accurate nor complete and self-consistent. Using materials from White et al. and new electron micrographs we assemble whole, self-consistent gap junction and chemical synapse networks of hermaphrodite C. elegans. We propose a method to visualize the wiring diagram, which reflects network signal flow. We calculate statistical and topological properties of the network, such as degree distributions, synaptic multiplicities, and small-world properties, that help in understanding network signal propagation. We identify neurons that may play central roles in information processing, and network motifs that could serve as functional modules of the network. We explore propagation of neuronal activity in response to sensory or artificial stimulation using linear systems theory and find several activity patterns that could serve as substrates of previously described behaviors. Finally, we analyze the interaction between the gap junction and the chemical synapse networks. Since several statistical properties of the C. elegans network, such as multiplicity and motif distributions are similar to those found in mammalian neocortex, they likely point to general principles of neuronal networks. The wiring diagram reported here can help in understanding the mechanistic basis of behavior by generating predictions about future experiments involving genetic perturbations, laser ablations, or monitoring propagation of neuronal activity in response to stimulation. PMID:21304930

  17. Synchrony and Control of Neuronal Networks.

    NASA Astrophysics Data System (ADS)

    Schiff, Steven

    2001-03-01

    Cooperative behavior in the brain stems from the nature and strength of the interactions between neurons within a networked ensemble. Normal network activity takes place in a state of partial synchrony between neurons, and some pathological behaviors, such as epilepsy and tremor, appear to share a common feature of increased interaction strength. We have focused on the parallel paths of both detecting and characterizing the nonlinear synchronization present within neuronal networks, and employing feedback control methodology using electrical fields to modulate that neuronal activity. From a theoretical perspective, we see evidence for nonlinear generalized synchrony in networks of neurons that linear techniques are incapable of detecting (PRE 54: 6708, 1996), and we have described a decoherence transition between asymmetric nonlinear systems that is experimentally observable (PRL 84: 1689, 2000). In addition, we have seen evidence for unstable dimension variability in real neuronal systems that indicates certain physical limits of modelability when observing such systems (PRL 85, 2490, 2000). From an experimental perspective, we have achieved success in modulating epileptic seizures in neuronal networks using electrical fields. Extracellular neuronal activity is continuously recorded during field application through differential extracellular recording techniques, and the applied electric field strength is continuously updated using a computer controlled proportional feedback algorithm. This approach appears capable of sustained amelioration of seizure events when used with negative feedback. In negative feedback mode, such findings may offer a novel technology for seizure control. In positive feedback mode, adaptively applied electric fields may offer a more physiological means for neural modulation for prosthetic purposes than previously possible (J. Neuroscience, 2001).

  18. Stability of Neuronal Networks with Homeostatic Regulation

    PubMed Central

    Harnack, Daniel; Pelko, Miha; Chaillet, Antoine; Chitour, Yacine; van Rossum, Mark C.W.

    2015-01-01

    Neurons are equipped with homeostatic mechanisms that counteract long-term perturbations of their average activity and thereby keep neurons in a healthy and information-rich operating regime. While homeostasis is believed to be crucial for neural function, a systematic analysis of homeostatic control has largely been lacking. The analysis presented here analyses the necessary conditions for stable homeostatic control. We consider networks of neurons with homeostasis and show that homeostatic control that is stable for single neurons, can destabilize activity in otherwise stable recurrent networks leading to strong non-abating oscillations in the activity. This instability can be prevented by slowing down the homeostatic control. The stronger the network recurrence, the slower the homeostasis has to be. Next, we consider how non-linearities in the neural activation function affect these constraints. Finally, we consider the case that homeostatic feedback is mediated via a cascade of multiple intermediate stages. Counter-intuitively, the addition of extra stages in the homeostatic control loop further destabilizes activity in single neurons and networks. Our theoretical framework for homeostasis thus reveals previously unconsidered constraints on homeostasis in biological networks, and identifies conditions that require the slow time-constants of homeostatic regulation observed experimentally. PMID:26154297

  19. Attractor dynamics in local neuronal networks

    PubMed Central

    Thivierge, Jean-Philippe; Comas, Rosa; Longtin, André

    2014-01-01

    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. PMID:24688457

  20. Neural network with dynamically adaptable neurons

    NASA Technical Reports Server (NTRS)

    Tawel, Raoul (Inventor)

    1994-01-01

    This invention is an adaptive neuron for use in neural network processors. The adaptive neuron participates in the supervised learning phase of operation on a co-equal basis with the synapse matrix elements by adaptively changing its gain in a similar manner to the change of weights in the synapse IO elements. In this manner, training time is decreased by as much as three orders of magnitude.

  1. Self-excited relaxation oscillations in networks of impulse neurons

    NASA Astrophysics Data System (ADS)

    Glyzin, S. D.; Kolesov, A. Yu; Rozov, N. Kh

    2015-06-01

    This paper addresses the problem of mathematical modelling of neuron activity. New classes of singularly perturbed differential-difference equations with Volterra-type delay are proposed and used to describe how single neurons and also neural networks function with various kinds of connections (electrical or chemical). Special asymptotic methods are developed which make it possible to analyse questions of the existence and stability of relaxation periodic motions in such systems. Bibliography: 56 titles.

  2. Towards Reproducible Descriptions of Neuronal Network Models

    PubMed Central

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

    2009-01-01

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

  3. Inhibition Controls Asynchronous States of Neuronal Networks

    PubMed Central

    Treviño, Mario

    2016-01-01

    Computations in cortical circuits require action potentials from excitatory and inhibitory neurons. In this mini-review, I first provide a quick overview of findings that indicate that GABAergic neurons play a fundamental role in coordinating spikes and generating synchronized network activity. Next, I argue that these observations helped popularize the notion that network oscillations require a high degree of spike correlations among interneurons which, in turn, produce synchronous inhibition of the local microcircuit. The aim of this text is to discuss some recent experimental and computational findings that support a complementary view: one in which interneurons participate actively in producing asynchronous states in cortical networks. This requires a proper mixture of shared excitation and inhibition leading to asynchronous activity between neighboring cells. Such contribution from interneurons would be extremely important because it would tend to reduce the spike correlation between neighboring pyramidal cells, a drop in redundancy that could enhance the information-processing capacity of neural networks. PMID:27274721

  4. Neuronal network analyses: premises, promises and uncertainties

    PubMed Central

    Parker, David

    2010-01-01

    Neuronal networks assemble the cellular components needed for sensory, motor and cognitive functions. Any rational intervention in the nervous system will thus require an understanding of network function. Obtaining this understanding is widely considered to be one of the major tasks facing neuroscience today. Network analyses have been performed for some years in relatively simple systems. In addition to the direct insights these systems have provided, they also illustrate some of the difficulties of understanding network function. Nevertheless, in more complex systems (including human), claims are made that the cellular bases of behaviour are, or will shortly be, understood. While the discussion is necessarily limited, this issue will examine these claims and highlight some traditional and novel aspects of network analyses and their difficulties. This introduction discusses the criteria that need to be satisfied for network understanding, and how they relate to traditional and novel approaches being applied to addressing network function. PMID:20603354

  5. Complexities and uncertainties of neuronal network function

    PubMed Central

    Parker, David

    2005-01-01

    The nervous system generates behaviours through the activity in groups of neurons assembled into networks. Understanding these networks is thus essential to our understanding of nervous system function. Understanding a network requires information on its component cells, their interactions and their functional properties. Few networks come close to providing complete information on these aspects. However, even if complete information were available it would still only provide limited insight into network function. This is because the functional and structural properties of a network are not fixed but are plastic and can change over time. The number of interacting network components, their (variable) functional properties, and various plasticity mechanisms endows networks with considerable flexibility, but these features inevitably complicate network analyses. This review will initially discuss the general approaches and problems of network analyses. It will then examine the success of these analyses in a model spinal cord locomotor network in the lamprey, to determine to what extent in this relatively simple vertebrate system it is possible to claim detailed understanding of network function and plasticity. PMID:16553310

  6. Label-Free Characterization of Emerging Human Neuronal Networks

    NASA Astrophysics Data System (ADS)

    Mir, Mustafa; Kim, Taewoo; Majumder, Anirban; Xiang, Mike; Wang, Ru; Liu, S. Chris; Gillette, Martha U.; Stice, Steven; Popescu, Gabriel

    2014-03-01

    The emergent self-organization of a neuronal network in a developing nervous system is the result of a remarkably orchestrated process involving a multitude of chemical, mechanical and electrical signals. Little is known about the dynamic behavior of a developing network (especially in a human model) primarily due to a lack of practical and non-invasive methods to measure and quantify the process. Here we demonstrate that by using a novel optical interferometric technique, we can non-invasively measure several fundamental properties of neural networks from the sub-cellular to the cell population level. We applied this method to quantify network formation in human stem cell derived neurons and show for the first time, correlations between trends in the growth, transport, and spatial organization of such a system. Quantifying the fundamental behavior of such cell lines without compromising their viability may provide an important new tool in future longitudinal studies.

  7. Integrated microfluidic platforms for investigating neuronal networks

    NASA Astrophysics Data System (ADS)

    Kim, Hyung Joon

    (multielectrode array) or nanowire electrode array to study electrophysiology in neuronal network. Also, "diode-like" microgrooves to control the number of neuronal processes is embedded in this platform. Chapter 6 concludes with a possible future direction of this work. Interfacing micro/nanotechnology with primary neuron culture would open many doors in fundamental neuroscience research and also biomedical innovation.

  8. GENERAL: Complete and phase synchronization in a heterogeneous small-world neuronal network

    NASA Astrophysics Data System (ADS)

    Han, Fang; Lu, Qi-Shao; Wiercigroch, Marian; Ji, Quan-Bao

    2009-02-01

    Synchronous firing of neurons is thought to be important for information communication in neuronal networks. This paper investigates the complete and phase synchronization in a heterogeneous small-world chaotic Hindmarsh-Rose neuronal network. The effects of various network parameters on synchronization behaviour are discussed with some biological explanations. Complete synchronization of small-world neuronal networks is studied theoretically by the master stability function method. It is shown that the coupling strength necessary for complete or phase synchronization decreases with the neuron number, the node degree and the connection density are increased. The effect of heterogeneity of neuronal networks is also considered and it is found that the network heterogeneity has an adverse effect on synchrony.

  9. Micropatterning Facilitates the Long-Term Growth and Analysis of iPSC-Derived Individual Human Neurons and Neuronal Networks.

    PubMed

    Burbulla, Lena F; Beaumont, Kristin G; Mrksich, Milan; Krainc, Dimitri

    2016-08-01

    The discovery of induced pluripotent stem cells (iPSCs) and their application to patient-specific disease models offers new opportunities for studying the pathophysiology of neurological disorders. However, current methods for culturing iPSC-derived neuronal cells result in clustering of neurons, which precludes the analysis of individual neurons and defined neuronal networks. To address this challenge, cultures of human neurons on micropatterned surfaces are developed that promote neuronal survival over extended periods of time. This approach facilitates studies of neuronal development, cellular trafficking, and related mechanisms that require assessment of individual neurons and specific network connections. Importantly, micropatterns support the long-term stability of cultured neurons, which enables time-dependent analysis of cellular processes in living neurons. The approach described in this paper allows mechanistic studies of human neurons, both in terms of normal neuronal development and function, as well as time-dependent pathological processes, and provides a platform for testing of new therapeutics in neuropsychiatric disorders. PMID:27108930

  10. Collective Dynamics for Heterogeneous Networks of Theta Neurons

    NASA Astrophysics Data System (ADS)

    Luke, Tanushree

    Collective behavior in neural networks has often been used as an indicator of communication between different brain areas. These collective synchronization and desynchronization patterns are also considered an important feature in understanding normal and abnormal brain function. To understand the emergence of these collective patterns, I create an analytic model that identifies all such macroscopic steady-states attainable by a network of Type-I neurons. This network, whose basic unit is the model "theta'' neuron, contains a mixture of excitable and spiking neurons coupled via a smooth pulse-like synapse. Applying the Ott-Antonsen reduction method in the thermodynamic limit, I obtain a low-dimensional evolution equation that describes the asymptotic dynamics of the macroscopic mean field of the network. This model can be used as the basis in understanding more complicated neuronal networks when additional dynamical features are included. From this reduced dynamical equation for the mean field, I show that the network exhibits three collective attracting steady-states. The first two are equilibrium states that both reflect partial synchronization in the network, whereas the third is a limit cycle in which the degree of network synchronization oscillates in time. In addition to a comprehensive identification of all possible attracting macro-states, this analytic model permits a complete bifurcation analysis of the collective behavior of the network with respect to three key network features: the degree of excitability of the neurons, the heterogeneity of the population, and the overall coupling strength. The network typically tends towards the two macroscopic equilibrium states when the neuron's intrinsic dynamics and the network interactions reinforce each other. In contrast, the limit cycle state, bifurcations, and multistability tend to occur when there is competition between these network features. I also outline here an extension of the above model where the

  11. Spike Code Flow in Cultured Neuronal Networks.

    PubMed

    Tamura, Shinichi; Nishitani, Yoshi; Hosokawa, Chie; Miyoshi, Tomomitsu; Sawai, Hajime; Kamimura, Takuya; Yagi, Yasushi; Mizuno-Matsumoto, Yuko; Chen, Yen-Wei

    2016-01-01

    We observed spike trains produced by one-shot electrical stimulation with 8 × 8 multielectrodes in cultured neuronal networks. Each electrode accepted spikes from several neurons. We extracted the short codes from spike trains and obtained a code spectrum with a nominal time accuracy of 1%. We then constructed code flow maps as movies of the electrode array to observe the code flow of "1101" and "1011," which are typical pseudorandom sequence such as that we often encountered in a literature and our experiments. They seemed to flow from one electrode to the neighboring one and maintained their shape to some extent. To quantify the flow, we calculated the "maximum cross-correlations" among neighboring electrodes, to find the direction of maximum flow of the codes with lengths less than 8. Normalized maximum cross-correlations were almost constant irrespective of code. Furthermore, if the spike trains were shuffled in interval orders or in electrodes, they became significantly small. Thus, the analysis suggested that local codes of approximately constant shape propagated and conveyed information across the network. Hence, the codes can serve as visible and trackable marks of propagating spike waves as well as evaluating information flow in the neuronal network. PMID:27217825

  12. Spike Code Flow in Cultured Neuronal Networks

    PubMed Central

    Tamura, Shinichi; Nishitani, Yoshi; Miyoshi, Tomomitsu; Sawai, Hajime; Kamimura, Takuya; Yagi, Yasushi; Mizuno-Matsumoto, Yuko; Chen, Yen-Wei

    2016-01-01

    We observed spike trains produced by one-shot electrical stimulation with 8 × 8 multielectrodes in cultured neuronal networks. Each electrode accepted spikes from several neurons. We extracted the short codes from spike trains and obtained a code spectrum with a nominal time accuracy of 1%. We then constructed code flow maps as movies of the electrode array to observe the code flow of “1101” and “1011,” which are typical pseudorandom sequence such as that we often encountered in a literature and our experiments. They seemed to flow from one electrode to the neighboring one and maintained their shape to some extent. To quantify the flow, we calculated the “maximum cross-correlations” among neighboring electrodes, to find the direction of maximum flow of the codes with lengths less than 8. Normalized maximum cross-correlations were almost constant irrespective of code. Furthermore, if the spike trains were shuffled in interval orders or in electrodes, they became significantly small. Thus, the analysis suggested that local codes of approximately constant shape propagated and conveyed information across the network. Hence, the codes can serve as visible and trackable marks of propagating spike waves as well as evaluating information flow in the neuronal network. PMID:27217825

  13. Recording axonal conduction to evaluate the integration of pluripotent cell-derived neurons into a neuronal network.

    PubMed

    Shimba, Kenta; Sakai, Koji; Takayama, Yuzo; Kotani, Kiyoshi; Jimbo, Yasuhiko

    2015-10-01

    Stem cell transplantation is a promising therapy to treat neurodegenerative disorders, and a number of in vitro models have been developed for studying interactions between grafted neurons and the host neuronal network to promote drug discovery. However, methods capable of evaluating the process by which stem cells integrate into the host neuronal network are lacking. In this study, we applied an axonal conduction-based analysis to a co-culture study of primary and differentiated neurons. Mouse cortical neurons and neuronal cells differentiated from P19 embryonal carcinoma cells, a model for early neural differentiation of pluripotent stem cells, were co-cultured in a microfabricated device. The somata of these cells were separated by the co-culture device, but their axons were able to elongate through microtunnels and then form synaptic contacts. Propagating action potentials were recorded from these axons by microelectrodes embedded at the bottom of the microtunnels and sorted into clusters representing individual axons. While the number of axons of cortical neurons increased until 14 days in vitro and then decreased, those of P19 neurons increased throughout the culture period. Network burst analysis showed that P19 neurons participated in approximately 80% of the bursting activity after 14 days in vitro. Interestingly, the axonal conduction delay of P19 neurons was significantly greater than that of cortical neurons, suggesting that there are some physiological differences in their axons. These results suggest that our method is feasible to evaluate the process by which stem cell-derived neurons integrate into a host neuronal network. PMID:26303583

  14. [Inhibitory interactions in neuronal networks including cells of the auditory cortex and the medial geniculate body].

    PubMed

    Sil'kis, I G

    1994-01-01

    Cross-correlation method was used for revealing effective inhibitory interactions in neural networks containing simultaneously recorded neurons from different loci of auditory cortex (A1) and medial geniculate body (MGB). It was shown that (i) inhibitory connections were "divergent", i. e., one neuron in A1 (MGB) depressed activity of neurons in different loci of A1 and MGB simultaneously; (ii) inputs to inhibitory neuron were "convergent", i.e., one neuron in A1 (MGB) was excited by neurons from different loci of A1 and MGB simultaneously. There were inhibitory neurons which selectively depressed activity of only one neighbouring neuron. The results allow to suggest that the same inhibitory neuron may be involved in afferent and feedback inhibition. We supposed that the principles of organization of inhibitory connections in thalamo-cortical networks underlie the observed exceptions to mapping (tonotopic) principle of organization of receptive fields of A1 and MGB. PMID:7879428

  15. Transition to Chaos in Random Neuronal Networks

    NASA Astrophysics Data System (ADS)

    Kadmon, Jonathan; Sompolinsky, Haim

    2015-10-01

    Firing patterns in the central nervous system often exhibit strong temporal irregularity and considerable heterogeneity in time-averaged response properties. Previous studies suggested that these properties are the outcome of the intrinsic chaotic dynamics of the neural circuits. Indeed, simplified rate-based neuronal networks with synaptic connections drawn from Gaussian distribution and sigmoidal nonlinearity are known to exhibit chaotic dynamics when the synaptic gain (i.e., connection variance) is sufficiently large. In the limit of an infinitely large network, there is a sharp transition from a fixed point to chaos, as the synaptic gain reaches a critical value. Near the onset, chaotic fluctuations are slow, analogous to the ubiquitous, slow irregular fluctuations observed in the firing rates of many cortical circuits. However, the existence of a transition from a fixed point to chaos in neuronal circuit models with more realistic architectures and firing dynamics has not been established. In this work, we investigate rate-based dynamics of neuronal circuits composed of several subpopulations with randomly diluted connections. Nonzero connections are either positive for excitatory neurons or negative for inhibitory ones, while single neuron output is strictly positive with output rates rising as a power law above threshold, in line with known constraints in many biological systems. Using dynamic mean field theory, we find the phase diagram depicting the regimes of stable fixed-point, unstable-dynamic, and chaotic-rate fluctuations. We focus on the latter and characterize the properties of systems near this transition. We show that dilute excitatory-inhibitory architectures exhibit the same onset to chaos as the single population with Gaussian connectivity. In these architectures, the large mean excitatory and inhibitory inputs dynamically balance each other, amplifying the effect of the residual fluctuations. Importantly, the existence of a transition to chaos

  16. Resynchronization in neuronal network divided by femtosecond laser processing.

    PubMed

    Hosokawa, Chie; Kudoh, Suguru N; Kiyohara, Ai; Taguchi, Takahisa

    2008-05-01

    We demonstrated scission of a living neuronal network on multielectrode arrays (MEAs) using a focused femtosecond laser and evaluated the resynchronization of spontaneous electrical activity within the network. By an irradiation of femtosecond laser into hippocampal neurons cultured on a multielectrode array dish, neurites were cut at the focal point. After the irradiation, synchronization of neuronal activity within the network drastically decreased over the divided area, indicating diminished functional connections between neurons. Cross-correlation analysis revealed that spontaneous activity between the divided areas gradually resynchronized within 10 days. These findings indicate that hippocampal neurons have the potential to regenerate functional connections and to reconstruct a network by self-assembly. PMID:18418255

  17. Coping with variability in small neuronal networks.

    PubMed

    Calabrese, Ronald L; Norris, Brian J; Wenning, Angela; Wright, Terrence M

    2011-12-01

    Experimental and corresponding modeling studies indicate that there is a 2- to 5-fold variation of intrinsic and synaptic parameters across animals while functional output is maintained. Here, we review experiments, using the heartbeat central pattern generator (CPG) in medicinal leeches, which explore the consequences of animal-to-animal variation in synaptic strength for coordinated motor output. We focus on a set of segmental heart motor neurons that all receive inhibitory synaptic input from the same four premotor interneurons. These four premotor inputs fire in a phase progression and the motor neurons also fire in a phase progression because of differences in synaptic strength profiles of the four inputs among segments. Our work tested the hypothesis that functional output is maintained in the face of animal-to-animal variation in the absolute strength of connections because relative strengths of the four inputs onto particular motor neurons is maintained across animals. Our experiments showed that relative strength is not strictly maintained across animals even as functional output is maintained, and animal-to-animal variations in strength of particular inputs do not correlate strongly with output phase. Further experiments measured the precise temporal pattern of the premotor inputs, the segmental synaptic strength profiles of their connections onto motor neurons, and the temporal pattern (phase progression) of those motor neurons all in the same animal for a series of 12 animals. The analysis of input and output in this sample of 12 individuals suggests that the number (four) of inputs to each motor neuron and the variability of the temporal pattern of input from the CPG across individuals weaken the influence of the strength of individual inputs. Moreover, the temporal pattern of the output varies as much across individuals as that of the input. Essentially, each animal arrives at a unique solution for how the network produces functional output. PMID

  18. Energy coding in neural network with inhibitory neurons.

    PubMed

    Wang, Ziyin; Wang, Rubin; Fang, Ruiyan

    2015-04-01

    This paper aimed at assessing and comparing the effects of the inhibitory neurons in the neural network on the neural energy distribution, and the network activities in the absence of the inhibitory neurons to understand the nature of neural energy distribution and neural energy coding. Stimulus, synchronous oscillation has significant difference between neural networks with and without inhibitory neurons, and this difference can be quantitatively evaluated by the characteristic energy distribution. In addition, the synchronous oscillation difference of the neural activity can be quantitatively described by change of the energy distribution if the network parameters are gradually adjusted. Compared with traditional method of correlation coefficient analysis, the quantitative indicators based on nervous energy distribution characteristics are more effective in reflecting the dynamic features of the neural network activities. Meanwhile, this neural coding method from a global perspective of neural activity effectively avoids the current defects of neural encoding and decoding theory and enormous difficulties encountered. Our studies have shown that neural energy coding is a new coding theory with high efficiency and great potential. PMID:25806094

  19. Hierarchical networks, power laws, and neuronal avalanches

    NASA Astrophysics Data System (ADS)

    Friedman, Eric J.; Landsberg, Adam S.

    2013-03-01

    We show that in networks with a hierarchical architecture, critical dynamical behaviors can emerge even when the underlying dynamical processes are not critical. This finding provides explicit insight into current studies of the brain's neuronal network showing power-law avalanches in neural recordings, and provides a theoretical justification of recent numerical findings. Our analysis shows how the hierarchical organization of a network can itself lead to power-law distributions of avalanche sizes and durations, scaling laws between anomalous exponents, and universal functions—even in the absence of self-organized criticality or critical points. This hierarchy-induced phenomenon is independent of, though can potentially operate in conjunction with, standard dynamical mechanisms for generating power laws.

  20. Irregular behavior in an excitatory-inhibitory neuronal network

    NASA Astrophysics Data System (ADS)

    Park, Choongseok; Terman, David

    2010-06-01

    Excitatory-inhibitory networks arise in many regions throughout the central nervous system and display complex spatiotemporal firing patterns. These neuronal activity patterns (of individual neurons and/or the whole network) are closely related to the functional status of the system and differ between normal and pathological states. For example, neurons within the basal ganglia, a group of subcortical nuclei that are responsible for the generation of movement, display a variety of dynamic behaviors such as correlated oscillatory activity and irregular, uncorrelated spiking. Neither the origins of these firing patterns nor the mechanisms that underlie the patterns are well understood. We consider a biophysical model of an excitatory-inhibitory network in the basal ganglia and explore how specific biophysical properties of the network contribute to the generation of irregular spiking. We use geometric dynamical systems and singular perturbation methods to systematically reduce the model to a simpler set of equations, which is suitable for analysis. The results specify the dependence on the strengths of synaptic connections and the intrinsic firing properties of the cells in the irregular regime when applied to the subthalamopallidal network of the basal ganglia.

  1. Inferring Network Dynamics and Neuron Properties from Population Recordings

    PubMed Central

    Linaro, Daniele; Storace, Marco; Mattia, Maurizio

    2011-01-01

    Understanding the computational capabilities of the nervous system means to “identify” its emergent multiscale dynamics. For this purpose, we propose a novel model-driven identification procedure and apply it to sparsely connected populations of excitatory integrate-and-fire neurons with spike frequency adaptation (SFA). Our method does not characterize the system from its microscopic elements in a bottom-up fashion, and does not resort to any linearization. We investigate networks as a whole, inferring their properties from the response dynamics of the instantaneous discharge rate to brief and aspecific supra-threshold stimulations. While several available methods assume generic expressions for the system as a black box, we adopt a mean-field theory for the evolution of the network transparently parameterized by identified elements (such as dynamic timescales), which are in turn non-trivially related to single-neuron properties. In particular, from the elicited transient responses, the input–output gain function of the neurons in the network is extracted and direct links to the microscopic level are made available: indeed, we show how to extract the decay time constant of the SFA, the absolute refractory period and the average synaptic efficacy. In addition and contrary to previous attempts, our method captures the system dynamics across bifurcations separating qualitatively different dynamical regimes. The robustness and the generality of the methodology is tested on controlled simulations, reporting a good agreement between theoretically expected and identified values. The assumptions behind the underlying theoretical framework make the method readily applicable to biological preparations like cultured neuron networks and in vitro brain slices. PMID:22016731

  2. Critical behavior in networks of real neurons

    NASA Astrophysics Data System (ADS)

    Tkacik, Gasper

    2014-03-01

    The patterns of joint activity in a population of retinal ganglion cells encode the complete information about the visual world, and thus place limits on what could be learned about the environment by the brain. We analyze the recorded simultaneous activity of more than a hundred such neurons from an interacting population responding to naturalistic stimuli, at the single spike level, by constructing accurate maximum entropy models for the distribution of network activity states. This - essentially an ``inverse spin glass'' - construction reveals strong frustration in the pairwise couplings between the neurons that results in a rugged energy landscape with many local extrema; strong collective interactions in subgroups of neurons despite weak individual pairwise correlations; and a joint distribution of activity that has an extremely wide dynamic range characterized by a zipf-like power law, strong deviations from ``typicality,'' and a number of signatures of critical behavior. We hypothesize that this tuning to a critical operating point might be a dynamic property of the system and suggest experiments to test this hypothesis.

  3. Serotonin modulation of cortical neurons and networks

    PubMed Central

    Celada, Pau; Puig, M. Victoria; Artigas, Francesc

    2013-01-01

    The serotonergic pathways originating in the dorsal and median raphe nuclei (DR and MnR, respectively) are critically involved in cortical function. Serotonin (5-HT), acting on postsynaptic and presynaptic receptors, is involved in cognition, mood, impulse control and motor functions by (1) modulating the activity of different neuronal types, and (2) varying the release of other neurotransmitters, such as glutamate, GABA, acetylcholine and dopamine. Also, 5-HT seems to play an important role in cortical development. Of all cortical regions, the frontal lobe is the area most enriched in serotonergic axons and 5-HT receptors. 5-HT and selective receptor agonists modulate the excitability of cortical neurons and their discharge rate through the activation of several receptor subtypes, of which the 5-HT1A, 5-HT1B, 5-HT2A, and 5-HT3 subtypes play a major role. Little is known, however, on the role of other excitatory receptors moderately expressed in cortical areas, such as 5-HT2C, 5-HT4, 5-HT6, and 5-HT7. In vitro and in vivo studies suggest that 5-HT1A and 5-HT2A receptors are key players and exert opposite effects on the activity of pyramidal neurons in the medial prefrontal cortex (mPFC). The activation of 5-HT1A receptors in mPFC hyperpolarizes pyramidal neurons whereas that of 5-HT2A receptors results in neuronal depolarization, reduction of the afterhyperpolarization and increase of excitatory postsynaptic currents (EPSCs) and of discharge rate. 5-HT can also stimulate excitatory (5-HT2A and 5-HT3) and inhibitory (5-HT1A) receptors in GABA interneurons to modulate synaptic GABA inputs onto pyramidal neurons. Likewise, the pharmacological manipulation of various 5-HT receptors alters oscillatory activity in PFC, suggesting that 5-HT is also involved in the control of cortical network activity. A better understanding of the actions of 5-HT in PFC may help to develop treatments for mood and cognitive disorders associated with an abnormal function of the frontal lobe

  4. Integrated workflows for spiking neuronal network simulations

    PubMed Central

    Antolík, Ján; Davison, Andrew P.

    2013-01-01

    The increasing availability of computational resources is enabling more detailed, realistic modeling in computational neuroscience, resulting in a shift toward more heterogeneous models of neuronal circuits, and employment of complex experimental protocols. This poses a challenge for existing tool chains, as the set of tools involved in a typical modeler's workflow is expanding concomitantly, with growing complexity in the metadata flowing between them. For many parts of the workflow, a range of tools is available; however, numerous areas lack dedicated tools, while integration of existing tools is limited. This forces modelers to either handle the workflow manually, leading to errors, or to write substantial amounts of code to automate parts of the workflow, in both cases reducing their productivity. To address these issues, we have developed Mozaik: a workflow system for spiking neuronal network simulations written in Python. Mozaik integrates model, experiment and stimulation specification, simulation execution, data storage, data analysis and visualization into a single automated workflow, ensuring that all relevant metadata are available to all workflow components. It is based on several existing tools, including PyNN, Neo, and Matplotlib. It offers a declarative way to specify models and recording configurations using hierarchically organized configuration files. Mozaik automatically records all data together with all relevant metadata about the experimental context, allowing automation of the analysis and visualization stages. Mozaik has a modular architecture, and the existing modules are designed to be extensible with minimal programming effort. Mozaik increases the productivity of running virtual experiments on highly structured neuronal networks by automating the entire experimental cycle, while increasing the reliability of modeling studies by relieving the user from manual handling of the flow of metadata between the individual workflow stages. PMID

  5. Identification of neuronal network properties from the spectral analysis of calcium imaging signals in neuronal cultures

    PubMed Central

    Tibau, Elisenda; Valencia, Miguel; Soriano, Jordi

    2013-01-01

    Neuronal networks in vitro are prominent systems to study the development of connections in living neuronal networks and the interplay between connectivity, activity and function. These cultured networks show a rich spontaneous activity that evolves concurrently with the connectivity of the underlying network. In this work we monitor the development of neuronal cultures, and record their activity using calcium fluorescence imaging. We use spectral analysis to characterize global dynamical and structural traits of the neuronal cultures. We first observe that the power spectrum can be used as a signature of the state of the network, for instance when inhibition is active or silent, as well as a measure of the network's connectivity strength. Second, the power spectrum identifies prominent developmental changes in the network such as GABAA switch. And third, the analysis of the spatial distribution of the spectral density, in experiments with a controlled disintegration of the network through CNQX, an AMPA-glutamate receptor antagonist in excitatory neurons, reveals the existence of communities of strongly connected, highly active neurons that display synchronous oscillations. Our work illustrates the interest of spectral analysis for the study of in vitro networks, and its potential use as a network-state indicator, for instance to compare healthy and diseased neuronal networks. PMID:24385953

  6. Communication through resonance in spiking neuronal networks.

    PubMed

    Hahn, Gerald; Bujan, Alejandro F; Frégnac, Yves; Aertsen, Ad; Kumar, Arvind

    2014-08-01

    The cortex processes stimuli through a distributed network of specialized brain areas. This processing requires mechanisms that can route neuronal activity across weakly connected cortical regions. Routing models proposed thus far are either limited to propagation of spiking activity across strongly connected networks or require distinct mechanisms that create local oscillations and establish their coherence between distant cortical areas. Here, we propose a novel mechanism which explains how synchronous spiking activity propagates across weakly connected brain areas supported by oscillations. In our model, oscillatory activity unleashes network resonance that amplifies feeble synchronous signals and promotes their propagation along weak connections ("communication through resonance"). The emergence of coherent oscillations is a natural consequence of synchronous activity propagation and therefore the assumption of different mechanisms that create oscillations and provide coherence is not necessary. Moreover, the phase-locking of oscillations is a side effect of communication rather than its requirement. Finally, we show how the state of ongoing activity could affect the communication through resonance and propose that modulations of the ongoing activity state could influence information processing in distributed cortical networks. PMID:25165853

  7. Effect of Transcranial Magnetic Stimulation on Neuronal Networks

    NASA Astrophysics Data System (ADS)

    Unsal, Ahmet; Hadimani, Ravi; Jiles, David

    2013-03-01

    The human brain contains around 100 billion nerve cells controlling our day to day activities. Consequently, brain disorders often result in impairments such as paralysis, loss of coordination and seizure. It has been said that 1 in 5 Americans suffer some diagnosable mental disorder. There is an urgent need to understand the disorders, prevent them and if possible, develop permanent cure for them. As a result, a significant amount of research activities is being directed towards brain research. Transcranial Magnetic Stimulation (TMS) is a promising tool for diagnosing and treating brain disorders. It is a non-invasive treatment method that produces a current flow in the brain which excites the neurons. Even though TMS has been verified to have advantageous effects on various brain related disorders, there have not been enough studies on the impact of TMS on cells. In this study, we are investigating the electrophysiological effects of TMS on one dimensional neuronal culture grown in a circular pathway. Electrical currents are produced on the neuronal networks depending on the directionality of the applied field. This aids in understanding how neuronal networks react under TMS treatment.

  8. Synchronization in neuronal oscillator networks with input heterogeneity and arbitrary network structure

    NASA Astrophysics Data System (ADS)

    Davison, Elizabeth; Dey, Biswadip; Leonard, Naomi

    Mathematical studies of synchronization in networks of neuronal oscillators offer insight into neuronal ensemble behavior in the brain. Systematic means to understand how network structure and external input affect synchronization in network models have the potential to improve methods for treating synchronization-related neurological disorders such as epilepsy and Parkinson's disease. To elucidate the complex relationships between network structure, external input, and synchronization, we investigate synchronous firing patterns in arbitrary networks of neuronal oscillators coupled through gap junctions with heterogeneous external inputs. We first apply a passivity-based Lyapunov analysis to undirected networks of homogeneous FitzHugh-Nagumo (FN) oscillators with homogeneous inputs and derive a sufficient condition on coupling strength that guarantees complete synchronization. In biologically relevant regimes, we employ Gronwall's inequality to obtain a bound tighter than those previously reported. We extend both analyses to a homogeneous FN network with heterogeneous inputs and show how cluster synchronization emerges under conditions on the symmetry of the coupling matrix and external inputs. Our results can be generalized to any network of semi-passive oscillators.

  9. Method Accelerates Training Of Some Neural Networks

    NASA Technical Reports Server (NTRS)

    Shelton, Robert O.

    1992-01-01

    Three-layer networks trained faster provided two conditions are satisfied: numbers of neurons in layers are such that majority of work done in synaptic connections between input and hidden layers, and number of neurons in input layer at least as great as number of training pairs of input and output vectors. Based on modified version of back-propagation method.

  10. Multiplex Networks of Cortical and Hippocampal Neurons Revealed at Different Timescales

    PubMed Central

    Timme, Nicholas; Ito, Shinya; Myroshnychenko, Maxym; Yeh, Fang-Chin; Hiolski, Emma; Hottowy, Pawel; Beggs, John M.

    2014-01-01

    Recent studies have emphasized the importance of multiplex networks – interdependent networks with shared nodes and different types of connections – in systems primarily outside of neuroscience. Though the multiplex properties of networks are frequently not considered, most networks are actually multiplex networks and the multiplex specific features of networks can greatly affect network behavior (e.g. fault tolerance). Thus, the study of networks of neurons could potentially be greatly enhanced using a multiplex perspective. Given the wide range of temporally dependent rhythms and phenomena present in neural systems, we chose to examine multiplex networks of individual neurons with time scale dependent connections. To study these networks, we used transfer entropy – an information theoretic quantity that can be used to measure linear and nonlinear interactions – to systematically measure the connectivity between individual neurons at different time scales in cortical and hippocampal slice cultures. We recorded the spiking activity of almost 12,000 neurons across 60 tissue samples using a 512-electrode array with 60 micrometer inter-electrode spacing and 50 microsecond temporal resolution. To the best of our knowledge, this preparation and recording method represents a superior combination of number of recorded neurons and temporal and spatial recording resolutions to any currently available in vivo system. We found that highly connected neurons (“hubs”) were localized to certain time scales, which, we hypothesize, increases the fault tolerance of the network. Conversely, a large proportion of non-hub neurons were not localized to certain time scales. In addition, we found that long and short time scale connectivity was uncorrelated. Finally, we found that long time scale networks were significantly less modular and more disassortative than short time scale networks in both tissue types. As far as we are aware, this analysis represents the first

  11. The role of dimensionality in neuronal network dynamics.

    PubMed

    Ulloa Severino, Francesco Paolo; Ban, Jelena; Song, Qin; Tang, Mingliang; Bianconi, Ginestra; Cheng, Guosheng; Torre, Vincent

    2016-01-01

    Recent results from network theory show that complexity affects several dynamical properties of networks that favor synchronization. Here we show that synchronization in 2D and 3D neuronal networks is significantly different. Using dissociated hippocampal neurons we compared properties of cultures grown on a flat 2D substrates with those formed on 3D graphene foam scaffolds. Both 2D and 3D cultures had comparable glia to neuron ratio and the percentage of GABAergic inhibitory neurons. 3D cultures because of their dimension have many connections among distant neurons leading to small-world networks and their characteristic dynamics. After one week, calcium imaging revealed moderately synchronous activity in 2D networks, but the degree of synchrony of 3D networks was higher and had two regimes: a highly synchronized (HS) and a moderately synchronized (MS) regime. The HS regime was never observed in 2D networks. During the MS regime, neuronal assemblies in synchrony changed with time as observed in mammalian brains. After two weeks, the degree of synchrony in 3D networks decreased, as observed in vivo. These results show that dimensionality determines properties of neuronal networks and that several features of brain dynamics are a consequence of its 3D topology. PMID:27404281

  12. The role of dimensionality in neuronal network dynamics

    PubMed Central

    Ulloa Severino, Francesco Paolo; Ban, Jelena; Song, Qin; Tang, Mingliang; Bianconi, Ginestra; Cheng, Guosheng; Torre, Vincent

    2016-01-01

    Recent results from network theory show that complexity affects several dynamical properties of networks that favor synchronization. Here we show that synchronization in 2D and 3D neuronal networks is significantly different. Using dissociated hippocampal neurons we compared properties of cultures grown on a flat 2D substrates with those formed on 3D graphene foam scaffolds. Both 2D and 3D cultures had comparable glia to neuron ratio and the percentage of GABAergic inhibitory neurons. 3D cultures because of their dimension have many connections among distant neurons leading to small-world networks and their characteristic dynamics. After one week, calcium imaging revealed moderately synchronous activity in 2D networks, but the degree of synchrony of 3D networks was higher and had two regimes: a highly synchronized (HS) and a moderately synchronized (MS) regime. The HS regime was never observed in 2D networks. During the MS regime, neuronal assemblies in synchrony changed with time as observed in mammalian brains. After two weeks, the degree of synchrony in 3D networks decreased, as observed in vivo. These results show that dimensionality determines properties of neuronal networks and that several features of brain dynamics are a consequence of its 3D topology. PMID:27404281

  13. Synchronization and rhythm dynamics of a neuronal network consisting of mixed bursting neurons with hybrid synapses

    NASA Astrophysics Data System (ADS)

    Shi, Xia; Xi, Wenqi

    2016-05-01

    In this paper, burst synchronization and rhythm dynamics of a small-world neuronal network consisting of mixed bursting types of neurons coupled via inhibitory-excitatory chemical synapses are explored. Two quantities, the synchronization parameter and average width factor, are used to characterize the synchronization degree and rhythm dynamics of the neuronal network. Numerical results show that the percentage of the inhibitory synapses in the network is the major factor for we get a similarly bell-shaped dependence of synchronization on it, and the decrease of the average width factor of the network. We also find that not only the value of the coupling strength can promote the synchronization degree, but the probability of random edges adding to the small-world network also can. The ratio of the long bursting neurons has little effect on the burst synchronization and rhythm dynamics of the network.

  14. Signal propagation through feedforward neuronal networks with different operational modes

    NASA Astrophysics Data System (ADS)

    Li, Jie; Liu, Feng; Xu, Ding; Wang, Wei

    2009-02-01

    How neuronal activity is propagated across multiple layers of neurons is a fundamental issue in neuroscience. Using numerical simulations, we explored how the operational mode of neurons —coincidence detector or temporal integrator— could affect the propagation of rate signals through a 10-layer feedforward network with sparse connectivity. Our study was based on two kinds of neuron models. The Hodgkin-Huxley (HH) neuron can function as a coincidence detector, while the leaky integrate-and-fire (LIF) neuron can act as a temporal integrator. When white noise is afferent to the input layer, rate signals can be stably propagated through both networks, while neurons in deeper layers fire synchronously in the absence of background noise; but the underlying mechanism for the development of synchrony is different. When an aperiodic signal is presented, the network of HH neurons can represent the temporal structure of the signal in firing rate. Meanwhile, synchrony is well developed and is resistant to background noise. In contrast, rate signals are somewhat distorted during the propagation through the network of LIF neurons, and only weak synchrony occurs in deeper layers. That is, coincidence detectors have a performance advantage over temporal integrators in propagating rate signals. Therefore, given weak synaptic conductance and sparse connectivity between layers in both networks, synchrony does greatly subserve the propagation of rate signals with fidelity, and coincidence detection could be of considerable functional significance in cortical processing.

  15. Stimulus-dependent synchronization in delayed-coupled neuronal networks.

    PubMed

    Esfahani, Zahra G; Gollo, Leonardo L; Valizadeh, Alireza

    2016-01-01

    Time delay is a general feature of all interactions. Although the effects of delayed interaction are often neglected when the intrinsic dynamics is much slower than the coupling delay, they can be crucial otherwise. We show that delayed coupled neuronal networks support transitions between synchronous and asynchronous states when the level of input to the network changes. The level of input determines the oscillation period of neurons and hence whether time-delayed connections are synchronizing or desynchronizing. We find that synchronizing connections lead to synchronous dynamics, whereas desynchronizing connections lead to out-of-phase oscillations in network motifs and to frustrated states with asynchronous dynamics in large networks. Since the impact of a neuronal network to downstream neurons increases when spikes are synchronous, networks with delayed connections can serve as gatekeeper layers mediating the firing transfer to other regions. This mechanism can regulate the opening and closing of communicating channels between cortical layers on demand. PMID:27001428

  16. Stimulus-dependent synchronization in delayed-coupled neuronal networks

    PubMed Central

    Esfahani, Zahra G.; Gollo, Leonardo L.; Valizadeh, Alireza

    2016-01-01

    Time delay is a general feature of all interactions. Although the effects of delayed interaction are often neglected when the intrinsic dynamics is much slower than the coupling delay, they can be crucial otherwise. We show that delayed coupled neuronal networks support transitions between synchronous and asynchronous states when the level of input to the network changes. The level of input determines the oscillation period of neurons and hence whether time-delayed connections are synchronizing or desynchronizing. We find that synchronizing connections lead to synchronous dynamics, whereas desynchronizing connections lead to out-of-phase oscillations in network motifs and to frustrated states with asynchronous dynamics in large networks. Since the impact of a neuronal network to downstream neurons increases when spikes are synchronous, networks with delayed connections can serve as gatekeeper layers mediating the firing transfer to other regions. This mechanism can regulate the opening and closing of communicating channels between cortical layers on demand. PMID:27001428

  17. Real-time tracking of neuronal network structure using data assimilation

    NASA Astrophysics Data System (ADS)

    Hamilton, Franz; Berry, Tyrus; Peixoto, Nathalia; Sauer, Timothy

    2013-11-01

    A nonlinear data assimilation technique is applied to determine and track effective connections between ensembles of cultured spinal cord neurons measured with multielectrode arrays. The method is statistical, depending only on confidence intervals, and requiring no form of arbitrary thresholding. In addition, the method updates connection strengths sequentially, enabling real-time tracking of nonstationary networks. The ensemble Kalman filter is used with a generic spiking neuron model to estimate connection strengths as well as other system parameters to deal with model mismatch. The method is validated on noisy synthetic data from Hodgkin-Huxley model neurons before being used to find network connections in the neural culture recordings.

  18. On The Use of Dynamic Bayesian Networks in Reconstructing Functional Neuronal Networks from Spike Train Ensembles

    PubMed Central

    Eldawlatly, Seif; Zhou, Yang; Jin, Rong; Oweiss, Karim G.

    2009-01-01

    Coordination among cortical neurons is believed to be key element in mediating many high level cortical processes such as perception, attention, learning and memory formation. Inferring the topology of the neural circuitry underlying this coordination is important to characterize the highly non-linear, time-varying interactions between cortical neurons in the presence of complex stimuli. In this work, we investigate the applicability of Dynamic Bayesian Networks (DBNs) in inferring the effective connectivity between spiking cortical neurons from their observed spike trains. We demonstrate that DBNs can infer the underlying non-linear and time-varying causal interactions between these neurons and can discriminate between mono and polysynaptic links between them under certain constraints governing their putative connectivity. We analyzed conditionally-Poisson spike train data mimicking spiking activity of cortical networks of small and moderately-large sizes. The performance was assessed and compared to other methods under systematic variations of the network structure to mimic a wide range of responses typically observed in the cortex. Results demonstrate the utility of DBN in inferring the effective connectivity in cortical networks. PMID:19852619

  19. Temporally tuned neuronal differentiation supports the functional remodeling of a neuronal network in Drosophila.

    PubMed

    Veverytsa, Lyubov; Allan, Douglas W

    2012-03-27

    During insect metamorphosis, neuronal networks undergo extensive remodeling by restructuring their connectivity and recruiting newborn neurons from postembryonic lineages. The neuronal network that directs the essential behavior, ecdysis, generates a distinct behavioral sequence at each developmental transition. Larval ecdysis replaces the cuticle between larval stages, and pupal ecdysis externalizes and expands the head and appendages to their adult position. However, the network changes that support these differences are unknown. Crustacean cardioactive peptide (CCAP) neurons and the peptide hormones they secrete are critical for ecdysis; their targeted ablation alters larval ecdysis progression and results in a failure of pupal ecdysis. In this study, we demonstrate that the CCAP neuron network is remodeled immediately before pupal ecdysis by the emergence of 12 late CCAP neurons. All 12 are CCAP efferents that exit the central nervous system. Importantly, these late CCAP neurons were found to be entirely sufficient for wild-type pupal ecdysis, even after targeted ablation of all other 42 CCAP neurons. Our evidence indicates that late CCAP neurons are derived from early, likely embryonic, lineages. However, they do not differentiate to express their peptide hormone battery, nor do they project an axon via lateral nerve trunks until pupariation, both of which are believed to be critical for the function of CCAP efferent neurons in ecdysis. Further analysis implicated ecdysone signaling via ecdysone receptors A/B1 and the nuclear receptor ftz-f1 as the differentiation trigger. These results demonstrate the utility of temporally tuned neuronal differentiation as a hard-wired developmental mechanism to remodel a neuronal network to generate a scheduled change in behavior. PMID:22393011

  20. Network of hypothalamic neurons that control appetite

    PubMed Central

    Sohn, Jong-Woo

    2015-01-01

    The central nervous system (CNS) controls food intake and energy expenditure via tight coordinations between multiple neuronal populations. Specifically, two distinct neuronal populations exist in the arcuate nucleus of hypothalamus (ARH): the anorexigenic (appetite-suppressing) pro-opiomelanocortin (POMC) neurons and the orexigenic (appetite-increasing) neuropeptide Y (NPY)/agouti-related peptide (AgRP) neurons. The coordinated regulation of neuronal circuit involving these neurons is essential in properly maintaining energy balance, and any disturbance therein may result in hyperphagia/obesity or hypophagia/starvation. Thus, adequate knowledge of the POMC and NPY/AgRP neuron physiology is mandatory to understand the pathophysiology of obesity and related metabolic diseases. This review will discuss the history and recent updates on the POMC and NPY/AgRP neuronal circuits, as well as the general anorexigenic and orexigenic circuits in the CNS. [BMB Reports 2015; 48(4): 229-233] PMID:25560696

  1. Small is beautiful: models of small neuronal networks

    PubMed Central

    Lamb, Damon G; Calabrese, Ronald L

    2013-01-01

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

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

    PubMed

    Lamb, Damon G; Calabrese, Ronald L

    2012-08-01

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

  3. Highly connected neurons spike less frequently in balanced networks

    NASA Astrophysics Data System (ADS)

    Pyle, Ryan; Rosenbaum, Robert

    2016-04-01

    Biological neuronal networks exhibit highly variable spiking activity. Balanced networks offer a parsimonious model of this variability in which strong excitatory synaptic inputs are canceled by strong inhibitory inputs on average, and irregular spiking activity is driven by fluctuating synaptic currents. Most previous studies of balanced networks assume a homogeneous or distance-dependent connectivity structure, but connectivity in biological cortical networks is more intricate. We use a heterogeneous mean-field theory of balanced networks to show that heterogeneous in-degrees can break balance. Moreover, heterogeneous architectures that achieve balance promote lower firing rates in neurons with larger in-degrees, consistent with some recent experimental observations.

  4. Highly connected neurons spike less frequently in balanced networks.

    PubMed

    Pyle, Ryan; Rosenbaum, Robert

    2016-04-01

    Biological neuronal networks exhibit highly variable spiking activity. Balanced networks offer a parsimonious model of this variability in which strong excitatory synaptic inputs are canceled by strong inhibitory inputs on average, and irregular spiking activity is driven by fluctuating synaptic currents. Most previous studies of balanced networks assume a homogeneous or distance-dependent connectivity structure, but connectivity in biological cortical networks is more intricate. We use a heterogeneous mean-field theory of balanced networks to show that heterogeneous in-degrees can break balance. Moreover, heterogeneous architectures that achieve balance promote lower firing rates in neurons with larger in-degrees, consistent with some recent experimental observations. PMID:27176240

  5. Colloid-guided assembly of oriented 3D neuronal networks

    PubMed Central

    Pautot, Sophie; Wyart, Claire; Isacoff, Ehud Y

    2009-01-01

    A central challenge in neuroscience is to understand the formation and function of three-dimensional (3D) neuronal networks. In vitro studies have been mainly limited to measurements of small numbers of neurons connected in two dimensions. Here we demonstrate the use of colloids as moveable supports for neuronal growth, maturation, transfection and manipulation, where the colloids serve as guides for the assembly of controlled 3D, millimeter-sized neuronal networks. Process growth can be guided into layered connectivity with a density similar to what is found in vivo. The colloidal superstructures are optically transparent, enabling remote stimulation and recording of neuronal activity using layer-specific expression of light-activated channels and indicator dyes. The modular approach toward in vitro circuit construction provides a stepping stone for applications ranging from basic neuroscience to neuron-based screening of targeted drugs. PMID:18641658

  6. Scalable Semisupervised Functional Neurocartography Reveals Canonical Neurons in Behavioral Networks.

    PubMed

    Frady, E Paxon; Kapoor, Ashish; Horvitz, Eric; Kristan, William B

    2016-08-01

    Large-scale data collection efforts to map the brain are underway at multiple spatial and temporal scales, but all face fundamental problems posed by high-dimensional data and intersubject variability. Even seemingly simple problems, such as identifying a neuron/brain region across animals/subjects, become exponentially more difficult in high dimensions, such as recognizing dozens of neurons/brain regions simultaneously. We present a framework and tools for functional neurocartography-the large-scale mapping of neural activity during behavioral states. Using a voltage-sensitive dye (VSD), we imaged the multifunctional responses of hundreds of leech neurons during several behaviors to identify and functionally map homologous neurons. We extracted simple features from each of these behaviors and combined them with anatomical features to create a rich medium-dimensional feature space. This enabled us to use machine learning techniques and visualizations to characterize and account for intersubject variability, piece together a canonical atlas of neural activity, and identify two behavioral networks. We identified 39 neurons (18 pairs, 3 unpaired) as part of a canonical swim network and 17 neurons (8 pairs, 1 unpaired) involved in a partially overlapping preparatory network. All neurons in the preparatory network rapidly depolarized at the onsets of each behavior, suggesting that it is part of a dedicated rapid-response network. This network is likely mediated by the S cell, and we referenced VSD recordings to an activity atlas to identify multiple cells of interest simultaneously in real time for further experiments. We targeted and electrophysiologically verified several neurons in the swim network and further showed that the S cell is presynaptic to multiple neurons in the preparatory network. This study illustrates the basic framework to map neural activity in high dimensions with large-scale recordings and how to extract the rich information necessary to perform

  7. Emerging dynamics in neuronal networks of diffusively coupled hard oscillators.

    PubMed

    Ponta, L; Lanza, V; Bonnin, M; Corinto, F

    2011-06-01

    Oscillatory networks are a special class of neural networks where each neuron exhibits time periodic behavior. They represent bio-inspired architectures which can be exploited to model biological processes such as the binding problem and selective attention. In this paper we investigate the dynamics of networks whose neurons are hard oscillators, namely they exhibit the coexistence of different stable attractors. We consider a constant external stimulus applied to each neuron, which influences the neuron's own natural frequency. We show that, due to the interaction between different kinds of attractors, as well as between attractors and repellors, new interesting dynamics arises, in the form of synchronous oscillations of various amplitudes. We also show that neurons subject to different stimuli are able to synchronize if their couplings are strong enough. PMID:21411276

  8. Intermittent synchronization in a network of bursting neurons

    NASA Astrophysics Data System (ADS)

    Park, Choongseok; Rubchinsky, Leonid L.

    2011-09-01

    Synchronized oscillations in networks of inhibitory and excitatory coupled bursting neurons are common in a variety of neural systems from central pattern generators to human brain circuits. One example of the latter is the subcortical network of the basal ganglia, formed by excitatory and inhibitory bursters of the subthalamic nucleus and globus pallidus, involved in motor control and affected in Parkinson's disease. Recent experiments have demonstrated the intermittent nature of the phase-locking of neural activity in this network. Here, we explore one potential mechanism to explain the intermittent phase-locking in a network. We simplify the network to obtain a model of two inhibitory coupled elements and explore its dynamics. We used geometric analysis and singular perturbation methods for dynamical systems to reduce the full model to a simpler set of equations. Mathematical analysis was completed using three slow variables with two different time scales. Intermittently, synchronous oscillations are generated by overlapped spiking which crucially depends on the geometry of the slow phase plane and the interplay between slow variables as well as the strength of synapses. Two slow variables are responsible for the generation of activity patterns with overlapped spiking, and the other slower variable enhances the robustness of an irregular and intermittent activity pattern. While the analyzed network and the explored mechanism of intermittent synchrony appear to be quite generic, the results of this analysis can be used to trace particular values of biophysical parameters (synaptic strength and parameters of calcium dynamics), which are known to be impacted in Parkinson's disease.

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

    PubMed Central

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

    2016-01-01

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

  10. Emergence of Slow-Switching Assemblies in Structured Neuronal Networks

    PubMed Central

    Schaub, Michael T.; Billeh, Yazan N.; Anastassiou, Costas A.; Koch, Christof; Barahona, Mauricio

    2015-01-01

    Unraveling the interplay between connectivity and spatio-temporal dynamics in neuronal networks is a key step to advance our understanding of neuronal information processing. Here we investigate how particular features of network connectivity underpin the propensity of neural networks to generate slow-switching assembly (SSA) dynamics, i.e., sustained epochs of increased firing within assemblies of neurons which transition slowly between different assemblies throughout the network. We show that the emergence of SSA activity is linked to spectral properties of the asymmetric synaptic weight matrix. In particular, the leading eigenvalues that dictate the slow dynamics exhibit a gap with respect to the bulk of the spectrum, and the associated Schur vectors exhibit a measure of block-localization on groups of neurons, thus resulting in coherent dynamical activity on those groups. Through simple rate models, we gain analytical understanding of the origin and importance of the spectral gap, and use these insights to develop new network topologies with alternative connectivity paradigms which also display SSA activity. Specifically, SSA dynamics involving excitatory and inhibitory neurons can be achieved by modifying the connectivity patterns between both types of neurons. We also show that SSA activity can occur at multiple timescales reflecting a hierarchy in the connectivity, and demonstrate the emergence of SSA in small-world like networks. Our work provides a step towards understanding how network structure (uncovered through advancements in neuroanatomy and connectomics) can impact on spatio-temporal neural activity and constrain the resulting dynamics. PMID:26176664

  11. Genotoxicants Target Distinct Molecular Networks in Neonatal Neurons

    PubMed Central

    Kisby, Glen E.; Olivas, Antoinette; Standley, Melissa; Lu, Xinfang; Pattee, Patrick; O’Malley, Jean; Li, Xiaorong; Muniz, Juan; Nagalla, Srinavasa R.

    2006-01-01

    Background Exposure of the brain to environmental agents during critical periods of neuronal development is considered a key factor underlying many neurologic disorders. Objectives In this study we examined the influence of genotoxicants on cerebellar function during early development by measuring global gene expression changes. Methods We measured global gene expression in immature cerebellar neurons (i.e., granule cells) after treatment with two distinct alkylating agents, methylazoxymethanol (MAM) and nitrogen mustard (HN2). Granule cell cultures were treated for 24 hr with MAM (10–1,000 μM) or HN2 (0.1–20 μM) and examined for cell viability, DNA damage, and markers of apoptosis. Results Neuronal viability was significantly reduced (p < 0.01) at concentrations > 500 μM for MAM and > 1.0 μM for HN2; this correlated with an increase in both DNA damage and markers of apoptosis. Neuronal cultures treated with sublethal concentrations of MAM (100 μM) or HN2 (1.0 μM) were then examined for gene expression using large-scale mouse cDNA microarrays (27,648). Gene expression results revealed that a) global gene expression was predominantly up-regulated by both genotoxicants; b) the number of down-regulated genes was approximately 3-fold greater for HN2 than for MAM; and c) distinct classes of molecules were influenced by MAM (i.e, neuronal differentiation, the stress and immune response, and signal transduction) and HN2 (i.e, protein synthesis and apoptosis). Conclusions These studies demonstrate that individual genotoxicants induce distinct gene expression signatures. Further study of these molecular networks may explain the variable response of the developing brain to different types of environmental genotoxicants. PMID:17107856

  12. Visualizing Neuronal Network Connectivity with Connectivity Pattern Tables

    PubMed Central

    Nordlie, Eilen; Plesser, Hans Ekkehard

    2009-01-01

    Complex ideas are best conveyed through well-designed illustrations. Up to now, computational neuroscientists have mostly relied on box-and-arrow diagrams of even complex neuronal networks, often using ad hoc notations with conflicting use of symbols from paper to paper. This significantly impedes the communication of ideas in neuronal network modeling. We present here Connectivity Pattern Tables (CPTs) as a clutter-free visualization of connectivity in large neuronal networks containing two-dimensional populations of neurons. CPTs can be generated automatically from the same script code used to create the actual network in the NEST simulator. Through aggregation, CPTs can be viewed at different levels, providing either full detail or summary information. We also provide the open source ConnPlotter tool as a means to create connectivity pattern tables. PMID:20140265

  13. An FPGA-Based Silicon Neuronal Network with Selectable Excitability Silicon Neurons.

    PubMed

    Li, Jing; Katori, Yuichi; Kohno, Takashi

    2012-01-01

    This paper presents a digital silicon neuronal network which simulates the nerve system in creatures and has the ability to execute intelligent tasks, such as associative memory. Two essential elements, the mathematical-structure-based digital spiking silicon neuron (DSSN) and the transmitter release based silicon synapse, allow us to tune the excitability of silicon neurons and are computationally efficient for hardware implementation. We adopt mixed pipeline and parallel structure and shift operations to design a sufficient large and complex network without excessive hardware resource cost. The network with 256 full-connected neurons is built on a Digilent Atlys board equipped with a Xilinx Spartan-6 LX45 FPGA. Besides, a memory control block and USB control block are designed to accomplish the task of data communication between the network and the host PC. This paper also describes the mechanism of associative memory performed in the silicon neuronal network. The network is capable of retrieving stored patterns if the inputs contain enough information of them. The retrieving probability increases with the similarity between the input and the stored pattern increasing. Synchronization of neurons is observed when the successful stored pattern retrieval occurs. PMID:23269911

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

    PubMed

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

    2016-08-01

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

  15. Cluster synchronization in networks of neurons with chemical synapses

    SciTech Connect

    Juang, Jonq; Liang, Yu-Hao

    2014-03-15

    In this work, we study the cluster synchronization of chemically coupled and generally formulated networks which are allowed to be nonidentical. The sufficient condition for the existence of stably synchronous clusters is derived. Specifically, we only need to check the stability of the origins of m decoupled linear systems. Here, m is the number of subpopulations. Examples of nonidentical networks such as Hindmarsh-Rose (HR) neurons with various choices of parameters in different subpopulations, or HR neurons in one subpopulation and FitzHugh-Nagumo neurons in the other subpopulation are provided. Explicit threshold for the coupling strength that guarantees the stably cluster synchronization can be obtained.

  16. Ordering spatiotemporal chaos in complex thermosensitive neuron networks

    NASA Astrophysics Data System (ADS)

    Gong, Yubing; Xu, Bo; Xu, Qiang; Yang, Chuanlu; Ren, Tingqi; Hou, Zhonghuai; Xin, Houwen

    2006-04-01

    We have studied the effect of random long-range connections in chaotic thermosensitive neuron networks with each neuron being capable of exhibiting diverse bursting behaviors, and found stochastic synchronization and optimal spatiotemporal patterns. For a given coupling strength, the chaotic burst-firings of the neurons become more and more synchronized as the number of random connections (or randomness) is increased and, rather, the most pronounced spatiotemporal pattern appears for an optimal randomness. As the coupling strength is increased, the optimal randomness shifts towards a smaller strength. This result shows that random long-range connections can tame the chaos in the neural networks and make the neurons more effectively reach synchronization. Since the model studied can be used to account for hypothalamic neurons of dogfish, catfish, etc., this result may reflect the significant role of random connections in transferring biological information.

  17. Developing neuronal networks: Self-organized criticality predicts the future

    NASA Astrophysics Data System (ADS)

    Pu, Jiangbo; Gong, Hui; Li, Xiangning; Luo, Qingming

    2013-01-01

    Self-organized criticality emerged in neural activity is one of the key concepts to describe the formation and the function of developing neuronal networks. The relationship between critical dynamics and neural development is both theoretically and experimentally appealing. However, whereas it is well-known that cortical networks exhibit a rich repertoire of activity patterns at different stages during in vitro maturation, dynamical activity patterns through the entire neural development still remains unclear. Here we show that a series of metastable network states emerged in the developing and ``aging'' process of hippocampal networks cultured from dissociated rat neurons. The unidirectional sequence of state transitions could be only observed in networks showing power-law scaling of distributed neuronal avalanches. Our data suggest that self-organized criticality may guide spontaneous activity into a sequential succession of homeostatically-regulated transient patterns during development, which may help to predict the tendency of neural development at early ages in the future.

  18. Structure-dynamics relationships in bursting neuronal networks revealed using a prediction framework.

    PubMed

    Mäki-Marttunen, Tuomo; Aćimović, Jugoslava; Ruohonen, Keijo; Linne, Marja-Leena

    2013-01-01

    The question of how the structure of a neuronal network affects its functionality has gained a lot of attention in neuroscience. However, the vast majority of the studies on structure-dynamics relationships consider few types of network structures and assess limited numbers of structural measures. In this in silico study, we employ a wide diversity of network topologies and search among many possibilities the aspects of structure that have the greatest effect on the network excitability. The network activity is simulated using two point-neuron models, where the neurons are activated by noisy fluctuation of the membrane potential and their connections are described by chemical synapse models, and statistics on the number and quality of the emergent network bursts are collected for each network type. We apply a prediction framework to the obtained data in order to find out the most relevant aspects of network structure. In this framework, predictors that use different sets of graph-theoretic measures are trained to estimate the activity properties, such as burst count or burst length, of the networks. The performances of these predictors are compared with each other. We show that the best performance in prediction of activity properties for networks with sharp in-degree distribution is obtained when the prediction is based on clustering coefficient. By contrast, for networks with broad in-degree distribution, the maximum eigenvalue of the connectivity graph gives the most accurate prediction. The results shown for small ([Formula: see text]) networks hold with few exceptions when different neuron models, different choices of neuron population and different average degrees are applied. We confirm our conclusions using larger ([Formula: see text]) networks as well. Our findings reveal the relevance of different aspects of network structure from the viewpoint of network excitability, and our integrative method could serve as a general framework for structure

  19. Structure-Dynamics Relationships in Bursting Neuronal Networks Revealed Using a Prediction Framework

    PubMed Central

    Mäki-Marttunen, Tuomo; Aćimović, Jugoslava; Ruohonen, Keijo; Linne, Marja-Leena

    2013-01-01

    The question of how the structure of a neuronal network affects its functionality has gained a lot of attention in neuroscience. However, the vast majority of the studies on structure-dynamics relationships consider few types of network structures and assess limited numbers of structural measures. In this in silico study, we employ a wide diversity of network topologies and search among many possibilities the aspects of structure that have the greatest effect on the network excitability. The network activity is simulated using two point-neuron models, where the neurons are activated by noisy fluctuation of the membrane potential and their connections are described by chemical synapse models, and statistics on the number and quality of the emergent network bursts are collected for each network type. We apply a prediction framework to the obtained data in order to find out the most relevant aspects of network structure. In this framework, predictors that use different sets of graph-theoretic measures are trained to estimate the activity properties, such as burst count or burst length, of the networks. The performances of these predictors are compared with each other. We show that the best performance in prediction of activity properties for networks with sharp in-degree distribution is obtained when the prediction is based on clustering coefficient. By contrast, for networks with broad in-degree distribution, the maximum eigenvalue of the connectivity graph gives the most accurate prediction. The results shown for small () networks hold with few exceptions when different neuron models, different choices of neuron population and different average degrees are applied. We confirm our conclusions using larger () networks as well. Our findings reveal the relevance of different aspects of network structure from the viewpoint of network excitability, and our integrative method could serve as a general framework for structure-dynamics studies in biosciences. PMID:23935998

  20. Transient electrical coupling regulates formation of neuronal networks.

    PubMed

    Szabo, Theresa M; Zoran, Mark J

    2007-01-19

    Electrical synapses are abundant before and during developmental windows of intense chemical synapse formation, and might therefore contribute to the establishment of neuronal networks. Transient electrical coupling develops and is then eliminated between regenerating Helisoma motoneurons 110 and 19 during a period of 48-72 h in vivo and in vitro following nerve injury. An inverse relationship exists between electrical coupling and chemical synaptic transmission at these synapses, such that the decline in electrical coupling is coincident with the emergence of cholinergic synaptic transmission. In this study, we have generated two- and three-cell neuronal networks to test whether predicted synaptogenic capabilities were affected by previous synaptic interactions. Electrophysiological analyses demonstrated that synapses formed in three-cell neuronal networks were not those predicted based on synaptogenic outcomes in two-cell networks. Thus, new electrical and chemical synapse formation within a neuronal network is dependent on existing connectivity of that network. In addition, new contacts formed with established networks have little impact on these existing connections. These results suggest that network-dependent mechanisms, particularly those mediated by gap junctional coupling, regulate synapse formation within simple neural networks. PMID:17156754

  1. Spike-timing error backpropagation in theta neuron networks.

    PubMed

    McKennoch, Sam; Voegtlin, Thomas; Bushnell, Linda

    2009-01-01

    The main contribution of this letter is the derivation of a steepest gradient descent learning rule for a multilayer network of theta neurons, a one-dimensional nonlinear neuron model. Central to our model is the assumption that the intrinsic neuron dynamics are sufficient to achieve consistent time coding, with no need to involve the precise shape of postsynaptic currents; this assumption departs from other related models such as SpikeProp and Tempotron learning. Our results clearly show that it is possible to perform complex computations by applying supervised learning techniques to the spike times and time response properties of nonlinear integrate and fire neurons. Networks trained with our multilayer training rule are shown to have similar generalization abilities for spike latency pattern classification as Tempotron learning. The rule is also able to train networks to perform complex regression tasks that neither SpikeProp or Tempotron learning appears to be capable of. PMID:19431278

  2. Network and neuronal membrane properties in hybrid networks reciprocally regulate selectivity to rapid thalamocortical inputs.

    PubMed

    Pesavento, Michael J; Pinto, David J

    2012-11-01

    Rapidly changing environments require rapid processing from sensory inputs. Varying deflection velocities of a rodent's primary facial vibrissa cause varying temporal neuronal activity profiles within the ventral posteromedial thalamic nucleus. Local neuron populations in a single somatosensory layer 4 barrel transform sparsely coded input into a spike count based on the input's temporal profile. We investigate this transformation by creating a barrel-like hybrid network with whole cell recordings of in vitro neurons from a cortical slice preparation, embedding the biological neuron in the simulated network by presenting virtual synaptic conductances via a conductance clamp. Utilizing the hybrid network, we examine the reciprocal network properties (local excitatory and inhibitory synaptic convergence) and neuronal membrane properties (input resistance) by altering the barrel population response to diverse thalamic input. In the presence of local network input, neurons are more selective to thalamic input timing; this arises from strong feedforward inhibition. Strongly inhibitory (damping) network regimes are more selective to timing and less selective to the magnitude of input but require stronger initial input. Input selectivity relies heavily on the different membrane properties of excitatory and inhibitory neurons. When inhibitory and excitatory neurons had identical membrane properties, the sensitivity of in vitro neurons to temporal vs. magnitude features of input was substantially reduced. Increasing the mean leak conductance of the inhibitory cells decreased the network's temporal sensitivity, whereas increasing excitatory leak conductance enhanced magnitude sensitivity. Local network synapses are essential in shaping thalamic input, and differing membrane properties of functional classes reciprocally modulate this effect. PMID:22896716

  3. Effects of extracellular potassium diffusion on electrically coupled neuron networks

    NASA Astrophysics Data System (ADS)

    Wu, Xing-Xing; Shuai, Jianwei

    2015-02-01

    Potassium accumulation and diffusion during neuronal epileptiform activity have been observed experimentally, and potassium lateral diffusion has been suggested to play an important role in nonsynaptic neuron networks. We adopt a hippocampal CA1 pyramidal neuron network in a zero-calcium condition to better understand the influence of extracellular potassium dynamics on the stimulus-induced activity. The potassium concentration in the interstitial space for each neuron is regulated by potassium currents, Na+-K+ pumps, glial buffering, and ion diffusion. In addition to potassium diffusion, nearby neurons are also coupled through gap junctions. Our results reveal that the latency of the first spike responding to stimulus monotonically decreases with increasing gap-junction conductance but is insensitive to potassium diffusive coupling. The duration of network oscillations shows a bell-like shape with increasing potassium diffusive coupling at weak gap-junction coupling. For modest electrical coupling, there is an optimal K+ diffusion strength, at which the flow of potassium ions among the network neurons appropriately modulates interstitial potassium concentrations in a degree that provides the most favorable environment for the generation and continuance of the action potential waves in the network.

  4. Balanced Networks of Spiking Neurons with Spatially Dependent Recurrent Connections

    NASA Astrophysics Data System (ADS)

    Rosenbaum, Robert; Doiron, Brent

    2014-04-01

    Networks of model neurons with balanced recurrent excitation and inhibition capture the irregular and asynchronous spiking activity reported in cortex. While mean-field theories of spatially homogeneous balanced networks are well understood, a mean-field analysis of spatially heterogeneous balanced networks has not been fully developed. We extend the analysis of balanced networks to include a connection probability that depends on the spatial separation between neurons. In the continuum limit, we derive that stable, balanced firing rate solutions require that the spatial spread of external inputs be broader than that of recurrent excitation, which in turn must be broader than or equal to that of recurrent inhibition. Notably, this implies that network models with broad recurrent inhibition are inconsistent with the balanced state. For finite size networks, we investigate the pattern-forming dynamics arising when balanced conditions are not satisfied. Our study highlights the new challenges that balanced networks pose for the spatiotemporal dynamics of complex systems.

  5. The estimation of neurotransmitter release probability in feedforward neuronal network based on adaptive synchronization

    NASA Astrophysics Data System (ADS)

    Xue, Ming; Wang, Jiang; Jia, Chenhui; Yu, Haitao; Deng, Bin; Wei, Xile; Che, Yanqiu

    2013-03-01

    In this paper, we proposed a new approach to estimate unknown parameters and topology of a neuronal network based on the adaptive synchronization control scheme. A virtual neuronal network is constructed as an observer to track the membrane potential of the corresponding neurons in the original network. When they achieve synchronization, the unknown parameters and topology of the original network are obtained. The method is applied to estimate the real-time status of the connection in the feedforward network and the neurotransmitter release probability of unreliable synapses is obtained by statistic computation. Numerical simulations are also performed to demonstrate the effectiveness of the proposed adaptive controller. The obtained results may have important implications in system identification in neural science.

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

    PubMed Central

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

    2011-01-01

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

  7. Vibrational resonance in a heterogeneous scale free network of neurons

    NASA Astrophysics Data System (ADS)

    Uzuntarla, Muhammet; Yilmaz, Ergin; Wagemakers, Alexandre; Ozer, Mahmut

    2015-05-01

    Vibrational resonance (VR) is a phenomenon whereby the response of some dynamical systems to a weak low-frequency signal can be maximized with the assistance of an optimal intensity of another high-frequency signal. In this paper, we study the VR in a heterogeneous neural system having a complex network topology. We consider a scale-free network of neurons where the heterogeneity is in the intrinsic excitability of the individual neurons. It is shown that emergence of VR in heterogeneous neuron population requires less energy than a homogeneous population. We also find that electrical coupling strength among neurons plays a key role in determining the weak signal processing capacity of the heterogeneous population. Lastly, we investigate the influence of interneuronal link density on the VR and demonstrate that the energy needed to obtain the resonance grows with the increase in average degree.

  8. Anticipated synchronization in neuronal network motifs

    NASA Astrophysics Data System (ADS)

    Matias, F. S.; Gollo, L. L.; Carelli, P. V.; Copelli, M.; Mirasso, C. R.

    2013-01-01

    Two identical dynamical systems coupled unidirectionally (in a so called master-slave configuration) exhibit anticipated synchronization (AS) if the one which receives the coupling (the slave) also receives a negative delayed self-feedback. In oscillatory neuronal systems AS is characterized by a phase-locking with negative time delay τ between the spikes of the master and of the slave (slave fires before the master), while in the usual delayed synchronization (DS) regime τ is positive (slave fires after the master). A 3-neuron motif in which the slave self-feedback is replaced by a feedback loop mediated by an interneuron can exhibits both AS and DS regimes. Here we show that AS is robust in the presence of noise in a 3 Hodgkin-Huxley type neuronal motif. We also show that AS is stable for large values of τ in a chain of connected slaves-interneurons.

  9. Minimal attractors in digraph system models of neuronal networks

    NASA Astrophysics Data System (ADS)

    Just, Winfried; Ahn, Sungwoo; Terman, David

    2008-12-01

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

  10. Network-induced chaos in integrate-and-fire neuronal ensembles

    NASA Astrophysics Data System (ADS)

    Zhou, Douglas; Rangan, Aaditya V.; Sun, Yi; Cai, David

    2009-09-01

    It has been shown that a single standard linear integrate-and-fire (IF) neuron under a general time-dependent stimulus cannot possess chaotic dynamics despite the firing-reset discontinuity. Here we address the issue of whether conductance-based, pulsed-coupled network interactions can induce chaos in an IF neuronal ensemble. Using numerical methods, we demonstrate that all-to-all, homogeneously pulse-coupled IF neuronal networks can indeed give rise to chaotic dynamics under an external periodic current drive. We also provide a precise characterization of the largest Lyapunov exponent for these high dimensional nonsmooth dynamical systems. In addition, we present a stable and accurate numerical algorithm for evaluating the largest Lyapunov exponent, which can overcome difficulties encountered by traditional methods for these nonsmooth dynamical systems with degeneracy induced by, e.g., refractoriness of neurons.

  11. How adaptation shapes spike rate oscillations in recurrent neuronal networks

    PubMed Central

    Augustin, Moritz; Ladenbauer, Josef; Obermayer, Klaus

    2012-01-01

    Neural mass signals from in-vivo recordings often show oscillations with frequencies ranging from <1 to 100 Hz. Fast rhythmic activity in the beta and gamma range can be generated by network-based mechanisms such as recurrent synaptic excitation-inhibition loops. Slower oscillations might instead depend on neuronal adaptation currents whose timescales range from tens of milliseconds to seconds. Here we investigate how the dynamics of such adaptation currents contribute to spike rate oscillations and resonance properties in recurrent networks of excitatory and inhibitory neurons. Based on a network of sparsely coupled spiking model neurons with two types of adaptation current and conductance-based synapses with heterogeneous strengths and delays we use a mean-field approach to analyze oscillatory network activity. For constant external input, we find that spike-triggered adaptation currents provide a mechanism to generate slow oscillations over a wide range of adaptation timescales as long as recurrent synaptic excitation is sufficiently strong. Faster rhythms occur when recurrent inhibition is slower than excitation and oscillation frequency increases with the strength of inhibition. Adaptation facilitates such network-based oscillations for fast synaptic inhibition and leads to decreased frequencies. For oscillatory external input, adaptation currents amplify a narrow band of frequencies and cause phase advances for low frequencies in addition to phase delays at higher frequencies. Our results therefore identify the different key roles of neuronal adaptation dynamics for rhythmogenesis and selective signal propagation in recurrent networks. PMID:23450654

  12. Autonomous Optimization of Targeted Stimulation of Neuronal Networks

    PubMed Central

    Kumar, Sreedhar S.; Wülfing, Jan; Okujeni, Samora; Boedecker, Joschka; Riedmiller, Martin

    2016-01-01

    Driven by clinical needs and progress in neurotechnology, targeted interaction with neuronal networks is of increasing importance. Yet, the dynamics of interaction between intrinsic ongoing activity in neuronal networks and their response to stimulation is unknown. Nonetheless, electrical stimulation of the brain is increasingly explored as a therapeutic strategy and as a means to artificially inject information into neural circuits. Strategies using regular or event-triggered fixed stimuli discount the influence of ongoing neuronal activity on the stimulation outcome and are therefore not optimal to induce specific responses reliably. Yet, without suitable mechanistic models, it is hardly possible to optimize such interactions, in particular when desired response features are network-dependent and are initially unknown. In this proof-of-principle study, we present an experimental paradigm using reinforcement-learning (RL) to optimize stimulus settings autonomously and evaluate the learned control strategy using phenomenological models. We asked how to (1) capture the interaction of ongoing network activity, electrical stimulation and evoked responses in a quantifiable ‘state’ to formulate a well-posed control problem, (2) find the optimal state for stimulation, and (3) evaluate the quality of the solution found. Electrical stimulation of generic neuronal networks grown from rat cortical tissue in vitro evoked bursts of action potentials (responses). We show that the dynamic interplay of their magnitudes and the probability to be intercepted by spontaneous events defines a trade-off scenario with a network-specific unique optimal latency maximizing stimulus efficacy. An RL controller was set to find this optimum autonomously. Across networks, stimulation efficacy increased in 90% of the sessions after learning and learned latencies strongly agreed with those predicted from open-loop experiments. Our results show that autonomous techniques can exploit

  13. Autonomous Optimization of Targeted Stimulation of Neuronal Networks.

    PubMed

    Kumar, Sreedhar S; Wülfing, Jan; Okujeni, Samora; Boedecker, Joschka; Riedmiller, Martin; Egert, Ulrich

    2016-08-01

    Driven by clinical needs and progress in neurotechnology, targeted interaction with neuronal networks is of increasing importance. Yet, the dynamics of interaction between intrinsic ongoing activity in neuronal networks and their response to stimulation is unknown. Nonetheless, electrical stimulation of the brain is increasingly explored as a therapeutic strategy and as a means to artificially inject information into neural circuits. Strategies using regular or event-triggered fixed stimuli discount the influence of ongoing neuronal activity on the stimulation outcome and are therefore not optimal to induce specific responses reliably. Yet, without suitable mechanistic models, it is hardly possible to optimize such interactions, in particular when desired response features are network-dependent and are initially unknown. In this proof-of-principle study, we present an experimental paradigm using reinforcement-learning (RL) to optimize stimulus settings autonomously and evaluate the learned control strategy using phenomenological models. We asked how to (1) capture the interaction of ongoing network activity, electrical stimulation and evoked responses in a quantifiable 'state' to formulate a well-posed control problem, (2) find the optimal state for stimulation, and (3) evaluate the quality of the solution found. Electrical stimulation of generic neuronal networks grown from rat cortical tissue in vitro evoked bursts of action potentials (responses). We show that the dynamic interplay of their magnitudes and the probability to be intercepted by spontaneous events defines a trade-off scenario with a network-specific unique optimal latency maximizing stimulus efficacy. An RL controller was set to find this optimum autonomously. Across networks, stimulation efficacy increased in 90% of the sessions after learning and learned latencies strongly agreed with those predicted from open-loop experiments. Our results show that autonomous techniques can exploit quantitative

  14. Rich club neurons dominate Information Transfer in local cortical networks

    NASA Astrophysics Data System (ADS)

    Nigam, Sunny; Shimono, Masanori; Sporns, Olaf; Beggs, John

    2015-03-01

    The performance of complex networks depends on how they route their traffic. It is unknown how information is transferred in local cortical networks of hundreds of closely-spaced neurons. To address this, it is necessary to record simultaneously from hundreds of neurons at a spacing that matches typical axonal connection distances, and at a temporal resolution that matches synaptic delays. We used a 512 electrode array (60 μm spacing) to record spontaneous activity at 20 kHz, simultaneously from up to 700 neurons in slice cultures of mouse somatosensory cortex for 1 hr at a time. We used transfer entropy to quantify directed information transfer (IT) between pairs of neurons. We found an approximately lognormal distribution of firing rates as reported in in-vivo. Pairwise information transfer strengths also were nearly lognormally distributed, similar to synaptic strengths. 20% of the neurons accounted for 70% of the total IT coming into, and going out of the network and were defined as rich nodes. These rich nodes were more densely and strongly connected to each other expected by chance, forming a rich club. This highly uneven distribution of IT has implications for the efficiency and robustness of local cortical networks, and gives clues to the plastic processes that shape them. JSPS.

  15. Non-Boltzmann Dynamics in Networks of Neurons

    NASA Astrophysics Data System (ADS)

    Crair, Michael Charles

    We present a theory for a network of neurons that communicate via action potentials. Our model balances the need for an accurate in detail picture for the functioning of neurons with the desire for a simple and tractable description. We view the problem at the mesoscopic level, with an abstract neural state capturing what we assume to be the relevant physical properties of all the ionic and molecular interactions that make up an active cell. We include in our description of the neural state a stochastic component which mimics the intracellular and extracellular commotion in a network of neurons. Because our model is based on a realistic spiking neural network, we can make firm predictions about the behavior of real biological networks of neurons. For instance, we find that attractor dynamics, a general property exhibited by standard models of neural networks, is preserved in our model but the symmetry which exists in standard models between the 'on' and 'off' neural state is broken in our description by the spike driven noisy dynamics. These predictions are generally corroborated by the limited experimental evidence available, and we make suggestions for further experiments that would clarify the validity of our description. The spiking properties of neurons also leads us to a model for learning which is based on modifying the temporal form of neural interactions instead of the usual connection strength. This suggests that a network of neurons can reinforce associative behavior by changing the time course of the neural interactions expressed in the synaptic potentials instead of changing the size of the synaptic interactions.

  16. FPGA implementation of motifs-based neuronal network and synchronization analysis

    NASA Astrophysics Data System (ADS)

    Deng, Bin; Zhu, Zechen; Yang, Shuangming; Wei, Xile; Wang, Jiang; Yu, Haitao

    2016-06-01

    Motifs in complex networks play a crucial role in determining the brain functions. In this paper, 13 kinds of motifs are implemented with Field Programmable Gate Array (FPGA) to investigate the relationships between the networks properties and motifs properties. We use discretization method and pipelined architecture to construct various motifs with Hindmarsh-Rose (HR) neuron as the node model. We also build a small-world network based on these motifs and conduct the synchronization analysis of motifs as well as the constructed network. We find that the synchronization properties of motif determine that of motif-based small-world network, which demonstrates effectiveness of our proposed hardware simulation platform. By imitation of some vital nuclei in the brain to generate normal discharges, our proposed FPGA-based artificial neuronal networks have the potential to replace the injured nuclei to complete the brain function in the treatment of Parkinson's disease and epilepsy.

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

    PubMed

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

    2003-05-01

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

  18. Brain extracellular matrix retains connectivity in neuronal networks

    PubMed Central

    Bikbaev, Arthur; Frischknecht, Renato; Heine, Martin

    2015-01-01

    The formation and maintenance of connectivity are critically important for the processing and storage of information in neuronal networks. The brain extracellular matrix (ECM) appears during postnatal development and surrounds most neurons in the adult mammalian brain. Importantly, the removal of the ECM was shown to improve plasticity and post-traumatic recovery in the CNS, but little is known about the mechanisms. Here, we investigated the role of the ECM in the regulation of the network activity in dissociated hippocampal cultures grown on microelectrode arrays (MEAs). We found that enzymatic removal of the ECM in mature cultures led to transient enhancement of neuronal activity, but prevented disinhibition-induced hyperexcitability that was evident in age-matched control cultures with intact ECM. Furthermore, the ECM degradation followed by disinhibition strongly affected the network interaction so that it strongly resembled the juvenile pattern seen in naïve developing cultures. Taken together, our results demonstrate that the ECM plays an important role in retention of existing connectivity in mature neuronal networks that can be exerted through synaptic confinement of glutamate. On the other hand, removal of the ECM can play a permissive role in modification of connectivity and adaptive exploration of novel network architecture. PMID:26417723

  19. Carbon nanotubes: artificial nanomaterials to engineer single neurons and neuronal networks.

    PubMed

    Fabbro, Alessandra; Bosi, Susanna; Ballerini, Laura; Prato, Maurizio

    2012-08-15

    In the past decade, nanotechnology applications to the nervous system have often involved the study and the use of novel nanomaterials to improve the diagnosis and therapy of neurological diseases. In the field of nanomedicine, carbon nanotubes are evaluated as promising materials for diverse therapeutic and diagnostic applications. Besides, carbon nanotubes are increasingly employed in basic neuroscience approaches, and they have been used in the design of neuronal interfaces or in that of scaffolds promoting neuronal growth in vitro. Ultimately, carbon nanotubes are thought to hold the potential for the development of innovative neurological implants. In this framework, it is particularly relevant to document the impact of interfacing such materials with nerve cells. Carbon nanotubes were shown, when modified with biologically active compounds or functionalized in order to alter their charge, to affect neurite outgrowth and branching. Notably, purified carbon nanotubes used as scaffolds can promote the formation of nanotube-neuron hybrid networks, able per se to affect neuron integrative abilities, network connectivity, and synaptic plasticity. We focus this review on our work over several years directed to investigate the ability of carbon nanotube platforms in providing a new tool for nongenetic manipulations of neuronal performance and network signaling. PMID:22896805

  20. Gap junctions in developing thalamic and neocortical neuronal networks.

    PubMed

    Niculescu, Dragos; Lohmann, Christian

    2014-12-01

    The presence of direct, cytoplasmatic, communication between neurons in the brain of vertebrates has been demonstrated a long time ago. These gap junctions have been characterized in many brain areas in terms of subunit composition, biophysical properties, neuronal connectivity patterns, and developmental regulation. Although interesting findings emerged, showing that different subunits are specifically regulated during development, or that excitatory and inhibitory neuronal networks exhibit various electrical connectivity patterns, gap junctions did not receive much further interest. Originally, it was believed that gap junctions represent simple passageways for electrical and biochemical coordination early in development. Today, we know that gap junction connectivity is tightly regulated, following independent developmental patterns for excitatory and inhibitory networks. Electrical connections are important for many specific functions of neurons, and are, for example, required for the development of neuronal stimulus tuning in the visual system. Here, we integrate the available data on neuronal connectivity and gap junction properties, as well as the most recent findings concerning the functional implications of electrical connections in the developing thalamus and neocortex. PMID:23843439

  1. Microglia Control Neuronal Network Excitability via BDNF Signalling

    PubMed Central

    2013-01-01

    Microglia-neuron interactions play a crucial role in several neurological disorders characterized by altered neural network excitability, such as epilepsy and neuropathic pain. While a series of potential messengers have been postulated as substrates of the communication between microglia and neurons, including cytokines, purines, prostaglandins, and nitric oxide, the specific links between messengers, microglia, neuronal networks, and diseases have remained elusive. Brain-derived neurotrophic factor (BDNF) released by microglia emerges as an exception in this riddle. Here, we review the current knowledge on the role played by microglial BDNF in controlling neuronal excitability by causing disinhibition. The efforts made by different laboratories during the last decade have collectively provided a robust mechanistic paradigm which elucidates the mechanisms involved in the synthesis and release of BDNF from microglia, the downstream TrkB-mediated signals in neurons, and the biophysical mechanism by which disinhibition occurs, via the downregulation of the K+-Cl− cotransporter KCC2, dysrupting Cl−homeostasis, and hence the strength of GABAA- and glycine receptor-mediated inhibition. The resulting altered network activity appears to explain several features of the associated pathologies. Targeting the molecular players involved in this canonical signaling pathway may lead to novel therapeutic approach for ameliorating a wide array of neural dysfunctions. PMID:24089642

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

    PubMed

    Siettos, Constantinos; Starke, Jens

    2016-09-01

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

  3. Autapse-induced multiple coherence resonance in single neurons and neuronal networks

    PubMed Central

    Yilmaz, Ergin; Ozer, Mahmut; Baysal, Veli; Perc, Matjaž

    2016-01-01

    We study the effects of electrical and chemical autapse on the temporal coherence or firing regularity of single stochastic Hodgkin-Huxley neurons and scale-free neuronal networks. Also, we study the effects of chemical autapse on the occurrence of spatial synchronization in scale-free neuronal networks. Irrespective of the type of autapse, we observe autaptic time delay induced multiple coherence resonance for appropriately tuned autaptic conductance levels in single neurons. More precisely, we show that in the presence of an electrical autapse, there is an optimal intensity of channel noise inducing the multiple coherence resonance, whereas in the presence of chemical autapse the occurrence of multiple coherence resonance is less sensitive to the channel noise intensity. At the network level, we find autaptic time delay induced multiple coherence resonance and synchronization transitions, occurring at approximately the same delay lengths. We show that these two phenomena can arise only at a specific range of the coupling strength, and that they can be observed independently of the average degree of the network. PMID:27480120

  4. Autapse-induced multiple coherence resonance in single neurons and neuronal networks.

    PubMed

    Yilmaz, Ergin; Ozer, Mahmut; Baysal, Veli; Perc, Matjaž

    2016-01-01

    We study the effects of electrical and chemical autapse on the temporal coherence or firing regularity of single stochastic Hodgkin-Huxley neurons and scale-free neuronal networks. Also, we study the effects of chemical autapse on the occurrence of spatial synchronization in scale-free neuronal networks. Irrespective of the type of autapse, we observe autaptic time delay induced multiple coherence resonance for appropriately tuned autaptic conductance levels in single neurons. More precisely, we show that in the presence of an electrical autapse, there is an optimal intensity of channel noise inducing the multiple coherence resonance, whereas in the presence of chemical autapse the occurrence of multiple coherence resonance is less sensitive to the channel noise intensity. At the network level, we find autaptic time delay induced multiple coherence resonance and synchronization transitions, occurring at approximately the same delay lengths. We show that these two phenomena can arise only at a specific range of the coupling strength, and that they can be observed independently of the average degree of the network. PMID:27480120

  5. Burst synchronization transitions in a neuronal network of subnetworks

    NASA Astrophysics Data System (ADS)

    Sun, Xiaojuan; Lei, Jinzhi; Perc, Matjaž; Kurths, Jürgen; Chen, Guanrong

    2011-03-01

    In this paper, the transitions of burst synchronization are explored in a neuronal network consisting of subnetworks. The studied network is composed of electrically coupled bursting Hindmarsh-Rose neurons. Numerical results show that two types of burst synchronization transitions can be induced not only by the variations of intra- and intercoupling strengths but also by changing the probability of random links between different subnetworks and the number of subnetworks. Furthermore, we find that the underlying mechanisms for these two bursting synchronization transitions are different: one is due to the change of spike numbers per burst, while the other is caused by the change of the bursting type. Considering that changes in the coupling strengths and neuronal connections are closely interlaced with brain plasticity, the presented results could have important implications for the role of the brain plasticity in some functional behavior that are associated with synchronization.

  6. Enhancement of synchronization in inter-intra-connected neuronal networks

    NASA Astrophysics Data System (ADS)

    Moukam Kakmeni, F. M.; Nguemaha, V. M.

    2016-01-01

    We study the enhancement of neural synchrony in a network of electrically coupled Hindmarsh Rose (HR) neurons. The behavior of the network under control by an external environment modeled by the Fitzhugh Nagumo (FN) is analyzed. Biologically, such a control system could mimic the modification of normal neuronal dynamics due to drugs or other chemical substances. We show that the environment could have as effect the suppression of chaos, enhancement of synchrony and favor interesting properties such as sub-threshold membrane oscillations, and oscillation death for relatively strong local coupling. Interestingly, we find that the electrical coupling between each two coupled HR and FN is less important to synchronization than the local coupling between the HR and the FN neurons. In other words, local interactions are found to play a stronger role in synchronization than long-range (global) interactions.

  7. COMMUNICATION: Neuron network activity scales exponentially with synapse density

    NASA Astrophysics Data System (ADS)

    Brewer, G. J.; Boehler, M. D.; Pearson, R. A.; DeMaris, A. A.; Ide, A. N.; Wheeler, B. C.

    2009-02-01

    Neuronal network output in the cortex as a function of synapse density during development has not been explicitly determined. Synaptic scaling in cortical brain networks seems to alter excitatory and inhibitory synaptic inputs to produce a representative rate of synaptic output. Here, we cultured rat hippocampal neurons over a three-week period to correlate synapse density with the increase in spontaneous spiking activity. We followed the network development as synapse formation and spike rate in two serum-free media optimized for either (a) neuron survival (Neurobasal/B27) or (b) spike rate (NbActiv4). We found that while synaptophysin synapse density increased linearly with development, spike rates increased exponentially in developing neuronal networks. Synaptic receptor components NR1, GluR1 and GABA-A also increase linearly but with more excitatory receptors than inhibitory. These results suggest that the brain's information processing capability gains more from increasing connectivity of the processing units than increasing processing units, much as Internet information flow increases much faster than the linear number of nodes and connections.

  8. NEURON: enabling autonomicity in wireless sensor networks.

    PubMed

    Zafeiropoulos, Anastasios; Gouvas, Panagiotis; Liakopoulos, Athanassios; Mentzas, Gregoris; Mitrou, Nikolas

    2010-01-01

    Future Wireless Sensor Networks (WSNs) will be ubiquitous, large-scale networks interconnected with the existing IP infrastructure. Autonomic functionalities have to be designed in order to reduce the complexity of their operation and management, and support the dissemination of knowledge within a WSN. In this paper a novel protocol for energy efficient deployment, clustering and routing in WSNs is proposed that focuses on the incorporation of autonomic functionalities in the existing approaches. The design of the protocol facilitates the design of innovative applications and services that are based on overlay topologies created through cooperation among the sensor nodes. PMID:22399931

  9. NEURON: Enabling Autonomicity in Wireless Sensor Networks

    PubMed Central

    Zafeiropoulos, Anastasios; Gouvas, Panagiotis; Liakopoulos, Athanassios; Mentzas, Gregoris; Mitrou, Nikolas

    2010-01-01

    Future Wireless Sensor Networks (WSNs) will be ubiquitous, large-scale networks interconnected with the existing IP infrastructure. Autonomic functionalities have to be designed in order to reduce the complexity of their operation and management, and support the dissemination of knowledge within a WSN. In this paper a novel protocol for energy efficient deployment, clustering and routing in WSNs is proposed that focuses on the incorporation of autonomic functionalities in the existing approaches. The design of the protocol facilitates the design of innovative applications and services that are based on overlay topologies created through cooperation among the sensor nodes. PMID:22399931

  10. Blur identification by multilayer neural network based on multivalued neurons.

    PubMed

    Aizenberg, Igor; Paliy, Dmitriy V; Zurada, Jacek M; Astola, Jaakko T

    2008-05-01

    A multilayer neural network based on multivalued neurons (MLMVN) is a neural network with a traditional feedforward architecture. At the same time, this network has a number of specific different features. Its backpropagation learning algorithm is derivative-free. The functionality of MLMVN is superior to that of the traditional feedforward neural networks and of a variety kernel-based networks. Its higher flexibility and faster adaptation to the target mapping enables to model complex problems using simpler networks. In this paper, the MLMVN is used to identify both type and parameters of the point spread function, whose precise identification is of crucial importance for the image deblurring. The simulation results show the high efficiency of the proposed approach. It is confirmed that the MLMVN is a powerful tool for solving classification problems, especially multiclass ones. PMID:18467216

  11. Elucidation of The Behavioral Program and Neuronal Network Encoded by Dorsal Raphe Serotonergic Neurons.

    PubMed

    Urban, Daniel J; Zhu, Hu; Marcinkiewcz, Catherine A; Michaelides, Michael; Oshibuchi, Hidehiro; Rhea, Darren; Aryal, Dipendra K; Farrell, Martilias S; Lowery-Gionta, Emily; Olsen, Reid H J; Wetsel, William C; Kash, Thomas L; Hurd, Yasmin L; Tecott, Laurence H; Roth, Bryan L

    2016-04-01

    Elucidating how the brain's serotonergic network mediates diverse behavioral actions over both relatively short (minutes-hours) and long period of time (days-weeks) remains a major challenge for neuroscience. Our relative ignorance is largely due to the lack of technologies with robustness, reversibility, and spatio-temporal control. Recently, we have demonstrated that our chemogenetic approach (eg, Designer Receptors Exclusively Activated by Designer Drugs (DREADDs)) provides a reliable and robust tool for controlling genetically defined neural populations. Here we show how short- and long-term activation of dorsal raphe nucleus (DRN) serotonergic neurons induces robust behavioral responses. We found that both short- and long-term activation of DRN serotonergic neurons induce antidepressant-like behavioral responses. However, only short-term activation induces anxiogenic-like behaviors. In parallel, these behavioral phenotypes were associated with a metabolic map of whole brain network activity via a recently developed non-invasive imaging technology DREAMM (DREADD Associated Metabolic Mapping). Our findings reveal a previously unappreciated brain network elicited by selective activation of DRN serotonin neurons and illuminate potential therapeutic and adverse effects of drugs targeting DRN neurons. PMID:26383016

  12. Effects of acute spinalization on neurons of postural networks

    PubMed Central

    Zelenin, Pavel V.; Lyalka, Vladimir F.; Hsu, Li-Ju; Orlovsky, Grigori N.; Deliagina, Tatiana G.

    2016-01-01

    Postural limb reflexes (PLRs) represent a substantial component of postural corrections. Spinalization results in loss of postural functions, including disappearance of PLRs. The aim of the present study was to characterize the effects of acute spinalization on two populations of spinal neurons (F and E) mediating PLRs, which we characterized previously. For this purpose, in decerebrate rabbits spinalized at T12, responses of interneurons from L5 to stimulation causing PLRs before spinalization, were recorded. The results were compared to control data obtained in our previous study. We found that spinalization affected the distribution of F- and E-neurons across the spinal grey matter, caused a significant decrease in their activity, as well as disturbances in processing of posture-related sensory inputs. A two-fold decrease in the proportion of F-neurons in the intermediate grey matter was observed. Location of populations of F- and E-neurons exhibiting significant decrease in their activity was determined. A dramatic decrease of the efficacy of sensory input from the ipsilateral limb to F-neurons, and from the contralateral limb to E-neurons was found. These changes in operation of postural networks underlie the loss of postural control after spinalization, and represent a starting point for the development of spasticity. PMID:27302149

  13. Effects of acute spinalization on neurons of postural networks.

    PubMed

    Zelenin, Pavel V; Lyalka, Vladimir F; Hsu, Li-Ju; Orlovsky, Grigori N; Deliagina, Tatiana G

    2016-01-01

    Postural limb reflexes (PLRs) represent a substantial component of postural corrections. Spinalization results in loss of postural functions, including disappearance of PLRs. The aim of the present study was to characterize the effects of acute spinalization on two populations of spinal neurons (F and E) mediating PLRs, which we characterized previously. For this purpose, in decerebrate rabbits spinalized at T12, responses of interneurons from L5 to stimulation causing PLRs before spinalization, were recorded. The results were compared to control data obtained in our previous study. We found that spinalization affected the distribution of F- and E-neurons across the spinal grey matter, caused a significant decrease in their activity, as well as disturbances in processing of posture-related sensory inputs. A two-fold decrease in the proportion of F-neurons in the intermediate grey matter was observed. Location of populations of F- and E-neurons exhibiting significant decrease in their activity was determined. A dramatic decrease of the efficacy of sensory input from the ipsilateral limb to F-neurons, and from the contralateral limb to E-neurons was found. These changes in operation of postural networks underlie the loss of postural control after spinalization, and represent a starting point for the development of spasticity. PMID:27302149

  14. Network architecture underlying maximal separation of neuronal representations

    PubMed Central

    Jortner, Ron A.

    2011-01-01

    One of the most basic and general tasks faced by all nervous systems is extracting relevant information from the organism's surrounding world. While physical signals available to sensory systems are often continuous, variable, overlapping, and noisy, high-level neuronal representations used for decision-making tend to be discrete, specific, invariant, and highly separable. This study addresses the question of how neuronal specificity is generated. Inspired by experimental findings on network architecture in the olfactory system of the locust, I construct a highly simplified theoretical framework which allows for analytic solution of its key properties. For generalized feed-forward systems, I show that an intermediate range of connectivity values between source- and target-populations leads to a combinatorial explosion of wiring possibilities, resulting in input spaces which are, by their very nature, exquisitely sparsely populated. In particular, connection probability ½, as found in the locust antennal-lobe–mushroom-body circuit, serves to maximize separation of neuronal representations across the target Kenyon cells (KCs), and explains their specific and reliable responses. This analysis yields a function expressing response specificity in terms of lower network parameters; together with appropriate gain control this leads to a simple neuronal algorithm for generating arbitrarily sparse and selective codes and linking network architecture and neural coding. I suggest a straightforward way to construct ecologically meaningful representations from this code. PMID:23316159

  15. Multitasking attractor networks with neuronal threshold noise.

    PubMed

    Agliari, Elena; Barra, Adriano; Galluzzi, Andrea; Isopi, Marco

    2014-01-01

    We consider the multitasking associative network in the low-storage limit and we study its phase diagram with respect to the noise level T and the degree d of dilution in pattern entries. We find that the system is characterized by a rich variety of stable states, including pure states, parallel retrieval states, hierarchically organized states and symmetric mixtures (remarkably, both even and odd), whose complexity increases as the number of patterns P grows. The analysis is performed both analytically and numerically: Exploiting techniques based on partial differential equations, we are able to get the self-consistencies for the order parameters. Such self-consistency equations are then solved and the solutions are further checked through stability theory to catalog their organizations into the phase diagram, which is outlined at the end. This is a further step towards the understanding of spontaneous parallel processing in associative networks. PMID:24121044

  16. Complexity in neuronal noise depends on network interconnectivity.

    PubMed

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

    2011-06-01

    "Noise," or noise-like activity (NLA), defines background electrical membrane potential fluctuations at the cellular level of the nervous system, comprising an important aspect of brain dynamics. Using whole-cell voltage recordings from fast-spiking stratum oriens interneurons and stratum pyramidale neurons located in the CA3 region of the intact mouse hippocampus, we applied complexity measures from dynamical systems theory (i.e., 1/f(γ) noise and correlation dimension) and found evidence for complexity in neuronal NLA, ranging from high- to low-complexity dynamics. Importantly, these high- and low-complexity signal features were largely dependent on gap junction and chemical synaptic transmission. Progressive neuronal isolation from the surrounding local network via gap junction blockade (abolishing gap junction-dependent spikelets) and then chemical synaptic blockade (abolishing excitatory and inhibitory post-synaptic potentials), or the reverse order of these treatments, resulted in emergence of high-complexity NLA dynamics. Restoring local network interconnectivity via blockade washout resulted in resolution to low-complexity behavior. These results suggest that the observed increase in background NLA complexity is the result of reduced network interconnectivity, thereby highlighting the potential importance of the NLA signal to the study of network state transitions arising in normal and abnormal brain dynamics (such as in epilepsy, for example). PMID:21347547

  17. The Geometry of Spontaneous Spiking in Neuronal Networks

    NASA Astrophysics Data System (ADS)

    Medvedev, Georgi S.; Zhuravytska, Svitlana

    2012-10-01

    The mathematical theory of pattern formation in electrically coupled networks of excitable neurons forced by small noise is presented in this work. Using the Freidlin-Wentzell large-deviation theory for randomly perturbed dynamical systems and the elements of the algebraic graph theory, we identify and analyze the main regimes in the network dynamics in terms of the key control parameters: excitability, coupling strength, and network topology. The analysis reveals the geometry of spontaneous dynamics in electrically coupled network. Specifically, we show that the location of the minima of a certain continuous function on the surface of the unit n-cube encodes the most likely activity patterns generated by the network. By studying how the minima of this function evolve under the variation of the coupling strength, we describe the principal transformations in the network dynamics. The minimization problem is also used for the quantitative description of the main dynamical regimes and transitions between them. In particular, for the weak and strong coupling regimes, we present asymptotic formulas for the network activity rate as a function of the coupling strength and the degree of the network. The variational analysis is complemented by the stability analysis of the synchronous state in the strong coupling regime. The stability estimates reveal the contribution of the network connectivity and the properties of the cycle subspace associated with the graph of the network to its synchronization properties. This work is motivated by the experimental and modeling studies of the ensemble of neurons in the Locus Coeruleus, a nucleus in the brainstem involved in the regulation of cognitive performance and behavior.

  18. Oscillations in the bistable regime of neuronal networks

    NASA Astrophysics Data System (ADS)

    Roxin, Alex; Compte, Albert

    2016-07-01

    Bistability between attracting fixed points in neuronal networks has been hypothesized to underlie persistent activity observed in several cortical areas during working memory tasks. In network models this kind of bistability arises due to strong recurrent excitation, sufficient to generate a state of high activity created in a saddle-node (SN) bifurcation. On the other hand, canonical network models of excitatory and inhibitory neurons (E-I networks) robustly produce oscillatory states via a Hopf (H) bifurcation due to the E-I loop. This mechanism for generating oscillations has been invoked to explain the emergence of brain rhythms in the β to γ bands. Although both bistability and oscillatory activity have been intensively studied in network models, there has not been much focus on the coincidence of the two. Here we show that when oscillations emerge in E-I networks in the bistable regime, their phenomenology can be explained to a large extent by considering coincident SN and H bifurcations, known as a codimension two Takens-Bogdanov bifurcation. In particular, we find that such oscillations are not composed of a stable limit cycle, but rather are due to noise-driven oscillatory fluctuations. Furthermore, oscillations in the bistable regime can, in principle, have arbitrarily low frequency.

  19. Oscillations in the bistable regime of neuronal networks.

    PubMed

    Roxin, Alex; Compte, Albert

    2016-07-01

    Bistability between attracting fixed points in neuronal networks has been hypothesized to underlie persistent activity observed in several cortical areas during working memory tasks. In network models this kind of bistability arises due to strong recurrent excitation, sufficient to generate a state of high activity created in a saddle-node (SN) bifurcation. On the other hand, canonical network models of excitatory and inhibitory neurons (E-I networks) robustly produce oscillatory states via a Hopf (H) bifurcation due to the E-I loop. This mechanism for generating oscillations has been invoked to explain the emergence of brain rhythms in the β to γ bands. Although both bistability and oscillatory activity have been intensively studied in network models, there has not been much focus on the coincidence of the two. Here we show that when oscillations emerge in E-I networks in the bistable regime, their phenomenology can be explained to a large extent by considering coincident SN and H bifurcations, known as a codimension two Takens-Bogdanov bifurcation. In particular, we find that such oscillations are not composed of a stable limit cycle, but rather are due to noise-driven oscillatory fluctuations. Furthermore, oscillations in the bistable regime can, in principle, have arbitrarily low frequency. PMID:27575167

  20. GABA-A receptor antagonists increase firing, bursting and synchrony of spontaneous activity in neuronal networks grown on microelectrode arrays: a step towards chemical "fingerprinting"

    EPA Science Inventory

    Assessment of effects on spontaneous network activity in neurons grown on MEAs is a proposed method to screen chemicals for potential neurotoxicity. In addition, differential effects on network activity (chemical "fingerprints") could be used to classify chemical modes of action....

  1. Thermodynamics and signatures of criticality in a network of neurons.

    PubMed

    Tkačik, Gašper; Mora, Thierry; Marre, Olivier; Amodei, Dario; Palmer, Stephanie E; Berry, Michael J; Bialek, William

    2015-09-15

    The activity of a neural network is defined by patterns of spiking and silence from the individual neurons. Because spikes are (relatively) sparse, patterns of activity with increasing numbers of spikes are less probable, but, with more spikes, the number of possible patterns increases. This tradeoff between probability and numerosity is mathematically equivalent to the relationship between entropy and energy in statistical physics. We construct this relationship for populations of up to N = 160 neurons in a small patch of the vertebrate retina, using a combination of direct and model-based analyses of experiments on the response of this network to naturalistic movies. We see signs of a thermodynamic limit, where the entropy per neuron approaches a smooth function of the energy per neuron as N increases. The form of this function corresponds to the distribution of activity being poised near an unusual kind of critical point. We suggest further tests of criticality, and give a brief discussion of its functional significance. PMID:26330611

  2. Continuous network of endoplasmic reticulum in cerebellar Purkinje neurons.

    PubMed Central

    Terasaki, M; Slater, N T; Fein, A; Schmidek, A; Reese, T S

    1994-01-01

    Purkinje neurons in rat cerebellar slices injected with an oil drop saturated with 1,1'-dihexadecyl-3,3,3',3'-tetramethylindocarbocyanine perchlorate [DiIC16(3) or DiI] to label the endoplasmic reticulum were observed by confocal microscopy. DiI spread throughout the cell body and dendrites and into the axon. DiI spreading is due to diffusion in a continuous bilayer and is not due to membrane trafficking because it also spreads in fixed neurons. DiI stained such features of the endoplasmic reticulum as densities at branch points, reticular networks in the cell body and dendrites, nuclear envelope, spines, and aggregates formed during anoxia nuclear envelope, spines, and aggregates formed during anoxia in low extracellular Ca2+. In cultured rat hippocampal neurons, where optical conditions provide more detail, DiI labeled a clearly delineated network of endoplasmic reticulum in the cell body. We conclude that there is a continuous compartment of endoplasmic reticulum extending from the cell body throughout the dendrites. This compartment may coordinate and integrate neuronal functions. Images PMID:7519781

  3. Midline thalamic neurons are differentially engaged during hippocampus network oscillations.

    PubMed

    Lara-Vásquez, Ariel; Espinosa, Nelson; Durán, Ernesto; Stockle, Marcelo; Fuentealba, Pablo

    2016-01-01

    The midline thalamus is reciprocally connected with the medial temporal lobe, where neural circuitry essential for spatial navigation and memory formation resides. Yet, little information is available on the dynamic relationship between activity patterns in the midline thalamus and medial temporal lobe. Here, we report on the functional heterogeneity of anatomically-identified thalamic neurons and the differential modulation of their activity with respect to dorsal hippocampal rhythms in the anesthetized mouse. Midline thalamic neurons expressing the calcium-binding protein calretinin, irrespective of their selective co-expression of calbindin, discharged at overall low levels, did not increase their activity during hippocampal theta oscillations, and their firing rates were inhibited during hippocampal sharp wave-ripples. Conversely, thalamic neurons lacking calretinin discharged at higher rates, increased their activity during hippocampal theta waves, but remained unaffected during sharp wave-ripples. Our results indicate that the midline thalamic system comprises at least two different classes of thalamic projection neuron, which can be partly defined by their differential engagement by hippocampal pathways during specific network oscillations that accompany distinct behavioral contexts. Thus, different midline thalamic neuronal populations might be selectively recruited to support distinct stages of memory processing, consistent with the thalamus being pivotal in the dialogue of cortical circuits. PMID:27411890

  4. Midline thalamic neurons are differentially engaged during hippocampus network oscillations

    PubMed Central

    Lara-Vásquez, Ariel; Espinosa, Nelson; Durán, Ernesto; Stockle, Marcelo; Fuentealba, Pablo

    2016-01-01

    The midline thalamus is reciprocally connected with the medial temporal lobe, where neural circuitry essential for spatial navigation and memory formation resides. Yet, little information is available on the dynamic relationship between activity patterns in the midline thalamus and medial temporal lobe. Here, we report on the functional heterogeneity of anatomically-identified thalamic neurons and the differential modulation of their activity with respect to dorsal hippocampal rhythms in the anesthetized mouse. Midline thalamic neurons expressing the calcium-binding protein calretinin, irrespective of their selective co-expression of calbindin, discharged at overall low levels, did not increase their activity during hippocampal theta oscillations, and their firing rates were inhibited during hippocampal sharp wave-ripples. Conversely, thalamic neurons lacking calretinin discharged at higher rates, increased their activity during hippocampal theta waves, but remained unaffected during sharp wave-ripples. Our results indicate that the midline thalamic system comprises at least two different classes of thalamic projection neuron, which can be partly defined by their differential engagement by hippocampal pathways during specific network oscillations that accompany distinct behavioral contexts. Thus, different midline thalamic neuronal populations might be selectively recruited to support distinct stages of memory processing, consistent with the thalamus being pivotal in the dialogue of cortical circuits. PMID:27411890

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

    PubMed

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

    2015-06-01

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

  6. Slow fluctuations in recurrent networks of spiking neurons

    NASA Astrophysics Data System (ADS)

    Wieland, Stefan; Bernardi, Davide; Schwalger, Tilo; Lindner, Benjamin

    2015-10-01

    Networks of fast nonlinear elements may display slow fluctuations if interactions are strong. We find a transition in the long-term variability of a sparse recurrent network of perfect integrate-and-fire neurons at which the Fano factor switches from zero to infinity and the correlation time is minimized. This corresponds to a bifurcation in a linear map arising from the self-consistency of temporal input and output statistics. More realistic neural dynamics with a leak current and refractory period lead to smoothed transitions and modified critical couplings that can be theoretically predicted.

  7. Translating network models to parallel hardware in NEURON

    PubMed Central

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

    2008-01-01

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

  8. Slow fluctuations in recurrent networks of spiking neurons.

    PubMed

    Wieland, Stefan; Bernardi, Davide; Schwalger, Tilo; Lindner, Benjamin

    2015-10-01

    Networks of fast nonlinear elements may display slow fluctuations if interactions are strong. We find a transition in the long-term variability of a sparse recurrent network of perfect integrate-and-fire neurons at which the Fano factor switches from zero to infinity and the correlation time is minimized. This corresponds to a bifurcation in a linear map arising from the self-consistency of temporal input and output statistics. More realistic neural dynamics with a leak current and refractory period lead to smoothed transitions and modified critical couplings that can be theoretically predicted. PMID:26565154

  9. Synchrony in stochastically driven neuronal networks with complex topologies

    NASA Astrophysics Data System (ADS)

    Newhall, Katherine A.; Shkarayev, Maxim S.; Kramer, Peter R.; Kovačič, Gregor; Cai, David

    2015-05-01

    We study the synchronization of a stochastically driven, current-based, integrate-and-fire neuronal model on a preferential-attachment network with scale-free characteristics and high clustering. The synchrony is induced by cascading total firing events where every neuron in the network fires at the same instant of time. We show that in the regime where the system remains in this highly synchronous state, the firing rate of the network is completely independent of the synaptic coupling, and depends solely on the external drive. On the other hand, the ability for the network to maintain synchrony depends on a balance between the fluctuations of the external input and the synaptic coupling strength. In order to accurately predict the probability of repeated cascading total firing events, we go beyond mean-field and treelike approximations and conduct a detailed second-order calculation taking into account local clustering. Our explicit analytical results are shown to give excellent agreement with direct numerical simulations for the particular preferential-attachment network model investigated.

  10. Emergence and robustness of target waves in a neuronal network

    NASA Astrophysics Data System (ADS)

    Xu, Ying; Jin, Wuyin; Ma, Jun

    2015-08-01

    Target waves in excitable media such as neuronal network can regulate the spatial distribution and orderliness as a continuous pacemaker. Three different schemes are used to develop stable target wave in the network, and the potential mechanism for emergence of target waves in the excitable media is investigated. For example, a local pacing driven by external periodical forcing can generate stable target wave in the excitable media, furthermore, heterogeneity and local feedback under self-feedback coupling are also effective to generate continuous target wave as well. To discern the difference of these target waves, a statistical synchronization factor is defined by using mean field theory and artificial defects are introduced into the network to block the target wave, thus the robustness of these target waves could be detected. However, these target waves developed from the above mentioned schemes show different robustness to the blocking from artificial defects. A regular network of Hindmarsh-Rose neurons is designed in a two-dimensional square array, target waves are induced by using three different ways, and then some artificial defects, which are associated with anatomical defects, are set in the network to detect the effect of defects blocking on the travelling waves. It confirms that the robustness of target waves to defects blocking depends on the intrinsic properties (ways to generate target wave) of target waves.

  11. Modularity Induced Gating and Delays in Neuronal Networks.

    PubMed

    Shein-Idelson, Mark; Cohen, Gilad; Ben-Jacob, Eshel; Hanein, Yael

    2016-04-01

    Neural networks, despite their highly interconnected nature, exhibit distinctly localized and gated activation. Modularity, a distinctive feature of neural networks, has been recently proposed as an important parameter determining the manner by which networks support activity propagation. Here we use an engineered biological model, consisting of engineered rat cortical neurons, to study the role of modular topology in gating the activity between cell populations. We show that pairs of connected modules support conditional propagation (transmitting stronger bursts with higher probability), long delays and propagation asymmetry. Moreover, large modular networks manifest diverse patterns of both local and global activation. Blocking inhibition decreased activity diversity and replaced it with highly consistent transmission patterns. By independently controlling modularity and disinhibition, experimentally and in a model, we pose that modular topology is an important parameter affecting activation localization and is instrumental for population-level gating by disinhibition. PMID:27104350

  12. Modularity Induced Gating and Delays in Neuronal Networks

    PubMed Central

    Shein-Idelson, Mark; Cohen, Gilad; Hanein, Yael

    2016-01-01

    Neural networks, despite their highly interconnected nature, exhibit distinctly localized and gated activation. Modularity, a distinctive feature of neural networks, has been recently proposed as an important parameter determining the manner by which networks support activity propagation. Here we use an engineered biological model, consisting of engineered rat cortical neurons, to study the role of modular topology in gating the activity between cell populations. We show that pairs of connected modules support conditional propagation (transmitting stronger bursts with higher probability), long delays and propagation asymmetry. Moreover, large modular networks manifest diverse patterns of both local and global activation. Blocking inhibition decreased activity diversity and replaced it with highly consistent transmission patterns. By independently controlling modularity and disinhibition, experimentally and in a model, we pose that modular topology is an important parameter affecting activation localization and is instrumental for population-level gating by disinhibition. PMID:27104350

  13. Novel Method for Neuronal Nanosurgical Connection

    PubMed Central

    Katchinskiy, Nir; Goez, Helly R.; Dutta, Indrani; Godbout, Roseline; Elezzabi, Abdulhakem Y.

    2016-01-01

    Neuronal injury may cause an irreversible damage to cellular, organ and organism function. While preventing neural injury is ideal, it is not always possible. There are multiple etiologies for neuronal injury including trauma, infection, inflammation, immune mediated disorders, toxins and hereditary conditions. We describe a novel laser application, utilizing femtosecond laser pulses, in order to connect neuronal axon to neuronal soma. We were able to maintain cellular viability, and demonstrate that this technique is universal as it is applicable to multiple cell types and media. PMID:26846892

  14. Network feedback regulates motor output across a range of modulatory neuron activity.

    PubMed

    Spencer, Robert M; Blitz, Dawn M

    2016-06-01

    Modulatory projection neurons alter network neuron synaptic and intrinsic properties to elicit multiple different outputs. Sensory and other inputs elicit a range of modulatory neuron activity that is further shaped by network feedback, yet little is known regarding how the impact of network feedback on modulatory neurons regulates network output across a physiological range of modulatory neuron activity. Identified network neurons, a fully described connectome, and a well-characterized, identified modulatory projection neuron enabled us to address this issue in the crab (Cancer borealis) stomatogastric nervous system. The modulatory neuron modulatory commissural neuron 1 (MCN1) activates and modulates two networks that generate rhythms via different cellular mechanisms and at distinct frequencies. MCN1 is activated at rates of 5-35 Hz in vivo and in vitro. Additionally, network feedback elicits MCN1 activity time-locked to motor activity. We asked how network activation, rhythm speed, and neuron activity levels are regulated by the presence or absence of network feedback across a physiological range of MCN1 activity rates. There were both similarities and differences in responses of the two networks to MCN1 activity. Many parameters in both networks were sensitive to network feedback effects on MCN1 activity. However, for most parameters, MCN1 activity rate did not determine the extent to which network output was altered by the addition of network feedback. These data demonstrate that the influence of network feedback on modulatory neuron activity is an important determinant of network output and feedback can be effective in shaping network output regardless of the extent of network modulation. PMID:27030739

  15. Convergent neuromodulation onto a network neuron can have divergent effects at the network level.

    PubMed

    Kintos, Nickolas; Nusbaum, Michael P; Nadim, Farzan

    2016-04-01

    Different neuromodulators often target the same ion channel. When such modulators act on different neuron types, this convergent action can enable a rhythmic network to produce distinct outputs. Less clear are the functional consequences when two neuromodulators influence the same ion channel in the same neuron. We examine the consequences of this seeming redundancy using a mathematical model of the crab gastric mill (chewing) network. This network is activated in vitro by the projection neuron MCN1, which elicits a half-center bursting oscillation between the reciprocally-inhibitory neurons LG and Int1. We focus on two neuropeptides which modulate this network, including a MCN1 neurotransmitter and the hormone crustacean cardioactive peptide (CCAP). Both activate the same voltage-gated current (I MI ) in the LG neuron. However, I MI-MCN1 , resulting from MCN1 released neuropeptide, has phasic dynamics in its maximal conductance due to LG presynaptic inhibition of MCN1, while I MI-CCAP retains the same maximal conductance in both phases of the gastric mill rhythm. Separation of time scales allows us to produce a 2D model from which phase plane analysis shows that, as in the biological system, I MI-MCN1 and I MI-CCAP primarily influence the durations of opposing phases of this rhythm. Furthermore, I MI-MCN1 influences the rhythmic output in a manner similar to the Int1-to-LG synapse, whereas I MI-CCAP has an influence similar to the LG-to-Int1 synapse. These results show that distinct neuromodulators which target the same voltage-gated ion channel in the same network neuron can nevertheless produce distinct effects at the network level, providing divergent neuromodulator actions on network activity. PMID:26798029

  16. Pharmacodynamics of potassium channel openers in cultured neuronal networks.

    PubMed

    Wu, Calvin; V Gopal, Kamakshi; Lukas, Thomas J; Gross, Guenter W; Moore, Ernest J

    2014-06-01

    A novel class of drugs - potassium (K(+)) channel openers or activators - has recently been shown to cause anticonvulsive and neuroprotective effects by activating hyperpolarizing K(+) currents, and therefore, may show efficacy for treating tinnitus. This study presents measurements of the modulatory effects of four K(+) channel openers on the spontaneous activity and action potential waveforms of neuronal networks. The networks were derived from mouse embryonic auditory cortices and grown on microelectrode arrays. Pentylenetetrazol was used to create hyperactivity states in the neuronal networks as a first approximation for mimicking tinnitus or tinnitus-like activity. We then compared the pharmacodynamics of the four channel activators, retigabine and flupirtine (voltage-gated K(+) channel KV7 activators), NS1619 and isopimaric acid ("big potassium" BK channel activators). The EC50 of retigabine, flupirtine, NS1619, and isopimaric acid were 8.0, 4.0, 5.8, and 7.8µM, respectively. The reduction of hyperactivity compared to the reference activity was significant. The present results highlight the notion of re-purposing the K(+) channel activators for reducing hyperactivity of spontaneously active auditory networks, serving as a platform for these drugs to show efficacy toward target identification, prevention, as well as treatment of tinnitus. PMID:24681057

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

    PubMed Central

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

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

  18. A Neuronal Network Model for Pitch Selectivity and Representation

    PubMed Central

    Huang, Chengcheng; Rinzel, John

    2016-01-01

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

  19. Collapse of ordered spatial pattern in neuronal network

    NASA Astrophysics Data System (ADS)

    Song, Xinlin; Wang, Chunni; Ma, Jun; Ren, Guodong

    2016-06-01

    Spatiotemporal systems can emerge some regular spatial patterns due to self organization or under external periodical pacing while external attack or intrinsic collapse can destroy the regularity in the spatial system. For an example, the electrical activities of neurons in nervous system show regular spatial distribution under appropriate coupling and connection. It is believed that distinct regularity could be induced in the media by appropriate forcing or feedback, while a diffusive collapse induced by continuous destruction can cause breakdown of the media. In this paper, the collapse of ordered spatial distribution is investigated in a regular network of neurons (Morris-Lecar, Hindmarsh-Rose) in two-dimensional array. A stable target wave is developed regular spatial distribution emerges by imposing appropriate external forcing with diversity, or generating heterogeneity (parameter diversity in space). The diffusive invasion could be produced by continuous parameter collapse or switch in local area, e.g, the diffusive poisoning in ion channels of potassium in Morris-Lecar neurons causes breakdown in conductance of channels. It is found that target wave-dominated regularity can be suppressed when the collapsed area is diffused in random. Statistical correlation functions for sampled nodes (neurons) are defined to detect the collapse of ordered state by series analysis.

  20. Mechanism of quasi-periodic lag jitter in bursting rhythms by a neuronal network

    NASA Astrophysics Data System (ADS)

    Barrio, R.; Rodríguez, Marcos; Serrano, S.; Shilnikov, Andrey

    2015-11-01

    We study a heteroclinic bifurcation leading to the onset of robust phase-lag jittering in bursting rhythms generated by a neuronal circuit. We show that the jitter phenomenon is associated with the occurrence of a stable invariant curve emerging through a torus bifurcation in 2D return maps for phase lags between three constituent bursters. To study biologically plausible and phenomenological models of rhythmic neuronal networks we have further developed parallel computational techniques for parameter continuations of all possible fixed points and invariant curves of such return maps. The method is based on a “fine” brute-force analysis of the large data set generated by the computational techniques.

  1. PyNN: A Common Interface for Neuronal Network Simulators

    PubMed Central

    Davison, Andrew P.; Brüderle, Daniel; Eppler, Jochen; Kremkow, Jens; Muller, Eilif; Pecevski, Dejan; Perrinet, Laurent; Yger, Pierre

    2008-01-01

    Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its own programming or configuration language, leading to considerable difficulty in porting models from one simulator to another. This impedes communication between investigators and makes it harder to reproduce and build on the work of others. On the other hand, simulation results can be cross-checked between different simulators, giving greater confidence in their correctness, and each simulator has different optimizations, so the most appropriate simulator can be chosen for a given modelling task. A common programming interface to multiple simulators would reduce or eliminate the problems of simulator diversity while retaining the benefits. PyNN is such an interface, making it possible to write a simulation script once, using the Python programming language, and run it without modification on any supported simulator (currently NEURON, NEST, PCSIM, Brian and the Heidelberg VLSI neuromorphic hardware). PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization and data-management tools. PyNN increases the reliability of modelling studies by making it much easier to check results on multiple simulators. PyNN is open-source software and is available from http://neuralensemble.org/PyNN. PMID:19194529

  2. PyNN: A Common Interface for Neuronal Network Simulators.

    PubMed

    Davison, Andrew P; Brüderle, Daniel; Eppler, Jochen; Kremkow, Jens; Muller, Eilif; Pecevski, Dejan; Perrinet, Laurent; Yger, Pierre

    2008-01-01

    Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its own programming or configuration language, leading to considerable difficulty in porting models from one simulator to another. This impedes communication between investigators and makes it harder to reproduce and build on the work of others. On the other hand, simulation results can be cross-checked between different simulators, giving greater confidence in their correctness, and each simulator has different optimizations, so the most appropriate simulator can be chosen for a given modelling task. A common programming interface to multiple simulators would reduce or eliminate the problems of simulator diversity while retaining the benefits. PyNN is such an interface, making it possible to write a simulation script once, using the Python programming language, and run it without modification on any supported simulator (currently NEURON, NEST, PCSIM, Brian and the Heidelberg VLSI neuromorphic hardware). PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization and data-management tools. PyNN increases the reliability of modelling studies by making it much easier to check results on multiple simulators. PyNN is open-source software and is available from http://neuralensemble.org/PyNN. PMID:19194529

  3. Information Transmission and Anderson Localization in two-dimensional networks of firing-rate neurons

    NASA Astrophysics Data System (ADS)

    Natale, Joseph; Hentschel, George

    Firing-rate networks offer a coarse model of signal propagation in the brain. Here we analyze sparse, 2D planar firing-rate networks with no synapses beyond a certain cutoff distance. Additionally, we impose Dale's Principle to ensure that each neuron makes only or inhibitory outgoing connections. Using spectral methods, we find that the number of neurons participating in excitations of the network becomes insignificant whenever the connectivity cutoff is tuned to a value near or below the average interneuron separation. Further, neural activations exceeding a certain threshold stay confined to a small region of space. This behavior is an instance of Anderson localization, a disorder-induced phase transition by which an information channel is rendered unable to transmit signals. We discuss several potential implications of localization for both local and long-range computation in the brain. This work was supported in part by Grants JSMF/ 220020321 and NSF/IOS/1208126.

  4. On the properties of input-to-output transformations in neuronal networks.

    PubMed

    Olypher, Andrey; Vaillant, Jean

    2016-06-01

    Information processing in neuronal networks in certain important cases can be considered as maps of binary vectors, where ones (spikes) and zeros (no spikes) of input neurons are transformed into spikes and no spikes of output neurons. A simple but fundamental characteristic of such a map is how it transforms distances between input vectors into distances between output vectors. We advanced earlier known results by finding an exact solution to this problem for McCulloch-Pitts neurons. The obtained explicit formulas allow for detailed analysis of how the network connectivity and neuronal excitability affect the transformation of distances in neurons. As an application, we explored a simple model of information processing in the hippocampus, a brain area critically implicated in learning and memory. We found network connectivity and neuronal excitability parameter values that optimize discrimination between similar and distinct inputs. A decrease of neuronal excitability, which in biological neurons may be associated with decreased inhibition, impaired the optimality of discrimination. PMID:27106188

  5. Neuronal response impedance mechanism implementing cooperative networks with low firing rates and μs precision

    PubMed Central

    Vardi, Roni; Goldental, Amir; Marmari, Hagar; Brama, Haya; Stern, Edward A.; Sardi, Shira; Sabo, Pinhas; Kanter, Ido

    2015-01-01

    Realizations of low firing rates in neural networks usually require globally balanced distributions among excitatory and inhibitory links, while feasibility of temporal coding is limited by neuronal millisecond precision. We show that cooperation, governing global network features, emerges through nodal properties, as opposed to link distributions. Using in vitro and in vivo experiments we demonstrate microsecond precision of neuronal response timings under low stimulation frequencies, whereas moderate frequencies result in a chaotic neuronal phase characterized by degraded precision. Above a critical stimulation frequency, which varies among neurons, response failures were found to emerge stochastically such that the neuron functions as a low pass filter, saturating the average inter-spike-interval. This intrinsic neuronal response impedance mechanism leads to cooperation on a network level, such that firing rates are suppressed toward the lowest neuronal critical frequency simultaneously with neuronal microsecond precision. Our findings open up opportunities of controlling global features of network dynamics through few nodes with extreme properties. PMID:26124707

  6. Neuronal response impedance mechanism implementing cooperative networks with low firing rates and μs precision.

    PubMed

    Vardi, Roni; Goldental, Amir; Marmari, Hagar; Brama, Haya; Stern, Edward A; Sardi, Shira; Sabo, Pinhas; Kanter, Ido

    2015-01-01

    Realizations of low firing rates in neural networks usually require globally balanced distributions among excitatory and inhibitory links, while feasibility of temporal coding is limited by neuronal millisecond precision. We show that cooperation, governing global network features, emerges through nodal properties, as opposed to link distributions. Using in vitro and in vivo experiments we demonstrate microsecond precision of neuronal response timings under low stimulation frequencies, whereas moderate frequencies result in a chaotic neuronal phase characterized by degraded precision. Above a critical stimulation frequency, which varies among neurons, response failures were found to emerge stochastically such that the neuron functions as a low pass filter, saturating the average inter-spike-interval. This intrinsic neuronal response impedance mechanism leads to cooperation on a network level, such that firing rates are suppressed toward the lowest neuronal critical frequency simultaneously with neuronal microsecond precision. Our findings open up opportunities of controlling global features of network dynamics through few nodes with extreme properties. PMID:26124707

  7. Echo state networks with filter neurons and a delay&sum readout.

    PubMed

    Holzmann, Georg; Hauser, Helmut

    2010-03-01

    Echo state networks (ESNs) are a novel approach to recurrent neural network training with the advantage of a very simple and linear learning algorithm. It has been demonstrated that ESNs outperform other methods on a number of benchmark tasks. Although the approach is appealing, there are still some inherent limitations in the original formulation. Here we suggest two enhancements of this network model. First, the previously proposed idea of filters in neurons is extended to arbitrary infinite impulse response (IIR) filter neurons. This enables such networks to learn multiple attractors and signals at different timescales, which is especially important for modeling real-world time series. Second, a delay&sum readout is introduced, which adds trainable delays in the synaptic connections of output neurons and therefore vastly improves the memory capacity of echo state networks. It is shown in commonly used benchmark tasks and real-world examples, that this new structure is able to significantly outperform standard ESNs and other state-of-the-art models for nonlinear dynamical system modeling. PMID:19625164

  8. Self-Organized Criticality in Developing Neuronal Networks

    PubMed Central

    Tetzlaff, Christian; Okujeni, Samora; Egert, Ulrich; Wörgötter, Florentin; Butz, Markus

    2010-01-01

    Recently evidence has accumulated that many neural networks exhibit self-organized criticality. In this state, activity is similar across temporal scales and this is beneficial with respect to information flow. If subcritical, activity can die out, if supercritical epileptiform patterns may occur. Little is known about how developing networks will reach and stabilize criticality. Here we monitor the development between 13 and 95 days in vitro (DIV) of cortical cell cultures (n = 20) and find four different phases, related to their morphological maturation: An initial low-activity state (≈19 DIV) is followed by a supercritical (≈20 DIV) and then a subcritical one (≈36 DIV) until the network finally reaches stable criticality (≈58 DIV). Using network modeling and mathematical analysis we describe the dynamics of the emergent connectivity in such developing systems. Based on physiological observations, the synaptic development in the model is determined by the drive of the neurons to adjust their connectivity for reaching on average firing rate homeostasis. We predict a specific time course for the maturation of inhibition, with strong onset and delayed pruning, and that total synaptic connectivity should be strongly linked to the relative levels of excitation and inhibition. These results demonstrate that the interplay between activity and connectivity guides developing networks into criticality suggesting that this may be a generic and stable state of many networks in vivo and in vitro. PMID:21152008

  9. Collective behavior of interacting locally synchronized oscillations in neuronal networks

    NASA Astrophysics Data System (ADS)

    Jalili, Mahdi

    2012-10-01

    Local circuits in the cortex and hippocampus are endowed with resonant, oscillatory firing properties which underlie oscillations in various frequency ranges (e.g. gamma range) frequently observed in the local field potentials, and in electroencephalography. Synchronized oscillations are thought to play important roles in information binding in the brain. This paper addresses the collective behavior of interacting locally synchronized oscillations in realistic neural networks. A network of five neurons is proposed in order to produce locally synchronized oscillations. The neuron models are Hindmarsh-Rose type with electrical and/or chemical couplings. We construct large-scale models using networks of such units which capture the essential features of the dynamics of cells and their connectivity patterns. The profile of the spike synchronization is then investigated considering different model parameters such as strength and ratio of excitatory/inhibitory connections. We also show that transmission time-delay might enhance the spike synchrony. The influence of spike-timing-dependence-plasticity is also studies on the spike synchronization.

  10. To Break or to Brake Neuronal Network Accelerated by Ammonium Ions?

    PubMed Central

    Dynnik, Vladimir V.; Kononov, Alexey V.; Sergeev, Alexander I.; Teplov, Iliya Y.; Tankanag, Arina V.; Zinchenko, Valery P.

    2015-01-01

    Purpose The aim of present study was to investigate the effects of ammonium ions on in vitro neuronal network activity and to search alternative methods of acute ammonia neurotoxicity prevention. Methods Rat hippocampal neuronal and astrocytes co-cultures in vitro, fluorescent microscopy and perforated patch clamp were used to monitor the changes in intracellular Ca2+- and membrane potential produced by ammonium ions and various modulators in the cells implicated in neural networks. Results Low concentrations of NH4Cl (0.1–4 mM) produce short temporal effects on network activity. Application of 5–8 mM NH4Cl: invariably transforms diverse network firing regimen to identical burst patterns, characterized by substantial neuronal membrane depolarization at plateau phase of potential and high-amplitude Ca2+-oscillations; raises frequency and average for period of oscillations Ca2+-level in all cells implicated in network; results in the appearance of group of «run out» cells with high intracellular Ca2+ and steadily diminished amplitudes of oscillations; increases astrocyte Ca2+-signalling, characterized by the appearance of groups of cells with increased intracellular Ca2+-level and/or chaotic Ca2+-oscillations. Accelerated network activity may be suppressed by the blockade of NMDA or AMPA/kainate-receptors or by overactivation of AMPA/kainite-receptors. Ammonia still activate neuronal firing in the presence of GABA(A) receptors antagonist bicuculline, indicating that «disinhibition phenomenon» is not implicated in the mechanisms of networks acceleration. Network activity may also be slowed down by glycine, agonists of metabotropic inhibitory receptors, betaine, L-carnitine, L-arginine, etc. Conclusions Obtained results demonstrate that ammonium ions accelerate neuronal networks firing, implicating ionotropic glutamate receptors, having preserved the activities of group of inhibitory ionotropic and metabotropic receptors. This may mean, that ammonia

  11. Dynamical state of the network determines the efficacy of single neuron properties in shaping the network activity

    PubMed Central

    Sahasranamam, Ajith; Vlachos, Ioannis; Aertsen, Ad; Kumar, Arvind

    2016-01-01

    Spike patterns are among the most common electrophysiological descriptors of neuron types. Surprisingly, it is not clear how the diversity in firing patterns of the neurons in a network affects its activity dynamics. Here, we introduce the state-dependent stochastic bursting neuron model allowing for a change in its firing patterns independent of changes in its input-output firing rate relationship. Using this model, we show that the effect of single neuron spiking on the network dynamics is contingent on the network activity state. While spike bursting can both generate and disrupt oscillations, these patterns are ineffective in large regions of the network state space in changing the network activity qualitatively. Finally, we show that when single-neuron properties are made dependent on the population activity, a hysteresis like dynamics emerges. This novel phenomenon has important implications for determining the network response to time-varying inputs and for the network sensitivity at different operating points. PMID:27212008

  12. Dynamical state of the network determines the efficacy of single neuron properties in shaping the network activity.

    PubMed

    Sahasranamam, Ajith; Vlachos, Ioannis; Aertsen, Ad; Kumar, Arvind

    2016-01-01

    Spike patterns are among the most common electrophysiological descriptors of neuron types. Surprisingly, it is not clear how the diversity in firing patterns of the neurons in a network affects its activity dynamics. Here, we introduce the state-dependent stochastic bursting neuron model allowing for a change in its firing patterns independent of changes in its input-output firing rate relationship. Using this model, we show that the effect of single neuron spiking on the network dynamics is contingent on the network activity state. While spike bursting can both generate and disrupt oscillations, these patterns are ineffective in large regions of the network state space in changing the network activity qualitatively. Finally, we show that when single-neuron properties are made dependent on the population activity, a hysteresis like dynamics emerges. This novel phenomenon has important implications for determining the network response to time-varying inputs and for the network sensitivity at different operating points. PMID:27212008

  13. Robust spatial memory maps in flickering neuronal networks: a topological model

    NASA Astrophysics Data System (ADS)

    Dabaghian, Yuri; Babichev, Andrey; Memoli, Facundo; Chowdhury, Samir; Rice University Collaboration; Ohio State University Collaboration

    It is widely accepted that the hippocampal place cells provide a substrate of the neuronal representation of the environment--the ``cognitive map''. However, hippocampal network, as any other network in the brain is transient: thousands of hippocampal neurons die every day and the connections formed by these cells constantly change due to various forms of synaptic plasticity. What then explains the remarkable reliability of our spatial memories? We propose a computational approach to answering this question based on a couple of insights. First, we propose that the hippocampal cognitive map is fundamentally topological, and hence it is amenable to analysis by topological methods. We then apply several novel methods from homology theory, to understand how dynamic connections between cells influences the speed and reliability of spatial learning. We simulate the rat's exploratory movements through different environments and study how topological invariants of these environments arise in a network of simulated neurons with ``flickering'' connectivity. We find that despite transient connectivity the network of place cells produces a stable representation of the topology of the environment.

  14. Computation emerges from adaptive synchronization of networking neurons.

    PubMed

    Zanin, Massimiliano; Del Pozo, Francisco; Boccaletti, Stefano

    2011-01-01

    The activity of networking neurons is largely characterized by the alternation of synchronous and asynchronous spiking sequences. One of the most relevant challenges that scientists are facing today is, then, relating that evidence with the fundamental mechanisms through which the brain computes and processes information, as well as with the arousal (or progress) of a number of neurological illnesses. In other words, the problem is how to associate an organized dynamics of interacting neural assemblies to a computational task. Here we show that computation can be seen as a feature emerging from the collective dynamics of an ensemble of networking neurons, which interact by means of adaptive dynamical connections. Namely, by associating logical states to synchronous neuron's dynamics, we show how the usual Boolean logics can be fully recovered, and a universal Turing machine can be constructed. Furthermore, we show that, besides the static binary gates, a wider class of logical operations can be efficiently constructed as the fundamental computational elements interact within an adaptive network, each operation being represented by a specific motif. Our approach qualitatively differs from the past attempts to encode information and compute with complex systems, where computation was instead the consequence of the application of control loops enforcing a desired state into the specific system's dynamics. Being the result of an emergent process, the computation mechanism here described is not limited to a binary Boolean logic, but it can involve a much larger number of states. As such, our results can enlighten new concepts for the understanding of the real computing processes taking place in the brain. PMID:22073167

  15. Automated Detection of Soma Location and Morphology in Neuronal Network Cultures

    PubMed Central

    Ozcan, Burcin; Negi, Pooran; Laezza, Fernanda; Papadakis, Manos; Labate, Demetrio

    2015-01-01

    Automated identification of the primary components of a neuron and extraction of its sub-cellular features are essential steps in many quantitative studies of neuronal networks. The focus of this paper is the development of an algorithm for the automated detection of the location and morphology of somas in confocal images of neuronal network cultures. This problem is motivated by applications in high-content screenings (HCS), where the extraction of multiple morphological features of neurons on large data sets is required. Existing algorithms are not very efficient when applied to the analysis of confocal image stacks of neuronal cultures. In addition to the usual difficulties associated with the processing of fluorescent images, these types of stacks contain a small number of images so that only a small number of pixels are available along the z-direction and it is challenging to apply conventional 3D filters. The algorithm we present in this paper applies a number of innovative ideas from the theory of directional multiscale representations and involves the following steps: (i) image segmentation based on support vector machines with specially designed multiscale filters; (ii) soma extraction and separation of contiguous somas, using a combination of level set method and directional multiscale filters. We also present an approach to extract the soma’s surface morphology using the 3D shearlet transform. Extensive numerical experiments show that our algorithms are computationally efficient and highly accurate in segmenting the somas and separating contiguous ones. The algorithms presented in this paper will facilitate the development of a high-throughput quantitative platform for the study of neuronal networks for HCS applications. PMID:25853656

  16. Neuronal Networks during Burst Suppression as Revealed by Source Analysis

    PubMed Central

    Reinicke, Christine; Moeller, Friederike; Anwar, Abdul Rauf; Mideksa, Kidist Gebremariam; Pressler, Ronit; Deuschl, Günther; Stephani, Ulrich; Siniatchkin, Michael

    2015-01-01

    Introduction Burst-suppression (BS) is an electroencephalography (EEG) pattern consisting of alternant periods of slow waves of high amplitude (burst) and periods of so called flat EEG (suppression). It is generally associated with coma of various etiologies (hypoxia, drug-related intoxication, hypothermia, and childhood encephalopathies, but also anesthesia). Animal studies suggest that both the cortex and the thalamus are involved in the generation of BS. However, very little is known about mechanisms of BS in humans. The aim of this study was to identify the neuronal network underlying both burst and suppression phases using source reconstruction and analysis of functional and effective connectivity in EEG. Material/Methods Dynamic imaging of coherent sources (DICS) was applied to EEG segments of 13 neonates and infants with burst and suppression EEG pattern. The brain area with the strongest power in the analyzed frequency (1–4 Hz) range was defined as the reference region. DICS was used to compute the coherence between this reference region and the entire brain. The renormalized partial directed coherence (RPDC) was used to describe the informational flow between the identified sources. Results/Conclusion Delta activity during the burst phases was associated with coherent sources in the thalamus and brainstem as well as bilateral sources in cortical regions mainly frontal and parietal, whereas suppression phases were associated with coherent sources only in cortical regions. Results of the RPDC analyses showed an upwards informational flow from the brainstem towards the thalamus and from the thalamus to cortical regions, which was absent during the suppression phases. These findings may support the theory that a “cortical deafferentiation” between the cortex and sub-cortical structures exists especially in suppression phases compared to burst phases in burst suppression EEGs. Such a deafferentiation may play a role in the poor neurological outcome of

  17. Neuronal network disturbance after focal ischemia in rats

    SciTech Connect

    Kataoka, K.; Hayakawa, T.; Yamada, K.; Mushiroi, T.; Kuroda, R.; Mogami, H. )

    1989-09-01

    We studied functional disturbances following left middle cerebral artery occlusion in rats. Neuronal function was evaluated by (14C)2-deoxyglucose autoradiography 1 day after occlusion. We analyzed the mechanisms of change in glucose utilization outside the infarct using Fink-Heimer silver impregnation, axonal transport of wheat germ agglutinin-conjugated-horseradish peroxidase, and succinate dehydrogenase histochemistry. One day after occlusion, glucose utilization was remarkably reduced in the areas surrounding the infarct. There were many silver grains indicating degeneration of the synaptic terminals in the cortical areas surrounding the infarct and the ipsilateral cingulate cortex. Moreover, in the left thalamus where the left middle cerebral artery supplied no blood, glucose utilization significantly decreased compared with sham-operated rats. In the left thalamus, massive silver staining of degenerated synaptic terminals and decreases in succinate dehydrogenase activity were observed 4 and 5 days after occlusion. The absence of succinate dehydrogenase staining may reflect early changes in retrograde degeneration of thalamic neurons after ischemic injury of the thalamocortical pathway. Terminal degeneration even affected areas remote from the infarct: there were silver grains in the contralateral hemisphere transcallosally connected to the infarct and in the ipsilateral substantia nigra. Axonal transport study showed disruption of the corticospinal tract by subcortical ischemia; the transcallosal pathways in the cortex surrounding the infarct were preserved. The relation between neural function and the neuronal network in the area surrounding the focal cerebral infarct is discussed with regard to ischemic penumbra and diaschisis.

  18. Microstate description of stable chaos in networks of spiking neurons

    NASA Astrophysics Data System (ADS)

    Puelma Touzel, Maximilian; Michael, Monteforte; Wolf, Fred

    2014-03-01

    Dynamic instabilities have been proposed to explain the decorrelation of stimulus-driven activity observed in sensory areas such as the olfactory bulb, but are sensitive to noise. Simple neuron models coupled through inhibition can nevertheless exhibit a negative maximum Lyapunov exponent, despite displaying irregular and asynchronous (AI) activity and having an exponential instability to finite-sized perturbations above a critical strength that scales with the size, density and activity of the circuit. This stable chaos, a phenomenon first found in coupled-map lattices, produces a large, finite set of locally-attracting, yet mutually-repelling AI spike sequences ideally suited for discrete, high-dimensional coding. We analyze the effects of finite-sized perturbations on the spiking microstate and reveal the mechanism underlying the stable chaos. From this, we can analytically derive the aforementioned scaling relations and estimate the critical value of previously observed transitions to conventional chaos. This work highlights the features of intra-neuron dynamics and inter-neuron coupling that generate this phase space structure, which might serve as an attractor reservoir that downstream networks can use to decode sensory input.

  19. Entropy driven artificial neuronal networks and sensorial representation; A proposal

    SciTech Connect

    VanHulle, M.M. )

    1989-04-01

    A hierarchical Artificial Neuronal Network (ANN) is proposed as a model senosorium wherein feedback is allowed to modify the categorization abilities of the system. In this way, the original representation, being abstract and precategorical, is refined, yielding a more concrete representation. As thermodynamical entropy is a hierarchical invariant and an explicitly time dependent and compact measure of state dynamics, it is chosen as feedback measure. The main features of the network are shown to be plausible from the point of view of the physiology and anatomy of the visual system of cats and primates and one of these, double-layered maps performing combinatorial processing and evaluation, respectively, is illustrated by simulations in the orientation domain.

  20. Fuzzy operators and cyclic behavior in formal neuronal networks

    NASA Technical Reports Server (NTRS)

    Labos, E.; Holden, A. V.; Laczko, J.; Orzo, L.; Labos, A. S.

    1992-01-01

    Formal neuronal networks (FNN), which are comprised of threshold gates, make use of the unit step function. It is regarded as a degenerated distribution function (DDF) and will be referred to here as a non-fuzzy threshold operator (nFTO). Special networks of this kind generating long cycles of states are modified by introduction of fuzzy threshold operators (FTO), i.e., non-degenerated distribution functions (nDDF). The cyclic behavior of the new nets is compared with the original ones. The interconnection matrix and threshold values are not modified. It is concluded that the original long cycles change the fixed points and short cycles, and as the computer simulations demonstrate, the aperiodic motion that is associated with chaotic behavior appears. The emergence of the above changes depend on the steepness of the threshold operators.

  1. Can Simple Rules Control Development of a Pioneer Vertebrate Neuronal Network Generating Behavior?

    PubMed Central

    Conte, Deborah; Hull, Mike; Merrison-Hort, Robert; al Azad, Abul Kalam; Buhl, Edgar; Borisyuk, Roman; Soffe, Stephen R.

    2014-01-01

    How do the pioneer networks in the axial core of the vertebrate nervous system first develop? Fundamental to understanding any full-scale neuronal network is knowledge of the constituent neurons, their properties, synaptic interconnections, and normal activity. Our novel strategy uses basic developmental rules to generate model networks that retain individual neuron and synapse resolution and are capable of reproducing correct, whole animal responses. We apply our developmental strategy to young Xenopus tadpoles, whose brainstem and spinal cord share a core vertebrate plan, but at a tractable complexity. Following detailed anatomical and physiological measurements to complete a descriptive library of each type of spinal neuron, we build models of their axon growth controlled by simple chemical gradients and physical barriers. By adding dendrites and allowing probabilistic formation of synaptic connections, we reconstruct network connectivity among up to 2000 neurons. When the resulting “network” is populated by model neurons and synapses, with properties based on physiology, it can respond to sensory stimulation by mimicking tadpole swimming behavior. This functioning model represents the most complete reconstruction of a vertebrate neuronal network that can reproduce the complex, rhythmic behavior of a whole animal. The findings validate our novel developmental strategy for generating realistic networks with individual neuron- and synapse-level resolution. We use it to demonstrate how early functional neuronal connectivity and behavior may in life result from simple developmental “rules,” which lay out a scaffold for the vertebrate CNS without specific neuron-to-neuron recognition. PMID:24403159

  2. How effective delays shape oscillatory dynamics in neuronal networks

    NASA Astrophysics Data System (ADS)

    Roxin, Alex; Montbrió, Ernest

    2011-02-01

    Synaptic, dendritic and single-cell kinetics generate significant time delays that shape the dynamics of large networks of spiking neurons. Previous work has shown that such effective delays can be taken into account with a rate model through the addition of an explicit, fixed delay (Roxin et al. (2005,2006) [29,30]). Here we extend this work to account for arbitrary symmetric patterns of synaptic connectivity and generic nonlinear transfer functions. Specifically, we conduct a weakly nonlinear analysis of the dynamical states arising via primary instabilities of the asynchronous state. In this way we determine analytically how the nature and stability of these states depend on the choice of transfer function and connectivity. We arrive at two general observations of physiological relevance that could not be explained in previous work. These are: 1 - fast oscillations are always supercritical for realistic transfer functions and 2 - traveling waves are preferred over standing waves given plausible patterns of local connectivity. We finally demonstrate that these results show good agreement with those obtained performing numerical simulations of a network of Hodgkin-Huxley neurons.

  3. In vitro neuronal network activity in NMDA receptor encephalitis

    PubMed Central

    2013-01-01

    Background Anti-NMDA-encephalitis is caused by antibodies against the N-methyl-D-aspartate receptor (NMDAR) and characterized by a severe encephalopathy with psychosis, epileptic seizures and autonomic disturbances. It predominantly occurs in young women and is associated in 59% with an ovarian teratoma. Results We describe effects of cerebrospinal fluid (CSF) from an anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis patient on in vitro neuronal network activity (ivNNA). In vitro NNA of dissociated primary rat cortical populations was recorded by the microelectrode array (MEA) system. The 23-year old patient was severely affected but showed an excellent recovery following multimodal immunomodulatory therapy and removal of an ovarian teratoma. Patient CSF (pCSF) taken during the initial weeks after disease onset suppressed global spike- and burst rates of ivNNA in contrast to pCSF sampled after clinical recovery and decrease of NMDAR antibody titers. The synchrony of pCSF-affected ivNNA remained unaltered during the course of the disease. Conclusion Patient CSF directly suppresses global activity of neuronal networks recorded by the MEA system. In contrast, pCSF did not regulate the synchrony of ivNNA suggesting that NMDAR antibodies selectively regulate distinct parameters of ivNNA while sparing their functional connectivity. Thus, assessing ivNNA could represent a new technique to evaluate functional consequences of autoimmune encephalitis-related CSF changes. PMID:23379293

  4. Simulation of Code Spectrum and Code Flow of Cultured Neuronal Networks.

    PubMed

    Tamura, Shinichi; Nishitani, Yoshi; Hosokawa, Chie; Miyoshi, Tomomitsu; Sawai, Hajime

    2016-01-01

    It has been shown that, in cultured neuronal networks on a multielectrode, pseudorandom-like sequences (codes) are detected, and they flow with some spatial decay constant. Each cultured neuronal network is characterized by a specific spectrum curve. That is, we may consider the spectrum curve as a "signature" of its associated neuronal network that is dependent on the characteristics of neurons and network configuration, including the weight distribution. In the present study, we used an integrate-and-fire model of neurons with intrinsic and instantaneous fluctuations of characteristics for performing a simulation of a code spectrum from multielectrodes on a 2D mesh neural network. We showed that it is possible to estimate the characteristics of neurons such as the distribution of number of neurons around each electrode and their refractory periods. Although this process is a reverse problem and theoretically the solutions are not sufficiently guaranteed, the parameters seem to be consistent with those of neurons. That is, the proposed neural network model may adequately reflect the behavior of a cultured neuronal network. Furthermore, such prospect is discussed that code analysis will provide a base of communication within a neural network that will also create a base of natural intelligence. PMID:27239189

  5. Simulation of Code Spectrum and Code Flow of Cultured Neuronal Networks

    PubMed Central

    Tamura, Shinichi; Nishitani, Yoshi; Hosokawa, Chie; Miyoshi, Tomomitsu; Sawai, Hajime

    2016-01-01

    It has been shown that, in cultured neuronal networks on a multielectrode, pseudorandom-like sequences (codes) are detected, and they flow with some spatial decay constant. Each cultured neuronal network is characterized by a specific spectrum curve. That is, we may consider the spectrum curve as a “signature” of its associated neuronal network that is dependent on the characteristics of neurons and network configuration, including the weight distribution. In the present study, we used an integrate-and-fire model of neurons with intrinsic and instantaneous fluctuations of characteristics for performing a simulation of a code spectrum from multielectrodes on a 2D mesh neural network. We showed that it is possible to estimate the characteristics of neurons such as the distribution of number of neurons around each electrode and their refractory periods. Although this process is a reverse problem and theoretically the solutions are not sufficiently guaranteed, the parameters seem to be consistent with those of neurons. That is, the proposed neural network model may adequately reflect the behavior of a cultured neuronal network. Furthermore, such prospect is discussed that code analysis will provide a base of communication within a neural network that will also create a base of natural intelligence. PMID:27239189

  6. Analyzing neuronal networks using discrete-time dynamics

    NASA Astrophysics Data System (ADS)

    Ahn, Sungwoo; Smith, Brian H.; Borisyuk, Alla; Terman, David

    2010-05-01

    We develop mathematical techniques for analyzing detailed Hodgkin-Huxley like models for excitatory-inhibitory neuronal networks. Our strategy for studying a given network is to first reduce it to a discrete-time dynamical system. The discrete model is considerably easier to analyze, both mathematically and computationally, and parameters in the discrete model correspond directly to parameters in the original system of differential equations. While these networks arise in many important applications, a primary focus of this paper is to better understand mechanisms that underlie temporally dynamic responses in early processing of olfactory sensory information. The models presented here exhibit several properties that have been described for olfactory codes in an insect’s Antennal Lobe. These include transient patterns of synchronization and decorrelation of sensory inputs. By reducing the model to a discrete system, we are able to systematically study how properties of the dynamics, including the complex structure of the transients and attractors, depend on factors related to connectivity and the intrinsic and synaptic properties of cells within the network.

  7. Neuron-Like Networks Between Ribosomal Proteins Within the Ribosome.

    PubMed

    Poirot, Olivier; Timsit, Youri

    2016-01-01

    From brain to the World Wide Web, information-processing networks share common scale invariant properties. Here, we reveal the existence of neural-like networks at a molecular scale within the ribosome. We show that with their extensions, ribosomal proteins form complex assortative interaction networks through which they communicate through tiny interfaces. The analysis of the crystal structures of 50S eubacterial particles reveals that most of these interfaces involve key phylogenetically conserved residues. The systematic observation of interactions between basic and aromatic amino acids at the interfaces and along the extension provides new structural insights that may contribute to decipher the molecular mechanisms of signal transmission within or between the ribosomal proteins. Similar to neurons interacting through "molecular synapses", ribosomal proteins form a network that suggest an analogy with a simple molecular brain in which the "sensory-proteins" innervate the functional ribosomal sites, while the "inter-proteins" interconnect them into circuits suitable to process the information flow that circulates during protein synthesis. It is likely that these circuits have evolved to coordinate both the complex macromolecular motions and the binding of the multiple factors during translation. This opens new perspectives on nanoscale information transfer and processing. PMID:27225526

  8. Block-Based Neural Networks with Pulsed Neuron Model

    NASA Astrophysics Data System (ADS)

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

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

  9. Neuron-Like Networks Between Ribosomal Proteins Within the Ribosome

    PubMed Central

    Poirot, Olivier; Timsit, Youri

    2016-01-01

    From brain to the World Wide Web, information-processing networks share common scale invariant properties. Here, we reveal the existence of neural-like networks at a molecular scale within the ribosome. We show that with their extensions, ribosomal proteins form complex assortative interaction networks through which they communicate through tiny interfaces. The analysis of the crystal structures of 50S eubacterial particles reveals that most of these interfaces involve key phylogenetically conserved residues. The systematic observation of interactions between basic and aromatic amino acids at the interfaces and along the extension provides new structural insights that may contribute to decipher the molecular mechanisms of signal transmission within or between the ribosomal proteins. Similar to neurons interacting through “molecular synapses”, ribosomal proteins form a network that suggest an analogy with a simple molecular brain in which the “sensory-proteins” innervate the functional ribosomal sites, while the “inter-proteins” interconnect them into circuits suitable to process the information flow that circulates during protein synthesis. It is likely that these circuits have evolved to coordinate both the complex macromolecular motions and the binding of the multiple factors during translation. This opens new perspectives on nanoscale information transfer and processing. PMID:27225526

  10. Analyzing Neuronal Networks Using Discrete-Time Dynamics

    PubMed Central

    Ahn, Sungwoo; Smith, Brian H.; Borisyuk, Alla; Terman, David

    2010-01-01

    We develop mathematical techniques for analyzing detailed Hodgkin-Huxley like models for excitatory-inhibitory neuronal networks. Our strategy for studying a given network is to first reduce it to a discrete-time dynamical system. The discrete model is considerably easier to analyze, both mathematically and computationally, and parameters in the discrete model correspond directly to parameters in the original system of differential equations. While these networks arise in many important applications, a primary focus of this paper is to better understand mechanisms that underlie temporally dynamic responses in early processing of olfactory sensory information. The models presented here exhibit several properties that have been described for olfactory codes in an insect's Antennal Lobe. These include transient patterns of synchronization and decorrelation of sensory inputs. By reducing the model to a discrete system, we are able to systematically study how properties of the dynamics, including the complex structure of the transients and attractors, depend on factors related to connectivity and the intrinsic and synaptic properties of cells within the network. PMID:20454529

  11. Recent Developments in VSD Imaging of Small Neuronal Networks

    ERIC Educational Resources Information Center

    Hill, Evan S.; Bruno, Angela M.; Frost, William N.

    2014-01-01

    Voltage-sensitive dye (VSD) imaging is a powerful technique that can provide, in single experiments, a large-scale view of network activity unobtainable with traditional sharp electrode recording methods. Here we review recent work using VSDs to study small networks and highlight several results from this approach. Topics covered include circuit…

  12. Quantification of degeneracy in Hodgkin-Huxley neurons on Newman-Watts small world network.

    PubMed

    Man, Menghua; Zhang, Ya; Ma, Guilei; Friston, Karl; Liu, Shanghe

    2016-08-01

    Degeneracy is a fundamental source of biological robustness, complexity and evolvability in many biological systems. However, degeneracy is often confused with redundancy. Furthermore, the quantification of degeneracy has not been addressed for realistic neuronal networks. The objective of this paper is to characterize degeneracy in neuronal network models via quantitative mathematic measures. Firstly, we establish Hodgkin-Huxley neuronal networks with Newman-Watts small world network architectures. Secondly, in order to calculate the degeneracy, redundancy and complexity in the ensuing networks, we use information entropy to quantify the information a neuronal response carries about the stimulus - and mutual information to measure the contribution of each subset of the neuronal network. Finally, we analyze the interdependency of degeneracy, redundancy and complexity - and how these three measures depend upon network architectures. Our results suggest that degeneracy can be applied to any neuronal network as a formal measure, and degeneracy is distinct from redundancy. Qualitatively degeneracy and complexity are more highly correlated over different network architectures, in comparison to redundancy. Quantitatively, the relationship between both degeneracy and redundancy depends on network coupling strength: both degeneracy and redundancy increase with complexity for small coupling strengths; however, as coupling strength increases, redundancy decreases with complexity (in contrast to degeneracy, which is relatively invariant). These results suggest that the degeneracy is a general topologic characteristic of neuronal networks, which could be applied quantitatively in neuroscience and connectomics. PMID:27155043

  13. Propagation of Spiking and Burst-Spiking Synchronous States in a Feed-Forward Neuronal Network

    NASA Astrophysics Data System (ADS)

    Zhang, Xi; Huang, Hong-Bin; Li, Pei-Jun; Wu, Fang-Ping; Wu, Wang-Jie; Jiang, Min

    2012-12-01

    Neuronal firing that carries information can propagate stably in neuronal networks. One important feature of the stable states is their spatiotemporal correlation (STC) developed in the propagation. The propagation of synchronous states of spiking and burst-spiking neuronal activities in a feed-forward neuronal network with high STC is studied. Different dynamic regions and synchronous regions of the second layer are clarified for spiking and burst-spiking neuronal activities. By calculating correlation, it is found that five layers are needed for stable propagation. Synchronous regions of the 4th layer and the 10th layer are compared.

  14. Multiparametric characterisation of neuronal network activity for in vitro agrochemical neurotoxicity assessment.

    PubMed

    Alloisio, Susanna; Nobile, Mario; Novellino, Antonio

    2015-05-01

    The last few decades have seen the marketing of hundreds of new pesticide products with a forecasted expansion of the global agrochemical industry. As several pesticides directly target nervous tissue as their mechanism of toxicity, alternative methods to routine in vivo animal testing, such as the Multi Electrode Array (MEAs)-based approach, have been proposed as an in vitro tool to perform sensitive, quick and low cost neuro-toxicological screening. Here, we examined the effects of a training set of eleven active substances known to have neuronal or non-neuronal targets, contained in the most commonly used agrochemicals, on the spontaneous electrical activity of cortical neuronal networks grown on MEAs. A multiparametric characterisation of neuronal network firing and bursting was performed with the aim of investigating how this can contribute to the efficient evaluation of in vitro chemical-induced neurotoxicity. The analysis of MFR, MBR, MBD, MISI_B and % Spikes_B parameters identified four different groups of chemicals: one wherein only inhibition is observed (chlorpyrifos, deltamethrin, orysastrobin, dimoxystrobin); a second one in which all parameters, except the MISI_B, are inhibited (carbaryl, quinmerac); a third in which increases at low chemical concentration are followed by decreases at high concentration, with exception of MISI_B that only decreased (fipronil); a fourth in which no effects are observed (paraquat, glyphosate, imidacloprid, mepiquat). The overall results demonstrated that the multiparametric description of the neuronal networks activity makes MEA-based screening platform an accurate and consistent tool for the evaluation of the toxic potential of chemicals. In particular, among the bursting parameters the MISI_B was the best that correlates with potency and may help to better define chemical toxicity when MFR is affected only at relatively high concentration. PMID:25845298

  15. From artificial neural networks to spiking neuron populations and back again.

    PubMed

    de Kamps, M; van der Velde, F

    2001-01-01

    In this paper, we investigate the relation between Artificial Neural Networks (ANNs) and networks of populations of spiking neurons. The activity of an artificial neuron is usually interpreted as the firing rate of a neuron or neuron population. Using a model of the visual cortex, we will show that this interpretation runs into serious difficulties. We propose to interpret the activity of an artificial neuron as the steady state of a cross-inhibitory circuit, in which one population codes for 'positive' artificial neuron activity and another for 'negative' activity. We will show that with this interpretation it is possible, under certain circumstances, to transform conventional ANNs (e.g. trained with 'back-propagation') into biologically plausible networks of spiking populations. However, in general, the use of biologically motivated spike response functions introduces artificial neurons that behave differently from the ones used in the classical ANN paradigm. PMID:11665784

  16. Patterning human neuronal networks on photolithographically engineered silicon dioxide substrates functionalized with glial analogues.

    PubMed

    Hughes, Mark A; Brennan, Paul M; Bunting, Andrew S; Cameron, Katherine; Murray, Alan F; Shipston, Mike J

    2014-05-01

    Interfacing neurons with silicon semiconductors is a challenge being tackled through various bioengineering approaches. Such constructs inform our understanding of neuronal coding and learning and ultimately guide us toward creating intelligent neuroprostheses. A fundamental prerequisite is to dictate the spatial organization of neuronal cells. We sought to pattern neurons using photolithographically defined arrays of polymer parylene-C, activated with fetal calf serum. We used a purified human neuronal cell line [Lund human mesencephalic (LUHMES)] to establish whether neurons remain viable when isolated on-chip or whether they require a supporting cell substrate. When cultured in isolation, LUHMES neurons failed to pattern and did not show any morphological signs of differentiation. We therefore sought a cell type with which to prepattern parylene regions, hypothesizing that this cellular template would enable secondary neuronal adhesion and network formation. From a range of cell lines tested, human embryonal kidney (HEK) 293 cells patterned with highest accuracy. LUHMES neurons adhered to pre-established HEK 293 cell clusters and this coculture environment promoted morphological differentiation of neurons. Neurites extended between islands of adherent cell somata, creating an orthogonally arranged neuronal network. HEK 293 cells appear to fulfill a role analogous to glia, dictating cell adhesion, and generating an environment conducive to neuronal survival. We next replaced HEK 293 cells with slower growing glioma-derived precursors. These primary human cells patterned accurately on parylene and provided a similarly effective scaffold for neuronal adhesion. These findings advance the use of this microfabrication-compatible platform for neuronal patterning. PMID:23733444

  17. Energy-efficient population coding constrains network size of a neuronal array system

    NASA Astrophysics Data System (ADS)

    Yu, Lianchun; Zhang, Chi; Liu, Liwei; Yu, Yuguo

    2016-01-01

    We consider the open issue of how the energy efficiency of the neural information transmission process, in a general neuronal array, constrains the network size, and how well this network size ensures the reliable transmission of neural information in a noisy environment. By direct mathematical analysis, we have obtained general solutions proving that there exists an optimal number of neurons in the network, where the average coding energy cost (defined as energy consumption divided by mutual information) per neuron passes through a global minimum for both subthreshold and superthreshold signals. With increases in background noise intensity, the optimal neuronal number decreases for subthreshold signals and increases for suprathreshold signals. The existence of an optimal number of neurons in an array network reveals a general rule for population coding that states that the neuronal number should be large enough to ensure reliable information transmission that is robust to the noisy environment but small enough to minimize energy cost.

  18. Integration of neuroblasts into a two-dimensional small world neuronal network

    NASA Astrophysics Data System (ADS)

    Schneider-Mizell, Casey; Zochowski, Michal; Sander, Leonard

    2009-03-01

    Neurogenesis in the adult brain has been suggested to be important for learning and functional robustness to the neuronal death. New neurons integrate themselves into existing neuronal networks by moving into a target destination, extending axonal and dendritic processes, and inducing synaptogenesis to connect to active neurons. We hypothesize that increased plasticity of the network to novel stimuli can arise from activity-dependent cell and process motility rules. In complement to a similar in vitro model, we investigate a computational model of a two-dimensional small world network of integrate and fire neurons. After steady-state activity is reached in the extant network, we introduce new neurons which move, stop, and connect themselves through rules governed by position and firing rate.

  19. Energy-efficient population coding constrains network size of a neuronal array system.

    PubMed

    Yu, Lianchun; Zhang, Chi; Liu, Liwei; Yu, Yuguo

    2016-01-01

    We consider the open issue of how the energy efficiency of the neural information transmission process, in a general neuronal array, constrains the network size, and how well this network size ensures the reliable transmission of neural information in a noisy environment. By direct mathematical analysis, we have obtained general solutions proving that there exists an optimal number of neurons in the network, where the average coding energy cost (defined as energy consumption divided by mutual information) per neuron passes through a global minimum for both subthreshold and superthreshold signals. With increases in background noise intensity, the optimal neuronal number decreases for subthreshold signals and increases for suprathreshold signals. The existence of an optimal number of neurons in an array network reveals a general rule for population coding that states that the neuronal number should be large enough to ensure reliable information transmission that is robust to the noisy environment but small enough to minimize energy cost. PMID:26781354

  20. Energy-efficient population coding constrains network size of a neuronal array system

    PubMed Central

    Yu, Lianchun; Zhang, Chi; Liu, Liwei; Yu, Yuguo

    2016-01-01

    We consider the open issue of how the energy efficiency of the neural information transmission process, in a general neuronal array, constrains the network size, and how well this network size ensures the reliable transmission of neural information in a noisy environment. By direct mathematical analysis, we have obtained general solutions proving that there exists an optimal number of neurons in the network, where the average coding energy cost (defined as energy consumption divided by mutual information) per neuron passes through a global minimum for both subthreshold and superthreshold signals. With increases in background noise intensity, the optimal neuronal number decreases for subthreshold signals and increases for suprathreshold signals. The existence of an optimal number of neurons in an array network reveals a general rule for population coding that states that the neuronal number should be large enough to ensure reliable information transmission that is robust to the noisy environment but small enough to minimize energy cost. PMID:26781354

  1. Search for periodicity in the observational data by means of artificial neuron networks

    NASA Astrophysics Data System (ADS)

    Baluev, R.

    2012-05-01

    The possibility of application of artificial neural networks is considered for two classical model problems of observational data reduction: (i) the identification of periodic oscillations in noisy time series and (ii) assessment of the frequency of this oscillation (on the existing time series). On the inputs of the neural networks the values of the time series are given, and on the output, respectively, we have either an indicatior of the presence of signal (from 0 to 1), or the assessment of its frequency. It is shown that the theoretical limit, which a neural network can achieve in the training to solve such problems, corresponds to the Bayesian theory of estimation and testing of statistical hypotheses. Training of the neural network was carried out with a help of means of open-source package FANN. The best results were achieved using the algorithm Cascade2, which allows finding the optimal number of network neurons (not just the weight of the connection between them). In comparison with traditional methods based on the periodogram, which require long calculations, the trained neural network works almost instantly. Thus, artificial neural networks are very promising for the processing of large data sets. However, the threshold of signal detection so far failed to bring to Bayesian theoretical limit. In addition, it is not yet possible to train the neural network to analyze time-series with arbitrarily-uneven distribution of observations. This indicates on a need for further investigations to improve the efficiency of the method.

  2. Neuronal oscillations and functional interactions between resting state networks.

    PubMed

    Lei, Xu; Wang, Yulin; Yuan, Hong; Mantini, Dante

    2014-07-01

    Functional magnetic imaging (fMRI) studies showed that resting state activity in the healthy brain is organized into multiple large-scale networks encompassing distant regions. A key finding of resting state fMRI studies is the anti-correlation typically observed between the dorsal attention network (DAN) and the default mode network (DMN), which - during task performance - are activated and deactivated, respectively. Previous studies have suggested that alcohol administration modulates the balance of activation/deactivation in brain networks, as well as it induces significant changes in oscillatory activity measured by electroencephalography (EEG). However, our knowledge of alcohol-induced changes in band-limited EEG power and their potential link with the functional interactions between DAN and DMN is still very limited. Here we address this issue, examining the neuronal effects of alcohol administration during resting state by using simultaneous EEG-fMRI. Our findings show increased EEG power in the theta frequency band (4-8 Hz) after administration of alcohol compared to placebo, which was prominent over the frontal cortex. More interestingly, increased frontal tonic EEG activity in this band was associated with greater anti-correlation between the DAN and the frontal component of the DMN. Furthermore, EEG theta power and DAN-DMN anti-correlation were relatively greater in subjects who reported a feeling of euphoria after alcohol administration, which may result from a diminished inhibition exerted by the prefrontal cortex. Overall, our findings suggest that slow brain rhythms are responsible for dynamic functional interactions between brain networks. They also confirm the applicability and potential usefulness of EEG-fMRI for central nervous system drug research. PMID:25050432

  3. Numbers And Gains Of Neurons In Winner-Take-All Networks

    NASA Technical Reports Server (NTRS)

    Brown, Timothy X.

    1993-01-01

    Report presents theoretical study of gains required in neurons to implement winner-take-all electronic neural network of given size and related question of maximum size of winner-take-all network in which neurons have specified sigmoid transfer or response function with specified gain.

  4. Analysis of connectivity map: Control to glutamate injured and phenobarbital treated neuronal network

    NASA Astrophysics Data System (ADS)

    Kamal, Hassan; Kanhirodan, Rajan; Srinivas, Kalyan V.; Sikdar, Sujit K.

    2010-04-01

    We study the responses of a cultured neural network when it is exposed to epileptogenesis glutamate injury causing epilepsy and subsequent treatment with phenobarbital by constructing connectivity map of neurons using correlation matrix. This study is particularly useful in understanding the pharmaceutical drug induced changes in the neuronal network properties with insights into changes at the systems biology level.

  5. Connectivity, excitability and activity patterns in neuronal networks

    NASA Astrophysics Data System (ADS)

    le Feber, Joost; Stoyanova, Irina I.; Chiappalone, Michela

    2014-06-01

    Extremely synchronized firing patterns such as those observed in brain diseases like epilepsy may result from excessive network excitability. Although network excitability is closely related to (excitatory) connectivity, a direct measure for network excitability remains unavailable. Several methods currently exist for estimating network connectivity, most of which are related to cross-correlation. An example is the conditional firing probability (CFP) analysis which calculates the pairwise probability (CFPi,j) that electrode j records an action potential at time t = τ, given that electrode i recorded a spike at t = 0. However, electrode i often records multiple spikes within the analysis interval, and CFP values are biased by the on-going dynamic state of the network. Here we show that in a linear approximation this bias may be removed by deconvoluting CFPi,j with the autocorrelation of i (i.e. CFPi,i), to obtain the single pulse response (SPRi,j)—the average response at electrode j to a single spike at electrode i. Thus, in a linear system SPRs would be independent of the dynamic network state. Nonlinear components of synaptic transmission, such as facilitation and short term depression, will however still affect SPRs. Therefore SPRs provide a clean measure of network excitability. We used carbachol and ghrelin to moderately activate cultured cortical networks to affect their dynamic state. Both neuromodulators transformed the bursting firing patterns of the isolated networks into more dispersed firing. We show that the influence of the dynamic state on SPRs is much smaller than the effect on CFPs, but not zero. The remaining difference reflects the alteration in network excitability. We conclude that SPRs are less contaminated by the dynamic network state and that mild excitation may decrease network excitability, possibly through short term synaptic depression.

  6. Communication Network Analysis Methods.

    ERIC Educational Resources Information Center

    Farace, Richard V.; Mabee, Timothy

    This paper reviews a variety of analytic procedures that can be applied to network data, discussing the assumptions and usefulness of each procedure when applied to the complexity of human communication. Special attention is paid to the network properties measured or implied by each procedure. Factor analysis and multidimensional scaling are among…

  7. a Simple Neuron Network Based on Hebb's Rule

    NASA Astrophysics Data System (ADS)

    Zhang, Gui-Qing; Yu, Zi; Yang, Qiu-Ying; Chen, Tian-Lun

    A weighted mechanism in neural networks is studied. This paper focuses on the neuron's behaviors in an area of brain. Our model could regenerate the power-law behaviors and finite size effects of neural avalanche. The probability density functions (PDFs) for the neural avalanche size differing at different times (lattice size) have fat tails with a q-Gaussian shape and the same parameter value of q in the thermodynamical limit. Above two kinds of behaviors show that our neural model can well present self-organized critical behavior. The robustness of PDFs shows the stability of self-organized criticality. Meanwhile, the avalanche scaling relation of the waiting time has been found.

  8. Simulation of restricted neural networks with reprogrammable neurons

    SciTech Connect

    Hartline, D.K. )

    1989-05-01

    This paper describes a network model composed of reprogrammable neurons. It incorporates the following design features: spikes can be generated by a model representing repetitive firing at axon (and dendritic) trigger zones; active responses (plateau potentials; delaying mechanisms) are simulated with Hodgkin-huxley type kinetics; synaptic interactions both spike-mediated and non-spiking chemical ('chemotonic'), simulate transmitter release and binding to postsynaptic receptors. Facilitation and antifacilitation of spike-mediated postsynaptic potentials (PSP's) are included. Chemical pools are used to simulate second messenger systems, trapping of ions in extracellular spaces, and electrogenic pumps, as well as biochemical reaction chains of quite general character. Modulation of any of the parameters of any compartment can be effected through the pools. Intracellular messengers of three kinds are simulated explicitly: those produced by voltage-gated processes (e.g. Ca); those dependent on transmitter (or hormone) binding; and those dependent on other internal messengers (e.g., internally released Ca; enzymatically activated pathways).

  9. FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model.

    PubMed

    Yaghini Bonabi, Safa; Asgharian, Hassan; Safari, Saeed; Nili Ahmadabadi, Majid

    2014-01-01

    A set of techniques for efficient implementation of Hodgkin-Huxley-based (H-H) model of a neural network on FPGA (Field Programmable Gate Array) is presented. The central implementation challenge is H-H model complexity that puts limits on the network size and on the execution speed. However, basics of the original model cannot be compromised when effect of synaptic specifications on the network behavior is the subject of study. To solve the problem, we used computational techniques such as CORDIC (Coordinate Rotation Digital Computer) algorithm and step-by-step integration in the implementation of arithmetic circuits. In addition, we employed different techniques such as sharing resources to preserve the details of model as well as increasing the network size in addition to keeping the network execution speed close to real time while having high precision. Implementation of a two mini-columns network with 120/30 excitatory/inhibitory neurons is provided to investigate the characteristic of our method in practice. The implementation techniques provide an opportunity to construct large FPGA-based network models to investigate the effect of different neurophysiological mechanisms, like voltage-gated channels and synaptic activities, on the behavior of a neural network in an appropriate execution time. Additional to inherent properties of FPGA, like parallelism and re-configurability, our approach makes the FPGA-based system a proper candidate for study on neural control of cognitive robots and systems as well. PMID:25484854

  10. FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model

    PubMed Central

    Yaghini Bonabi, Safa; Asgharian, Hassan; Safari, Saeed; Nili Ahmadabadi, Majid

    2014-01-01

    A set of techniques for efficient implementation of Hodgkin-Huxley-based (H-H) model of a neural network on FPGA (Field Programmable Gate Array) is presented. The central implementation challenge is H-H model complexity that puts limits on the network size and on the execution speed. However, basics of the original model cannot be compromised when effect of synaptic specifications on the network behavior is the subject of study. To solve the problem, we used computational techniques such as CORDIC (Coordinate Rotation Digital Computer) algorithm and step-by-step integration in the implementation of arithmetic circuits. In addition, we employed different techniques such as sharing resources to preserve the details of model as well as increasing the network size in addition to keeping the network execution speed close to real time while having high precision. Implementation of a two mini-columns network with 120/30 excitatory/inhibitory neurons is provided to investigate the characteristic of our method in practice. The implementation techniques provide an opportunity to construct large FPGA-based network models to investigate the effect of different neurophysiological mechanisms, like voltage-gated channels and synaptic activities, on the behavior of a neural network in an appropriate execution time. Additional to inherent properties of FPGA, like parallelism and re-configurability, our approach makes the FPGA-based system a proper candidate for study on neural control of cognitive robots and systems as well. PMID:25484854

  11. Self-organization of synchronous activity propagation in neuronal networks driven by local excitation.

    PubMed

    Bayati, Mehdi; Valizadeh, Alireza; Abbassian, Abdolhossein; Cheng, Sen

    2015-01-01

    Many experimental and theoretical studies have suggested that the reliable propagation of synchronous neural activity is crucial for neural information processing. The propagation of synchronous firing activity in so-called synfire chains has been studied extensively in feed-forward networks of spiking neurons. However, it remains unclear how such neural activity could emerge in recurrent neuronal networks through synaptic plasticity. In this study, we investigate whether local excitation, i.e., neurons that fire at a higher frequency than the other, spontaneously active neurons in the network, can shape a network to allow for synchronous activity propagation. We use two-dimensional, locally connected and heterogeneous neuronal networks with spike-timing dependent plasticity (STDP). We find that, in our model, local excitation drives profound network changes within seconds. In the emergent network, neural activity propagates synchronously through the network. This activity originates from the site of the local excitation and propagates through the network. The synchronous activity propagation persists, even when the local excitation is removed, since it derives from the synaptic weight matrix. Importantly, once this connectivity is established it remains stable even in the presence of spontaneous activity. Our results suggest that synfire-chain-like activity can emerge in a relatively simple way in realistic neural networks by locally exciting the desired origin of the neuronal sequence. PMID:26089794

  12. Ca^2+ Dynamics and Propagating Waves in Neural Networks with Excitatory and Inhibitory Neurons.

    NASA Astrophysics Data System (ADS)

    Bondarenko, Vladimir E.

    2008-03-01

    Dynamics of neural spikes, intracellular Ca^2+, and Ca^2+ in intracellular stores was investigated both in isolated Chay's neurons and in the neurons coupled in networks. Three types of neural networks were studied: a purely excitatory neural network, with only excitatory (AMPA) synapses; a purely inhibitory neural network with only inhibitory (GABA) synapses; and a hybrid neural network, with both AMPA and GABA synapses. In the hybrid neural network, the ratio of excitatory to inhibitory neurons was 4:1. For each case, we considered two types of connections, ``all-with-all" and 20 connections per neuron. Each neural network contained 100 neurons with randomly distributed connection strengths. In the neural networks with ``all-with-all" connections and AMPA/GABA synapses an increase in average synaptic strength yielded bursting activity with increased/decreased number of spikes per burst. The neural bursts and Ca^2+ transients were synchronous at relatively large connection strengths despite random connection strengths. Simulations of the neural networks with 20 connections per neuron and with only AMPA synapses showed synchronous oscillations, while the neural networks with GABA or hybrid synapses generated propagating waves of membrane potential and Ca^2+ transients.

  13. Experiments in clustered neuronal networks: A paradigm for complex modular dynamics

    NASA Astrophysics Data System (ADS)

    Teller, Sara; Soriano, Jordi

    2016-06-01

    Uncovering the interplay activity-connectivity is one of the major challenges in neuroscience. To deepen in the understanding of how a neuronal circuit shapes network dynamics, neuronal cultures have emerged as remarkable systems given their accessibility and easy manipulation. An attractive configuration of these in vitro systems consists in an ensemble of interconnected clusters of neurons. Using calcium fluorescence imaging to monitor spontaneous activity in these clustered neuronal networks, we were able to draw functional maps and reveal their topological features. We also observed that these networks exhibit a hierarchical modular dynamics, in which clusters fire in small groups that shape characteristic communities in the network. The structure and stability of these communities is sensitive to chemical or physical action, and therefore their analysis may serve as a proxy for network health. Indeed, the combination of all these approaches is helping to develop models to quantify damage upon network degradation, with promising applications for the study of neurological disorders in vitro.

  14. Microbial light-activatable proton pumps as neuronal inhibitors to functionally dissect neuronal networks in C. elegans.

    PubMed

    Husson, Steven J; Liewald, Jana F; Schultheis, Christian; Stirman, Jeffrey N; Lu, Hang; Gottschalk, Alexander

    2012-01-01

    Essentially any behavior in simple and complex animals depends on neuronal network function. Currently, the best-defined system to study neuronal circuits is the nematode Caenorhabditis elegans, as the connectivity of its 302 neurons is exactly known. Individual neurons can be activated by photostimulation of Channelrhodopsin-2 (ChR2) using blue light, allowing to directly probe the importance of a particular neuron for the respective behavioral output of the network under study. In analogy, other excitable cells can be inhibited by expressing Halorhodopsin from Natronomonas pharaonis (NpHR) and subsequent illumination with yellow light. However, inhibiting C. elegans neurons using NpHR is difficult. Recently, proton pumps from various sources were established as valuable alternative hyperpolarizers. Here we show that archaerhodopsin-3 (Arch) from Halorubrum sodomense and a proton pump from the fungus Leptosphaeria maculans (Mac) can be utilized to effectively inhibit excitable cells in C. elegans. Arch is the most powerful hyperpolarizer when illuminated with yellow or green light while the action spectrum of Mac is more blue-shifted, as analyzed by light-evoked behaviors and electrophysiology. This allows these tools to be combined in various ways with ChR2 to analyze different subsets of neurons within a circuit. We exemplify this by means of the polymodal aversive sensory ASH neurons, and the downstream command interneurons to which ASH neurons signal to trigger a reversal followed by a directional turn. Photostimulating ASH and subsequently inhibiting command interneurons using two-color illumination of different body segments, allows investigating temporal aspects of signaling downstream of ASH. PMID:22815873

  15. The role of electrical coupling in generating and modulating oscillations in a neuronal network.

    PubMed

    Mouser, Christina; Bose, Amitabha; Nadim, Farzan

    2016-08-01

    A simplified model of the crustacean gastric mill network is considered. Rhythmic activity in this network has largely been attributed to half center oscillations driven by mutual inhibition. We use mathematical modeling and dynamical systems theory to show that rhythmic oscillations in this network may also depend on, or even arise from, a voltage-dependent electrical coupling between one of the cells in the half-center network and a projection neuron that lies outside of the network. This finding uncovers a potentially new mechanism for the generation of oscillations in neuronal networks. PMID:27188714

  16. The effect of noise on a neural network with spiking neurons

    NASA Astrophysics Data System (ADS)

    Inchiosa, Mario E.

    1993-08-01

    We study a class of neural network associative memories which include noise and transmission delays, code information in the timing of spikes, use long-range Hebbian couplings plus local, inhibitory couplings, and feature low, biologically realistic neuronal activity. Recall of a pattern consists of a synchronized, periodic firing of neurons. We find a Lyapunov functional for the noiseless network dynamics, and using statistical mechanics and numerical simulation, we find that noisy dynamics improves the network's ability to discriminate stored from unknown patterns.

  17. Autaptic self-feedback-induced synchronization transitions in Newman-Watts neuronal network with time delays

    NASA Astrophysics Data System (ADS)

    Wang, Qi; Gong, Yubing; Wu, Yanan

    2015-04-01

    Autapse is a special synapse that connects a neuron to itself. In this work, we numerically study the effect of chemical autapse on the synchronization of Newman-Watts Hodgkin-Huxley neuron network with time delays. It is found that the neurons exhibit synchronization transitions as autaptic self-feedback delay is varied, and the phenomenon enhances when autaptic self-feedback strength increases. Moreover, this phenomenon becomes strongest when network time delay or coupling strength is optimal. It is also found that the synchronization transitions by network time delay can be enhanced by autaptic activity and become strongest when autaptic delay is optimal. These results show that autaptic delayed self-feedback activity can intermittently enhance and reduce the synchronization of the neuronal network and hence plays an important role in regulating the synchronization of the neurons. These findings could find potential implications for the information processing and transmission in neural systems.

  18. Effects of glial release and somatic receptors on bursting in synchronized neuronal networks

    NASA Astrophysics Data System (ADS)

    Zhan, Xuan; Lai, Pik-Yin; Chan, C. K.

    2011-07-01

    A model is constructed to study the phenomenon of bursting in cultured neuronal networks by considering the effects of glial release and the extrasynaptic receptors on neurons. In the frequently observed situations of synchronized bursting, the whole neuronal network can be described by a mean-field model. In this model, the dynamics of the synchronized network in the presence of glia is represented by an effective two-compartment neuron with stimulations on both the dendrite and soma. Numerical simulations of this model show that most of the experimental observations in bursting, in particular the high plateau and the slow repolarization, can be reproduced. Our findings suggest that the effects of glia release and extrasynaptic receptors, which are usually neglected in neuronal models, can become important in intense network activities. Furthermore, simulations of the model are also performed for the case of glia-suppressed cultures to compare with recent experimental results.

  19. Effects of channel noise on firing coherence of small-world Hodgkin-Huxley neuronal networks

    NASA Astrophysics Data System (ADS)

    Sun, X. J.; Lei, J. Z.; Perc, M.; Lu, Q. S.; Lv, S. J.

    2011-01-01

    We investigate the effects of channel noise on firing coherence of Watts-Strogatz small-world networks consisting of biophysically realistic HH neurons having a fraction of blocked voltage-gated sodium and potassium ion channels embedded in their neuronal membranes. The intensity of channel noise is determined by the number of non-blocked ion channels, which depends on the fraction of working ion channels and the membrane patch size with the assumption of homogeneous ion channel density. We find that firing coherence of the neuronal network can be either enhanced or reduced depending on the source of channel noise. As shown in this paper, sodium channel noise reduces firing coherence of neuronal networks; in contrast, potassium channel noise enhances it. Furthermore, compared with potassium channel noise, sodium channel noise plays a dominant role in affecting firing coherence of the neuronal network. Moreover, we declare that the observed phenomena are independent of the rewiring probability.

  20. Human Neuron Cultures: Micropatterning Facilitates the Long-Term Growth and Analysis of iPSC-Derived Individual Human Neurons and Neuronal Networks (Adv. Healthcare Mater. 15/2016).

    PubMed

    Burbulla, Lena F; Beaumont, Kristin G; Mrksich, Milan; Krainc, Dimitri

    2016-08-01

    Dimitri Krainc, Milan Mrksich, and co-workers demonstrate the utility of microcontact printing technology for culturing of human neurons in defined patterns over extended periods of time on page 1894. This approach facilitates studies of neuronal development, cellular trafficking, and related mechanisms that require assessment of individual neurons and neuronal networks. PMID:27511952

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

    PubMed Central

    Pyka, Martin; Klatt, Sebastian; Cheng, Sen

    2014-01-01

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

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

    PubMed

    Pyka, Martin; Klatt, Sebastian; Cheng, Sen

    2014-01-01

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

  3. Intermittent chaos, self-organization, and learning from synchronous synaptic activity in model neuron networks.

    PubMed Central

    Hoppensteadt, F C

    1989-01-01

    Self-organization of frequencies is studied by using model neurons called VCONs (voltage-controlled oscillator neuron models). These models give direct access to frequency information, in contrast to all-or-none neuron models, and they generate voltage spikes that phase-lock to oscillatory stimulation, similar to phase-locking of action potentials to oscillatory voltage stimulation observed in Hodgkin-Huxley preparations of squid axons. The rotation vector method is described and used to study how networks synchronize, even in the presence of noise or when damaged; the entropy of ratios of phases is used to construct an energy function that characterizes organized behavior. Computer simulations show that rotation numbers (output frequency/input frequency) describe both chaotic and nonchaotic behavior. Learning occurs when synaptic connections strengthen in response to stimulation that is synchronous with cell activity. It is shown that intermittent chaotic firing is suppressed and simple stable responses are enhanced by such learning in VCON networks. This analysis provides a rigorous basis for further investigation of the ideas of Wiener [Wiener, N. (1961) Cybernetics (MIT Press, Cambridge, MA), p. 191] on the origin of slow brain waves due to "the pulling together of frequencies." PMID:2717606

  4. Granger Causality Network Reconstruction of Conductance-Based Integrate-and-Fire Neuronal Systems

    PubMed Central

    Zhou, Douglas; Xiao, Yanyang; Zhang, Yaoyu; Xu, Zhiqin; Cai, David

    2014-01-01

    Reconstruction of anatomical connectivity from measured dynamical activities of coupled neurons is one of the fundamental issues in the understanding of structure-function relationship of neuronal circuitry. Many approaches have been developed to address this issue based on either electrical or metabolic data observed in experiment. The Granger causality (GC) analysis remains one of the major approaches to explore the dynamical causal connectivity among individual neurons or neuronal populations. However, it is yet to be clarified how such causal connectivity, i.e., the GC connectivity, can be mapped to the underlying anatomical connectivity in neuronal networks. We perform the GC analysis on the conductance-based integrate-and-fire (IF) neuronal networks to obtain their causal connectivity. Through numerical experiments, we find that the underlying synaptic connectivity amongst individual neurons or subnetworks, can be successfully reconstructed by the GC connectivity constructed from voltage time series. Furthermore, this reconstruction is insensitive to dynamical regimes and can be achieved without perturbing systems and prior knowledge of neuronal model parameters. Surprisingly, the synaptic connectivity can even be reconstructed by merely knowing the raster of systems, i.e., spike timing of neurons. Using spike-triggered correlation techniques, we establish a direct mapping between the causal connectivity and the synaptic connectivity for the conductance-based IF neuronal networks, and show the GC is quadratically related to the coupling strength. The theoretical approach we develop here may provide a framework for examining the validity of the GC analysis in other settings. PMID:24586285

  5. The Role of Adult-Born Neurons in the Constantly Changing Olfactory Bulb Network

    PubMed Central

    Malvaut, Sarah; Saghatelyan, Armen

    2016-01-01

    The adult mammalian brain is remarkably plastic and constantly undergoes structurofunctional modifications in response to environmental stimuli. In many regions plasticity is manifested by modifications in the efficacy of existing synaptic connections or synapse formation and elimination. In a few regions, however, plasticity is brought by the addition of new neurons that integrate into established neuronal networks. This type of neuronal plasticity is particularly prominent in the olfactory bulb (OB) where thousands of neuronal progenitors are produced on a daily basis in the subventricular zone (SVZ) and migrate along the rostral migratory stream (RMS) towards the OB. In the OB, these neuronal precursors differentiate into local interneurons, mature, and functionally integrate into the bulbar network by establishing output synapses with principal neurons. Despite continuous progress, it is still not well understood how normal functioning of the OB is preserved in the constantly remodelling bulbar network and what role adult-born neurons play in odor behaviour. In this review we will discuss different levels of morphofunctional plasticity effected by adult-born neurons and their functional role in the adult OB and also highlight the possibility that different subpopulations of adult-born cells may fulfill distinct functions in the OB neuronal network and odor behaviour. PMID:26839709

  6. Developmental Transcriptional Networks Are Required to Maintain Neuronal Subtype Identity in the Mature Nervous System

    PubMed Central

    Eade, Kevin T.; Fancher, Hailey A.; Ridyard, Marc S.; Allan, Douglas W.

    2012-01-01

    During neurogenesis, transcription factors combinatorially specify neuronal fates and then differentiate subtype identities by inducing subtype-specific gene expression profiles. But how is neuronal subtype identity maintained in mature neurons? Modeling this question in two Drosophila neuronal subtypes (Tv1 and Tv4), we test whether the subtype transcription factor networks that direct differentiation during development are required persistently for long-term maintenance of subtype identity. By conditional transcription factor knockdown in adult Tv neurons after normal development, we find that most transcription factors within the Tv1/Tv4 subtype transcription networks are indeed required to maintain Tv1/Tv4 subtype-specific gene expression in adults. Thus, gene expression profiles are not simply “locked-in,” but must be actively maintained by persistent developmental transcription factor networks. We also examined the cross-regulatory relationships between all transcription factors that persisted in adult Tv1/Tv4 neurons. We show that certain critical cross-regulatory relationships that had existed between these transcription factors during development were no longer present in the mature adult neuron. This points to key differences between developmental and maintenance transcriptional regulatory networks in individual neurons. Together, our results provide novel insight showing that the maintenance of subtype identity is an active process underpinned by persistently active, combinatorially-acting, developmental transcription factors. These findings have implications for understanding the maintenance of all long-lived cell types and the functional degeneration of neurons in the aging brain. PMID:22383890

  7. Transition from double coherence resonances to single coherence resonance in a neuronal network with phase noise.

    PubMed

    Jia, Yanbing; Gu, Huaguang

    2015-12-01

    The effect of phase noise on the coherence dynamics of a neuronal network composed of FitzHugh-Nagumo (FHN) neurons is investigated. Phase noise can induce dissimilar coherence resonance (CR) effects for different coupling strength regimes. When the coupling strength is small, phase noise can induce double CRs. One corresponds to the average frequency of phase noise, and the other corresponds to the intrinsic firing frequency of the FHN neuron. When the coupling strength is large enough, phase noise can only induce single CR, and the CR corresponds to the intrinsic firing frequency of the FHN neuron. The results show a transition from double CRs to single CR with the increase in the coupling strength. The transition can be well interpreted based on the dynamics of a single neuron stimulated by both phase noise and the coupling current. When the coupling strength is small, the coupling current is weak, and phase noise mainly determines the dynamics of the neuron. Moreover, the phase-noise-induced double CRs in the neuronal network are similar to the phase-noise-induced double CRs in an isolated FHN neuron. When the coupling strength is large enough, the coupling current is strong and plays a key role in the occurrence of the single CR in the network. The results provide a novel phenomenon and may have important implications in understanding the dynamics of neuronal networks. PMID:26723163

  8. Micro-electrode array recordings reveal reductions in both excitation and inhibition in cultured cortical neuron networks lacking Shank3.

    PubMed

    Lu, C; Chen, Q; Zhou, T; Bozic, D; Fu, Z; Pan, J Q; Feng, G

    2016-02-01

    Numerous risk genes have recently been implicated in susceptibility to autism and schizophrenia. Translating such genetic findings into disease-relevant neurobiological mechanisms is challenging due to the lack of throughput assays that can be used to assess their functions on an appropriate scale. To address this issue, we explored the feasibility of using a micro-electrode array (MEA) as a potentially scalable assay to identify the electrical network phenotypes associated with risk genes. We first characterized local and global network firing in cortical neurons with MEAs, and then developed methods to analyze the alternation between the network active period (NAP) and the network inactive period (NIP), each of which lasts tens of seconds. We then evaluated the electric phenotypes of neurons derived from Shank3 knockout (KO) mice. Cortical neurons cultured on MEAs displayed a rich repertoire of spontaneous firing, and Shank3 deletion led to reduced firing activity. Enhancing excitation with CX546 rescued the deficit in the spike rate in the Shank3 KO network. In addition, the Shank3 KO network produced a shorter NIP, and this altered network firing pattern was normalized by clonazepam, a positive modulator of the GABAA receptor. MEA recordings revealed electric phenotypes that displayed altered excitation and inhibition in the network lacking Shank3. Thus, our study highlights MEAs as an experimental framework for measuring multiple robust neurobiological end points in dynamic networks and as an assay system that could be used to identify electric phenotypes in cultured neuronal networks and to analyze additional risk genes identified in psychiatric genetics. PMID:26598066

  9. On controllability of neuronal networks with constraints on the average of control gains.

    PubMed

    Tang, Yang; Wang, Zidong; Gao, Huijun; Qiao, Hong; Kurths, Jürgen

    2014-12-01

    Control gains play an important role in the control of a natural or a technical system since they reflect how much resource is required to optimize a certain control objective. This paper is concerned with the controllability of neuronal networks with constraints on the average value of the control gains injected in driver nodes, which are in accordance with engineering and biological backgrounds. In order to deal with the constraints on control gains, the controllability problem is transformed into a constrained optimization problem (COP). The introduction of the constraints on the control gains unavoidably leads to substantial difficulty in finding feasible as well as refining solutions. As such, a modified dynamic hybrid framework (MDyHF) is developed to solve this COP, based on an adaptive differential evolution and the concept of Pareto dominance. By comparing with statistical methods and several recently reported constrained optimization evolutionary algorithms (COEAs), we show that our proposed MDyHF is competitive and promising in studying the controllability of neuronal networks. Based on the MDyHF, we proceed to show the controlling regions under different levels of constraints. It is revealed that we should allocate the control gains economically when strong constraints are considered. In addition, it is found that as the constraints become more restrictive, the driver nodes are more likely to be selected from the nodes with a large degree. The results and methods presented in this paper will provide useful insights into developing new techniques to control a realistic complex network efficiently. PMID:24733036

  10. Intrinsic Neuronal Properties Switch the Mode of Information Transmission in Networks

    PubMed Central

    Gjorgjieva, Julijana; Mease, Rebecca A.; Moody, William J.; Fairhall, Adrienne L.

    2014-01-01

    Diverse ion channels and their dynamics endow single neurons with complex biophysical properties. These properties determine the heterogeneity of cell types that make up the brain, as constituents of neural circuits tuned to perform highly specific computations. How do biophysical properties of single neurons impact network function? We study a set of biophysical properties that emerge in cortical neurons during the first week of development, eventually allowing these neurons to adaptively scale the gain of their response to the amplitude of the fluctuations they encounter. During the same time period, these same neurons participate in large-scale waves of spontaneously generated electrical activity. We investigate the potential role of experimentally observed changes in intrinsic neuronal properties in determining the ability of cortical networks to propagate waves of activity. We show that such changes can strongly affect the ability of multi-layered feedforward networks to represent and transmit information on multiple timescales. With properties modeled on those observed at early stages of development, neurons are relatively insensitive to rapid fluctuations and tend to fire synchronously in response to wave-like events of large amplitude. Following developmental changes in voltage-dependent conductances, these same neurons become efficient encoders of fast input fluctuations over few layers, but lose the ability to transmit slower, population-wide input variations across many layers. Depending on the neurons' intrinsic properties, noise plays different roles in modulating neuronal input-output curves, which can dramatically impact network transmission. The developmental change in intrinsic properties supports a transformation of a networks function from the propagation of network-wide information to one in which computations are scaled to local activity. This work underscores the significance of simple changes in conductance parameters in governing how neurons

  11. Intrinsic neuronal properties switch the mode of information transmission in networks.

    PubMed

    Gjorgjieva, Julijana; Mease, Rebecca A; Moody, William J; Fairhall, Adrienne L

    2014-12-01

    Diverse ion channels and their dynamics endow single neurons with complex biophysical properties. These properties determine the heterogeneity of cell types that make up the brain, as constituents of neural circuits tuned to perform highly specific computations. How do biophysical properties of single neurons impact network function? We study a set of biophysical properties that emerge in cortical neurons during the first week of development, eventually allowing these neurons to adaptively scale the gain of their response to the amplitude of the fluctuations they encounter. During the same time period, these same neurons participate in large-scale waves of spontaneously generated electrical activity. We investigate the potential role of experimentally observed changes in intrinsic neuronal properties in determining the ability of cortical networks to propagate waves of activity. We show that such changes can strongly affect the ability of multi-layered feedforward networks to represent and transmit information on multiple timescales. With properties modeled on those observed at early stages of development, neurons are relatively insensitive to rapid fluctuations and tend to fire synchronously in response to wave-like events of large amplitude. Following developmental changes in voltage-dependent conductances, these same neurons become efficient encoders of fast input fluctuations over few layers, but lose the ability to transmit slower, population-wide input variations across many layers. Depending on the neurons' intrinsic properties, noise plays different roles in modulating neuronal input-output curves, which can dramatically impact network transmission. The developmental change in intrinsic properties supports a transformation of a networks function from the propagation of network-wide information to one in which computations are scaled to local activity. This work underscores the significance of simple changes in conductance parameters in governing how neurons

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

    NASA Astrophysics Data System (ADS)

    Zemanová, Lucia; Zhou, Changsong; Kurths, Jürgen

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

  13. Spontaneous Neuronal Activity in Developing Neocortical Networks: From Single Cells to Large-Scale Interactions

    PubMed Central

    Luhmann, Heiko J.; Sinning, Anne; Yang, Jenq-Wei; Reyes-Puerta, Vicente; Stüttgen, Maik C.; Kirischuk, Sergei; Kilb, Werner

    2016-01-01

    Neuronal activity has been shown to be essential for the proper formation of neuronal circuits, affecting developmental processes like neurogenesis, migration, programmed cell death, cellular differentiation, formation of local and long-range axonal connections, synaptic plasticity or myelination. Accordingly, neocortical areas reveal distinct spontaneous and sensory-driven neuronal activity patterns already at early phases of development. At embryonic stages, when immature neurons start to develop voltage-dependent channels, spontaneous activity is highly synchronized within small neuronal networks and governed by electrical synaptic transmission. Subsequently, spontaneous activity patterns become more complex, involve larger networks and propagate over several neocortical areas. The developmental shift from local to large-scale network activity is accompanied by a gradual shift from electrical to chemical synaptic transmission with an initial excitatory action of chloride-gated channels activated by GABA, glycine and taurine. Transient neuronal populations in the subplate (SP) support temporary circuits that play an important role in tuning early neocortical activity and the formation of mature neuronal networks. Thus, early spontaneous activity patterns control the formation of developing networks in sensory cortices, and disturbances of these activity patterns may lead to long-lasting neuronal deficits. PMID:27252626

  14. Cryopreserved rat cortical cells develop functional neuronal networks on microelectrode arrays.

    PubMed

    Otto, Frauke; Görtz, Philipp; Fleischer, Wiebke; Siebler, Mario

    2003-09-30

    Neurons growing on microelectrode arrays (MEAs) are promising tools to investigate principal neuronal network mechanisms and network responses to pharmaceutical substances. However, broad application of these tools, e.g. in pharmaceutical substance screening, requires neuronal cells that provide stable activity on MEAs. Cryopreserved cortical neurons (CCx) from embryonic rats were cultured on MEAs and their immunocytochemical and electrophysiological properties were compared with acutely dissociated neurons (Cx). Both cell types formed neuritic networks and expressed the neuron-specific markers microtubule associated protein 2, synaptophysin, neurofilament and gamma-aminobutyric acid (GABA). Spontaneous spike activity (SSA) was recorded after 9 up to 74 days in vitro (DIV) in CCx and from 5 to 30 DIV in Cx, respectively. Cx and CCx exhibited synchronized burst activity with similar spiking characteristics. Tetrodotoxin (TTX) abolished the SSA of both cell types reversibly. In CCx SSA-inhibition occurred with an IC50 of 1.1 nM for TTX, 161 microM for magnesium, 18 microM for D,L-2-amino-5-phosphonovaleric acid (APV) and 1 microM for GABA. CCx cells were easy to handle and developed long living, stable and active neuronal networks on MEAs with similar characteristics as Cx. Thus, these neurochips seem to be suitable for studying neuronal network properties and screening in pharmaceutical research. PMID:12948560

  15. Spontaneous Neuronal Activity in Developing Neocortical Networks: From Single Cells to Large-Scale Interactions.

    PubMed

    Luhmann, Heiko J; Sinning, Anne; Yang, Jenq-Wei; Reyes-Puerta, Vicente; Stüttgen, Maik C; Kirischuk, Sergei; Kilb, Werner

    2016-01-01

    Neuronal activity has been shown to be essential for the proper formation of neuronal circuits, affecting developmental processes like neurogenesis, migration, programmed cell death, cellular differentiation, formation of local and long-range axonal connections, synaptic plasticity or myelination. Accordingly, neocortical areas reveal distinct spontaneous and sensory-driven neuronal activity patterns already at early phases of development. At embryonic stages, when immature neurons start to develop voltage-dependent channels, spontaneous activity is highly synchronized within small neuronal networks and governed by electrical synaptic transmission. Subsequently, spontaneous activity patterns become more complex, involve larger networks and propagate over several neocortical areas. The developmental shift from local to large-scale network activity is accompanied by a gradual shift from electrical to chemical synaptic transmission with an initial excitatory action of chloride-gated channels activated by GABA, glycine and taurine. Transient neuronal populations in the subplate (SP) support temporary circuits that play an important role in tuning early neocortical activity and the formation of mature neuronal networks. Thus, early spontaneous activity patterns control the formation of developing networks in sensory cortices, and disturbances of these activity patterns may lead to long-lasting neuronal deficits. PMID:27252626

  16. Investigating local and long-range neuronal network dynamics by simultaneous optogenetics, reverse microdialysis and silicon probe recordings in vivo

    PubMed Central

    Taylor, Hannah; Schmiedt, Joscha T.; Çarçak, Nihan; Onat, Filiz; Di Giovanni, Giuseppe; Lambert, Régis; Leresche, Nathalie; Crunelli, Vincenzo; David, Francois

    2014-01-01

    Background The advent of optogenetics has given neuroscientists the opportunity to excite or inhibit neuronal population activity with high temporal resolution and cellular selectivity. Thus, when combined with recordings of neuronal ensemble activity in freely moving animals optogenetics can provide an unprecedented snapshot of the contribution of neuronal assemblies to (patho)physiological conditions in vivo. Still, the combination of optogenetic and silicone probe (or tetrode) recordings does not allow investigation of the role played by voltage- and transmitter-gated channels of the opsin-transfected neurons and/or other adjacent neurons in controlling neuronal activity. New method and results We demonstrate that optogenetics and silicone probe recordings can be combined with intracerebral reverse microdialysis for the long-term delivery of neuroactive drugs around the optic fiber and silicone probe. In particular, we show the effect of antagonists of T-type Ca2+ channels, hyperpolarization-activated cyclic nucleotide-gated channels and metabotropic glutamate receptors on silicone probe-recorded activity of the local opsin-transfected neurons in the ventrobasal thalamus, and demonstrate the changes that the block of these thalamic channels/receptors brings about in the network dynamics of distant somatotopic cortical neuronal ensembles. Comparison with existing methods This is the first demonstration of successfully combining optogenetics and neuronal ensemble recordings with reverse microdialysis. This combination of techniques overcomes some of the disadvantages that are associated with the use of intracerebral injection of a drug-containing solution at the site of laser activation. Conclusions The combination of reverse microdialysis, silicone probe recordings and optogenetics can unravel the short and long-term effects of specific transmitter- and voltage-gated channels on laser-modulated firing at the site of optogenetic stimulation and the actions that

  17. Effects of distance-dependent delay on small-world neuronal networks

    NASA Astrophysics Data System (ADS)

    Zhu, Jinjie; Chen, Zhen; Liu, Xianbin

    2016-04-01

    We study firing behaviors and the transitions among them in small-world noisy neuronal networks with electrical synapses and information transmission delay. Each neuron is modeled by a two-dimensional Rulkov map neuron. The distance between neurons, which is a main source of the time delay, is taken into consideration. Through spatiotemporal patterns and interspike intervals as well as the interburst intervals, the collective behaviors are revealed. It is found that the networks switch from resting state into intermittent firing state under Gaussian noise excitation. Initially, noise-induced firing behaviors are disturbed by small time delays. Periodic firing behaviors with irregular zigzag patterns emerge with an increase of the delay and become progressively regular after a critical value is exceeded. More interestingly, in accordance with regular patterns, the spiking frequency doubles compared with the former stage for the spiking neuronal network. A growth of frequency persists for a larger delay and a transition to antiphase synchronization is observed. Furthermore, it is proved that these transitions are generic also for the bursting neuronal network and the FitzHugh-Nagumo neuronal network. We show these transitions due to the increase of time delay are robust to the noise strength, coupling strength, network size, and rewiring probability.

  18. Low-Density Neuronal Networks Cultured using Patterned Poly-L-Lysine on Microelectrode Arrays

    PubMed Central

    Jun, Sang Beom; Hynd, Matthew R.; Dowell-Mesfin, Natalie; Smith, Karen L.; Turner, James N.; Shain, William; Kim, Sung June

    2009-01-01

    Synaptic activity recorded from low-density networks of cultured rat hippocampal neurons was monitored using microelectrode arrays (MEAs). Neuronal networks were patterned with poly-L-lysine (PLL) using microcontact printing (µCP). Polydimethysiloxane (PDMS) stamps were fabricated with relief structures resulting in patterns of 2 µm-wide lines for directing process growth and 20 µm-diameter circles for cell soma attachment. These circles were aligned to electrode sites. Different densities of neurons were plated in order to assess the minimal neuron density required for development of an active network. Spontaneous activity was observed at 10–14 days in networks using neuron densities as low as 200 cells/mm2. Immunocytochemistry demonstrated the distribution of dendrites along the lines and the location of foci of the presynaptic protein, synaptophysin, on neuron somas and dendrites. Scanning electron microscopy demonstrated that single fluorescent tracks contained multiple processes. Evoked responses of selected portions of the networks were produced by stimulation of specific electrode sites. In addition, the neuronal excitability of the network was increased by the bath application of high K+ (10–12 mM). Application of DNQX, an AMPA antagonist, blocked all spontaneous activity, suggesting that the activity is excitatory and mediated through glutamate receptors. PMID:17049614

  19. Detection of neuron membranes in electron microscopy images using a serial neural network architecture.

    PubMed

    Jurrus, Elizabeth; Paiva, Antonio R C; Watanabe, Shigeki; Anderson, James R; Jones, Bryan W; Whitaker, Ross T; Jorgensen, Erik M; Marc, Robert E; Tasdizen, Tolga

    2010-12-01

    Study of nervous systems via the connectome, the map of connectivities of all neurons in that system, is a challenging problem in neuroscience. Towards this goal, neurobiologists are acquiring large electron microscopy datasets. However, the shear volume of these datasets renders manual analysis infeasible. Hence, automated image analysis methods are required for reconstructing the connectome from these very large image collections. Segmentation of neurons in these images, an essential step of the reconstruction pipeline, is challenging because of noise, anisotropic shapes and brightness, and the presence of confounding structures. The method described in this paper uses a series of artificial neural networks (ANNs) in a framework combined with a feature vector that is composed of image intensities sampled over a stencil neighborhood. Several ANNs are applied in series allowing each ANN to use the classification context provided by the previous network to improve detection accuracy. We develop the method of serial ANNs and show that the learned context does improve detection over traditional ANNs. We also demonstrate advantages over previous membrane detection methods. The results are a significant step towards an automated system for the reconstruction of the connectome. PMID:20598935

  20. Detection of Neuron Membranes in Electron Microscopy Images using a Serial Neural Network Architecture

    PubMed Central

    Jurrus, Elizabeth; Paiva, Antonio R. C.; Watanabe, Shigeki; Anderson, James R.; Jones, Bryan W.; Whitaker, Ross T.; Jorgensen, Erik M.; Marc, Robert E.; Tasdizen, Tolga

    2010-01-01

    Study of nervous systems via the connectome, the map of connectivities of all neurons in that system, is a challenging problem in neuroscience. Towards this goal, neurobiologists are acquiring large electron microscopy datasets. However, the shear volume of these datasets renders manual analysis infeasible. Hence, automated image analysis methods are required for reconstructing the connectome from these very large image collections. Segmentation of neurons in these images, an essential step of the reconstruction pipeline, is challenging because of noise, anisotropic shapes and brightness, and the presence of confounding structures. The method described in this paper uses a series of artificial neural networks (ANNs) in a framework combined with a feature vector that is composed of image intensities sampled over a stencil neighborhood. Several ANNs are applied in series allowing each ANN to use the classification context provided by the previous network to improve detection accuracy. We develop the method of serial ANNs and show that the learned context does improve detection over traditional ANNs. We also demonstrate advantages over previous membrane detection methods. The results are a significant step towards an automated system for the reconstruction of the connectome. PMID:20598935

  1. In Vitro Reconstruction of Neuronal Networks Derived from Human iPS Cells Using Microfabricated Devices.

    PubMed

    Takayama, Yuzo; Kida, Yasuyuki S

    2016-01-01

    Morphology and function of the nervous system is maintained via well-coordinated processes both in central and peripheral nervous tissues, which govern the homeostasis of organs/tissues. Impairments of the nervous system induce neuronal disorders such as peripheral neuropathy or cardiac arrhythmia. Although further investigation is warranted to reveal the molecular mechanisms of progression in such diseases, appropriate model systems mimicking the patient-specific communication between neurons and organs are not established yet. In this study, we reconstructed the neuronal network in vitro either between neurons of the human induced pluripotent stem (iPS) cell derived peripheral nervous system (PNS) and central nervous system (CNS), or between PNS neurons and cardiac cells in a morphologically and functionally compartmentalized manner. Networks were constructed in photolithographically microfabricated devices with two culture compartments connected by 20 microtunnels. We confirmed that PNS and CNS neurons connected via synapses and formed a network. Additionally, calcium-imaging experiments showed that the bundles originating from the PNS neurons were functionally active and responded reproducibly to external stimuli. Next, we confirmed that CNS neurons showed an increase in calcium activity during electrical stimulation of networked bundles from PNS neurons in order to demonstrate the formation of functional cell-cell interactions. We also confirmed the formation of synapses between PNS neurons and mature cardiac cells. These results indicate that compartmentalized culture devices are promising tools for reconstructing network-wide connections between PNS neurons and various organs, and might help to understand patient-specific molecular and functional mechanisms under normal and pathological conditions. PMID:26848955

  2. In Vitro Reconstruction of Neuronal Networks Derived from Human iPS Cells Using Microfabricated Devices

    PubMed Central

    Takayama, Yuzo; Kida, Yasuyuki S.

    2016-01-01

    Morphology and function of the nervous system is maintained via well-coordinated processes both in central and peripheral nervous tissues, which govern the homeostasis of organs/tissues. Impairments of the nervous system induce neuronal disorders such as peripheral neuropathy or cardiac arrhythmia. Although further investigation is warranted to reveal the molecular mechanisms of progression in such diseases, appropriate model systems mimicking the patient-specific communication between neurons and organs are not established yet. In this study, we reconstructed the neuronal network in vitro either between neurons of the human induced pluripotent stem (iPS) cell derived peripheral nervous system (PNS) and central nervous system (CNS), or between PNS neurons and cardiac cells in a morphologically and functionally compartmentalized manner. Networks were constructed in photolithographically microfabricated devices with two culture compartments connected by 20 microtunnels. We confirmed that PNS and CNS neurons connected via synapses and formed a network. Additionally, calcium-imaging experiments showed that the bundles originating from the PNS neurons were functionally active and responded reproducibly to external stimuli. Next, we confirmed that CNS neurons showed an increase in calcium activity during electrical stimulation of networked bundles from PNS neurons in order to demonstrate the formation of functional cell-cell interactions. We also confirmed the formation of synapses between PNS neurons and mature cardiac cells. These results indicate that compartmentalized culture devices are promising tools for reconstructing network-wide connections between PNS neurons and various organs, and might help to understand patient-specific molecular and functional mechanisms under normal and pathological conditions. PMID:26848955

  3. Physical and Biological Regulation of Neuron Regenerative Growth and Network Formation on Recombinant Dragline Silks

    PubMed Central

    Huang, Wenwen; He, Jiuyang; Jones, Justin; Lewis, Randolph V.; Kaplan, David L.

    2015-01-01

    Recombinant spider silks produced in transgenic goat milk were studied as cell culture matrices for neuronal growth. Major ampullate spidroin 1 (MaSp1) supported neuronal growth, axon extension and network connectivity, with cell morphology comparable to the gold standard poly-lysine. In addition, neurons growing on MaSp1 films had increased neural cell adhesion molecule (NCAM) expression at both mRNA and protein levels. The results indicate that MaSp1 films present useful surface charge and substrate stiffness to support the growth of primary rat cortical neurons. Moreover, a putative neuron-specific surface binding sequence GRGGL within MaSp1 may contribute to the biological regulation of neuron growth. These findings indicate that MaSp1 could regulate neuron growth through its physical and biological features. This dual regulation mode of MaSp1 could provide an alternative strategy for generating functional silk materials for neural tissue engineering. PMID:25701039

  4. Size-dependent regulation of synchronized activity in living neuronal networks

    NASA Astrophysics Data System (ADS)

    Yamamoto, Hideaki; Kubota, Shigeru; Chida, Yudai; Morita, Mayu; Moriya, Satoshi; Akima, Hisanao; Sato, Shigeo; Hirano-Iwata, Ayumi; Tanii, Takashi; Niwano, Michio

    2016-07-01

    We study the effect of network size on synchronized activity in living neuronal networks. Dissociated cortical neurons form synaptic connections in culture and generate synchronized spontaneous activity within 10 days in vitro. Using micropatterned surfaces to extrinsically control the size of neuronal networks, we show that synchronized activity can emerge in a network as small as 12 cells. Furthermore, a detailed comparison of small (˜20 cells), medium (˜100 cells), and large (˜400 cells) networks reveal that synchronized activity becomes destabilized in the small networks. A computational modeling of neural activity is then employed to explore the underlying mechanism responsible for the size effect. We find that the generation and maintenance of the synchronized activity can be minimally described by: (1) the stochastic firing of each neuron in the network, (2) enhancement in the network activity in a positive feedback loop of excitatory synapses, and (3) Ca-dependent suppression of bursting activity. The model further shows that the decrease in total synaptic input to a neuron that drives the positive feedback amplification of correlated activity is a key factor underlying the destabilization of synchrony in smaller networks. Spontaneous neural activity plays a critical role in cortical information processing, and our work constructively clarifies an aspect of the structural basis behind this.

  5. Size-dependent regulation of synchronized activity in living neuronal networks.

    PubMed

    Yamamoto, Hideaki; Kubota, Shigeru; Chida, Yudai; Morita, Mayu; Moriya, Satoshi; Akima, Hisanao; Sato, Shigeo; Hirano-Iwata, Ayumi; Tanii, Takashi; Niwano, Michio

    2016-07-01

    We study the effect of network size on synchronized activity in living neuronal networks. Dissociated cortical neurons form synaptic connections in culture and generate synchronized spontaneous activity within 10 days in vitro. Using micropatterned surfaces to extrinsically control the size of neuronal networks, we show that synchronized activity can emerge in a network as small as 12 cells. Furthermore, a detailed comparison of small (∼20 cells), medium (∼100 cells), and large (∼400 cells) networks reveal that synchronized activity becomes destabilized in the small networks. A computational modeling of neural activity is then employed to explore the underlying mechanism responsible for the size effect. We find that the generation and maintenance of the synchronized activity can be minimally described by: (1) the stochastic firing of each neuron in the network, (2) enhancement in the network activity in a positive feedback loop of excitatory synapses, and (3) Ca-dependent suppression of bursting activity. The model further shows that the decrease in total synaptic input to a neuron that drives the positive feedback amplification of correlated activity is a key factor underlying the destabilization of synchrony in smaller networks. Spontaneous neural activity plays a critical role in cortical information processing, and our work constructively clarifies an aspect of the structural basis behind this. PMID:27575164

  6. Coherent and intermittent ensemble oscillations emerge from networks of irregular spiking neurons.

    PubMed

    Hoseini, Mahmood S; Wessel, Ralf

    2016-01-01

    Local field potential (LFP) recordings from spatially distant cortical circuits reveal episodes of coherent gamma oscillations that are intermittent, and of variable peak frequency and duration. Concurrently, single neuron spiking remains largely irregular and of low rate. The underlying potential mechanisms of this emergent network activity have long been debated. Here we reproduce such intermittent ensemble oscillations in a model network, consisting of excitatory and inhibitory model neurons with the characteristics of regular-spiking (RS) pyramidal neurons, and fast-spiking (FS) and low-threshold spiking (LTS) interneurons. We find that fluctuations in the external inputs trigger reciprocally connected and irregularly spiking RS and FS neurons in episodes of ensemble oscillations, which are terminated by the recruitment of the LTS population with concurrent accumulation of inhibitory conductance in both RS and FS neurons. The model qualitatively reproduces experimentally observed phase drift, oscillation episode duration distributions, variation in the peak frequency, and the concurrent irregular single-neuron spiking at low rate. Furthermore, consistent with previous experimental studies using optogenetic manipulation, periodic activation of FS, but not RS, model neurons causes enhancement of gamma oscillations. In addition, increasing the coupling between two model networks from low to high reveals a transition from independent intermittent oscillations to coherent intermittent oscillations. In conclusion, the model network suggests biologically plausible mechanisms for the generation of episodes of coherent intermittent ensemble oscillations with irregular spiking neurons in cortical circuits. PMID:26561602

  7. Extracellular Recording from Neuronal Networks Cultured on Hydrogel-coated Microelectrode Array

    NASA Astrophysics Data System (ADS)

    Goto, Miho; Moriguchi, Hiroyuki; Takayama, Yuzo; Saito, Aki; Kotani, Kiyoshi; Jimbo, Yasuhiko

    Microelectrode array (MEA) has been widely used for ensemble recording. One of the advantages of MEA recording is its capability of studying correlation between network structures and the ensemble activity-patterns. Simple neuronal networks, from which activities of individual cells can be identified, are promising for this purpose. We have developed a mask-free cell-patterning method named “micropipette drawing”. In this method, a thin hydrogel layer is formed on the surface of MEA substrates, which acts as the support for growth-guidance patterns. Here in this work, we tested whether electrical signals could be detected through this gel layer. Rat cortical neurons were cultured on substrates with guiding patterns. Electrical activities could be detected after 7 days in vitro (DIV) in both patterned and normal cell cultures, though the signal to noise ratio in the normal culture was clearly higher than that in the patterned culture. Frequency analysis demonstrated that the difference of the power spectra between these cultures was particularly significant in high frequency regions. Decreases in high-frequency components were more prominent in the signals obtained from the patterned cultures. This result suggested that the hydrogel layer acted as low-pass filters probably due to its capacitive properties. The next step is to establish a method to form hydrogel layers, which maintain growth-guidance properties and have better frequency characteristics.

  8. Patterning human neuronal networks on photolithographically engineered silicon dioxide substrates functionalized with glial analogues

    PubMed Central

    Hughes, Mark A; Brennan, Paul M; Bunting, Andrew S; Cameron, Katherine; Murray, Alan F; Shipston, Mike J

    2014-01-01

    Interfacing neurons with silicon semiconductors is a challenge being tackled through various bioengineering approaches. Such constructs inform our understanding of neuronal coding and learning and ultimately guide us toward creating intelligent neuroprostheses. A fundamental prerequisite is to dictate the spatial organization of neuronal cells. We sought to pattern neurons using photolithographically defined arrays of polymer parylene-C, activated with fetal calf serum. We used a purified human neuronal cell line [Lund human mesencephalic (LUHMES)] to establish whether neurons remain viable when isolated on-chip or whether they require a supporting cell substrate. When cultured in isolation, LUHMES neurons failed to pattern and did not show any morphological signs of differentiation. We therefore sought a cell type with which to prepattern parylene regions, hypothesizing that this cellular template would enable secondary neuronal adhesion and network formation. From a range of cell lines tested, human embryonal kidney (HEK) 293 cells patterned with highest accuracy. LUHMES neurons adhered to pre-established HEK 293 cell clusters and this coculture environment promoted morphological differentiation of neurons. Neurites extended between islands of adherent cell somata, creating an orthogonally arranged neuronal network. HEK 293 cells appear to fulfill a role analogous to glia, dictating cell adhesion, and generating an environment conducive to neuronal survival. We next replaced HEK 293 cells with slower growing glioma-derived precursors. These primary human cells patterned accurately on parylene and provided a similarly effective scaffold for neuronal adhesion. These findings advance the use of this microfabrication-compatible platform for neuronal patterning. © 2013 The Authors. Journal ofBiomedicalMaterials Research Part APublished byWiley Periodicals, Inc.Wiley Periodicals, Inc. J Biomed Mater Res Part A: 102A: 1350–1360, 2014. PMID:23733444

  9. Autapse-induced target wave, spiral wave in regular network of neurons

    NASA Astrophysics Data System (ADS)

    Qin, HuiXin; Ma, Jun; Wang, ChunNi; Chu, RunTong

    2014-10-01

    Autapse is a type of synapse that connects axon and dendrites of the same neuron, and the effect is often detected by close-loop feedback in axonal action potentials to the owned dendritic tree. An artificial autapse was introduced into the Hindmarsh-Rose neuron model, and a regular network was designed to detect the regular pattern formation induced by autapse. It was found that target wave emerged in the network even when only a single autapse was considered. By increasing the (autapse density) number of neurons with autapse, for example, a regular area (2×2, 3×3, 4×4, 5×5 neurons) under autapse induced target wave by selecting the feedback gain and time-delay in autapse. Spiral waves were also observed under optimized feedback gain and time delay in autapses because of coherence-like resonance in the network induced by some electric autapses connected to some neurons. This confirmed that the electric autapse has a critical role in exciting and regulating the collective behaviors of neurons by generating stable regular waves (target waves, spiral waves) in the network. The wave length of the induced travelling wave (target wave, spiral wave), because of local effect of autapse, was also calculated to understand the waveprofile in the network of neurons.

  10. Synchronization and Partial Synchronization Experiments with Networks of Time-Delay Coupled Hindmarsh-Rose Neurons

    NASA Astrophysics Data System (ADS)

    Steur, Erik; Murguia, Carlos; Fey, Rob H. B.; Nijmeijer, Henk

    2016-06-01

    We study experimentally synchronization and partial synchronization in networks of Hindmarsh-Rose model neurons that interact through linear time-delay couplings. Our experimental setup consists of electric circuit board realizations of the Hindmarsh-Rose model neuron and a coupling interface in which the interaction between the circuits is defined. With this experimental setup we test the predictive value of theoretical results about synchronization and partial synchronization in networks.

  11. Phase synchronization of bursting neurons in clustered small-world networks

    NASA Astrophysics Data System (ADS)

    Batista, C. A. S.; Lameu, E. L.; Batista, A. M.; Lopes, S. R.; Pereira, T.; Zamora-López, G.; Kurths, J.; Viana, R. L.

    2012-07-01

    We investigate the collective dynamics of bursting neurons on clustered networks. The clustered network model is composed of subnetworks, each of them presenting the so-called small-world property. This model can also be regarded as a network of networks. In each subnetwork a neuron is connected to other ones with regular as well as random connections, the latter with a given intracluster probability. Moreover, in a given subnetwork each neuron has an intercluster probability to be connected to the other subnetworks. The local neuron dynamics has two time scales (fast and slow) and is modeled by a two-dimensional map. In such small-world network the neuron parameters are chosen to be slightly different such that, if the coupling strength is large enough, there may be synchronization of the bursting (slow) activity. We give bounds for the critical coupling strength to obtain global burst synchronization in terms of the network structure, that is, the probabilities of intracluster and intercluster connections. We find that, as the heterogeneity in the network is reduced, the network global synchronizability is improved. We show that the transitions to global synchrony may be abrupt or smooth depending on the intercluster probability.

  12. Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons

    PubMed Central

    Burbank, Kendra S.

    2015-01-01

    The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field’s Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP), leading to a symmetric combined rule we call Mirrored STDP (mSTDP). We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks. PMID:26633645

  13. Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons.

    PubMed

    Burbank, Kendra S

    2015-12-01

    The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field's Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP), leading to a symmetric combined rule we call Mirrored STDP (mSTDP). We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks. PMID:26633645

  14. Impact of bounded noise and shortcuts on the spatiotemporal dynamics of neuronal networks

    NASA Astrophysics Data System (ADS)

    Yang, X. L.; Jia, Y. B.; Zhang, L.

    2014-01-01

    The influences of bounded noise together with shortcuts on the spatiotemporal collective behaviors of temporal coherence and spatial synchronization are discussed in neuronal networks. Firstly, we focus on the case of regular neuronal networks. With the increase of noise amplitude, we find that the spatial synchronizability among coupled neurons is always impaired and coherence resonance, however, occurs at an appropriately tuned level of noise amplitude. Then, we introduce shortcuts to the regular neuronal networks to formulate small-world neuronal networks. The results indicate that the spatial synchronization and temporal coherence in the networks can be enhanced with the addition of shortcuts. Moreover, we verify that there exists an optimal amount of added shortcuts such that the small-world networks reach much more ordered spatiotemporal patterns, i.e., coupled neurons are nearly synchronized in space and most coherent in time. In addition, the shortcuts-induced much more ordered states are confirmed to be robust against changes of the intensity of the unit Wiener process.

  15. A microfluidic platform for controlled biochemical stimulation of twin neuronal networks.

    PubMed

    Biffi, Emilia; Piraino, Francesco; Pedrocchi, Alessandra; Fiore, Gianfranco B; Ferrigno, Giancarlo; Redaelli, Alberto; Menegon, Andrea; Rasponi, Marco

    2012-06-01

    Spatially and temporally resolved delivery of soluble factors is a key feature for pharmacological applications. In this framework, microfluidics coupled to multisite electrophysiology offers great advantages in neuropharmacology and toxicology. In this work, a microfluidic device for biochemical stimulation of neuronal networks was developed. A micro-chamber for cell culturing, previously developed and tested for long term neuronal growth by our group, was provided with a thin wall, which partially divided the cell culture region in two sub-compartments. The device was reversibly coupled to a flat micro electrode array and used to culture primary neurons in the same microenvironment. We demonstrated that the two fluidically connected compartments were able to originate two parallel neuronal networks with similar electrophysiological activity but functionally independent. Furthermore, the device allowed to connect the outlet port to a syringe pump and to transform the static culture chamber in a perfused one. At 14 days invitro, sub-networks were independently stimulated with a test molecule, tetrodotoxin, a neurotoxin known to block action potentials, by means of continuous delivery. Electrical activity recordings proved the ability of the device configuration to selectively stimulate each neuronal network individually. The proposed microfluidic approach represents an innovative methodology to perform biological, pharmacological, and electrophysiological experiments on neuronal networks. Indeed, it allows for controlled delivery of substances to cells, and it overcomes the limitations due to standard drug stimulation techniques. Finally, the twin network configuration reduces biological variability, which has important outcomes on pharmacological and drug screening. PMID:22655017

  16. Chaotic burst synchronization in a two-small-world-layer neuronal network

    NASA Astrophysics Data System (ADS)

    Zheng, Yanhong; Wang, Haixia

    2015-09-01

    Chaotic burst synchronization in a two-small-world-layer neuronal network is studied in this paper. For a neuronal network coupled by two single-small-world-layer networks with link probability differences between layers, the two-layer network can achieve synchrony as the interlayer coupling strength increases. When chaotic layer network is coupled with chaotic-burst-synchronization layer network, the latter is dominant at small interlayer coupling strength, so it can make the layer with the irregular pattern show some regular and also exhibit the same pattern with the other layer. However, when chaotic layer is coupled with firing synchronization layer, the ordered layer is dominated by a disordered one with the interlayer coupling strength increasing. When the interlayer coupling strength is large enough, both networks are chaotic burst synchronization. Therefore, the synchronous states strongly depend on the interlayer coupling strength and the link probability. Moreover, the spatiotemporal pattern synchronization between the networks is robust to small noise.

  17. Nonlinear functional approximation with networks using adaptive neurons

    NASA Technical Reports Server (NTRS)

    Tawel, Raoul

    1992-01-01

    A novel mathematical framework for the rapid learning of nonlinear mappings and topological transformations is presented. It is based on allowing the neuron's parameters to adapt as a function of learning. This fully recurrent adaptive neuron model (ANM) has been successfully applied to complex nonlinear function approximation problems such as the highly degenerate inverse kinematics problem in robotics.

  18. Maximum-entropy closures for kinetic theories of neuronal network dynamics.

    PubMed

    Rangan, Aaditya V; Cai, David

    2006-05-01

    We analyze (1 + 1)D kinetic equations for neuronal network dynamics, which are derived via an intuitive closure from a Boltzmann-like equation governing the evolution of a one-particle (i.e., one-neuron) probability density function. We demonstrate that this intuitive closure is a generalization of moment closures based on the maximum-entropy principle. By invoking maximum-entropy closures, we show how to systematically extend this kinetic theory to obtain higher-order, kinetic equations and to include coupled networks of both excitatory and inhibitory neurons. PMID:16712338

  19. Interrogation Methods and Terror Networks

    NASA Astrophysics Data System (ADS)

    Baccara, Mariagiovanna; Bar-Isaac, Heski

    We examine how the structure of terror networks varies with legal limits on interrogation and the ability of authorities to extract information from detainees. We assume that terrorist networks are designed to respond optimally to a tradeoff caused by information exchange: Diffusing information widely leads to greater internal efficiency, but it leaves the organization more vulnerable to law enforcement. The extent of this vulnerability depends on the law enforcement authority’s resources, strategy and interrogation methods. Recognizing that the structure of a terrorist network responds to the policies of law enforcement authorities allows us to begin to explore the most effective policies from the authorities’ point of view.

  20. Fuzzy Neuron: Method and Hardware Realization

    NASA Technical Reports Server (NTRS)

    Krasowski, Michael J.; Prokop, Norman F.

    2014-01-01

    This innovation represents a method by which single-to-multi-input, single-to-many-output system transfer functions can be estimated from input/output data sets. This innovation can be run in the background while a system is operating under other means (e.g., through human operator effort), or may be utilized offline using data sets created from observations of the estimated system. It utilizes a set of fuzzy membership functions spanning the input space for each input variable. Linear combiners associated with combinations of input membership functions are used to create the output(s) of the estimator. Coefficients are adjusted online through the use of learning algorithms.

  1. Multiple network interface core apparatus and method

    SciTech Connect

    Underwood, Keith D.; Hemmert, Karl Scott

    2011-04-26

    A network interface controller and network interface control method comprising providing a single integrated circuit as a network interface controller and employing a plurality of network interface cores on the single integrated circuit.

  2. Holographic fiber bundle system for patterned optogenetic activation of large-scale neuronal networks.

    PubMed

    Farah, Nairouz; Levinsky, Alexandra; Brosh, Inbar; Kahn, Itamar; Shoham, Shy

    2015-10-01

    Optogenetic perturbation has become a fundamental tool in controlling activity in neurons. Used to control activity in cell cultures, slice preparations, anesthetized and awake behaving animals, optical control of cell-type specific activity enables the interrogation of complex systems. A remaining challenge in developing optical control tools is the ability to produce defined light patterns such that power-efficient, precise control of neuronal populations is obtained. Here, we describe a system for patterned stimulation that enables the generation of structured activity in neurons by transmitting optical patterns from computer-generated holograms through an optical fiber bundle. The system couples the optical system to versatile fiber bundle configurations, including coherent or incoherent bundles composed of hundreds of up to several meters long fibers. We describe the components of the system, a method for calibration, and a detailed power efficiency and spatial specificity quantification. Next, we use the system to precisely control single-cell activity as measured by extracellular electrophysiological recordings in ChR2-expressing cortical cell cultures. The described system complements recent descriptions of optical control systems, presenting a system suitable for high-resolution spatiotemporal optical control of wide-area neural networks in vitro and in vivo, yielding a tool for precise neural system interrogation. PMID:26793741

  3. Excitement and synchronization of small-world neuronal networks with short-term synaptic plasticity.

    PubMed

    Han, Fang; Wiercigroch, Marian; Fang, Jian-An; Wang, Zhijie

    2011-10-01

    Excitement and synchronization of electrically and chemically coupled Newman-Watts (NW) small-world neuronal networks with a short-term synaptic plasticity described by a modified Oja learning rule are investigated. For each type of neuronal network, the variation properties of synaptic weights are examined first. Then the effects of the learning rate, the coupling strength and the shortcut-adding probability on excitement and synchronization of the neuronal network are studied. It is shown that the synaptic learning suppresses the over-excitement, helps synchronization for the electrically coupled network but impairs synchronization for the chemically coupled one. Both the introduction of shortcuts and the increase of the coupling strength improve synchronization and they are helpful in increasing the excitement for the chemically coupled network, but have little effect on the excitement of the electrically coupled one. PMID:21956933

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

    PubMed Central

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

    2013-01-01

    We describe simulations of large-scale networks of excitatory and inhibitory spiking neurons that can generate dynamically stable winner-take-all (WTA) behavior. The network connectivity is a variant of center-surround architecture that we call center-annular-surround (CAS). In this architecture each neuron is excited by nearby neighbors and inhibited by more distant neighbors in an annular-surround region. The neural units of these networks simulate conductance-based spiking neurons that interact via mechanisms susceptible to both short-term synaptic plasticity and STDP. We show that such CAS networks display robust WTA behavior unlike the center-surround networks and other control architectures that we have studied. We find that a large-scale network of spiking neurons with separate populations of excitatory and inhibitory neurons can give rise to smooth maps of sensory input. In addition, we show that a humanoid brain-based-device (BBD) under the control of a spiking WTA neural network can learn to reach to target positions in its visual field, thus demonstrating the acquisition of sensorimotor coordination. PMID:23515493

  5. Tumor Diagnosis Using Backpropagation Neural Network Method

    NASA Astrophysics Data System (ADS)

    Ma, Lixing; Looney, Carl; Sukuta, Sydney; Bruch, Reinhard; Afanasyeva, Natalia

    1998-05-01

    For characterization of skin cancer, an artificial neural network (ANN) method has been developed to diagnose normal tissue, benign tumor and melanoma. The pattern recognition is based on a three-layer neural network fuzzy learning system. In this study, the input neuron data set is the Fourier Transform infrared (FT-IR)spectrum obtained by a new Fiberoptic Evanescent Wave Fourier Transform Infrared (FEW-FTIR) spectroscopy method in the range of 1480 to 1850 cm-1. Ten input features are extracted from the absorbency values in this region. A single hidden layer of neural nodes with sigmoids activation functions clusters the feature space into small subclasses and the output nodes are separated in different nonconvex classes to permit nonlinear discrimination of disease states. The output is classified as three classes: normal tissue, benign tumor and melanoma. The results obtained from the neural network pattern recognition are shown to be consistent with traditional medical diagnosis. Input features have also been extracted from the absorbency spectra using chemical factor analysis. These abstract features or factors are also used in the classification.

  6. From 2D to 3D: novel nanostructured scaffolds to investigate signalling in reconstructed neuronal networks.

    PubMed

    Bosi, Susanna; Rauti, Rossana; Laishram, Jummi; Turco, Antonio; Lonardoni, Davide; Nieus, Thierry; Prato, Maurizio; Scaini, Denis; Ballerini, Laura

    2015-01-01

    To recreate in vitro 3D neuronal circuits will ultimately increase the relevance of results from cultured to whole-brain networks and will promote enabling technologies for neuro-engineering applications. Here we fabricate novel elastomeric scaffolds able to instruct 3D growth of living primary neurons. Such systems allow investigating the emerging activity, in terms of calcium signals, of small clusters of neurons as a function of the interplay between the 2D or 3D architectures and network dynamics. We report the ability of 3D geometry to improve functional organization and synchronization in small neuronal assemblies. We propose a mathematical modelling of network dynamics that supports such a result. Entrapping carbon nanotubes in the scaffolds remarkably boosted synaptic activity, thus allowing for the first time to exploit nanomaterial/cell interfacing in 3D growth support. Our 3D system represents a simple and reliable construct, able to improve the complexity of current tissue culture models. PMID:25910072

  7. Self-Organized Information Processing in Neuronal Networks: Replacing Layers in Deep Networks by Dynamics

    NASA Astrophysics Data System (ADS)

    Kirst, Christoph

    It is astonishing how the sub-parts of a brain co-act to produce coherent behavior. What are mechanism that coordinate information processing and communication and how can those be changed flexibly in order to cope with variable contexts? Here we show that when information is encoded in the deviations around a collective dynamical reference state of a recurrent network the propagation of these fluctuations is strongly dependent on precisely this underlying reference. Information here 'surfs' on top of the collective dynamics and switching between states enables fast and flexible rerouting of information. This in turn affects local processing and consequently changes in the global reference dynamics that re-regulate the distribution of information. This provides a generic mechanism for self-organized information processing as we demonstrate with an oscillatory Hopfield network that performs contextual pattern recognition. Deep neural networks have proven to be very successful recently. Here we show that generating information channels via collective reference dynamics can effectively compress a deep multi-layer architecture into a single layer making this mechanism a promising candidate for the organization of information processing in biological neuronal networks.

  8. Biophysical regions identification using an artificial neuronal network: A case study in the South Western Atlantic

    NASA Astrophysics Data System (ADS)

    Saraceno, Martin; Provost, Christine; Lebbah, Mustapha

    A classification method based on an artificial neuronal network is used to identify biophysical regions in the South Western Atlantic (SWA). The method comprises a probabilistic version of the Kohonen’s self-organizing map and a Hierarchical Ascending Clustering algorithm. It objectively defines the optimal number of classes and the class boundaries. The method is applied to ocean surface data provided by satellite: chlorophyll-a, sea surface temperature and sea surface temperature gradient, first to means and then, in an attempt to examine seasonal variations, to monthly climatologies. Both results reflect the presence of the major circulation patterns and frontal positions in the SWA. The provinces retrieved using mean fields are compared to previous results and show a more accurate description of the SWA. The classification obtained with monthly climatologies offers the flexibility to include the time dimension; the boundaries of biophysical regions established are not fixed, but vary in time. Perspectives and limitations of the methodology are discussed.

  9. Parallel hierarchical method in networks

    NASA Astrophysics Data System (ADS)

    Malinochka, Olha; Tymchenko, Leonid

    2007-09-01

    This method of parallel-hierarchical Q-transformation offers new approach to the creation of computing medium - of parallel -hierarchical (PH) networks, being investigated in the form of model of neurolike scheme of data processing [1-5]. The approach has a number of advantages as compared with other methods of formation of neurolike media (for example, already known methods of formation of artificial neural networks). The main advantage of the approach is the usage of multilevel parallel interaction dynamics of information signals at different hierarchy levels of computer networks, that enables to use such known natural features of computations organization as: topographic nature of mapping, simultaneity (parallelism) of signals operation, inlaid cortex, structure, rough hierarchy of the cortex, spatially correlated in time mechanism of perception and training [5].

  10. TETRAMETHRIN AND DDT INHIBIT SPONTANEOUS FIRING IN CORTICAL NEURONAL NETWORKS

    EPA Science Inventory

    The insecticidal and neurotoxic effects of pyrethroids result from prolonged sodium channel inactivation, which causes alterations in neuronal firing and communication. Previously, we determined the relative potencies of 11 type I and type II pyrethroid insecticides using microel...

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

    PubMed

    Roxin, Alex

    2011-01-01

    Neuronal network models often assume a fixed probability of connection between neurons. This assumption leads to random networks with binomial in-degree and out-degree distributions which are relatively narrow. Here I study the effect of broad degree distributions on network dynamics by interpolating between a binomial and a truncated power-law distribution for the in-degree and out-degree independently. This is done both for an inhibitory network (I network) as well as for the recurrent excitatory connections in a network of excitatory and inhibitory neurons (EI network). In both cases increasing the width of the in-degree distribution affects the global state of the network by driving transitions between asynchronous behavior and oscillations. This effect is reproduced in a simplified rate model which includes the heterogeneity in neuronal input due to the in-degree of cells. On the other hand, broadening the out-degree distribution is shown to increase the fraction of common inputs to pairs of neurons. This leads to increases in the amplitude of the cross-correlation (CC) of synaptic currents. In the case 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 spike-count. In the asynchronous regime of the EI network, broadening the out-degree increases the amplitude of CCs in the recurrent excitatory currents, while CC of the total current is essentially unaffected as are pairwise spiking correlations. This is due to a dynamic balance between excitatory and inhibitory synaptic currents. In the oscillatory regime, changes in the out-degree can have a large effect on spiking correlations and even on the qualitative dynamical state of the network. PMID:21556129

  12. DELTAMETHRIN AND ESFENVALERATE INHIBIT SPONTANEOUS NETWORK ACTIVITY IN RAT CORTICAL NEURONS IN VITRO.

    EPA Science Inventory

    Understanding pyrethroid actions on neuronal networks will help to establish a mode of action for these compounds, which is needed for cumulative risk decisions under the Food Quality Protection Act of 1996. However, pyrethroid effects on spontaneous activity in networks of inter...

  13. Neuron-synapse IC chip-set for large-scale chaotic neural networks.

    PubMed

    Horio, Y; Aihara, K; Yamamoto, O

    2003-01-01

    We propose a neuron-synapse integrated circuit (IC) chip-set for large-scale chaotic neural networks. We use switched-capacitor (SC) circuit techniques to implement a three-internal-state transiently-chaotic neural network model. The SC chaotic neuron chip faithfully reproduces complex chaotic dynamics in real numbers through continuous state variables of the analog circuitry. We can digitally control most of the model parameters by means of programmable capacitive arrays embedded in the SC chaotic neuron chip. Since the output of the neuron is transfered into a digital pulse according to the all-or-nothing property of an axon, we design a synapse chip with digital circuits. We propose a memory-based synapse circuit architecture to achieve a rapid calculation of a vast number of weighted summations. Both of the SC neuron and the digital synapse circuits have been fabricated as IC forms. We have tested these IC chips extensively, and confirmed the functions and performance of the chip-set. The proposed neuron-synapse IC chip-set makes it possible to construct a scalable and reconfigurable large-scale chaotic neural network with 10000 neurons and 10000/sup 2/ synaptic connections. PMID:18244585

  14. Reduced synaptic activity in neuronal networks derived from embryonic stem cells of murine Rett syndrome model

    PubMed Central

    Barth, Lydia; Sütterlin, Rosmarie; Nenniger, Markus; Vogt, Kaspar E.

    2014-01-01

    Neurodevelopmental diseases such as the Rett syndrome (RTT) have received renewed attention, since the mechanisms involved may underlie a broad range of neuropsychiatric disorders such as schizophrenia and autism. In vertebrates early stages in the functional development of neurons and neuronal networks are difficult to study. Embryonic stem cell-derived neurons provide an easily accessible tool to investigate neuronal differentiation and early network formation. We used in vitro cultures of neurons derived from murine embryonic stem cells missing the methyl-CpG-binding protein 2 (MECP2) gene (MeCP2-/y) and from wild type cells of the corresponding background. Cultures were assessed using whole-cell patch-clamp electrophysiology and immunofluorescence. We studied the functional maturation of developing neurons and the activity of the synaptic connections they formed. Neurons exhibited minor differences in the developmental patterns for their intrinsic parameters, such as resting membrane potential and excitability; with the MeCP2-/y cells showing a slightly accelerated development, with shorter action potential half-widths at early stages. There was no difference in the early phase of synapse development, but as the cultures matured, significant deficits became apparent, particularly for inhibitory synaptic activity. MeCP2-/y embryonic stem cell-derived neuronal cultures show clear developmental deficits that match phenotypes observed in slice preparations and thus provide a compelling tool to further investigate the mechanisms behind RTT pathophysiology. PMID:24723848

  15. Reduced synaptic activity in neuronal networks derived from embryonic stem cells of murine Rett syndrome model.

    PubMed

    Barth, Lydia; Sütterlin, Rosmarie; Nenniger, Markus; Vogt, Kaspar E

    2014-01-01

    Neurodevelopmental diseases such as the Rett syndrome (RTT) have received renewed attention, since the mechanisms involved may underlie a broad range of neuropsychiatric disorders such as schizophrenia and autism. In vertebrates early stages in the functional development of neurons and neuronal networks are difficult to study. Embryonic stem cell-derived neurons provide an easily accessible tool to investigate neuronal differentiation and early network formation. We used in vitro cultures of neurons derived from murine embryonic stem cells missing the methyl-CpG-binding protein 2 (MECP2) gene (MeCP2-/y) and from wild type cells of the corresponding background. Cultures were assessed using whole-cell patch-clamp electrophysiology and immunofluorescence. We studied the functional maturation of developing neurons and the activity of the synaptic connections they formed. Neurons exhibited minor differences in the developmental patterns for their intrinsic parameters, such as resting membrane potential and excitability; with the MeCP2-/y cells showing a slightly accelerated development, with shorter action potential half-widths at early stages. There was no difference in the early phase of synapse development, but as the cultures matured, significant deficits became apparent, particularly for inhibitory synaptic activity. MeCP2-/y embryonic stem cell-derived neuronal cultures show clear developmental deficits that match phenotypes observed in slice preparations and thus provide a compelling tool to further investigate the mechanisms behind RTT pathophysiology. PMID:24723848

  16. Short Conduction Delays Cause Inhibition Rather than Excitation to Favor Synchrony in Hybrid Neuronal Networks of the Entorhinal Cortex

    PubMed Central

    Fernandez, Fernando R.; White, John A.; Canavier, Carmen C.

    2012-01-01

    How stable synchrony in neuronal networks is sustained in the presence of conduction delays is an open question. The Dynamic Clamp was used to measure phase resetting curves (PRCs) for entorhinal cortical cells, and then to construct networks of two such neurons. PRCs were in general Type I (all advances or all delays) or weakly type II with a small region at early phases with the opposite type of resetting. We used previously developed theoretical methods based on PRCs under the assumption of pulsatile coupling to predict the delays that synchronize these hybrid circuits. For excitatory coupling, synchrony was predicted and observed only with no delay and for delays greater than half a network period that cause each neuron to receive an input late in its firing cycle and almost immediately fire an action potential. Synchronization for these long delays was surprisingly tight and robust to the noise and heterogeneity inherent in a biological system. In contrast to excitatory coupling, inhibitory coupling led to antiphase for no delay, very short delays and delays close to a network period, but to near-synchrony for a wide range of relatively short delays. PRC-based methods show that conduction delays can stabilize synchrony in several ways, including neutralizing a discontinuity introduced by strong inhibition, favoring synchrony in the case of noisy bistability, and avoiding an initial destabilizing region of a weakly type II PRC. PRCs can identify optimal conduction delays favoring synchronization at a given frequency, and also predict robustness to noise and heterogeneity. PMID:22241969

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

    NASA Astrophysics Data System (ADS)

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

    2016-06-01

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

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

    NASA Astrophysics Data System (ADS)

    Echeveste, Rodrigo; Gros, Claudius

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

  19. A knowledge driven supervised learning approach to identify gene network of differentially up-regulated genes during neuronal senescence in Rattus norvegicus.

    PubMed

    Dholaniya, Pankaj Singh; Ghosh, Soumitra; Surampudi, Bapi Raju; Kondapi, Anand K

    2015-09-01

    Various approaches have been described to infer the gene interaction network from expression data. Several models based on computational and mathematical methods are available. The fundamental thing in the identification of the gene interaction is their biological relevance. Two genes belonging to the same pathway are more likely to affect the expression of each other than the genes of two different pathways. In the present study, interaction network of genes is described based on upregulated genes during neuronal senescence in the Cerebellar granule neurons of rat. We have adopted a supervised learning method and used it in combination with biological pathway information of the genes to develop a gene interaction network. Further modular analysis of the network has been done to identify senescence-related marker genes. Currently there is no adequate information available about the genes implicated in neuronal senescence. Thus identifying multipath genes belonging to the pathway affected by senescence might be very useful in studying the senescence process. PMID:26163927

  20. Glycolysis and oxidative phosphorylation in neurons and astrocytes during network activity in hippocampal slices

    PubMed Central

    Ivanov, Anton I; Malkov, Anton E; Waseem, Tatsiana; Mukhtarov, Marat; Buldakova, Svetlana; Gubkina, Olena; Zilberter, Misha; Zilberter, Yuri

    2014-01-01

    Network activation triggers a significant energy metabolism increase in both neurons and astrocytes. Questions of the primary neuronal energy substrate (e.g., glucose vs. lactate) as well as the relative contributions of glycolysis and oxidative phosphorylation and their cellular origin (neurons vs. astrocytes) are still a matter of debates. Using simultaneous measurements of electrophysiological and metabolic parameters during synaptic stimulation in hippocampal slices from mature mice, we show that neurons and astrocytes use both glycolysis and oxidative phosphorylation to meet their energy demands. Supplementation or replacement of glucose in artificial cerebrospinal fluid (ACSF) with pyruvate or lactate strongly modifies parameters related to network activity-triggered energy metabolism. These effects are not induced by changes in ATP content, pHi, [Ca2+]i or accumulation of reactive oxygen species. Our results suggest that during network activation, a significant fraction of NAD(P)H response (its overshoot phase) corresponds to glycolysis and the changes in cytosolic NAD(P)H and mitochondrial FAD are coupled. Our data do not support the hypothesis of a preferential utilization of astrocyte-released lactate by neurons during network activation in slices—instead, we show that during such activity glucose is an effective energy substrate for both neurons and astrocytes. PMID:24326389

  1. RBFOX1 regulates both splicing and transcriptional networks in human neuronal development

    PubMed Central

    Fogel, Brent L.; Wexler, Eric; Wahnich, Amanda; Friedrich, Tara; Vijayendran, Chandran; Gao, Fuying; Parikshak, Neelroop; Konopka, Genevieve; Geschwind, Daniel H.

    2012-01-01

    RNA splicing plays a critical role in the programming of neuronal differentiation and, consequently, normal human neurodevelopment, and its disruption may underlie neurodevelopmental and neuropsychiatric disorders. The RNA-binding protein, fox-1 homolog (RBFOX1; also termed A2BP1 or FOX1), is a neuron-specific splicing factor predicted to regulate neuronal splicing networks clinically implicated in neurodevelopmental disease, including autism spectrum disorder (ASD), but only a few targets have been experimentally identified. We used RNA sequencing to identify the RBFOX1 splicing network at a genome-wide level in primary human neural stem cells during differentiation. We observe that RBFOX1 regulates a wide range of alternative splicing events implicated in neuronal development and maturation, including transcription factors, other splicing factors and synaptic proteins. Downstream alterations in gene expression define an additional transcriptional network regulated by RBFOX1 involved in neurodevelopmental pathways remarkably parallel to those affected by splicing. Several of these differentially expressed genes are further implicated in ASD and related neurodevelopmental diseases. Weighted gene co-expression network analysis demonstrates a high degree of connectivity among these disease-related genes, highlighting RBFOX1 as a key factor coordinating the regulation of both neurodevelopmentally important alternative splicing events and clinically relevant neuronal transcriptional programs in the development of human neurons. PMID:22730494

  2. [Multiscale functional imaging: reconstructing network dynamics from the synaptic echoes recorded in a single visual cortex neuron].

    PubMed

    Fregnac, Yves; Baudot, Pierre; Chavane, Frédéric; Marre, Olivier; Monier, Cyril; Pananceau, Marc; Sadoc, Gérard

    2009-04-01

    In vivo intracellular electrophysiology offers the unique possibility of listening to the "synaptic rumor " of the cortical network, captured by a recording electrode in a single V1 cell. It allows one to reconstruct the distribution of input sources in space and time, i.e. the effective network dynamics. We have used a reverse engineering method to demonstrate the propagation of visually evoked activity through lateral (and feedback) connectivity in the primary cortex of higher mammals. This approach, based on synaptic echography, is compared here with a real-time brain imaging technique based on voltage-sensitive dye imaging. The former method gives access to the microscopic convergence processes of single neurons, whereas the latter describes the macroscopic divergence process on the neuronal map. A combination of the two techniques can be used to elucidate the cortical origin of low-level (non attentive) binding processes participating in the emergence of Gestalt percepts. PMID:20120274

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

    PubMed Central

    Lütcke, Henry; Gerhard, Felipe; Zenke, Friedemann; Gerstner, Wulfram; Helmchen, Fritjof

    2013-01-01

    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. PMID:24399936

  4. Impact of delays on the synchronization transitions of modular neuronal networks with hybrid synapses

    NASA Astrophysics Data System (ADS)

    Liu, Chen; Wang, Jiang; Yu, Haitao; Deng, Bin; Wei, Xile; Tsang, Kaiming; Chan, Wailok

    2013-09-01

    The combined effects of the information transmission delay and the ratio of the electrical and chemical synapses on the synchronization transitions in the hybrid modular neuronal network are investigated in this paper. Numerical results show that the synchronization of neuron activities can be either promoted or destroyed as the information transmission delay increases, irrespective of the probability of electrical synapses in the hybrid-synaptic network. Interestingly, when the number of the electrical synapses exceeds a certain level, further increasing its proportion can obviously enhance the spatiotemporal synchronization transitions. Moreover, the coupling strength has a significant effect on the synchronization transition. The dominated type of the synapse always has a more profound effect on the emergency of the synchronous behaviors. Furthermore, the results of the modular neuronal network structures demonstrate that excessive partitioning of the modular network may result in the dramatic detriment of neuronal synchronization. Considering that information transmission delays are inevitable in intra- and inter-neuronal networks communication, the obtained results may have important implications for the exploration of the synchronization mechanism underlying several neural system diseases such as Parkinson's Disease.

  5. Caged Neuron MEA: A system for long-term investigation of cultured neural network connectivity

    PubMed Central

    Erickson, Jonathan; Tooker, Angela; Tai, Y-C.; Pine, Jerome

    2008-01-01

    Traditional techniques for investigating cultured neural networks, such as the patch clamp and multi-electrode array, are limited by: 1) the number of identified cells which can be simultaneously electrically contacted, 2) the length of time for which cells can be studied, and 3) the lack of one-to-one neuron-to-electrode specificity. Here, we present a new device—the caged neuron multi-electrode array—which overcomes these limitations. This micro-machined device consists of an array of neurocages which mechanically trap a neuron near an extracellular electrode. While the cell body is trapped, the axon and dendrites can freely grow into the surrounding area to form a network. The electrode is bi-directional, capable of both stimulating and recording action potentials. This system is non-invasive, so that all constituent neurons of a network can be studied over its lifetime with stable one-to-one neuron-to-electrode correspondence. Proof-of-concept experiments are described to illustrate that functional networks form in a neurochip system of 16 cages in a 4×4 array, and that suprathreshold connectivity can be fully mapped over several weeks. The neurochip opens a new domain in neurobiology for studying small cultured neural networks. PMID:18775453

  6. Effects of Aβ exposure on long-term associative memory and its neuronal mechanisms in a defined neuronal network

    PubMed Central

    Ford, Lenzie; Crossley, Michael; Williams, Thomas; Thorpe, Julian R.; Serpell, Louise C.; Kemenes, György

    2015-01-01

    Amyloid beta (Aβ) induced neuronal death has been linked to memory loss, perhaps the most devastating symptom of Alzheimer’s disease (AD). Although Aβ-induced impairment of synaptic or intrinsic plasticity is known to occur before any cell death, the links between these neurophysiological changes and the loss of specific types of behavioral memory are not fully understood. Here we used a behaviorally and physiologically tractable animal model to investigate Aβ-induced memory loss and electrophysiological changes in the absence of neuronal death in a defined network underlying associative memory. We found similar behavioral but different neurophysiological effects for Aβ 25-35 and Aβ 1-42 in the feeding circuitry of the snail Lymnaea stagnalis. Importantly, we also established that both the behavioral and neuronal effects were dependent upon the animals having been classically conditioned prior to treatment, since Aβ application before training caused neither memory impairment nor underlying neuronal changes over a comparable period of time following treatment. PMID:26024049

  7. A new optical neuron device for all-optical neural networks

    NASA Astrophysics Data System (ADS)

    Akiyama, Koji; Takimoto, Akio; Miyauchi, Michihiro; Kuratomi, Yasunori; Asayama, Junko; Ogawa, Hisahito

    1991-12-01

    A new optical neuron device has been developed. The device can perform both summation and thresholding operations in optics, and consists of a PIN a Si:H photoreceptor, aluminum neuron electrodes and a ferroelectric liquid crystal light modulator. The a-Si:H photoreceptor shows characteristics of an ideal quantum efficiency and a good linearity. The optical neuron device exhibits a response time of about 30 microns for incident light power of 9 microW and a contrast ratio of 300:1. Using this neuron device, a lenslet array and a memory mask, an all-optical neural network has been constructed. The network demonstrates an associate memory function on purely optical parallel processing without any help from electric computation.

  8. Cultured networks of excitatory projection neurons and inhibitory interneurons for studying human cortical neurotoxicity.

    PubMed

    Xu, Jin-Chong; Fan, Jing; Wang, Xueqing; Eacker, Stephen M; Kam, Tae-In; Chen, Li; Yin, Xiling; Zhu, Juehua; Chi, Zhikai; Jiang, Haisong; Chen, Rong; Dawson, Ted M; Dawson, Valina L

    2016-04-01

    Translating neuroprotective treatments from discovery in cell and animal models to the clinic has proven challenging. To reduce the gap between basic studies of neurotoxicity and neuroprotection and clinically relevant therapies, we developed a human cortical neuron culture system from human embryonic stem cells or human inducible pluripotent stem cells that generated both excitatory and inhibitory neuronal networks resembling the composition of the human cortex. This methodology used timed administration of retinoic acid to FOXG1(+)neural precursor cells leading to differentiation of neuronal populations representative of the six cortical layers with both excitatory and inhibitory neuronal networks that were functional and homeostatically stable. In human cortical neuronal cultures, excitotoxicity or ischemia due to oxygen and glucose deprivation led to cell death that was dependent onN-methyl-d-aspartate (NMDA) receptors, nitric oxide (NO), and poly(ADP-ribose) polymerase (PARP) (a cell death pathway called parthanatos that is distinct from apoptosis, necroptosis, and other forms of cell death). Neuronal cell death was attenuated by PARP inhibitors that are currently in clinical trials for cancer treatment. This culture system provides a new platform for the study of human cortical neurotoxicity and suggests that PARP inhibitors may be useful for ameliorating excitotoxic and ischemic cell death in human neurons. PMID:27053772

  9. Long-term electromagnetic exposure of developing neuronal networks: A flexible experimental setup.

    PubMed

    Oster, Stefan; Daus, Andreas W; Erbes, Christian; Goldhammer, Michael; Bochtler, Ulrich; Thielemann, Christiane

    2016-05-01

    Neuronal networks in vitro are considered one of the most promising targets of research to assess potential electromagnetic field induced effects on neuronal functionality. A few exposure studies revealed there is currently no evidence of any adverse health effects caused by weak electromagnetic fields. Nevertheless, some published results are inconsistent. Particularly, doubts have been raised regarding possible athermal biological effects in the young brain during neuronal development. Therefore, we developed and characterized a flexible experimental setup based on a transverse electromagnetic waveguide, allowing controlled, reproducible exposure of developing neuronal networks in vitro. Measurement of S-parameters confirmed very good performance of the Stripline in the band of 800-1000 MHz. Simulations suggested a flexible positioning of cell culture dishes throughout a large exposure area, as specific absorption rate values were quite independent of their position (361.7 ± 11.4 mW/kg) at 1 W, 900 MHz. During exposure, thermal drift inside cellular medium did not exceed 0.1 K. Embryonic rat cortical neurons were cultivated on microelectrode array chips to non-invasively assess electrophysiological properties of electrogenic networks. Measurements were taken for several weeks, which attest to the experimental setup being a reliable system for long-term studies on developing neuronal tissue. Bioelectromagnetics. 37:264-278, 2016. © 2016 Wiley Periodicals, Inc. PMID:27070808

  10. Causal Interrogation of Neuronal Networks and Behavior through Virally Transduced Ivermectin Receptors.

    PubMed

    Obenhaus, Horst A; Rozov, Andrei; Bertocchi, Ilaria; Tang, Wannan; Kirsch, Joachim; Betz, Heinrich; Sprengel, Rolf

    2016-01-01

    The causal interrogation of neuronal networks involved in specific behaviors requires the spatially and temporally controlled modulation of neuronal activity. For long-term manipulation of neuronal activity, chemogenetic tools provide a reasonable alternative to short-term optogenetic approaches. Here we show that virus mediated gene transfer of the ivermectin (IVM) activated glycine receptor mutant GlyRα1 (AG) can be used for the selective and reversible silencing of specific neuronal networks in mice. In the striatum, dorsal hippocampus, and olfactory bulb, GlyRα1 (AG) promoted IVM dependent effects in representative behavioral assays. Moreover, GlyRα1 (AG) mediated silencing had a strong and reversible impact on neuronal ensemble activity and c-Fos activation in the olfactory bulb. Together our results demonstrate that long-term, reversible and re-inducible neuronal silencing via GlyRα1 (AG) is a promising tool for the interrogation of network mechanisms underlying the control of behavior and memory formation. PMID:27625595

  11. Causal Interrogation of Neuronal Networks and Behavior through Virally Transduced Ivermectin Receptors

    PubMed Central

    Obenhaus, Horst A.; Rozov, Andrei; Bertocchi, Ilaria; Tang, Wannan; Kirsch, Joachim; Betz, Heinrich; Sprengel, Rolf

    2016-01-01

    The causal interrogation of neuronal networks involved in specific behaviors requires the spatially and temporally controlled modulation of neuronal activity. For long-term manipulation of neuronal activity, chemogenetic tools provide a reasonable alternative to short-term optogenetic approaches. Here we show that virus mediated gene transfer of the ivermectin (IVM) activated glycine receptor mutant GlyRα1AG can be used for the selective and reversible silencing of specific neuronal networks in mice. In the striatum, dorsal hippocampus, and olfactory bulb, GlyRα1AG promoted IVM dependent effects in representative behavioral assays. Moreover, GlyRα1AG mediated silencing had a strong and reversible impact on neuronal ensemble activity and c-Fos activation in the olfactory bulb. Together our results demonstrate that long-term, reversible and re-inducible neuronal silencing via GlyRα1AG is a promising tool for the interrogation of network mechanisms underlying the control of behavior and memory formation. PMID:27625595

  12. Scale-free and economical features of functional connectivity in neuronal networks.

    PubMed

    Thivierge, Jean-Philippe

    2014-08-01

    A form of activity that is highly studied in cultured cortical networks is the neuronal avalanche, characterized by bursts whose distribution follows a power law. While the statistics of neuronal avalanches are well characterized, much less is known about the neuronal interactions from which they arise. We examined statistical dependencies between pairs of cells in spontaneously active cultures of cortical neurons using an information measure of transfer entropy. We show that the distribution of transfer entropy follows a power law with a slope near 3/2. Using graph-theoretic approaches of weighted networks, we demonstrate that this power law maximizes a measure of global economy that accounts for both the efficiency of neuronal interactions as well as the overall traffic in the network. Finally, we describe a pairwise Poisson model that captures the statistics of information transfer in a population of spiking neurons. Using this model, we show that avalanches can occur in systems with weak pairwise interactions, and that strong pairwise interactions can arise without avalanches, suggesting that these two measures capture distinct properties of brain dynamics. PMID:25215772

  13. Scale-free and economical features of functional connectivity in neuronal networks

    NASA Astrophysics Data System (ADS)

    Thivierge, Jean-Philippe

    2014-08-01

    A form of activity that is highly studied in cultured cortical networks is the neuronal avalanche, characterized by bursts whose distribution follows a power law. While the statistics of neuronal avalanches are well characterized, much less is known about the neuronal interactions from which they arise. We examined statistical dependencies between pairs of cells in spontaneously active cultures of cortical neurons using an information measure of transfer entropy. We show that the distribution of transfer entropy follows a power law with a slope near 3/2. Using graph-theoretic approaches of weighted networks, we demonstrate that this power law maximizes a measure of global economy that accounts for both the efficiency of neuronal interactions as well as the overall traffic in the network. Finally, we describe a pairwise Poisson model that captures the statistics of information transfer in a population of spiking neurons. Using this model, we show that avalanches can occur in systems with weak pairwise interactions, and that strong pairwise interactions can arise without avalanches, suggesting that these two measures capture distinct properties of brain dynamics.

  14. Interplay between population firing stability and single neuron dynamics in hippocampal networks.

    PubMed

    Slomowitz, Edden; Styr, Boaz; Vertkin, Irena; Milshtein-Parush, Hila; Nelken, Israel; Slutsky, Michael; Slutsky, Inna

    2015-01-01

    Neuronal circuits' ability to maintain the delicate balance between stability and flexibility in changing environments is critical for normal neuronal functioning. However, to what extent individual neurons and neuronal populations maintain internal firing properties remains largely unknown. In this study, we show that distributions of spontaneous population firing rates and synchrony are subject to accurate homeostatic control following increase of synaptic inhibition in cultured hippocampal networks. Reduction in firing rate triggered synaptic and intrinsic adaptive responses operating as global homeostatic mechanisms to maintain firing macro-stability, without achieving local homeostasis at the single-neuron level. Adaptive mechanisms, while stabilizing population firing properties, reduced short-term facilitation essential for synaptic discrimination of input patterns. Thus, invariant ongoing population dynamics emerge from intrinsically unstable activity patterns of individual neurons and synapses. The observed differences in the precision of homeostatic control at different spatial scales challenge cell-autonomous theory of network homeostasis and suggest the existence of network-wide regulation rules. PMID:25556699

  15. Learning Precise Spike Train-to-Spike Train Transformations in Multilayer Feedforward Neuronal Networks.

    PubMed

    Banerjee, Arunava

    2016-05-01

    We derive a synaptic weight update rule for learning temporally precise spike train-to-spike train transformations in multilayer feedforward networks of spiking neurons. The framework, aimed at seamlessly generalizing error backpropagation to the deterministic spiking neuron setting, is based strictly on spike timing and avoids invoking concepts pertaining to spike rates or probabilistic models of spiking. The derivation is founded on two innovations. First, an error functional is proposed that compares the spike train emitted by the output neuron of the network to the desired spike train by way of their putative impact on a virtual postsynaptic neuron. This formulation sidesteps the need for spike alignment and leads to closed-form solutions for all quantities of interest. Second, virtual assignment of weights to spikes rather than synapses enables a perturbation analysis of individual spike times and synaptic weights of the output, as well as all intermediate neurons in the network, which yields the gradients of the error functional with respect to the said entities. Learning proceeds via a gradient descent mechanism that leverages these quantities. Simulation experiments demonstrate the efficacy of the proposed learning framework. The experiments also highlight asymmetries between synapses on excitatory and inhibitory neurons. PMID:26942750

  16. A microfluidic method for dopamine uptake measurements in dopaminergic neurons.

    PubMed

    Yu, Yue; Shamsi, Mohtashim H; Krastev, Dimitar L; Dryden, Michael D M; Leung, Yen; Wheeler, Aaron R

    2016-02-01

    Dopamine (DA) is a classical neurotransmitter and dysfunction in its synaptic handling underlies many neurological disorders, including addiction, depression, and neurodegeneration. A key to understanding DA dysfunction is the accurate measurement of dopamine uptake by dopaminergic neurons. Current methods that allow for the analysis of dopamine uptake rely on standard multiwell-plate based ELISA, or on carbon-fibre microelectrodes used in in vivo recording techniques. The former suffers from challenges associated with automation and analyte degradation, while the latter has low throughput and is not ideal for laboratory screening. In response to these challenges, we introduce a digital microfluidic platform to evaluate dopamine homeostasis in in vitro neuron culture. The method features voltammetric dopamine sensors with limit of detection of 30 nM integrated with cell culture sites for multi-day neuron culture and differentiation. We demonstrate the utility of the new technique for DA uptake assays featuring in-line culture and analysis, with a determination of uptake of approximately ∼32 fmol in 10 min per virtual microwell (each containing ∼200 differentiated SH-SY5Y cells). We propose that future generations of this technique will be useful for drug discovery for neurodegenerative disease as well as for a wide range of applications that would benefit from integrated cell culture and electroanalysis. PMID:26725686

  17. Vibrational resonance in adaptive small-world neuronal networks with spike-timing-dependent plasticity

    NASA Astrophysics Data System (ADS)

    Yu, Haitao; Guo, Xinmeng; Wang, Jiang; Deng, Bin; Wei, Xile

    2015-10-01

    The phenomenon of vibrational resonance is investigated in adaptive Newman-Watts small-world neuronal networks, where the strength of synaptic connections between neurons is modulated based on spike-timing-dependent plasticity. Numerical results demonstrate that there exists appropriate amplitude of high-frequency driving which is able to optimize the neural ensemble response to the weak low-frequency periodic signal. The effect of networked vibrational resonance can be significantly affected by spike-timing-dependent plasticity. It is shown that spike-timing-dependent plasticity with dominant depression can always improve the efficiency of vibrational resonance, and a small adjusting rate can promote the transmission of weak external signal in small-world neuronal networks. In addition, the network topology plays an important role in the vibrational resonance in spike-timing-dependent plasticity-induced neural systems, where the system response to the subthreshold signal is maximized by an optimal network structure. Furthermore, it is demonstrated that the introduction of inhibitory synapses can considerably weaken the phenomenon of vibrational resonance in the hybrid small-world neuronal networks with spike-timing-dependent plasticity.

  18. MorphoNeuroNet: an automated method for dense neurite network analysis.

    PubMed

    Pani, Giuseppe; De Vos, Winnok H; Samari, Nada; de Saint-Georges, Louis; Baatout, Sarah; Van Oostveldt, Patrick; Benotmane, Mohammed Abderrafi

    2014-02-01

    High content cell-based screens are rapidly gaining popularity in the context of neuronal regeneration studies. To analyze neuronal morphology, automatic image analysis pipelines have been conceived, which accurately quantify the shape changes of neurons in cell cultures with non-dense neurite networks. However, most existing methods show poor performance for well-connected and differentiated neuronal networks, which may serve as valuable models for inter alia synaptogenesis. Here, we present a fully automated method for quantifying the morphology of neurons and the density of neurite networks, in dense neuronal cultures, which are grown for more than 10 days. MorphoNeuroNet, written as a script for ImageJ, Java based freeware, automatically determines various morphological parameters of the soma and the neurites (size, shape, starting points, and fractional occupation). The image analysis pipeline consists of a multi-tier approach in which the somas are segmented by adaptive region growing using nuclei as seeds, and the neurites are delineated by a combination of various intensity and edge detection algorithms. Quantitative comparison showed a superior performance of MorphoNeuroNet to existing analysis tools, especially for revealing subtle changes in thin neurites, which have weak fluorescence intensity compared to the rest of the network. The proposed method will help determining the effects of compounds on cultures with dense neurite networks, thereby boosting physiological relevance of cell-based assays in the context of neuronal diseases. PMID:24222510

  19. Autaptic activity-induced synchronization transitions in Newman-Watts network of Hodgkin-Huxley neurons.

    PubMed

    Wu, Yanan; Gong, Yubing; Wang, Qi

    2015-04-01

    In this paper, we numerically study the effect of autapse on the synchronization of Newman-Watts small-world Hodgkin-Huxley neuron network. It is found that the neurons exhibit synchronization transitions as autaptic self-feedback delay is varied, and the phenomenon becomes strongest when autaptic self-feedback strength is optimal. This phenomenon also changes with the change of coupling strength and network randomness and become strongest when they are optimal. There are similar synchronization transitions for electrical and chemical autapse, but the synchronization transitions for chemical autapse occur more frequently and are stronger than those for electrical synapse. The underlying mechanisms are briefly discussed in quality. These results show that autaptic activity plays a subtle role in the synchronization of the neuronal network. These findings may find potential implications of autapse for the information processing and transmission in neural systems. PMID:25933661

  20. Autaptic activity-induced synchronization transitions in Newman-Watts network of Hodgkin-Huxley neurons

    NASA Astrophysics Data System (ADS)

    Wu, Yanan; Gong, Yubing; Wang, Qi

    2015-04-01

    In this paper, we numerically study the effect of autapse on the synchronization of Newman-Watts small-world Hodgkin-Huxley neuron network. It is found that the neurons exhibit synchronization transitions as autaptic self-feedback delay is varied, and the phenomenon becomes strongest when autaptic self-feedback strength is optimal. This phenomenon also changes with the change of coupling strength and network randomness and become strongest when they are optimal. There are similar synchronization transitions for electrical and chemical autapse, but the synchronization transitions for chemical autapse occur more frequently and are stronger than those for electrical synapse. The underlying mechanisms are briefly discussed in quality. These results show that autaptic activity plays a subtle role in the synchronization of the neuronal network. These findings may find potential implications of autapse for the information processing and transmission in neural systems.

  1. Neurons with hysteresis form a network that can learn without any changes in synaptic connection strengths

    NASA Astrophysics Data System (ADS)

    Hoffmann, Geoffrey W.; Benson, Maurice W.

    1986-08-01

    A neural network concept derived from an analogy between the immune system and the central nerous system is outlined. The theory is based on a nervous that is slightly more complicated than the conventional McCullogh-Pitts type of neuron, in that it exhibits hysteresis at the single cell level. This added complication is compensated by the fact that a network of such neurons is able to learn without the necessity for any changes in synaptic connection strengths. The learning occurs as a natural consequence of interactions between the network and its enviornment, with environmental stimuli moving the system around in an N-dimensional phase space, until a point in phase space is reached such that the system's responses are appropriate for dealing with the stimuli. Due to the hysteresis associated with each neuron, the system tends to stay in the region of phase space where it is located. The theory includes a role for sleep in learning.

  2. Prediction of the NO2 concentration data in an urban area using multiple regression and neuronal networks

    NASA Astrophysics Data System (ADS)

    Dragomir, Carmelia Mariana; Voiculescu, Mirela; Constantin, Daniel-Eduard; Georgescu, Lucian Puiu

    2015-12-01

    The probability of exceeding EU limit values for NO2 concentrations has increased in many European cities. Meteorological parameters have an extremely important role in evaluating the dispersion of pollutants in various city areas. This paper focuses on meteorological variations and their impact on urban background NO2 concentrations in the city of Braila for 2009-2013. The dependence between measured NO2 data and meteorological parameters are analyzed using two modeling methods: multiple linear regression and artificial neuronal networks. The dataset calculated using the proposed models indicate that artificial neural networks can be applied in the analysis and forecasting of air quality.

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

    PubMed

    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

  4. Detection and clustering of features in aerial images by neuron network-based algorithm

    NASA Astrophysics Data System (ADS)

    Vozenilek, Vit

    2015-12-01

    The paper presents the algorithm for detection and clustering of feature in aerial photographs based on artificial neural networks. The presented approach is not focused on the detection of specific topographic features, but on the combination of general features analysis and their use for clustering and backward projection of clusters to aerial image. The basis of the algorithm is a calculation of the total error of the network and a change of weights of the network to minimize the error. A classic bipolar sigmoid was used for the activation function of the neurons and the basic method of backpropagation was used for learning. To verify that a set of features is able to represent the image content from the user's perspective, the web application was compiled (ASP.NET on the Microsoft .NET platform). The main achievements include the knowledge that man-made objects in aerial images can be successfully identified by detection of shapes and anomalies. It was also found that the appropriate combination of comprehensive features that describe the colors and selected shapes of individual areas can be useful for image analysis.

  5. Hybrid multiphoton volumetric functional imaging of large-scale bioengineered neuronal networks

    NASA Astrophysics Data System (ADS)

    Dana, Hod; Marom, Anat; Paluch, Shir; Dvorkin, Roman; Brosh, Inbar; Shoham, Shy

    2014-06-01

    Planar neural networks and interfaces serve as versatile in vitro models of central nervous system physiology, but adaptations of related methods to three dimensions (3D) have met with limited success. Here, we demonstrate for the first time volumetric functional imaging in a bioengineered neural tissue growing in a transparent hydrogel with cortical cellular and synaptic densities, by introducing complementary new developments in nonlinear microscopy and neural tissue engineering. Our system uses a novel hybrid multiphoton microscope design combining a 3D scanning-line temporal-focusing subsystem and a conventional laser-scanning multiphoton microscope to provide functional and structural volumetric imaging capabilities: dense microscopic 3D sampling at tens of volumes per second of structures with mm-scale dimensions containing a network of over 1,000 developing cells with complex spontaneous activity patterns. These developments open new opportunities for large-scale neuronal interfacing and for applications of 3D engineered networks ranging from basic neuroscience to the screening of neuroactive substances.

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

    PubMed Central

    Naze, Sebastien; Bernard, Christophe; Jirsa, Viktor

    2015-01-01

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

  7. Mechanisms of epileptiform synchronization in cortical neuronal networks.

    PubMed

    Avoli, M

    2014-01-01

    Neuronal synchronization supports different physiological states such as cognitive functions and sleep, and it is mirrored by identifiable EEG patterns ranging from gamma to delta oscillations. However, excessive neuronal synchronization is often the hallmark of epileptic activity in both generalized and partial epileptic disorders. Here, I will review the synchronizing mechanisms involved in generating epileptiform activity in the limbic system, which is closely involved in the pathophysiogenesis of temporal lobe epilepsy (TLE). TLE is often associated to a typical pattern of brain damage known as mesial temporal sclerosis, and it is one of the most refractory adult form of partial epilepsy. This epileptic disorder can be reproduced in animals by topical or systemic injection of pilocarpine or kainic acid, or by repetitive electrical stimulation; these procedures induce an initial status epilepticus and cause 1-4 weeks later a chronic condition of recurrent limbic seizures. Remarkably, a similar, seizure-free, latent period can be identified in TLE patients who suffered an initial insult in childhood and develop partial seizures in adolescence or early adulthood. Specifically, I will focus here on the neuronal mechanisms underlying three abnormal types of neuronal synchronization seen in both TLE patients and animal models mimicking this disorder: (i) interictal spikes; (ii) high frequency oscillations (80-500 Hz); and (iii) ictal (i.e., seizure) discharges. In addition, I will discuss the relationship between interictal spikes and ictal activity as well as recent evidence suggesting that specific seizure onsets in the pilocarpine model of TLE are characterized by distinctive patterns of spiking (also termed preictal) and high frequency oscillations. PMID:24251567

  8. Mechanisms of Epileptiform Synchronization in Cortical Neuronal Networks

    PubMed Central

    Avoli, M.

    2016-01-01

    Neuronal synchronization supports different physiological states such as cognitive functions and sleep, and it is mirrored by identifiable EEG patterns ranging from gamma to delta oscillations. However, excessive neuronal synchronization is often the hallmark of epileptic activity in both generalized and partial epileptic disorders. Here, I will review the synchronizing mechanisms involved in generating epileptiform activity in the limbic system, which is closely involved in the pathophysiogenesis of temporal lobe epilepsy (TLE). TLE is often associated to a typical pattern of brain damage known as mesial temporal sclerosis, and it is one of the most refractory adult form of partial epilepsy. This epileptic disorder can be reproduced in animals by topical or systemic injection of pilocarpine or kainic acid, or by repetitive electrical stimulation; these procedures induce an initial status epilepticus and cause 1–4 weeks later a chronic condition of recurrent limbic seizures. Remarkably, a similar, seizure-free, latent period can be identified in TLE patients who suffered an initial insult in childhood and develop partial seizures in adolescence or early adulthood. Specifically, I will focus here on the neuronal mechanisms underlying three abnormal types of neuronal synchronization seen in both TLE patients and animal models mimicking this disorder: (i) interictal spikes; (ii) high frequency oscillations (80–500 Hz); and (iii) ictal (i.e., seizure) discharges. In addition, I will discuss the relationship between interictal spikes and ictal activity as well as recent evidence suggesting that specific seizure onsets in the pilocarpine model of TLE are characterized by distinctive patterns of spiking (also termed preictal) and high frequency oscillations. PMID:24251567

  9. Synaptic dynamics and neuronal network connectivity are reflected in the distribution of times in Up states.

    PubMed

    Dao Duc, Khanh; Parutto, Pierre; Chen, Xiaowei; Epsztein, Jérôme; Konnerth, Arthur; Holcman, David

    2015-01-01

    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 time 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. PMID:26283956

  10. Neuronal Avalanches Imply Maximum Dynamic Range in Cortical Networks at Criticality

    PubMed Central

    Shew, Woodrow L.; Yang, Hongdian; Petermann, Thomas; Roy, Rajarshi

    2009-01-01

    Spontaneous neuronal activity is a ubiquitous feature of cortex. Its spatiotemporal organization reflects past input and modulates future network output. Here we study whether a particular type of spontaneous activity is generated by a network that is optimized for input processing. Neuronal avalanches are a type of spontaneous activity observed in superficial cortical layers in vitro and in vivo with statistical properties expected from a network operating at “criticality.” Theory predicts that criticality and, therefore, neuronal avalanches are optimal for input processing, but until now, this has not been tested in experiments. Here, we use cortex slice cultures grown on planar microelectrode arrays to demonstrate that cortical networks that generate neuronal avalanches benefit from a maximized dynamic range, i.e., the ability to respond to the greatest range of stimuli. By changing the ratio of excitation and inhibition in the cultures, we derive a network tuning curve for stimulus processing as a function of distance from criticality in agreement with predictions from our simulations. Our findings suggest that in the cortex, (1) balanced excitation and inhibition establishes criticality, which maximizes the range of inputs that can be processed, and (2) spontaneous activity and input processing are unified in the context of critical phenomena. PMID:20007483

  11. Synaptic dynamics and neuronal network connectivity are reflected in the distribution of times in Up states

    PubMed Central

    Dao Duc, Khanh; Parutto, Pierre; Chen, Xiaowei; Epsztein, Jérôme; Konnerth, Arthur; Holcman, David

    2015-01-01

    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 time 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. PMID:26283956

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

    NASA Astrophysics Data System (ADS)

    Goychuk, Igor; Goychuk, Andriy

    2015-04-01

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

  13. Integrated Brain Circuits: Astrocytic Networks Modulate Neuronal Activity and Behavior

    PubMed Central

    Halassa, Michael M.; Haydon, Philip G.

    2011-01-01

    The past decade has seen an explosion of research on roles of neuron-astrocyte interactions in the control of brain function. We highlight recent studies performed on the tripartite synapse, the structure consisting of pre- and postsynaptic elements of the synapse and an associated astrocytic process. Astrocytes respond to neuronal activity and neuro-transmitters, through the activation of metabotropic receptors, and can release the gliotransmitters ATP, D-serine, and glutamate, which act on neurons. Astrocyte-derived ATP modulates synaptic transmission, either directly or through its metabolic product adenosine. D-serine modulates NMDA receptor function, whereas glia-derived glutamate can play important roles in relapse following withdrawal from drugs of abuse. Cell type–specific molecular genetics has allowed a new level of examination of the function of astrocytes in brain function and has revealed an important role of these glial cells that is mediated by adenosine accumulation in the control of sleep and in cognitive impairments that follow sleep deprivation. PMID:20148679

  14. Plasticity of Neuron-Glial Transmission: Equipping Glia for Long-Term Integration of Network Activity

    PubMed Central

    Croft, Wayne; Dobson, Katharine L.; Bellamy, Tomas C.

    2015-01-01

    The capacity of synaptic networks to express activity-dependent changes in strength and connectivity is essential for learning and memory processes. In recent years, glial cells (most notably astrocytes) have been recognized as active participants in the modulation of synaptic transmission and synaptic plasticity, implicating these electrically nonexcitable cells in information processing in the brain. While the concept of bidirectional communication between neurons and glia and the mechanisms by which gliotransmission can modulate neuronal function are well established, less attention has been focussed on the computational potential of neuron-glial transmission itself. In particular, whether neuron-glial transmission is itself subject to activity-dependent plasticity and what the computational properties of such plasticity might be has not been explored in detail. In this review, we summarize current examples of plasticity in neuron-glial transmission, in many brain regions and neurotransmitter pathways. We argue that induction of glial plasticity typically requires repetitive neuronal firing over long time periods (minutes-hours) rather than the short-lived, stereotyped trigger typical of canonical long-term potentiation. We speculate that this equips glia with a mechanism for monitoring average firing rates in the synaptic network, which is suited to the longer term roles proposed for astrocytes in neurophysiology. PMID:26339509

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

    PubMed

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

    2016-01-01

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

  16. Information in a Network of Neuronal Cells: Effect of Cell Density and Short-Term Depression

    PubMed Central

    Onesto, Valentina; Cosentino, Carlo; Di Fabrizio, Enzo; Cesarelli, Mario; Amato, Francesco; Gentile, Francesco

    2016-01-01

    Neurons are specialized, electrically excitable cells which use electrical to chemical signals to transmit and elaborate information. Understanding how the cooperation of a great many of neurons in a grid may modify and perhaps improve the information quality, in contrast to few neurons in isolation, is critical for the rational design of cell-materials interfaces for applications in regenerative medicine, tissue engineering, and personalized lab-on-a-chips. In the present paper, we couple an integrate-and-fire model with information theory variables to analyse the extent of information in a network of nerve cells. We provide an estimate of the information in the network in bits as a function of cell density and short-term depression time. In the model, neurons are connected through a Delaunay triangulation of not-intersecting edges; in doing so, the number of connecting synapses per neuron is approximately constant to reproduce the early time of network development in planar neural cell cultures. In simulations where the number of nodes is varied, we observe an optimal value of cell density for which information in the grid is maximized. In simulations in which the posttransmission latency time is varied, we observe that information increases as the latency time decreases and, for specific configurations of the grid, it is largely enhanced in a resonance effect. PMID:27403421

  17. Information in a Network of Neuronal Cells: Effect of Cell Density and Short-Term Depression.

    PubMed

    Onesto, Valentina; Cosentino, Carlo; Di Fabrizio, Enzo; Cesarelli, Mario; Amato, Francesco; Gentile, Francesco

    2016-01-01

    Neurons are specialized, electrically excitable cells which use electrical to chemical signals to transmit and elaborate information. Understanding how the cooperation of a great many of neurons in a grid may modify and perhaps improve the information quality, in contrast to few neurons in isolation, is critical for the rational design of cell-materials interfaces for applications in regenerative medicine, tissue engineering, and personalized lab-on-a-chips. In the present paper, we couple an integrate-and-fire model with information theory variables to analyse the extent of information in a network of nerve cells. We provide an estimate of the information in the network in bits as a function of cell density and short-term depression time. In the model, neurons are connected through a Delaunay triangulation of not-intersecting edges; in doing so, the number of connecting synapses per neuron is approximately constant to reproduce the early time of network development in planar neural cell cultures. In simulations where the number of nodes is varied, we observe an optimal value of cell density for which information in the grid is maximized. In simulations in which the posttransmission latency time is varied, we observe that information increases as the latency time decreases and, for specific configurations of the grid, it is largely enhanced in a resonance effect. PMID:27403421

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

    PubMed Central

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

    2016-01-01

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

  19. Rhythmic Oscillations of Excitatory Bursting Hodkin-Huxley Neuronal Network with Synaptic Learning

    PubMed Central

    Shi, Qi; Han, Fang; Wang, Zhijie; Li, Caiyun

    2016-01-01

    Rhythmic oscillations of neuronal network are actually kind of synchronous behaviors, which play an important role in neural systems. In this paper, the properties of excitement degree and oscillation frequency of excitatory bursting Hodkin-Huxley neuronal network which incorporates a synaptic learning rule are studied. The effects of coupling strength, synaptic learning rate, and other parameters of chemical synapses, such as synaptic delay and decay time constant, are explored, respectively. It is found that the increase of the coupling strength can weaken the extent of excitement, whereas increasing the synaptic learning rate makes the network more excited in a certain range; along with the increasing of the delay time and the decay time constant, the excitement degree increases at the beginning, then decreases, and keeps stable. It is also found that, along with the increase of the synaptic learning rate, the coupling strength, the delay time, and the decay time constant, the oscillation frequency of the network decreases monotonically. PMID:27073393

  20. Specific Neuron Placement on Gold and Silicon Nitride-Patterned Substrates through a Two-Step Functionalization Method.

    PubMed

    Mescola, Andrea; Canale, Claudio; Prato, Mirko; Diaspro, Alberto; Berdondini, Luca; Maccione, Alessandro; Dante, Silvia

    2016-06-28

    The control of neuron-substrate adhesion has been always a challenge for fabricating neuron-based cell chips and in particular for multielectrode array (MEA) devices, which warrants the investigation of the electrophysiological activity of neuronal networks. The recent introduction of high-density chips based on the complementary metal oxide semiconductor (CMOS) technology, integrating thousands of electrodes, improved the possibility to sense large networks and raised the challenge to develop newly adapted functionalization techniques to further increase neuron electrode localization to avoid the positioning of cells out of the recording area. Here, we present a simple and straightforward chemical functionalization method that leads to the precise and exclusive positioning of the neural cell bodies onto modified electrodes and inhibits, at the same time, cellular adhesion in the surrounding insulator areas. Different from other approaches, this technique does not require any adhesion molecule as well as complex patterning technique such as μ-contact printing. The functionalization was first optimized on gold (Au) and silicon nitride (Si3N4)-patterned surfaces. The procedure consisted of the introduction of a passivating layer of hydrophobic silane molecules (propyltriethoxysilane [PTES]) followed by a treatment of the Au surface using 11-amino-1-undecanethiol hydrochloride (AT). On model substrates, well-ordered neural networks and an optimal coupling between a single neuron and single micrometric functionalized Au surface were achieved. In addition, we presented the preliminary results of this functionalization method directly applied on a CMOS-MEA: the electrical spontaneous spiking and bursting activities of the network recorded for up to 4 weeks demonstrate an excellent and stable neural adhesion and functional behavior comparable with what expected using a standard adhesion factor, such as polylysine or laminin, thus demonstrating that this procedure can be

  1. Microglia protect against brain injury and their selective elimination dysregulates neuronal network activity after stroke

    PubMed Central

    Szalay, Gergely; Martinecz, Bernadett; Lénárt, Nikolett; Környei, Zsuzsanna; Orsolits, Barbara; Judák, Linda; Császár, Eszter; Fekete, Rebeka; West, Brian L.; Katona, Gergely; Rózsa, Balázs; Dénes, Ádám

    2016-01-01

    Microglia are the main immune cells of the brain and contribute to common brain diseases. However, it is unclear how microglia influence neuronal activity and survival in the injured brain in vivo. Here we develop a precisely controlled model of brain injury induced by cerebral ischaemia combined with fast in vivo two-photon calcium imaging and selective microglial manipulation. We show that selective elimination of microglia leads to a striking, 60% increase in infarct size, which is reversed by microglial repopulation. Microglia-mediated protection includes reduction of excitotoxic injury, since an absence of microglia leads to dysregulated neuronal calcium responses, calcium overload and increased neuronal death. Furthermore, the incidence of spreading depolarization (SD) is markedly reduced in the absence of microglia. Thus, microglia are involved in changes in neuronal network activity and SD after brain injury in vivo that could have important implications for common brain diseases. PMID:27139776

  2. Microglia protect against brain injury and their selective elimination dysregulates neuronal network activity after stroke.

    PubMed

    Szalay, Gergely; Martinecz, Bernadett; Lénárt, Nikolett; Környei, Zsuzsanna; Orsolits, Barbara; Judák, Linda; Császár, Eszter; Fekete, Rebeka; West, Brian L; Katona, Gergely; Rózsa, Balázs; Dénes, Ádám

    2016-01-01

    Microglia are the main immune cells of the brain and contribute to common brain diseases. However, it is unclear how microglia influence neuronal activity and survival in the injured brain in vivo. Here we develop a precisely controlled model of brain injury induced by cerebral ischaemia combined with fast in vivo two-photon calcium imaging and selective microglial manipulation. We show that selective elimination of microglia leads to a striking, 60% increase in infarct size, which is reversed by microglial repopulation. Microglia-mediated protection includes reduction of excitotoxic injury, since an absence of microglia leads to dysregulated neuronal calcium responses, calcium overload and increased neuronal death. Furthermore, the incidence of spreading depolarization (SD) is markedly reduced in the absence of microglia. Thus, microglia are involved in changes in neuronal network activity and SD after brain injury in vivo that could have important implications for common brain diseases. PMID:27139776

  3. Visible rodent brain-wide networks at single-neuron resolution

    PubMed Central

    Yuan, Jing; Gong, Hui; Li, Anan; Li, Xiangning; Chen, Shangbin; Zeng, Shaoqun; Luo, Qingming

    2015-01-01

    There are some unsolvable fundamental questions, such as cell type classification, neural circuit tracing and neurovascular coupling, though great progresses are being made in neuroscience. Because of the structural features of neurons and neural circuits, the solution of these questions needs us to break through the current technology of neuroanatomy for acquiring the exactly fine morphology of neuron and vessels and tracing long-distant circuit at axonal resolution in the whole brain of mammals. Combined with fast-developing labeling techniques, efficient whole-brain optical imaging technology emerging at the right moment presents a huge potential in the structure and function research of specific-function neuron and neural circuit. In this review, we summarize brain-wide optical tomography techniques, review the progress on visible brain neuronal/vascular networks benefit from these novel techniques, and prospect the future technical development. PMID:26074784

  4. Development of coherent neuronal activity patterns in mammalian cortical networks: common principles and local hetereogeneity.

    PubMed

    Egorov, Alexei V; Draguhn, Andreas

    2013-01-01

    Many mammals are born in a very immature state and develop their rich repertoire of behavioral and cognitive functions postnatally. This development goes in parallel with changes in the anatomical and functional organization of cortical structures which are involved in most complex activities. The emerging spatiotemporal activity patterns in multi-neuronal cortical networks may indeed form a direct neuronal correlate of systemic functions like perception, sensorimotor integration, decision making or memory formation. During recent years, several studies--mostly in rodents--have shed light on the ontogenesis of such highly organized patterns of network activity. While each local network has its own peculiar properties, some general rules can be derived. We therefore review and compare data from the developing hippocampus, neocortex and--as an intermediate region--entorhinal cortex. All cortices seem to follow a characteristic sequence starting with uncorrelated activity in uncoupled single neurons where transient activity seems to have mostly trophic effects. In rodents, before and shortly after birth, cortical networks develop weakly coordinated multineuronal discharges which have been termed synchronous plateau assemblies (SPAs). While these patterns rely mostly on electrical coupling by gap junctions, the subsequent increase in number and maturation of chemical synapses leads to the generation of large-scale coherent discharges. These patterns have been termed giant depolarizing potentials (GDPs) for predominantly GABA-induced events or early network oscillations (ENOs) for mostly glutamatergic bursts, respectively. During the third to fourth postnatal week, cortical areas reach their final activity patterns with distinct network oscillations and highly specific neuronal discharge sequences which support adult behavior. While some of the mechanisms underlying maturation of network activity have been elucidated much work remains to be done in order to fully

  5. Control of bursting synchronization in networks of Hodgkin-Huxley-type neurons with chemical synapses

    NASA Astrophysics Data System (ADS)

    Batista, C. A. S.; Viana, R. L.; Ferrari, F. A. S.; Lopes, S. R.; Batista, A. M.; Coninck, J. C. P.

    2013-04-01

    Thermally sensitive neurons present bursting activity for certain temperature ranges, characterized by fast repetitive spiking of action potential followed by a short quiescent period. Synchronization of bursting activity is possible in networks of coupled neurons, and it is sometimes an undesirable feature. Control procedures can suppress totally or partially this collective behavior, with potential applications in deep-brain stimulation techniques. We investigate the control of bursting synchronization in small-world networks of Hodgkin-Huxley-type thermally sensitive neurons with chemical synapses through two different strategies. One is the application of an external time-periodic electrical signal and another consists of a time-delayed feedback signal. We consider the effectiveness of both strategies in terms of protocols of applications suitable to be applied by pacemakers.

  6. Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    PubMed Central

    Tessadori, Jacopo; Chiappalone, Michela

    2015-01-01

    Information coding in the Central Nervous System (CNS) remains unexplored. There is mounting evidence that, even at a very low level, the representation of a given stimulus might be dependent on context and history. If this is actually the case, bi-directional interactions between the brain (or if need be a reduced model of it) and sensory-motor system can shed a light on how encoding and decoding of information is performed. Here an experimental system is introduced and described in which the activity of a neuronal element (i.e., a network of neurons extracted from embryonic mammalian hippocampi) is given context and used to control the movement of an artificial agent, while environmental information is fed back to the culture as a sequence of electrical stimuli. This architecture allows a quick selection of diverse encoding, decoding, and learning algorithms to test different hypotheses on the computational properties of neuronal networks. PMID:25867052

  7. The formation and distribution of hippocampal synapses on patterned neuronal networks

    NASA Astrophysics Data System (ADS)

    Dowell-Mesfin, Natalie M.

    Communication within the central nervous system is highly orchestrated with neurons forming trillions of specialized junctions called synapses. In vivo, biochemical and topographical cues can regulate neuronal growth. Biochemical cues also influence synaptogenesis and synaptic plasticity. The effects of topography on the development of synapses have been less studied. In vitro, neuronal growth is unorganized and complex making it difficult to study the development of networks. Patterned topographical cues guide and control the growth of neuronal processes (axons and dendrites) into organized networks. The aim of this dissertation was to determine if patterned topographical cues can influence synapse formation and distribution. Standard fabrication and compression molding procedures were used to produce silicon masters and polystyrene replicas with topographical cues presented as 1 mum high pillars with diameters of 0.5 and 2.0 mum and gaps of 1.0 to 5.0 mum. Embryonic rat hippocampal neurons grown unto patterned surfaces. A developmental analysis with immunocytochemistry was used to assess the distribution of pre- and post-synaptic proteins. Activity-dependent pre-synaptic vesicle uptake using functional imaging dyes was also performed. Adaptive filtering computer algorithms identified synapses by segmenting juxtaposed pairs of pre- and post-synaptic labels. Synapse number and area were automatically extracted from each deconvolved data set. In addition, neuronal processes were traced automatically to assess changes in synapse distribution. The results of these experiments demonstrated that patterned topographic cues can induce organized and functional neuronal networks that can serve as models for the study of synapse formation and plasticity as well as for the development of neuroprosthetic devices.

  8. Spiny neurons of amygdala, striatum, and cortex use dendritic plateau potentials to detect network UP states

    PubMed Central

    Oikonomou, Katerina D.; Singh, Mandakini B.; Sterjanaj, Enas V.; Antic, Srdjan D.

    2014-01-01

    Spiny neurons of amygdala, striatum, and cerebral cortex share four interesting features: (1) they are the most abundant cell type within their respective brain area, (2) covered by thousands of thorny protrusions (dendritic spines), (3) possess high levels of dendritic NMDA conductances, and (4) experience sustained somatic depolarizations in vivo and in vitro (UP states). In all spiny neurons of the forebrain, adequate glutamatergic inputs generate dendritic plateau potentials (“dendritic UP states”) characterized by (i) fast rise, (ii) plateau phase lasting several hundred milliseconds, and (iii) abrupt decline at the end of the plateau phase. The dendritic plateau potential propagates toward the cell body decrementally to induce a long-lasting (longer than 100 ms, most often 200–800 ms) steady depolarization (∼20 mV amplitude), which resembles a neuronal UP state. Based on voltage-sensitive dye imaging, the plateau depolarization in the soma is precisely time-locked to the regenerative plateau potential taking place in the dendrite. The somatic plateau rises after the onset of the dendritic voltage transient and collapses with the breakdown of the dendritic plateau depolarization. We hypothesize that neuronal UP states in vivo reflect the occurrence of dendritic plateau potentials (dendritic UP states). We propose that the somatic voltage waveform during a neuronal UP state is determined by dendritic plateau potentials. A mammalian spiny neuron uses dendritic plateau potentials to detect and transform coherent network activity into a ubiquitous neuronal UP state. The biophysical properties of dendritic plateau potentials allow neurons to quickly attune to the ongoing network activity, as well as secure the stable amplitudes of successive UP states. PMID:25278841

  9. [Identification of hashish samples with inductively coupled high-frequency plasma emission spectrometry and neutron activation analysis and data handling with neuronal networks. 1. Methods for the quantitative determination of characteristic trace elements].

    PubMed

    Lahl, H; Henke, G

    1997-11-01

    Neutron activation analysis (NAA) and inductively coupled plasma emission spectrometry (ICP-AES) were used to quantify the relative contents of Fe, Sc, Ce, Pa, Cr, Co, respectively the absolute contents of Cr, Zn, Mn, Fe, Mg, Al, Cu, Ti, Ca, Sr in hashish samples, seized in different countries. The samples were processed after dry ashing by means of instrumental NAA and after wet mineralization by means of ICP-AES. For determination of the sampling and measurement errors, one of the samples was analyzed repeatedly with both methods. Classifying hashish samples with regard to concentration of certain elements could be done by artificial neural networks with a modified backpropagation algorithm. By this way, identity and non identity of one unknown sample with one of many different samples as data pool can be ascertained, on principle. PMID:9446107

  10. A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations

    PubMed Central

    Hahne, Jan; Helias, Moritz; Kunkel, Susanne; Igarashi, Jun; Bolten, Matthias; Frommer, Andreas; Diesmann, Markus

    2015-01-01

    Contemporary simulators for networks of point and few-compartment model neurons come with a plethora of ready-to-use neuron and synapse models and support complex network topologies. Recent technological advancements have broadened the spectrum of application further to the efficient simulation of brain-scale networks on supercomputers. In distributed network simulations the amount of spike data that accrues per millisecond and process is typically low, such that a common optimization strategy is to communicate spikes at relatively long intervals, where the upper limit is given by the shortest synaptic transmission delay in the network. This approach is well-suited for simulations that employ only chemical synapses but it has so far impeded the incorporation of gap-junction models, which require instantaneous neuronal interactions. Here, we present a numerical algorithm based on a waveform-relaxation technique which allows for network simulations with gap junctions in a way that is compatible with the delayed communication strategy. Using a reference implementation in the NEST simulator, we demonstrate that the algorithm and the required data structures can be smoothly integrated with existing code such that they complement the infrastructure for spiking connections. To show that the unified framework for gap-junction and spiking interactions achieves high performance and delivers high accuracy in the presence of gap junctions, we present benchmarks for workstations, clusters, and supercomputers. Finally, we discuss limitations of the novel technology. PMID:26441628

  11. A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations.

    PubMed

    Hahne, Jan; Helias, Moritz; Kunkel, Susanne; Igarashi, Jun; Bolten, Matthias; Frommer, Andreas; Diesmann, Markus

    2015-01-01

    Contemporary simulators for networks of point and few-compartment model neurons come with a plethora of ready-to-use neuron and synapse models and support complex network topologies. Recent technological advancements have broadened the spectrum of application further to the efficient simulation of brain-scale networks on supercomputers. In distributed network simulations the amount of spike data that accrues per millisecond and process is typically low, such that a common optimization strategy is to communicate spikes at relatively long intervals, where the upper limit is given by the shortest synaptic transmission delay in the network. This approach is well-suited for simulations that employ only chemical synapses but it has so far impeded the incorporation of gap-junction models, which require instantaneous neuronal interactions. Here, we present a numerical algorithm based on a waveform-relaxation technique which allows for network simulations with gap junctions in a way that is compatible with the delayed communication strategy. Using a reference implementation in the NEST simulator, we demonstrate that the algorithm and the required data structures can be smoothly integrated with existing code such that they complement the infrastructure for spiking connections. To show that the unified framework for gap-junction and spiking interactions achieves high performance and delivers high accuracy in the presence of gap junctions, we present benchmarks for workstations, clusters, and supercomputers. Finally, we discuss limitations of the novel technology. PMID:26441628

  12. The generation of the synchronized burst in the cultured neuronal networks

    NASA Astrophysics Data System (ADS)

    Li, Xiangning; Sun, Jing; Chen, Wenjuan; Zeng, Shaoqun; Luo, Qingming

    2009-02-01

    The spontaneous synchronous activity is a common behavior in a developing brain and plays a critical role in establishing appropriate connections and certain clinical diseases. Therefore, the investigation of the synchronous firing is important for understanding the formation of functional circuits and their implications in the network plasticity. In a limited period of time during development, the neuronal networks show synchronous activities, which occur simultaneously on a large amount of cells and varies wildly among different preparations. In this study, the spontaneous synchronous bursts are observed during the development of cultured neuron networks on multi-electrode array. The initiating site of a round of spontaneous synchronous burst, estimated from the relative delays of onsets of activities between electrodes, distributed randomly from each burst, while our statistical results confirmed that the positions of such initiating sites are stable. By calculating the cross-correlation function of the spike trains recorded from different electrodes simultaneously, the spreading mode and the spreading topography of the synchronized bursting activity were described. To access the changes in firing patterns in disinhibited cultured networks, the spontaneous activities were compared with the firings when the network exposed to bicuculline, the blocker of GABAA receptor. The results showed that the generation of synchronous bursts in cultured neuron networks is governed by the level of spontaneous activities and by the balance between excitation and inhibition circuits.

  13. Large-scale modeling of epileptic seizures: scaling properties of two parallel neuronal network simulation algorithms.

    PubMed

    Pesce, Lorenzo L; Lee, Hyong C; Hereld, Mark; Visser, Sid; Stevens, Rick L; Wildeman, Albert; van Drongelen, Wim

    2013-01-01

    Our limited understanding of the relationship between the behavior of individual neurons and large neuronal networks is an important limitation in current epilepsy research and may be one of the main causes of our inadequate ability to treat it. Addressing this problem directly via experiments is impossibly complex; thus, we have been developing and studying medium-large-scale simulations of detailed neuronal networks to guide us. Flexibility in the connection schemas and a complete description of the cortical tissue seem necessary for this purpose. In this paper we examine some of the basic issues encountered in these multiscale simulations. We have determined the detailed behavior of two such simulators on parallel computer systems. The observed memory and computation-time scaling behavior for a distributed memory implementation were very good over the range studied, both in terms of network sizes (2,000 to 400,000 neurons) and processor pool sizes (1 to 256 processors). Our simulations required between a few megabytes and about 150 gigabytes of RAM and lasted between a few minutes and about a week, well within the capability of most multinode clusters. Therefore, simulations of epileptic seizures on networks with millions of cells should be feasible on current supercomputers. PMID:24416069

  14. Large-Scale Modeling of Epileptic Seizures: Scaling Properties of Two Parallel Neuronal Network Simulation Algorithms

    DOE PAGESBeta

    Pesce, Lorenzo L.; Lee, Hyong C.; Hereld, Mark; Visser, Sid; Stevens, Rick L.; Wildeman, Albert; van Drongelen, Wim

    2013-01-01

    Our limited understanding of the relationship between the behavior of individual neurons and large neuronal networks is an important limitation in current epilepsy research and may be one of the main causes of our inadequate ability to treat it. Addressing this problem directly via experiments is impossibly complex; thus, we have been developing and studying medium-large-scale simulations of detailed neuronal networks to guide us. Flexibility in the connection schemas and a complete description of the cortical tissue seem necessary for this purpose. In this paper we examine some of the basic issues encountered in these multiscale simulations. We have determinedmore » the detailed behavior of two such simulators on parallel computer systems. The observed memory and computation-time scaling behavior for a distributed memory implementation were very good over the range studied, both in terms of network sizes (2,000 to 400,000 neurons) and processor pool sizes (1 to 256 processors). Our simulations required between a few megabytes and about 150 gigabytes of RAM and lasted between a few minutes and about a week, well within the capability of most multinode clusters. Therefore, simulations of epileptic seizures on networks with millions of cells should be feasible on current supercomputers.« less

  15. Effects of Organophosphorus Flame Retardants on Spontaneous Activity in Neuronal Networks Grown on Microelectrode Arrays

    EPA Science Inventory

    EFFECTS OF ORGANOPHOSPHORUS FLAME RETARDANTS ON SPONTANEOUS ACTIVITY IN NEURONAL NETWORKS GROWN ON MICROELECTRODE ARRAYS TJ Shafer1, K Wallace1, WR Mundy1, M Behl2,. 1Integrated Systems Toxicology Division, NHEERL, USEPA, RTP, NC, USA, 2National Toxicology Program, NIEHS, RTP, NC...

  16. Repeated Stimulation of Cultured Networks of Rat Cortical Neurons Induces Parallel Memory Traces

    ERIC Educational Resources Information Center

    le Feber, Joost; Witteveen, Tim; van Veenendaal, Tamar M.; Dijkstra, Jelle

    2015-01-01

    During systems consolidation, memories are spontaneously replayed favoring information transfer from hippocampus to neocortex. However, at present no empirically supported mechanism to accomplish a transfer of memory from hippocampal to extra-hippocampal sites has been offered. We used cultured neuronal networks on multielectrode arrays and…

  17. Detection of marine toxins, brevetoxin-3 and saxitoxin, in seawater using neuronal networks.

    PubMed

    Kulagina, Nadezhda V; Mikulski, Christina M; Gray, Samuel; Ma, Wu; Doucette, Gregory J; Ramsdell, John S; Pancrazio, Joseph J

    2006-01-15

    There is a need for assay systems that can detect known and unanticipated neurotoxins associated with harmful algal blooms. The present work describes our attempt to monitor the presence of brevetoxin-3 (PbTx-3) and saxitoxin (STX) in a seawater matrix using the neuronal network biosensor (NNB). The NNB relies on cultured mammalian neurons grown over microelectrode arrays, where the inherent bioelectrical activity of the network manifested as extracellular action potentials can be monitored noninvasively. Spinal cord neuronal networks were prepared from embryonic mice and the mean spike rate across the network was analyzed before and during exposure to the toxins. Extracellular action potentials from the network are highly sensitive not only to purified STX and PbTx-3, but also when in combination with matrixes such as natural seawater and algal growth medium. Detection limits for STX and PbTx-3, respectively, are 0.031 and 0.33 nM in recording buffer and 0.076 and 0.48 nM in the presence of 25-fold-diluted seawater. Our results demonstrated that neuronal networks could be used for analysis of Alexandrium fundyense (STX-producer) and Karenia brevis (PbTx-producer) algal samples lysed directly in the seawater-based growth medium and appropriately diluted with HEPES-buffered recording medium. The cultured network responded by changes in mean spike rate to the presence of STX-or PbTx-producing algae but not to the samples of two non-STX and non-PbTx isolates of the same algal genera. This work provides evidence that the NNB has the capacity to rapidly detect toxins associated with cells of toxic algal species or as dissolved forms present in seawater and hasthe potential for monitoring toxin levels during harmful algal blooms. PMID:16468405

  18. Functional Evaluation of Biological Neurotoxins in Networked Cultures of Stem Cell-derived Central Nervous System Neurons

    PubMed Central

    Hubbard, Kyle; Beske, Phillip; Lyman, Megan; McNutt, Patrick

    2015-01-01

    Therapeutic and mechanistic studies of the presynaptically targeted clostridial neurotoxins (CNTs) have been limited by the need for a scalable, cell-based model that produces functioning synapses and undergoes physiological responses to intoxication. Here we describe a simple and robust method to efficiently differentiate murine embryonic stem cells (ESCs) into defined lineages of synaptically active, networked neurons. Following an 8 day differentiation protocol, mouse embryonic stem cell-derived neurons (ESNs) rapidly express and compartmentalize neurotypic proteins, form neuronal morphologies and develop intrinsic electrical responses. By 18 days after differentiation (DIV 18), ESNs exhibit active glutamatergic and γ-aminobutyric acid (GABA)ergic synapses and emergent network behaviors characterized by an excitatory:inhibitory balance. To determine whether intoxication with CNTs functionally antagonizes synaptic neurotransmission, thereby replicating the in vivo pathophysiology that is responsible for clinical manifestations of botulism or tetanus, whole-cell patch clamp electrophysiology was used to quantify spontaneous miniature excitatory post-synaptic currents (mEPSCs) in ESNs exposed to tetanus neurotoxin (TeNT) or botulinum neurotoxin (BoNT) serotypes /A-/G. In all cases, ESNs exhibited near-complete loss of synaptic activity within 20 hr. Intoxicated neurons remained viable, as demonstrated by unchanged resting membrane potentials and intrinsic electrical responses. To further characterize the sensitivity of this approach, dose-dependent effects of intoxication on synaptic activity were measured 20 hr after addition of BoNT/A. Intoxication with 0.005 pM BoNT/A resulted in a significant decrement in mEPSCs, with a median inhibitory concentration (IC50) of 0.013 pM. Comparisons of median doses indicate that functional measurements of synaptic inhibition are faster, more specific and more sensitive than SNARE cleavage assays or the mouse lethality assay

  19. Deriving functional structure of neuronal networks from spike train data

    NASA Astrophysics Data System (ADS)

    Feldt, Sarah; Hetrick, Vaughn; Berke, Joshua; Zochowski, Michal

    2009-03-01

    We present a novel algorithm for the detection of functional clusters in neural data. In contrast to many clustering techniques which convert functional interactions to topological distances to determine groupings, our algorithm directly utilizes the dynamics of the neurons to obtain functional groupings. No prior knowledge of the number of groups is needed, as the algorithm determines statistically significant clusters through a comparison to surrogate data sets. Additionally, we introduce a new synchronization measure and use this measure in the algorithm to observe known groupings in simulated data. We then apply our algorithm to experimental data obtained from the hippocampus of a freely moving mouse and show that it detects known changes in neural states associated with exploration and slow wave sleep. Finally, we show that the new synchronization measure can detect changes which are consistent with known neurophysiological processes involved in memory consolidation.

  20. Spontaneous Neuronal Network Dynamics Reveal Circuit’s Functional Adaptations for Behavior

    PubMed Central

    Romano, Sebastián A.; Pietri, Thomas; Pérez-Schuster, Verónica; Jouary, Adrien; Haudrechy, Mathieu; Sumbre, Germán

    2015-01-01

    Summary Spontaneous neuronal activity is spatiotemporally structured, influencing brain computations. Nevertheless, the neuronal interactions underlying these spontaneous activity patterns, and their biological relevance, remain elusive. Here, we addressed these questions using two-photon calcium imaging of intact zebrafish larvae to monitor the neuron-to-neuron spontaneous activity fine structure in the tectum, a region involved in visual spatial detection. Spontaneous activity was organized in topographically compact assemblies, grouping functionally similar neurons rather than merely neighboring ones, reflecting the tectal retinotopic map despite being independent of retinal drive. Assemblies represent all-or-none-like sub-networks shaped by competitive dynamics, mechanisms advantageous for visual detection in noisy natural environments. Notably, assemblies were tuned to the same angular sizes and spatial positions as prey-detection performance in behavioral assays, and their spontaneous activation predicted directional tail movements. Therefore, structured spontaneous activity represents “preferred” network states, tuned to behaviorally relevant features, emerging from the circuit’s intrinsic non-linear dynamics, adapted for its functional role. PMID:25704948

  1. Two Functionally Distinct Networks of Gap Junction-Coupled Inhibitory Neurons in the Thalamic Reticular Nucleus

    PubMed Central

    Patrick, Saundra L.; Richardson, Kristen A.

    2014-01-01

    Gap junctions (GJs) electrically couple GABAergic neurons of the forebrain. The spatial organization of neuron clusters coupled by GJs is an important determinant of network function, yet it is poorly described for nearly all mammalian brain regions. Here we used a novel dye-coupling technique to show that GABAergic neurons in the thalamic reticular nucleus (TRN) of mice and rats form two types of GJ-coupled clusters with distinctive patterns and axonal projections. Most clusters are elongated narrowly along functional modules within the plane of the TRN, with axons that selectively inhibit local groups of relay neurons. However, some coupled clusters have neurons arrayed across the thickness of the TRN and target their axons to both first- and higher-order relay nuclei. Dye coupling was reduced, but not abolished, among cells of connexin36 knock-out mice. Our results suggest that GJs form two distinct types of inhibitory networks that correlate activity either within or across functional modules of the thalamus. PMID:25253862

  2. A method for immunolabeling neurons in intact cuticularized insect appendages.

    PubMed

    Ehrhardt, Erica; Kleele, Tatjana; Boyan, George

    2015-06-01

    The antennae of the grasshopper Schistocerca gregaria possess a pair of nerve pathways which are established by so-called pioneer neurons early in embryonic development. Subsequently, sensory cell clusters mediating olfaction, flight, optomotor responses, and phase changes differentiate from the antennal epithelium at stereotypic locations and direct their axons onto those of the pioneers to then project to the brain. Early in embryonic development, before the antennae become cuticularized, immunolabeling can be used to follow axogenesis in these pioneers and sensory cells. At later stages, immunolabeling becomes problematical as the cuticle is laid down and masks internal antigen sites. In order to immunolabel the nervous system of cuticularized late embryonic and first instar grasshopper antennae, we modified a procedure known as sonication in which the appendage is exposed to ultrasound thereby rendering it porous to antibodies. Comparisons of the immunolabeled nervous system of sectioned and sonicated antennae show that the cellular organization of sensory clusters and their axon projections is intact. The expression patterns of neuron-specific, microtubule-specific, and proliferative cell-specific labels in treated antennae are consistent with those reported for earlier developmental stages where sonication is not necessary, suggesting that these molecular epitopes are also conserved. The method ensures reliable immunolabeling in intact, cuticularized appendages so that the peripheral nervous system can be reconstructed directly via confocal microscopy throughout development. PMID:25868908

  3. Stability analysis of associative memory network composed of stochastic neurons and dynamic synapses

    PubMed Central

    Katori, Yuichi; Otsubo, Yosuke; Okada, Masato; Aihara, Kazuyuki

    2013-01-01

    We investigate the dynamical properties of an associative memory network consisting of stochastic neurons and dynamic synapses that show short-term depression and facilitation. In the stochastic neuron model used in this study, the efficacy of the synaptic transmission changes according to the short-term depression or facilitation mechanism. We derive a macroscopic mean field model that captures the overall dynamical properties of the stochastic model. We analyze the stability and bifurcation structure of the mean field model, and show the dependence of the memory retrieval performance on the noise intensity and parameters that determine the properties of the dynamic synapses, i.e., time constants for depressing and facilitating processes. The associative memory network exhibits a variety of dynamical states, including the memory and pseudo-memory states, as well as oscillatory states among memory patterns. This study provides comprehensive insight into the dynamical properties of the associative memory network with dynamic synapses. PMID:23440567

  4. Oscillations, complex spatiotemporal behavior, and information transport in networks of excitatory and inhibitory neurons

    SciTech Connect

    Destexhe, A. )

    1994-08-01

    Various types of spatiotemporal behavior are described for two-dimensional networks of excitatory and inhibitory neurons with time delayed interactions. It is described how the network behaves as several structural parameters are varied, such as the number of neurons, the connectivity, and the values of synaptic weights. A transition from spatially uniform oscillations to spatiotemporal chaos via intermittentlike behavior is observed. The properties of spatiotemporally chaotic solutions are investigated by evaluating the largest positive Lyapunov exponent and the loss of correlation with distance. Finally, properties of information transport are evaluated during uniform oscillations and spatiotemporal chaos. It is shown that the diffusion coefficient increases significantly in the spatiotemporal phase similar to the increase of transport coefficients at the onset of fluid turbulence. It is proposed that such a property should be seen in other media, such as chemical turbulence or networks of oscillators. The possibility of measuring information transport from appropriate experiments is also discussed.

  5. Lactate Effectively Covers Energy Demands during Neuronal Network Activity in Neonatal Hippocampal Slices

    PubMed Central

    Ivanov, Anton; Mukhtarov, Marat; Bregestovski, Piotr; Zilberter, Yuri

    2011-01-01

    Although numerous experimental data indicate that lactate is efficiently used for energy by the mature brain, the direct measurements of energy metabolism parameters during neuronal network activity in early postnatal development have not been performed. Therefore, the role of lactate in the energy metabolism of neurons at this age remains unclear. In this study, we monitored field potentials and contents of oxygen and NAD(P)H in correlation with oxidative metabolism during intense network activity in the CA1 hippocampal region of neonatal brain slices. We show that in the presence of glucose, lactate is effectively utilized as an energy substrate, causing an augmentation of oxidative metabolism. Moreover, in the absence of glucose lactate is fully capable of maintaining synaptic function. Therefore, during network activity in neonatal slices, lactate can be an efficient energy substrate capable of sustaining and enhancing aerobic energy metabolism. PMID:21602909

  6. Accelerated intoxication of GABAergic synapses by botulinum neurotoxin A disinhibits stem cell-derived neuron networks prior to network silencing

    PubMed Central

    Beske, Phillip H.; Scheeler, Stephen M.; Adler, Michael; McNutt, Patrick M.

    2015-01-01

    Botulinum neurotoxins (BoNTs) are extremely potent toxins that specifically cleave SNARE proteins in peripheral synapses, preventing neurotransmitter release. Neuronal responses to BoNT intoxication are traditionally studied by quantifying SNARE protein cleavage in vitro or monitoring physiological paralysis in vivo. Consequently, the dynamic effects of intoxication on synaptic behaviors are not well-understood. We have reported that mouse embryonic stem cell-derived neurons (ESNs) are highly sensitive to BoNT based on molecular readouts of intoxication. Here we study the time-dependent changes in synapse- and network-level behaviors following addition of BoNT/A to spontaneously active networks of glutamatergic and GABAergic ESNs. Whole-cell patch-clamp recordings indicated that BoNT/A rapidly blocked synaptic neurotransmission, confirming that ESNs replicate the functional pathophysiology responsible for clinical botulism. Quantitation of spontaneous neurotransmission in pharmacologically isolated synapses revealed accelerated silencing of GABAergic synapses compared to glutamatergic synapses, which was consistent with the selective accumulation of cleaved SNAP-25 at GAD1+ pre-synaptic terminals at early timepoints. Different latencies of intoxication resulted in complex network responses to BoNT/A addition, involving rapid disinhibition of stochastic firing followed by network silencing. Synaptic activity was found to be highly sensitive to SNAP-25 cleavage, reflecting the functional consequences of the localized cleavage of the small subpopulation of SNAP-25 that is engaged in neurotransmitter release in the nerve terminal. Collectively these findings illustrate that use of synaptic function assays in networked neurons cultures offers a novel and highly sensitive approach for mechanistic studies of toxin:neuron interactions and synaptic responses to BoNT. PMID:25954159

  7. Spike-timing-dependent plasticity in spiking neuron networks for robot navigation control

    NASA Astrophysics Data System (ADS)

    Arena, Paolo; Danieli, Fabio; Fortuna, Luigi; Frasca, Mattia; Patane, Luca

    2005-06-01

    In this paper a biologically-inspired network of spiking neurons is used for robot navigation control. The implemented scheme is able to process information coming from the robot contact sensors in order to avoid obstacles and on the basis of these actions to learn how to respond to stimuli coming from range finder sensors. The implemented network is therefore able of reinforcement learning through a mechanism based on operant conditioning. This learning takes place according to a plasticity law in the synapses, based on spike timing. Simulation results discussed in the paper show the suitability of the approach and interesting adaptive properties of the network.

  8. Simulated generation of evoked potentials components using networks with distinct excitatory and inhibitory neurons.

    PubMed

    Ventouras, E; Uzunoglu, N K; Koutsouris, D; Papageorgiou, C; Rabavilas, A; Stefanis, C

    2000-09-01

    Long latency evoked potentials (EP's) are electrical potentials related to brain information processing mechanisms. In this paper, three-layered neurophysiologically based artificial neural network model is presented whose neurons obey to Dale's law. The first two layers of the network can memorize and recall sparsely coded patterns, oscillating at biologically plausible frequencies. Excitatory low-pass filtering synapses, from the second to the third layer, create evoked current dipoles, when the network retrieves memories related to stimuli. Based on psychophysiological indications, simulated intracranial dipoles are straightforwardly transformed into long latency EP components such as N100 and P300 that match laboratory-measured scalp EP's. PMID:11026594

  9. Interplay between Graph Topology and Correlations of Third Order in Spiking Neuronal Networks

    PubMed Central

    Jovanović, Stojan

    2016-01-01

    The study of processes evolving on networks has recently become a very popular research field, not only because of the rich mathematical theory that underpins it, but also because of its many possible applications, a number of them in the field of biology. Indeed, molecular signaling pathways, gene regulation, predator-prey interactions and the communication between neurons in the brain can be seen as examples of networks with complex dynamics. The properties of such dynamics depend largely on the topology of the underlying network graph. In this work, we want to answer the following question: Knowing network connectivity, what can be said about the level of third-order correlations that will characterize the network dynamics? We consider a linear point process as a model for pulse-coded, or spiking activity in a neuronal network. Using recent results from theory of such processes, we study third-order correlations between spike trains in such a system and explain which features of the network graph (i.e. which topological motifs) are responsible for their emergence. Comparing two different models of network topology—random networks of Erdős-Rényi type and networks with highly interconnected hubs—we find that, in random networks, the average measure of third-order correlations does not depend on the local connectivity properties, but rather on global parameters, such as the connection probability. This, however, ceases to be the case in networks with a geometric out-degree distribution, where topological specificities have a strong impact on average correlations. PMID:27271768

  10. Interplay between Graph Topology and Correlations of Third Order in Spiking Neuronal Networks.

    PubMed

    Jovanović, Stojan; Rotter, Stefan

    2016-06-01

    The study of processes evolving on networks has recently become a very popular research field, not only because of the rich mathematical theory that underpins it, but also because of its many possible applications, a number of them in the field of biology. Indeed, molecular signaling pathways, gene regulation, predator-prey interactions and the communication between neurons in the brain can be seen as examples of networks with complex dynamics. The properties of such dynamics depend largely on the topology of the underlying network graph. In this work, we want to answer the following question: Knowing network connectivity, what can be said about the level of third-order correlations that will characterize the network dynamics? We consider a linear point process as a model for pulse-coded, or spiking activity in a neuronal network. Using recent results from theory of such processes, we study third-order correlations between spike trains in such a system and explain which features of the network graph (i.e. which topological motifs) are responsible for their emergence. Comparing two different models of network topology-random networks of Erdős-Rényi type and networks with highly interconnected hubs-we find that, in random networks, the average measure of third-order correlations does not depend on the local connectivity properties, but rather on global parameters, such as the connection probability. This, however, ceases to be the case in networks with a geometric out-degree distribution, where topological specificities have a strong impact on average correlations. PMID:27271768

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

    PubMed

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

    2011-12-01

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

  12. Neurons with hysteresis from a network that can learn without any changes in synaptic connection strengths

    SciTech Connect

    Hoffmann, G.W.; Benson, M.W.

    1986-01-01

    A neural network concept derived from an analogy between the immune system and the central nervous system is outlined. The theory is based on a neuron that is slightly more complicated than the conventional McCullogh-Pitts type of neuron, in that is exhibits hysteresis at the single cell level. This added complication is compensated by the fact that a network of such neurons is able to learn without the necessity for any changes in synaptic connection strengths. The learning occurs as a neural consequence of interactions between the network and its environment, with environmental stimuli moving the system around in an N-dimensional phase space, until a point in phase space is reached such that the system's responses are appropriate for dealing with the stimuli. Due to the hysteresis associated with each neuron, the system tends to stay in the region of phase space where it is located. The theory includes a role for sleep in learning. 18 refs., 2 figs.

  13. Dynamics of Disordered Network of Coupled Hindmarsh-Rose Neuronal Models

    NASA Astrophysics Data System (ADS)

    Dtchetgnia Djeundam, S. R.; Yamapi, R.; Filatrella, G.; Kofane, T. C.

    We investigate the effects of disorder on the synchronized state of a network of Hindmarsh-Rose neuronal models. Disorder, introduced as a perturbation of the neuronal parameters, destroys the network activity by wrecking the synchronized state. The dynamics of the synchronized state is analyzed through the Kuramoto order parameter, adapted to the neuronal Hindmarsh-Rose model. We find that the coupling deeply alters the dynamics of the single units, thus demonstrating that coupling not only affects the relative motion of the units, but also the dynamical behavior of each neuron; Thus, synchronization results in a structural change of the dynamics. The Kuramoto order parameter allows to clarify the nature of the transition from perfect phase synchronization to the disordered states, supporting the notion of an abrupt, second order-like, dynamical phase transition. We find that the system is resilient up to a certain disorder threshold, after that the network abruptly collapses to a desynchronized state. The loss of perfect synchronization seems to occur even for vanishingly small values of the disorder, but the degree of synchronization (as measured by the Kuramoto order parameter) gently decreases, and the completely disordered state is never reached.

  14. Stability and chaos of Rulkov map-based neuron network with electrical synapse

    NASA Astrophysics Data System (ADS)

    Wang, Caixia; Cao, Hongjun

    2015-02-01

    In this paper, stability and chaos of a simple system consisting of two identical Rulkov map-based neurons with the bidirectional electrical synapse are investigated in detail. On the one hand, as a function of control parameters and electrical coupling strengthes, the conditions for stability of fixed points of this system are obtained by using the qualitative analysis. On the other hand, chaos in the sense of Marotto is proved by a strict mathematical way. These results could be useful for building-up large-scale neurons networks with specific dynamics and rich biophysical phenomena.

  15. A Short-Circuit Method for Networks.

    ERIC Educational Resources Information Center

    Ong, P. P.

    1983-01-01

    Describes a method of network analysis that allows avoidance of Kirchoff's Laws (providing the network is symmetrical) by reduction to simple series/parallel resistances. The method can be extended to symmetrical alternating current, capacitance or inductance if corresponding theorems are used. Symmetric cubic network serves as an example. (JM)

  16. Suppression of bursting synchronization in clustered scale-free (rich-club) neuronal networks

    NASA Astrophysics Data System (ADS)

    Lameu, E. L.; Batista, C. A. S.; Batista, A. M.; Iarosz, K.; Viana, R. L.; Lopes, S. R.; Kurths, J.

    2012-12-01

    Functional brain networks are composed of cortical areas that are anatomically and functionally connected. One of the cortical networks for which more information is available in the literature is the cat cerebral cortex. Statistical analyses of the latter suggest that its structure can be described as a clustered network, in which each cluster is a scale-free network possessing highly connected hubs. Those hubs are, on their hand, connected together in a strong fashion ("rich-club" network). We have built a clustered scale-free network inspired in the cat cortex structure so as to study their dynamical properties. In this article, we focus on the synchronization of bursting activity of the cortical areas and how it can be suppressed by means of neuron deactivation through suitably applied light pulses. We show that it is possible to effectively suppress bursting synchronization by acting on a single, yet suitably chosen neuron, as long as it is highly connected, thanks to the "rich-club" structure of the network.

  17. Neuronal oscillations form parietal/frontal networks during contour integration

    PubMed Central

    Castellano, Marta; Plöchl, Michael; Vicente, Raul; Pipa, Gordon

    2014-01-01

    The ability to integrate visual features into a global coherent percept that can be further categorized and manipulated are fundamental abilities of the neural system. While the processing of visual information involves activation of early visual cortices, the recruitment of parietal and frontal cortices has been shown to be crucial for perceptual processes. Yet is it not clear how both cortical and long-range oscillatory activity leads to the integration of visual features into a coherent percept. Here, we will investigate perceptual grouping through the analysis of a contour categorization task, where the local elements that form contour must be linked into a coherent structure, which is then further processed and manipulated to perform the categorization task. The contour formation in our visual stimulus is a dynamic process where, for the first time, visual perception of contours is disentangled from the onset of visual stimulation or from motor preparation, cognitive processes that until now have been behaviorally attached to perceptual processes. Our main finding is that, while local and long-range synchronization at several frequencies seem to be an ongoing phenomena, categorization of a contour could only be predicted through local oscillatory activity within parietal/frontal sources, which in turn, would synchronize at gamma (>30 Hz) frequency. Simultaneously, fronto-parietal beta (13–30 Hz) phase locking forms a network spanning across neural sources that are not category specific. Both long range networks, i.e., the gamma network that is category specific, and the beta network that is not category specific, are functionally distinct but spatially overlapping. Altogether, we show that a critical mechanism underlying contour categorization involves oscillatory activity within parietal/frontal cortices, as well as its synchronization across distal cortical sites. PMID:25165437

  18. Endogenous cholinergic tone modulates spontaneous network level neuronal activity in primary cortical cultures grown on multi-electrode arrays

    PubMed Central

    2013-01-01

    Background Cortical cultures grown long-term on multi-electrode arrays (MEAs) are frequently and extensively used as models of cortical networks in studies of neuronal firing activity, neuropharmacology, toxicology and mechanisms underlying synaptic plasticity. However, in contrast to the predominantly asynchronous neuronal firing activity exhibited by intact cortex, electrophysiological activity of mature cortical cultures is dominated by spontaneous epileptiform-like global burst events which hinders their effective use in network-level studies, particularly for neurally-controlled animat (‘artificial animal’) applications. Thus, the identification of culture features that can be exploited to produce neuronal activity more representative of that seen in vivo could increase the utility and relevance of studies that employ these preparations. Acetylcholine has a recognised neuromodulatory role affecting excitability, rhythmicity, plasticity and information flow in vivo although its endogenous production by cortical cultures and subsequent functional influence upon neuronal excitability remains unknown. Results Consequently, using MEA electrophysiological recording supported by immunohistochemical and RT-qPCR methods, we demonstrate for the first time, the presence of intrinsic cholinergic neurons and significant, endogenous cholinergic tone in cortical cultures with a characterisation of the muscarinic and nicotinic components that underlie modulation of spontaneous neuronal activity. We found that tonic muscarinic ACh receptor (mAChR) activation affects global excitability and burst event regularity in a culture age-dependent manner whilst, in contrast, tonic nicotinic ACh receptor (nAChR) activation can modulate burst duration and the proportion of spikes occurring within bursts in a spatio-temporal fashion. Conclusions We suggest that the presence of significant endogenous cholinergic tone in cortical cultures and the comparability of its modulatory effects

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

    PubMed Central

    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

  20. Transient spatiotemporal chaos in a diffusively and synaptically coupled Morris-Lecar neuronal network

    NASA Astrophysics Data System (ADS)

    Lafranceschina, Jacopo

    Transient spatiotemporal chaos was reported in models for chemical reactions and in experiments for turbulence in shear flow. This study shows that transient spatiotemporal chaos also exists in a diffusively coupled Morris-Lecar (ML) neuronal network, with a collapse to either a global rest state or to a state of pulse propagation. Adding synaptic coupling to this network reduces the average lifetime of spatiotemporal chaos for small to intermediate coupling strengths and almost all numbers of synapses. For large coupling strengths, close to the threshold of excitation, the average lifetime increases beyond the value for only diffusive coupling, and the collapse to the rest state dominates over the collapse to a traveling pulse state. The regime of spatiotemporal chaos is characterized by a slightly increasing Lyapunov exponent and degree of phase coherence as the number of synaptic links increases. In contrast to the diffusive network, the pulse solution must not be asymptotic in the presence of synapses. The fact that chaos could be transient in higher dimensional systems, such as the one being explored in this study, point to its presence in every day life. Transient spatiotemporal chaos in a network of coupled neurons and the associated chaotic saddle provide a possibility for switching between metastable states observed in information processing and brain function. Such transient dynamics have been observed experimentally by Mazor, when stimulating projection neurons in the locust antennal lobe with different odors.

  1. Mean-field models for heterogeneous networks of two-dimensional integrate and fire neurons

    PubMed Central

    Nicola, Wilten; Campbell, Sue Ann

    2013-01-01

    We analytically derive mean-field models for all-to-all coupled networks of heterogeneous, adapting, two-dimensional integrate and fire neurons. The class of models we consider includes the Izhikevich, adaptive exponential and quartic integrate and fire models. The heterogeneity in the parameters leads to different moment closure assumptions that can be made in the derivation of the mean-field model from the population density equation for the large network. Three different moment closure assumptions lead to three different mean-field systems. These systems can be used for distinct purposes such as bifurcation analysis of the large networks, prediction of steady state firing rate distributions, parameter estimation for actual neurons and faster exploration of the parameter space. We use the mean-field systems to analyze adaptation induced bursting under realistic sources of heterogeneity in multiple parameters. Our analysis demonstrates that the presence of heterogeneity causes the Hopf bifurcation associated with the emergence of bursting to change from sub-critical to super-critical. This is confirmed with numerical simulations of the full network for biologically reasonable parameter values. This change decreases the plausibility of adaptation being the cause of bursting in hippocampal area CA3, an area with a sizable population of heavily coupled, strongly adapting neurons. PMID:24416013

  2. Beyond blow-up in excitatory integrate and fire neuronal networks: Refractory period and spontaneous activity.

    PubMed

    Cáceres, María J; Perthame, Benoît

    2014-06-01

    The Network Noisy Leaky Integrate and Fire equation is among the simplest model allowing for a self-consistent description of neural networks and gives a rule to determine the probability to find a neuron at the potential v. However, its mathematical structure is still poorly understood and, concerning its solutions, very few results are available. In the midst of them, a recent result shows blow-up in finite time for fully excitatory networks. The intuitive explanation is that each firing neuron induces a discharge of the others; thus increases the activity and consequently the discharge rate of the full network. In order to better understand the details of the phenomena and show that the equation is more complex and fruitful than expected, we analyze further the model. We extend the finite time blow-up result to the case when neurons, after firing, enter a refractory state for a given period of time. We also show that spontaneous activity may occur when, additionally, randomness is included on the firing potential VF in regimes where blow-up occurs for a fixed value of VF. PMID:24533963

  3. Dynamic stability of sequential stimulus representations in adapting neuronal networks

    PubMed Central

    Duarte, Renato C. F.; Morrison, Abigail

    2014-01-01

    The ability to acquire and maintain appropriate representations of time-varying, sequential stimulus events is a fundamental feature of neocortical circuits and a necessary first step toward more specialized information processing. The dynamical properties of such representations depend on the current state of the circuit, which is determined primarily by the ongoing, internally generated activity, setting the ground state from which input-specific transformations emerge. Here, we begin by demonstrating that timing-dependent synaptic plasticity mechanisms have an important role to play in the active maintenance of an ongoing dynamics characterized by asynchronous and irregular firing, closely resembling cortical activity in vivo. Incoming stimuli, acting as perturbations of the local balance of excitation and inhibition, require fast adaptive responses to prevent the development of unstable activity regimes, such as those characterized by a high degree of population-wide synchrony. We establish a link between such pathological network activity, which is circumvented by the action of plasticity, and a reduced computational capacity. Additionally, we demonstrate that the action of plasticity shapes and stabilizes the transient network states exhibited in the presence of sequentially presented stimulus events, allowing the development of adequate and discernible stimulus representations. The main feature responsible for the increased discriminability of stimulus-driven population responses in plastic networks is shown to be the decorrelating action of inhibitory plasticity and the consequent maintenance of the asynchronous irregular dynamic regime both for ongoing activity and stimulus-driven responses, whereas excitatory plasticity is shown to play only a marginal role. PMID:25374534

  4. Object-Oriented NeuroSys: Parallel Programs for Simulating Large Networks of Biologically Accurate Neurons

    SciTech Connect

    Pacheco, P; Miller, P; Kim, J; Leese, T; Zabiyaka, Y

    2003-05-07

    Object-oriented NeuroSys (ooNeuroSys) is a collection of programs for simulating very large networks of biologically accurate neurons on distributed memory parallel computers. It includes two principle programs: ooNeuroSys, a parallel program for solving the large systems of ordinary differential equations arising from the interconnected neurons, and Neurondiz, a parallel program for visualizing the results of ooNeuroSys. Both programs are designed to be run on clusters and use the MPI library to obtain parallelism. ooNeuroSys also includes an easy-to-use Python interface. This interface allows neuroscientists to quickly develop and test complex neuron models. Both ooNeuroSys and Neurondiz have a design that allows for both high performance and relative ease of maintenance.

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

    NASA Astrophysics Data System (ADS)

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

    2008-09-01

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

  6. Modeling the electrical behavior of anatomically complex neurons using a network analysis program: excitable membrane.

    PubMed

    Bunow, B; Segev, I; Fleshman, J W

    1985-01-01

    We present methods for using the general-purpose network analysis program, SPICE, to construct computer models of excitable membrane displaying Hodgkin-Huxley-like kinetics. The four non-linear partial differential equations of Hodgkin and Huxley (H-H; 1952) are implemented using electrical circuit elements. The H-H rate constants, alpha and beta, are approximated by polynomial functions rather than exponential functions, since the former are handled more efficiently by SPICE. The process of developing code to implement the H-H sodium conductance is described in detail. The Appendix contains a complete listing of the code required to simulate an H-H action potential. The behavior of models so constructed is validated by comparison with the space-clamped and propagating action potentials of Hodgkin and Huxley. SPICE models of multiply branched axons were tested and found to behave as predicted by previous numerical solutions for propagation in inhomogeneous axons. New results are presented for two cases. First, a detailed, anatomically based model is constructed of group Ia input to an alpha-motoneuron with an excitable soma, a myelinated axon and passive dendrites. Second, we simulate interactions among clusters of mixed excitable and passive dendritic spines on an idealized neuron. The methods presented in this paper and its companion (Segev et al. 1985) should permit neurobiologists to construct and explore models which simulate much more closely the real morphological and physiological characteristics of nerve cells. PMID:3841014

  7. Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses

    PubMed Central

    Ocker, Gabriel Koch; Litwin-Kumar, Ashok; Doiron, Brent

    2015-01-01

    The synaptic connectivity of cortical networks features an overrepresentation of certain wiring motifs compared to simple random-network models. This structure is shaped, in part, by synaptic plasticity that promotes or suppresses connections between neurons depending on their joint spiking activity. Frequently, theoretical studies focus on how feedforward inputs drive plasticity to create this network structure. We study the complementary scenario of self-organized structure in a recurrent network, with spike timing-dependent plasticity driven by spontaneous dynamics. We develop a self-consistent theory for the evolution of network structure by combining fast spiking covariance with a slow evolution of synaptic weights. Through a finite-size expansion of network dynamics we obtain a low-dimensional set of nonlinear differential equations for the evolution of two-synapse connectivity motifs. With this theory in hand, we explore how the form of the plasticity rule drives the evolution of microcircuits in cortical networks. When potentiation and depression are in approximate balance, synaptic dynamics depend on weighted divergent, convergent, and chain motifs. For additive, Hebbian STDP these motif interactions create instabilities in synaptic dynamics that either promote or suppress the initial network structure. Our work provides a consistent theoretical framework for studying how spiking activity in recurrent networks interacts with synaptic plasticity to determine network structure. PMID:26291697

  8. Microbial Rhodopsin Optogenetic Tools: Application for Analyses of Synaptic Transmission and of Neuronal Network Activity in Behavior.

    PubMed

    Glock, Caspar; Nagpal, Jatin; Gottschalk, Alexander

    2015-01-01

    Optogenetics was introduced as a new technology in the neurosciences about a decade ago (Zemelman et al., Neuron 33:15-22, 2002; Boyden et al., Nat Neurosci 8:1263-1268, 2005; Nagel et al., Curr Biol 15:2279-2284, 2005; Zemelman et al., Proc Natl Acad Sci USA 100:1352-1357, 2003). It combines optics, genetics, and bioengineering to render neurons sensitive to light, in order to achieve a precise, exogenous, and noninvasive control of membrane potential, intracellular signaling, network activity, or behavior (Rein and Deussing, Mol Genet Genomics 287:95-109, 2012; Yizhar et al., Neuron 71:9-34, 2011). As C. elegans is transparent, genetically amenable, has a small nervous system mapped with synapse resolution, and exhibits a rich behavioral repertoire, it is especially open to optogenetic methods (White et al., Philos Trans R Soc Lond B Biol Sci 314:1-340, 1986; De Bono et al., Optogenetic actuation, inhibition, modulation and readout for neuronal networks generating behavior in the nematode Caenorhabditis elegans, In: Hegemann P, Sigrist SJ (eds) Optogenetics, De Gruyter, Berlin, 2013; Husson et al., Biol Cell 105:235-250, 2013; Xu and Kim, Nat Rev Genet 12:793-801, 2011). Optogenetics, by now an "exploding" field, comprises a repertoire of different tools ranging from transgenically expressed photo-sensor proteins (Boyden et al., Nat Neurosci 8:1263-1268, 2005; Nagel et al., Curr Biol 15:2279-2284, 2005) or cascades (Zemelman et al., Neuron 33:15-22, 2002) to chemical biology approaches, using photochromic ligands of endogenous channels (Szobota et al., Neuron 54:535-545, 2007). Here, we will focus only on optogenetics utilizing microbial rhodopsins, as these are most easily and most widely applied in C. elegans. For other optogenetic tools, for example the photoactivated adenylyl cyclases (PACs, that drive neuronal activity by increasing synaptic vesicle priming, thus exaggerating rather than overriding the intrinsic activity of a neuron, as occurs with

  9. NeuroD6 Genomic Signature Bridging Neuronal Differentiation to Survival via the Molecular Chaperone Network

    PubMed Central

    Uittenbogaard, Martine; Baxter, Kristin K; Chiaramello, Anne

    2009-01-01

    During neurogenesis, expression of the basic Helix-Loop-Helix NeuroD6/Nex1/MATH-2 transcription factor parallels neuronal differentiation, and is maintained in differentiated neurons in the adult brain. To further dissect NeuroD6 differentiation properties, we previously generated a NeuroD6-overexpressing stable PC12 cell line, PC12-ND6, which displays a neuronal phenotype characterized by spontaneous neuritogenesis, accelerated NGF-induced differentiation, and increased regenerative capacity. Furthermore, we reported that NeuroD6 promotes long-term neuronal survival upon serum deprivation. In this study, we identified the NeuroD6-mediated transcriptional regulatory pathways linking neuronal differentiation to survival, by conducting a genome-wide microarray analysis using PC12-ND6 cells and serum deprivation as a stress paradigm. Through a series of filtering steps and a gene-ontology analysis, we found that NeuroD6 promotes distinct but overlapping gene networks, consistent with the differentiation, regeneration, and survival properties of PC12-ND6 cells. Using a gene set enrichment analysis, we provide the first evidence of a compelling link between NeuroD6 and a set of heat shock proteins in the absence of stress, which may be instrumental to confer stress tolerance to PC12-ND6 cells. Immunocytochemistry results showed that HSP27 and HSP70 interact with cytoskeletal elements, consistent with their roles in neuritogenesis and preserving cellular integrity. HSP70 also colocalizes with mitochondria located in the soma, growing neurites and growth cones of PC12-ND6 cells prior to and upon stress stimulus, consistent with its neuroprotective functions. Collectively, our findings support the notion that NeuroD6 links neuronal differentiation to survival via the network of molecular chaperones and endows the cells with increased stress tolerance. PMID:19610105

  10. Novel cell separation method for molecular analysis of neuron-astrocyte co-cultures.

    PubMed

    Goudriaan, Andrea; Camargo, Nutabi; Carney, Karen E; Oliet, Stéphane H R; Smit, August B; Verheijen, Mark H G

    2014-01-01

    Over the last decade, the importance of astrocyte-neuron communication in neuronal development and synaptic plasticity has become increasingly clear. Since neuron-astrocyte interactions represent highly dynamic and reciprocal processes, we hypothesized that many astrocyte genes may be regulated as a consequence of their interactions with maturing neurons. In order to identify such neuron-responsive astrocyte genes in vitro, we sought to establish an expedited technique for separation of neurons from co-cultured astrocytes. Our newly established method makes use of cold jet, which exploits different adhesion characteristics of subpopulations of cells (Jirsova etal., 1997), and is rapid, performed under ice-cold conditions and avoids protease-mediated isolation of astrocytes or time-consuming centrifugation, yielding intact astrocyte mRNA with approximately 90% of neuronal RNA removed. Using this purification method, we executed genome-wide profiling in which RNA derived from astrocyte-only cultures was compared with astrocyte RNA derived from differentiating neuron-astrocyte co-cultures. Data analysis determined that many astrocytic mRNAs and biological processes are regulated by neuronal interaction. Our results validate the cold jet as an efficient method to separate astrocytes from neurons in co-culture, and reveals that neurons induce robust gene-expression changes in co-cultured astrocytes. PMID:24523672

  11. Neuronal-glial networks as substrate for CNS integration.

    PubMed

    Verkhratsky, A; Toescu, E C

    2006-01-01

    Astrocytes have been considered, for a long time, as the support and house-keeping cells of the nervous system. Indeed, the astrocytes play very important metabolic roles in the brain, but the catalogue of nervous system functions or activities that involve directly glial participation has extended dramatically in the last decade. In addition to the further refining of the signalling capacity of the neuroglial networks and the detailed reassessment of the interactions between glia and vascular bed in the brain, one of the important salient features of the increased glioscience activity in the last few years was the morphological and functional demonstration that protoplasmic astrocytes occupy well defined spatial territories, with only limited areas of morphological overlapping, but still able to communicate with adjacent neighbours through intercellular junctions. All these features form the basis for a possible reassessment of the nature of integration of activity in the central nervous system that could raise glia to a role of central integrator. PMID:17125587

  12. Neuronal-glial networks as substrate for CNS integration

    PubMed Central

    Verkhratsky, A; Toescu, E C

    2006-01-01

    Astrocytes have been considered, for a long time, as the support and house-keeping cells of the nervous system. Indeed, the astrocytes play very important metabolic roles in the brain, but the catalogue of nervous system functions or activities that involve directly glial participation has extended dramatically in the last decade. In addition to the further refining of the signalling capacity of the neuroglial networks and the detailed reassessment of the interactions between glia and vascular bed in the brain, one of the important salient features of the increased glioscience activity in the last few years was the morphological and functional demonstration that protoplasmic astrocytes occupy well defined spatial territories, with only limited areas of morphological overlapping, but still able to communicate with adjacent neighbours through intercellular junctions. All these features form the basis for a possible reassessment of the nature of integration of activity in the central nervous system that could raise glia to a role of central integrator.

  13. Activity and High-Order Effective Connectivity Alterations in Sanfilippo C Patient-Specific Neuronal Networks

    PubMed Central

    Canals, Isaac; Soriano, Jordi; Orlandi, Javier G.; Torrent, Roger; Richaud-Patin, Yvonne; Jiménez-Delgado, Senda; Merlin, Simone; Follenzi, Antonia; Consiglio, Antonella; Vilageliu, Lluïsa; Grinberg, Daniel; Raya, Angel

    2015-01-01

    Summary Induced pluripotent stem cell (iPSC) technology has been successfully used to recapitulate phenotypic traits of several human diseases in vitro. Patient-specific iPSC-based disease models are also expected to reveal early functional phenotypes, although this remains to be proved. Here, we generated iPSC lines from two patients with Sanfilippo type C syndrome, a lysosomal storage disorder with inheritable progressive neurodegeneration. Mature neurons obtained from patient-specific iPSC lines recapitulated the main known phenotypes of the disease, not present in genetically corrected patient-specific iPSC-derived cultures. Moreover, neuronal networks organized in vitro from mature patient-derived neurons showed early defects in neuronal activity, network-wide degradation, and altered effective connectivity. Our findings establish the importance of iPSC-based technology to identify early functional phenotypes, which can in turn shed light on the pathological mechanisms occurring in Sanfilippo syndrome. This technology also has the potential to provide valuable readouts to screen compounds, which can prevent the onset of neurodegeneration. PMID:26411903

  14. Neuronal Representation of Numerosity Zero in the Primate Parieto-Frontal Number Network.

    PubMed

    Ramirez-Cardenas, Araceli; Moskaleva, Maria; Nieder, Andreas

    2016-05-23

    Neurons in the primate parieto-frontal network represent the number of visual items in a collection, but it is unknown whether this system encodes empty sets as conveying null quantity. We recorded from the ventral intraparietal area (VIP) and the prefrontal cortex (PFC) of monkeys performing a matching task including empty sets and countable numerosities as stimuli. VIP neurons encoded empty sets predominantly as a distinct category from numerosities. In contrast, PFC neurons represented empty sets more similarly to numerosity one than to larger numerosities, exhibiting numerical distance and size effects. Moreover, prefrontal neurons represented empty sets abstractly and irrespective of stimulus variations. Compared to VIP, the activity of numerosity neurons in PFC correlated better with the behavioral outcome of empty-set trials. Our results suggest a hierarchy in the processing from VIP to PFC, along which empty sets are steadily detached from visual properties and gradually positioned in a numerical continuum. These findings elucidate how the brain transforms the absence of countable items, nothing, into an abstract quantitative category, zero. PMID:27112297

  15. Bursting frequency versus phase synchronization in time-delayed neuron networks

    NASA Astrophysics Data System (ADS)

    Nordenfelt, Anders; Used, Javier; Sanjuán, Miguel A. F.

    2013-05-01

    We investigate the dependence of the average bursting frequency on time delay for neuron networks with randomly distributed time-delayed chemical synapses. The result is compared with the corresponding curve for the phase synchronization and it turns out that, in some intervals, these have a very similar shape and appear as almost mirror images of each other. We have analyzed both the map-based chaotic Rulkov model and the continuous Hindmarsh-Rose model, yielding the same conclusions. In order to gain further insight, we also analyzed time-delayed Kuramoto models displaying an overall behavior similar to that observed on the neuron network models. For the Kuramoto models, we were able to derive analytical formulas providing an implicit functional relationship between the mean frequency and the phase synchronization. These formulas suggest a strong dependence between those two measures, which could explain the similarities in shape between the curves.

  16. Identifying firing mammalian neurons in networks with high-resolution multi-transistor array (MTA)

    NASA Astrophysics Data System (ADS)

    Lambacher, A.; Vitzthum, V.; Zeitler, R.; Eickenscheidt, M.; Eversmann, B.; Thewes, R.; Fromherz, P.

    2011-01-01

    The electrical activity of a network of mammalian neurons is mapped with a Multi-Transistor Array (MTA) fabricated with extended CMOS technology. The spatial resolution is 7.4 μm on an area of 1 mm2 at a sampling frequency of 6 kHz for a complete readout of 16,384 sensor transistors. Action potentials give rise to extracellular voltages with amplitudes in a range of 500 μV. On the basis of the high resolution in space and time, correlation algorithms are used to identify single action potentials with amplitudes as low as about 200μV, and to assign the signals to the activity of individual neurons even in a dense network.

  17. A Functionally Conserved Gene Regulatory Network Module Governing Olfactory Neuron Diversity.

    PubMed

    Li, Qingyun; Barish, Scott; Okuwa, Sumie; Maciejewski, Abigail; Brandt, Alicia T; Reinhold, Dominik; Jones, Corbin D; Volkan, Pelin Cayirlioglu

    2016-01-01

    Sensory neuron diversity is required for organisms to decipher complex environmental cues. In Drosophila, the olfactory environment is detected by 50 different olfactory receptor neuron (ORN) classes that are clustered in combinations within distinct sensilla subtypes. Each sensilla subtype houses stereotypically clustered 1-4 ORN identities that arise through asymmetric divisions from a single multipotent sensory organ precursor (SOP). How each class of SOPs acquires a unique differentiation potential that accounts for ORN diversity is unknown. Previously, we reported a critical component of SOP diversification program, Rotund (Rn), increases ORN diversity by generating novel developmental trajectories from existing precursors within each independent sensilla type lineages. Here, we show that Rn, along with BarH1/H2 (Bar), Bric-à-brac (Bab), Apterous (Ap) and Dachshund (Dac), constitutes a transcription factor (TF) network that patterns the developing olfactory tissue. This network was previously shown to pattern the segmentation of the leg, which suggests that this network is functionally conserved. In antennal imaginal discs, precursors with diverse ORN differentiation potentials are selected from concentric rings defined by unique combinations of these TFs along the proximodistal axis of the developing antennal disc. The combinatorial code that demarcates each precursor field is set up by cross-regulatory interactions among different factors within the network. Modifications of this network lead to predictable changes in the diversity of sensilla subtypes and ORN pools. In light of our data, we propose a molecular map that defines each unique SOP fate. Our results highlight the importance of the early prepatterning gene regulatory network as a modulator of SOP and terminally differentiated ORN diversity. Finally, our model illustrates how conserved developmental strategies are used to generate neuronal diversity. PMID:26765103

  18. A Functionally Conserved Gene Regulatory Network Module Governing Olfactory Neuron Diversity

    PubMed Central

    Okuwa, Sumie; Maciejewski, Abigail; Brandt, Alicia T.; Reinhold, Dominik; Jones, Corbin D.; Volkan, Pelin Cayirlioglu

    2016-01-01

    Sensory neuron diversity is required for organisms to decipher complex environmental cues. In Drosophila, the olfactory environment is detected by 50 different olfactory receptor neuron (ORN) classes that are clustered in combinations within distinct sensilla subtypes. Each sensilla subtype houses stereotypically clustered 1–4 ORN identities that arise through asymmetric divisions from a single multipotent sensory organ precursor (SOP). How each class of SOPs acquires a unique differentiation potential that accounts for ORN diversity is unknown. Previously, we reported a critical component of SOP diversification program, Rotund (Rn), increases ORN diversity by generating novel developmental trajectories from existing precursors within each independent sensilla type lineages. Here, we show that Rn, along with BarH1/H2 (Bar), Bric-à-brac (Bab), Apterous (Ap) and Dachshund (Dac), constitutes a transcription factor (TF) network that patterns the developing olfactory tissue. This network was previously shown to pattern the segmentation of the leg, which suggests that this network is functionally conserved. In antennal imaginal discs, precursors with diverse ORN differentiation potentials are selected from concentric rings defined by unique combinations of these TFs along the proximodistal axis of the developing antennal disc. The combinatorial code that demarcates each precursor field is set up by cross-regulatory interactions among different factors within the network. Modifications of this network lead to predictable changes in the diversity of sensilla subtypes and ORN pools. In light of our data, we propose a molecular map that defines each unique SOP fate. Our results highlight the importance of the early prepatterning gene regulatory network as a modulator of SOP and terminally differentiated ORN diversity. Finally, our model illustrates how conserved developmental strategies are used to generate neuronal diversity. PMID:26765103

  19. Autaptic pacemaker mediated propagation of weak rhythmic activity across small-world neuronal networks

    NASA Astrophysics Data System (ADS)

    Yilmaz, Ergin; Baysal, Veli; Ozer, Mahmut; Perc, Matjaž

    2016-02-01

    We study the effects of an autapse, which is mathematically described as a self-feedback loop, on the propagation of weak, localized pacemaker activity across a Newman-Watts small-world network consisting of stochastic Hodgkin-Huxley neurons. We consider that only the pacemaker neuron, which is stimulated by a subthreshold periodic signal, has an electrical autapse that is characterized by a coupling strength and a delay time. We focus on the impact of the coupling strength, the network structure, the properties of the weak periodic stimulus, and the properties of the autapse on the transmission of localized pacemaker activity. Obtained results indicate the existence of optimal channel noise intensity for the propagation of the localized rhythm. Under optimal conditions, the autapse can significantly improve the propagation of pacemaker activity, but only for a specific range of the autaptic coupling strength. Moreover, the autaptic delay time has to be equal to the intrinsic oscillation period of the Hodgkin-Huxley neuron or its integer multiples. We analyze the inter-spike interval histogram and show that the autapse enhances or suppresses the propagation of the localized rhythm by increasing or decreasing the phase locking between the spiking of the pacemaker neuron and the weak periodic signal. In particular, when the autaptic delay time is equal to the intrinsic period of oscillations an optimal phase locking takes place, resulting in a dominant time scale of the spiking activity. We also investigate the effects of the network structure and the coupling strength on the propagation of pacemaker activity. We find that there exist an optimal coupling strength and an optimal network structure that together warrant an optimal propagation of the localized rhythm.

  20. DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons.

    PubMed

    Taherkhani, Aboozar; Belatreche, Ammar; Li, Yuhua; Maguire, Liam P

    2015-12-01

    Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, the exact learning mechanism in which the neuron is trained to fire at precise times remains an open problem. The majority of the existing learning methods for SNNs are based on weight adjustment. However, there is also biological evidence that the synaptic delay is not constant. In this paper, a learning method for spiking neurons, called delay learning remote supervised method (DL-ReSuMe), is proposed to merge the delay shift approach and ReSuMe-based weight adjustment to enhance the learning performance. DL-ReSuMe uses more biologically plausible properties, such as delay learning, and needs less weight adjustment than ReSuMe. Simulation results have shown that the proposed DL-ReSuMe approach achieves learning accuracy and learning speed improvements compared with ReSuMe. PMID:25794401

  1. Quantum neuron design

    NASA Astrophysics Data System (ADS)

    Behrman, Elizabeth; Steck, James

    2014-03-01

    In previous work, we have developed quantum systems that can learn and do information processing much like artificial neural networks. These learning methods have some advantages over other implementations of quantum computing in that they construct their own algorithms and could be robust to noise and decoherence. Here we take the next step, by designing quantum neurons that have some of the important behaviors of biological neurons, yet have the advantage of being complex valued and having quantum computing power. Our neuron model consists of a two-level system coupled to a Gaussian bath representing the environment. Simulations of a interconnected network of these neurons show that the model can both learn standard AI tasks, as similar networks of classical neurons have been shown to do, and, in addition, perform quantum mechanical calculations.

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

    NASA Astrophysics Data System (ADS)

    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.

  3. Emergence of Small-World Anatomical Networks in Self-Organizing Clustered Neuronal Cultures

    PubMed Central

    de Santos-Sierra, Daniel; Sendiña-Nadal, Irene; Leyva, Inmaculada; Almendral, Juan A.; Anava, Sarit; Ayali, Amir; Papo, David; Boccaletti, Stefano

    2014-01-01

    In vitro primary cultures of dissociated invertebrate neurons from locust ganglia are used to experimentally investigate the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. At all the different stages of the culture's development, identification of neurons' and neurites' location by means of a dedicated software allows to ultimately extract an adjacency matrix from each image of the culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main network's characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graph's micro- and meso-scale properties emerge. Finally, we identify the main physical processes ruling the culture's morphological transformations, and embed them into a simplified growth model qualitatively reproducing the overall set of experimental observations. PMID:24489675

  4. Neuronal networks with NMDARs and lateral inhibition implement winner-takes-all

    PubMed Central

    Shoemaker, Patrick A.

    2015-01-01

    A neural circuit that relies on the electrical properties of NMDA synaptic receptors is shown by numerical and theoretical analysis to be capable of realizing the winner-takes-all function, a powerful computational primitive that is often attributed to biological nervous systems. This biophysically-plausible model employs global lateral inhibition in a simple feedback arrangement. As its inputs increase, high-gain and then bi- or multi-stable equilibrium states may be assumed in which there is significant depolarization of a single neuron and hyperpolarization or very weak depolarization of other neurons in the network. The state of the winning neuron conveys analog information about its input. The winner-takes-all characteristic depends on the nonmonotonic current-voltage relation of NMDA receptor ion channels, as well as neural thresholding, and the gain and nature of the inhibitory feedback. Dynamical regimes vary with input strength. Fixed points may become unstable as the network enters a winner-takes-all regime, which can lead to entrained oscillations. Under some conditions, oscillatory behavior can be interpreted as winner-takes-all in nature. Stable winner-takes-all behavior is typically recovered as inputs increase further, but with still larger inputs, the winner-takes-all characteristic is ultimately lost. Network stability may be enhanced by biologically plausible mechanisms. PMID:25741276

  5. Neuronal synchrony reveals working memory networks and predicts individual memory capacity

    PubMed Central

    Palva, J. Matias; Monto, Simo; Kulashekhar, Shrikanth; Palva, Satu

    2010-01-01

    Visual working memory (VWM) is used to maintain sensory information for cognitive operations, and its deficits are associated with several neuropsychological disorders. VWM is based on sustained neuronal activity in a complex cortical network of frontal, parietal, occipital, and temporal areas. The neuronal mechanisms that coordinate this distributed processing to sustain coherent mental images and the mechanisms that set the behavioral capacity limit have remained unknown. We mapped the anatomical and dynamic structures of network synchrony supporting VWM by using a neuro informatics approach and combined magnetoencephalography and electroencephalography. Interareal phase synchrony was sustained and stable during the VWM retention period among frontoparietal and visual areas in α- (10–13 Hz), β- (18–24 Hz), and γ- (30–40 Hz) frequency bands. Furthermore, synchrony was strengthened with increasing memory load among the frontoparietal regions known to underlie executive and attentional functions during memory maintenance. On the other hand, the subjects’ individual behavioral VWM capacity was predicted by synchrony in a network in which the intraparietal sulcus was the most central hub. These data suggest that interareal phase synchrony in the α-, β-, and γ-frequency bands among frontoparietal and visual regions could be a systems level mechanism for coordinating and regulating the maintenance of neuronal object representations in VWM. PMID:20368447

  6. Chaotic phase synchronization in small-world networks of bursting neurons

    NASA Astrophysics Data System (ADS)

    Yu, Haitao; Wang, Jiang; Deng, Bin; Wei, Xile; Wong, Y. K.; Chan, W. L.; Tsang, K. M.; Yu, Ziqi

    2011-03-01

    We investigate the chaotic phase synchronization in a system of coupled bursting neurons in small-world networks. A transition to mutual phase synchronization takes place on the bursting time scale of coupled oscillators, while on the spiking time scale, they behave asynchronously. It is shown that phase synchronization is largely facilitated by a large fraction of shortcuts, but saturates when it exceeds a critical value. We also study the external chaotic phase synchronization of bursting oscillators in the small-world network by a periodic driving signal applied to a single neuron. It is demonstrated that there exists an optimal small-world topology, resulting in the largest peak value of frequency locking interval in the parameter plane, where bursting synchronization is maintained, even with the external driving. The width of this interval increases with the driving amplitude, but decrease rapidly with the network size. We infer that the externally applied driving parameters outside the frequency locking region can effectively suppress pathologically synchronized rhythms of bursting neurons in the brain.

  7. Effects of the spike timing-dependent plasticity on the synchronisation in a random Hodgkin-Huxley neuronal network

    NASA Astrophysics Data System (ADS)

    Borges, R. R.; Borges, F. S.; Lameu, E. L.; Batista, A. M.; Iarosz, K. C.; Caldas, I. L.; Viana, R. L.; Sanjuán, M. A. F.

    2016-05-01

    In this paper, we study the effects of spike timing-dependent plasticity on synchronisation in a network of Hodgkin-Huxley neurons. Neuron plasticity is a flexible property of a neuron and its network to change temporarily or permanently their biochemical, physiological, and morphological characteristics, in order to adapt to the environment. Regarding the plasticity, we consider Hebbian rules, specifically for spike timing-dependent plasticity (STDP), and with regard to network, we consider that the connections are randomly distributed. We analyse the synchronisation and desynchronisation according to an input level and probability of connections. Moreover, we verify that the transition for synchronisation depends on the neuronal network architecture, and the external perturbation level.

  8. An improved automated ultrasonic NDE system by wavelet and neuron networks.

    PubMed

    Bettayeb, Fairouz; Rachedi, Tarek; Benbartaoui, Hamid

    2004-04-01

    Despite of the widespread and increasing use of digitized signals, the ultrasonic testing community has not realized yet the full potential of the electronic processing. The performance of an ultrasonic flaw detection method is evaluated by the success of distinguishing the flaw echoes from those scattered by microstructures. So, de-noising of ultrasonic signals is extremely important as to correctly identify smaller defects, because the probability of detection usually decreases as the defect size decreases, while the probability of false call does increase. In this paper, the wavelet transform has been successfully experimented to suppress noise and to enhance flaw location from ultrasonic signal, with a good defect localization. The obtained result is then directed to an automatic Artificial Neuronal Networks classification and learning algorithm of defects from A-scan data. Since there is some uncertainty connected with the testing technique, the system needs a numerical modelling. So, knowing the technical characteristics of the transducer, we can preview which are the defects that experimental inspection should find. Indeed, the system performs simulation of the ultrasonic wave propagation in the material, and gives a very helpful tool to get information and physical phenomena understanding, which can help to a suitable prediction of the service life of the component. PMID:15047396

  9. The mechanisms of generation and propagation of synchronized bursting in developing networks of cortical neurons.

    PubMed

    Maeda, E; Robinson, H P; Kawana, A

    1995-10-01

    The characteristics and mechanisms of synchronized firing in developing networks of cultured cortical neurons were studied using multisite recording through planar electrode arrays (PEAs). With maturation of the network (from 3 to 40 d after plating), the frequency and propagation velocity of bursts increased markedly (approximately from 0.01 to 0.5 Hz and from 5 to 100 mm/sec, respectively), and the sensitivity to extracellular magnesium concentration (0-10 mM) decreased. The source of spontaneous bursts, estimated from the relative delay of onset of activity between electrodes, varied randomly with each burst. Physical separation of synchronously bursting networks into several parts using an ultraviolet laser, divided synchronous bursting into different frequencies and phases in each part. Focal stimulation through the PEA was effective at multiple sites in eliciting bursts, which propagated over the network from the site of stimulation. Stimulated bursts exhibited both an absolute refractory period and a relative refractory period, in which partially propagating bursts could be elicited. Periodic electrical stimulation (at 1 to 30 sec intervals) produced slower propagation velocities and smaller numbers of spikes per burst at shorter stimulation intervals. These results suggest that the generation and propagation of spontaneous synchronous bursts in cultured cortical neurons is governed by the level of spontaneous presynaptic firing, by the degree of connectivity of the network, and by a distributed balance between excitation and recovery processes. PMID:7472441

  10. A Neuronal Network Switch for Approach-Avoidance Toggled by Appetitive State

    PubMed Central

    Hirayama, Keiko; Gillette, Rhanor

    2011-01-01

    Summary Concrete examples of computation and implementation of cost-benefit decisions at the level of neuronal circuits are largely lacking. Such decisions are based on appetitive state, which is the integration of sensation, internal state and memory. Value-based decisions are accessible in neuronal circuitry of simple systems [1]. In one such, the predatory sea-slug Pleurobranchaea, appetite is readily quantified in behavior [2] and related to approach-avoidance decision [3]. Moreover, motor aspects of feeding and turning can be observed as fictive motor output in the isolated CNS [4,5]. Here we found that the excitation state of the feeding motor network both manifested appetitive state and controlled expression of orienting vs. avoidance. In isolated CNS’s spontaneous feeding network activity varied proportionally to donor feeding thresholds. CNS’s from low and high feeding threshold donors expressed fictive orienting or avoidance, respectively, in response to brief stimulation of sensory nerves. Artificially exciting the feeding network converted fictive avoidance to orienting. Thus, the feeding network embodied appetitive state and toggled approach-avoidance decision by configuring response symmetry of the premotor turn network. A resulting model suggests a basic cost-benefit decision module from which to consider evolutionary elaboration of the circuitry to serve more intricate valuation processes in complex animals. PMID:22197246

  11. An Asynchronous Recurrent Network of Cellular Automaton-Based Neurons and Its Reproduction of Spiking Neural Network Activities.

    PubMed

    Matsubara, Takashi; Torikai, Hiroyuki

    2016-04-01

    Modeling and implementation approaches for the reproduction of input-output relationships in biological nervous tissues contribute to the development of engineering and clinical applications. However, because of high nonlinearity, the traditional modeling and implementation approaches encounter difficulties in terms of generalization ability (i.e., performance when reproducing an unknown data set) and computational resources (i.e., computation time and circuit elements). To overcome these difficulties, asynchronous cellular automaton-based neuron (ACAN) models, which are described as special kinds of cellular automata that can be implemented as small asynchronous sequential logic circuits have been proposed. This paper presents a novel type of such ACAN and a theoretical analysis of its excitability. This paper also presents a novel network of such neurons, which can mimic input-output relationships of biological and nonlinear ordinary differential equation model neural networks. Numerical analyses confirm that the presented network has a higher generalization ability than other major modeling and implementation approaches. In addition, Field-Programmable Gate Array-implementations confirm that the presented network requires lower computational resources. PMID:25974951

  12. The iso-response method: measuring neuronal stimulus integration with closed-loop experiments

    PubMed Central

    Gollisch, Tim; Herz, Andreas V. M.

    2012-01-01

    Throughout the nervous system, neurons integrate high-dimensional input streams and transform them into an output of their own. This integration of incoming signals involves filtering processes and complex non-linear operations. The shapes of these filters and non-linearities determine the computational features of single neurons and their functional roles within larger networks. A detailed characterization of signal integration is thus a central ingredient to understanding information processing in neural circuits. Conventional methods for measuring single-neuron response properties, such as reverse correlation, however, are often limited by the implicit assumption that stimulus integration occurs in a linear fashion. Here, we review a conceptual and experimental alternative that is based on exploring the space of those sensory stimuli that result in the same neural output. As demonstrated by recent results in the auditory and visual system, such iso-response stimuli can be used to identify the non-linearities relevant for stimulus integration, disentangle consecutive neural processing steps, and determine their characteristics with unprecedented precision. Automated closed-loop experiments are crucial for this advance, allowing rapid search strategies for identifying iso-response stimuli during experiments. Prime targets for the method are feed-forward neural signaling chains in sensory systems, but the method has also been successfully applied to feedback systems. Depending on the specific question, “iso-response” may refer to a predefined firing rate, single-spike probability, first-spike latency, or other output measures. Examples from different studies show that substantial progress in understanding neural dynamics and coding can be achieved once rapid online data analysis and stimulus generation, adaptive sampling, and computational modeling are tightly integrated into experiments. PMID:23267315

  13. The origin of spontaneous synchronized burst in cultured neuronal networks based on multi-electrode arrays.

    PubMed

    Chen, Chuanping; Chen, Lin; Lin, Yunsheng; Zeng, Shaoqun; Luo, Qingming

    2006-08-01

    Many neural networks in mammalian central nervous system (CNS) fire single spike and complex spike burst. In fact, the conditions for triggering burst are not well understood. In the paper multi-electrode arrays (MEA) are used to record the spontaneous electrophysiological activities of cultured rat hippocampal neuronal network for a long time. After about 3 weeks culture, a transition from single spike to burst is observed in several networks. All of these spikes fire quickly before burst begins. The firing rate during the burst is lower than that just before the burst, but differences of inter-spike intervals (ISIs) between two firing patterns are not clear. Moreover, the electrical activities on neighboring electrodes show strong synchrony during the burst activities. In a word, the generation of the burst requires that network should have a sufficient level of excitation as well as a balance of synaptic inhibition. PMID:16533555

  14. The locust olfactory system as a case study for modeling dynamics of neurobiological networks: from discrete time neurons to continuous time neurons.

    PubMed

    Quenet, B; Horcholle-Bossavit, G

    2007-11-01

    Both chaotic and periodic activities are observed in networks of the central nervous systems. We choose the locust olfactory system as a good case study to analyze the relationships between networks' structure and the types of dynamics involved in coding mechanisms. In our modeling approach, we first build a fully connected recurrent network of synchronously updated McCulloch and Pitts neurons (MC-P type). In order to measure the use of the temporal dimension in the complex spatio-temporal patterns produced by the networks, we have defined an index the Normalized Euclidian Distance NED. We find that for appropriate parameters of input and connectivity, when adding some strong connections to the initial random synaptic matrices, it was easy to get the emergence of both robust oscillations and distributed synchrony in the spatiotemporal patterns. Then, in order to validate the MC-P model as a tool for analysis for network properties, we examine the dynamic behavior of networks of continuous time model neuron (Izhikevitch Integrate and Fire model -IFI-), implementing the same network characteristics. In both models, similarly to biological PN, the activity of excitatory neurons are phase-locked to different cycles of oscillations which remind the ones of the local field potential (LFP), and nevertheless exhibit dynamic behavior complex enough to be the basis of spatio-temporal codes. PMID:18075120

  15. Automatic Generation of Connectivity for Large-Scale Neuronal Network Models through Structural Plasticity.

    PubMed

    Diaz-Pier, Sandra; Naveau, Mikaël; Butz-Ostendorf, Markus; Morrison, Abigail

    2016-01-01

    With the emergence of new high performance computation technology in the last decade, the simulation of large scale neural networks which are able to reproduce the behavior and structure of the brain has finally become an achievable target of neuroscience. Due to the number of synaptic connections between neurons and the complexity of biological networks, most contemporary models have manually defined or static connectivity. However, it is expected that modeling the dynamic generation and deletion of the links among neurons, locally and between different regions of the brain, is crucial to unravel important mechanisms associated with learning, memory and healing. Moreover, for many neural circuits that could potentially be modeled, activity data is more readily and reliably available than connectivity data. Thus, a framework that enables networks to wire themselves on the basis of specified activity targets can be of great value in specifying network models where connectivity data is incomplete or has large error margins. To address these issues, in the present work we present an implementation of a model of structural plasticity in the neural network simulator NEST. In this model, synapses consist of two parts, a pre- and a post-synaptic element. Synapses are created and deleted during the execution of the simulation following local homeostatic rules until a mean level of electrical activity is reached in the network. We assess the scalability of the implementation in order to evaluate its potential usage in the self generation of connectivity of large scale networks. We show and discuss the results of simulations on simple two population networks and more complex models of the cortical microcircuit involving 8 populations and 4 layers using the new framework. PMID:27303272

  16. Automatic Generation of Connectivity for Large-Scale Neuronal Network Models through Structural Plasticity

    PubMed Central

    Diaz-Pier, Sandra; Naveau, Mikaël; Butz-Ostendorf, Markus; Morrison, Abigail

    2016-01-01

    With the emergence of new high performance computation technology in the last decade, the simulation of large scale neural networks which are able to reproduce the behavior and structure of the brain has finally become an achievable target of neuroscience. Due to the number of synaptic connections between neurons and the complexity of biological networks, most contemporary models have manually defined or static connectivity. However, it is expected that modeling the dynamic generation and deletion of the links among neurons, locally and between different regions of the brain, is crucial to unravel important mechanisms associated with learning, memory and healing. Moreover, for many neural circuits that could potentially be modeled, activity data is more readily and reliably available than connectivity data. Thus, a framework that enables networks to wire themselves on the basis of specified activity targets can be of great value in specifying network models where connectivity data is incomplete or has large error margins. To address these issues, in the present work we present an implementation of a model of structural plasticity in the neural network simulator NEST. In this model, synapses consist of two parts, a pre- and a post-synaptic element. Synapses are created and deleted during the execution of the simulation following local homeostatic rules until a mean level of electrical activity is reached in the network. We assess the scalability of the implementation in order to evaluate its potential usage in the self generation of connectivity of large scale networks. We show and discuss the results of simulations on simple two population networks and more complex models of the cortical microcircuit involving 8 populations and 4 layers using the new framework. PMID:27303272

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

    PubMed Central

    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. PMID:26973503

  18. Combined Exposure to Simulated Microgravity and Acute or Chronic Radiation Reduces Neuronal Network Integrity and Survival

    PubMed Central

    Quintens, Roel; Samari, Nada; de Saint-Georges, Louis; van Oostveldt, Patrick; Baatout, Sarah; Benotmane, Mohammed Abderrafi

    2016-01-01

    During orbital or interplanetary space flights, astronauts are exposed to cosmic radiations and microgravity. However, most earth-based studies on the potential health risks of space conditions have investigated the effects of these two conditions separately. This study aimed at assessing the combined effect of radiation exposure and microgravity on neuronal morphology and survival in vitro. In particular, we investigated the effects of simulated microgravity after acute (X-rays) or during chronic (Californium-252) exposure to ionizing radiation using mouse mature neuron cultures. Acute exposure to low (0.1 Gy) doses of X-rays caused a delay in neurite outgrowth and a reduction in soma size, while only the high dose impaired neuronal survival. Of interest, the strongest effect on neuronal morphology and survival was evident in cells exposed to microgravity and in particular in cells exposed to both microgravity and radiation. Removal of neurons from simulated microgravity for a period of 24 h was not sufficient to recover neurite length, whereas the soma size showed a clear re-adaptation to normal ground conditions. Genome-wide gene expression analysis confirmed a modulation of genes involved in neurite extension, cell survival and synaptic communication, suggesting that these changes might be responsible for the observed morphological effects. In general, the observed synergistic changes in neuronal network integrity and cell survival induced by simulated space conditions might help to better evaluate the astronaut's health risks and underline the importance of investigating the central nervous system and long-term cognition during and after a space flight. PMID:27203085

  19. Combined Exposure to Simulated Microgravity and Acute or Chronic Radiation Reduces Neuronal Network Integrity and Survival.

    PubMed

    Pani, Giuseppe; Verslegers, Mieke; Quintens, Roel; Samari, Nada; de Saint-Georges, Louis; van Oostveldt, Patrick; Baatout, Sarah; Benotmane, Mohammed Abderrafi

    2016-01-01

    During orbital or interplanetary space flights, astronauts are exposed to cosmic radiations and microgravity. However, most earth-based studies on the potential health risks of space conditions have investigated the effects of these two conditions separately. This study aimed at assessing the combined effect of radiation exposure and microgravity on neuronal morphology and survival in vitro. In particular, we investigated the effects of simulated microgravity after acute (X-rays) or during chronic (Californium-252) exposure to ionizing radiation using mouse mature neuron cultures. Acute exposure to low (0.1 Gy) doses of X-rays caused a delay in neurite outgrowth and a reduction in soma size, while only the high dose impaired neuronal survival. Of interest, the strongest effect on neuronal morphology and survival was evident in cells exposed to microgravity and in particular in cells exposed to both microgravity and radiation. Removal of neurons from simulated microgravity for a period of 24 h was not sufficient to recover neurite length, whereas the soma size showed a clear re-adaptation to normal ground conditions. Genome-wide gene expression analysis confirmed a modulation of genes involved in neurite extension, cell survival and synaptic communication, suggesting that these changes might be responsible for the observed morphological effects. In general, the observed synergistic changes in neuronal network integrity and cell survival induced by simulated space conditions might help to better evaluate the astronaut's health risks and underline the importance of investigating the central nervous system and long-term cognition during and after a space flight. PMID:27203085

  20. A Framework for Modeling the Growth and Development of Neurons and Networks

    PubMed Central

    Zubler, Frederic; Douglas, Rodney

    2009-01-01

    The development of neural tissue is a complex organizing process, in which it is difficult to grasp how the various localized interactions between dividing cells leads relentlessly to global network organization. Simulation is a useful tool for exploring such complex processes because it permits rigorous analysis of observed global behavior in terms of the mechanistic axioms declared in the simulated model. We describe a novel simulation tool, CX3D, for modeling the development of large realistic neural networks such as the neocortex, in a physical 3D space. In CX3D, as in biology, neurons arise by the replication and migration of precursors, which mature into cells able to extend axons and dendrites. Individual neurons are discretized into spherical (for the soma) and cylindrical (for neurites) elements that have appropriate mechanical properties. The growth functions of each neuron are encapsulated in set of pre-defined modules that are automatically distributed across its segments during growth. The extracellular space is also discretized, and allows for the diffusion of extracellular signaling molecules, as well as the physical interactions of the many developing neurons. We demonstrate the utility of CX3D by simulating three interesting developmental processes: neocortical lamination based on mechanical properties of tissues; a growth model of a neocortical pyramidal cell based on layer-specific guidance cues; and the formation of a neural network in vitro by employing neurite fasciculation. We also provide some examples in which previous models from the literature are re-implemented in CX3D. Our results suggest that CX3D is a powerful tool for understanding neural development. PMID:19949465

  1. LASER BIOLOGY: Peculiarities of studying an isolated neuron by the method of laser interference microscopy

    NASA Astrophysics Data System (ADS)

    Yusipovich, Alexander I.; Novikov, Sergey M.; Kazakova, Tatiana A.; Erokhova, Liudmila A.; Brazhe, Nadezda A.; Lazarev, Grigory L.; Maksimov, Georgy V.

    2006-09-01

    Actual aspects of using a new method of laser interference microscopy (LIM) for studying nerve cells are discussed. The peculiarities of the LIM display of neurons are demonstrated by the example of isolated neurons of a pond snail Lymnaea stagnalis. A comparative analysis of the images of the cell and subcellular structures of a neuron obtained by the methods of interference microscopy, optical transmission microscopy, and confocal microscopy is performed. Various aspects of the application of LIM for studying the lateral dimensions and internal structure of the cytoplasm and organelles of a neuron in cytology and cell physiology are discussed.

  2. Peculiarities of studying an isolated neuron by the method of laser interference microscopy

    SciTech Connect

    Yusipovich, Alexander I; Kazakova, Tatiana A; Erokhova, Liudmila A; Brazhe, Nadezda A; Maksimov, Georgy V; Novikov, Sergey M; Lazarev, Grigory L

    2006-09-30

    Actual aspects of using a new method of laser interference microscopy (LIM) for studying nerve cells are discussed. The peculiarities of the LIM display of neurons are demonstrated by the example of isolated neurons of a pond snail Lymnaea stagnalis. A comparative analysis of the images of the cell and subcellular structures of a neuron obtained by the methods of interference microscopy, optical transmission microscopy, and confocal microscopy is performed. Various aspects of the application of LIM for studying the lateral dimensions and internal structure of the cytoplasm and organelles of a neuron in cytology and cell physiology are discussed. (laser biology)

  3. Synchronization and array-enhanced resonances in delayed coupled neuronal network with channel noise.

    PubMed

    Chen, Jianchun; Ding, Shaojie; Li, Hui; He, Guolong; Zhang, Xuejuan

    2014-09-01

    This paper studies the combined effect of transmission delay and channel fluctuations on population behaviors of an excitatory Erdös-Rényi neuronal network. First, it is found that the network reaches a perfect spatial temporal coherence at a suitable membrane size. Such a coherence resonance is stimulus-free and is array-enhanced. Second, the presence of transmission delay can induce intermittent changes of the population dynamics. Besides, two resonant peaks of the population firing rate are observed as delay changes: one is at τd≈7ms for all membrane areas, which reflects the resonance between the delayed interaction and the intrinsic period of channel kinetics; the other occurs when the transmission delay equals to the mean inter-spike intervals of the population firings in the absence of delay, which reflects the resonance between the delayed interaction and the firing period of the non-delayed system. Third, concerning the impact of network topology and population size, it is found that decreasing the connection probability does not change the range of transmission delay but broadens the range of synaptic coupling that supports population neurons to generate action potentials synchronously and temporally coherently. Furthermore, there exists a critical connection probability that distinguishes the population dynamics into an asynchronous and synchronous state. All the results we obtained are based on networks of size N = 500, which are shown to be robust to further increasing the population size. PMID:25273211

  4. Inferring network properties of cortical neurons with synaptic coupling and parameter dispersion.

    PubMed

    Roy, Dipanjan; Jirsa, Viktor

    2013-01-01

    Computational models at different space-time scales allow us to understand the fundamental mechanisms that govern neural processes and relate uniquely these processes to neuroscience data. In this work, we propose a novel neurocomputational unit (a mesoscopic model which tell us about the interaction between local cortical nodes in a large scale neural mass model) of bursters that qualitatively captures the complex dynamics exhibited by a full network of parabolic bursting neurons. We observe that the temporal dynamics and fluctuation of mean synaptic action term exhibits a high degree of correlation with the spike/burst activity of our population. With heterogeneity in the applied drive and mean synaptic coupling derived from fast excitatory synapse approximations we observe long term behavior in our population dynamics such as partial oscillations, incoherence, and synchrony. In order to understand the origin of multistability at the population level as a function of mean synaptic coupling and heterogeneity in the firing rate threshold we employ a simple generative model for parabolic bursting recently proposed by Ghosh et al. (2009). Further, we use here a mean coupling formulated for fast spiking neurons for our analysis of generic model. Stability analysis of this mean field network allow us to identify all the relevant network states found in the detailed biophysical model. We derive here analytically several boundary solutions, a result which holds for any number of spikes per burst. These findings illustrate the role of oscillations occurring at slow time scales (bursts) on the global behavior of the network. PMID:23533147

  5. Cell Assembly Dynamics of Sparsely-Connected Inhibitory Networks: A Simple Model for the Collective Activity of Striatal Projection Neurons

    PubMed Central

    Angulo-Garcia, David; Berke, Joshua D.; Torcini, Alessandro

    2016-01-01

    Striatal projection neurons form a sparsely-connected inhibitory network, and this arrangement may be essential for the appropriate temporal organization of behavior. Here we show that a simplified, sparse inhibitory network of Leaky-Integrate-and-Fire neurons can reproduce some key features of striatal population activity, as observed in brain slices. In particular we develop a new metric to determine the conditions under which sparse inhibitory networks form anti-correlated cell assemblies with time-varying activity of individual cells. We find that under these conditions the network displays an input-specific sequence of cell assembly switching, that effectively discriminates similar inputs. Our results support the proposal that GABAergic connections between striatal projection neurons allow stimulus-selective, temporally-extended sequential activation of cell assemblies. Furthermore, we help to show how altered intrastriatal GABAergic signaling may produce aberrant network-level information processing in disorders such as Parkinson’s and Huntington’s diseases. PMID:26915024

  6. Altered neuronal network and rescue in a human MECP2 duplication model.

    PubMed

    Nageshappa, S; Carromeu, C; Trujillo, C A; Mesci, P; Espuny-Camacho, I; Pasciuto, E; Vanderhaeghen, P; Verfaillie, C M; Raitano, S; Kumar, A; Carvalho, C M B; Bagni, C; Ramocki, M B; Araujo, B H S; Torres, L B; Lupski, J R; Van Esch, H; Muotri, A R

    2016-02-01

    Increased dosage of methyl-CpG-binding protein-2 (MeCP2) results in a dramatic neurodevelopmental phenotype with onset at birth. We generated induced pluripotent stem cells (iPSCs) from patients with the MECP2 duplication syndrome (MECP2dup), carrying different duplication sizes, to study the impact of increased MeCP2 dosage in human neurons. We show that cortical neurons derived from these different MECP2dup iPSC lines have increased synaptogenesis and dendritic complexity. In addition, using multi-electrodes arrays, we show that neuronal network synchronization was altered in MECP2dup-derived neurons. Given MeCP2 functions at the epigenetic level, we tested whether these alterations were reversible using a library of compounds with defined activity on epigenetic pathways. One histone deacetylase inhibitor, NCH-51, was validated as a potential clinical candidate. Interestingly, this compound has never been considered before as a therapeutic alternative for neurological disorders. Our model recapitulates early stages of the human MECP2 duplication syndrome and represents a promising cellular tool to facilitate therapeutic drug screening for severe neurodevelopmental disorders. PMID:26347316

  7. Recent studies of ovine neuronal ceroid lipofuscinoses from BARN, the Batten Animal Research Network.

    PubMed

    Palmer, David N; Neverman, Nicole J; Chen, Jarol Z; Chang, Chia-Tien; Houweling, Peter J; Barry, Lucy A; Tammen, Imke; Hughes, Stephanie M; Mitchell, Nadia L

    2015-10-01

    Studies on naturally occurring New Zealand and Australian ovine models of the neuronal ceroid-lipofuscinoses (Batten disease, NCLs) have greatly aided our understanding of these diseases. Close collaborations between the New Zealand groups at Lincoln University and the University of Otago, Dunedin, and a group at the University of Sydney, Australia, led to the formation of BARN, the Batten Animal Research Network. This review focusses on presentations at the 14th International Conference on Neuronal Ceroid Lipofuscinoses (Batten Disease), recent relevant background work, and previews of work in preparation for publication. Themes include CLN5 and CLN6 neuronal cell culture studies, studies on tissues from affected and control animals and whole animal in vivo studies. Topics include the effect of a CLN6 mutation on endoplasmic reticulum proteins, lysosomal function and the interactions of CLN6 with other lysosomal activities and trafficking, scoping gene-based therapies, a molecular dissection of neuroinflammation, identification of differentially expressed genes in brain tissue, an attempted therapy with an anti-inflammatory drug in vivo and work towards gene therapy in ovine models of the NCLs. This article is part of a Special Issue entitled: "Current Research on the Neuronal Ceroid Lipofuscinoses (Batten Disease)". PMID:26073432

  8. Altered neuronal network and rescue in a human MECP2 duplication model

    PubMed Central

    Nageshappa, Savitha; Carromeu, Cassiano; Trujillo, Cleber A.; Mesci, Pinar; Espuny-Camacho, Ira; Pasciuto, Emanuela; Vanderhaeghen, Pierre; Verfaillie, Catherine; Raitano, Susanna; Kumar, Anujith; Carvalho, Claudia M.B.; Bagni, Claudia; Ramocki, Melissa B.; Araujo, Bruno H. S.; Torres, Laila B.; Lupski, James R.; Van Esch, Hilde; Muotri, Alysson R.

    2015-01-01

    Increased dosage of MeCP2 results in a dramatic neurodevelopmental phenotype with onset at birth. We generated induced pluripotent stem cells (iPSC) from patients with the MECP2 duplication syndrome (MECP2dup), carrying different duplication sizes, to study the impact of increased MeCP2 dosage in human neurons. We show that cortical neurons derived from these different MECP2dup iPSC lines have increase synaptogenesis and dendritic complexity. Additionally, using multi-electrodes arrays, we show that neuronal network synchronization was altered in MECP2dup-derived neurons. Given MeCP2 function at the epigenetic level, we tested if these alterations were reversible using a library of compounds with defined activity on epigenetic pathways. One histone deacetylase inhibitor, NCH-51, was validated as a potential clinical candidate. Interestingly, this compound has never been considered before as a therapeutic alternative for neurological disorders. Our model recapitulates early stages of the human MECP2 duplication syndrome and represents a promising cellular tool to facilitate therapeutic drug screening for severe neurodevelopmental disorders. PMID:26347316

  9. Searching for collective behavior in a large network of sensory neurons.

    PubMed

    Tkačik, Gašper; Marre, Olivier; Amodei, Dario; Schneidman, Elad; Bialek, William; Berry, Michael J

    2014-01-01

    Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this principle to construct probabilistic models which describe the correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies. Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global interaction that controls the distribution of synchrony in the population. Here we show that such "K-pairwise" models--being systematic extensions of the previously used pairwise Ising models--provide an excellent account of the data. We explore the properties of the neural vocabulary by: 1) estimating its entropy, which constrains the population's capacity to represent visual information; 2) classifying activity patterns into a small set of metastable collective modes; 3) showing that the neural codeword ensembles are extremely inhomogenous; 4) demonstrating that the state of individual neurons is highly predictable from the rest of the population, allowing the capacity for error correction. PMID:24391485

  10. Searching for Collective Behavior in a Large Network of Sensory Neurons

    PubMed Central

    Tkačik, Gašper; Marre, Olivier; Amodei, Dario; Schneidman, Elad; Bialek, William; Berry, Michael J.

    2014-01-01

    Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this principle to construct probabilistic models which describe the correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies. Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global interaction that controls the distribution of synchrony in the population. Here we show that such “K-pairwise” models—being systematic extensions of the previously used pairwise Ising models—provide an excellent account of the data. We explore the properties of the neural vocabulary by: 1) estimating its entropy, which constrains the population's capacity to represent visual information; 2) classifying activity patterns into a small set of metastable collective modes; 3) showing that the neural codeword ensembles are extremely inhomogenous; 4) demonstrating that the state of individual neurons is highly predictable from the rest of the population, allowing the capacity for error correction. PMID:24391485

  11. Intrinsic excitability state of local neuronal population modulates signal propagation in feed-forward neural networks.

    PubMed

    Han, Ruixue; Wang, Jiang; Yu, Haitao; Deng, Bin; Wei, Xilei; Qin, Yingmei; Wang, Haixu

    2015-04-01

    Reliable signal propagation across distributed brain areas is an essential requirement for cognitive function, and it has been investigated extensively in computational studies where feed-forward network (FFN) is taken as a generic model. But it is still unclear how distinct local network states, which are intrinsically generated by synaptic interactions within each layer, would affect the ability of FFN to transmit information. Here we investigate the impact of such network states on propagating transient synchrony (synfire) and firing rate by a combination of numerical simulations and analytical approach. Specifically, local network dynamics is attributed to the competition between excitatory and inhibitory neurons within each layer. Our results show that concomitant with different local network states, the performance of signal propagation differs dramatically. For both synfire propagation and firing rate propagation, there exists an optimal local excitability state, respectively, that optimizes the performance of signal propagation. Furthermore, we find that long-range connections strongly change the dependence of spiking activity propagation on local network state and propose that these two factors work jointly to determine information transmission across distributed networks. Finally, a simple mean field approach that bridges response properties of long-range connectivity and local subnetworks is utilized to reveal the underlying mechanism. PMID:25933656

  12. Intrinsic excitability state of local neuronal population modulates signal propagation in feed-forward neural networks

    NASA Astrophysics Data System (ADS)

    Han, Ruixue; Wang, Jiang; Yu, Haitao; Deng, Bin; Wei, Xilei; Qin, Yingmei; Wang, Haixu

    2015-04-01

    Reliable signal propagation across distributed brain areas is an essential requirement for cognitive function, and it has been investigated extensively in computational studies where feed-forward network (FFN) is taken as a generic model. But it is still unclear how distinct local network states, which are intrinsically generated by synaptic interactions within each layer, would affect the ability of FFN to transmit information. Here we investigate the impact of such network states on propagating transient synchrony (synfire) and firing rate by a combination of numerical simulations and analytical approach. Specifically, local network dynamics is attributed to the competition between excitatory and inhibitory neurons within each layer. Our results show that concomitant with different local network states, the performance of signal propagation differs dramatically. For both synfire propagation and firing rate propagation, there exists an optimal local excitability state, respectively, that optimizes the performance of signal propagation. Furthermore, we find that long-range connections strongly change the dependence of spiking activity propagation on local network state and propose that these two factors work jointly to determine information transmission across distributed networks. Finally, a simple mean field approach that bridges response properties of long-range connectivity and local subnetworks is utilized to reveal the underlying mechanism.

  13. Detection of M-sequences from spike sequence in neuronal networks.

    PubMed

    Nishitani, Yoshi; Hosokawa, Chie; Mizuno-Matsumoto, Yuko; Miyoshi, Tomomitsu; Sawai, Hajime; Tamura, Shinichi

    2012-01-01

    In circuit theory, it is well known that a linear feedback shift register (LFSR) circuit generates pseudorandom bit sequences (PRBS), including an M-sequence with the maximum period of length. In this study, we tried to detect M-sequences known as a pseudorandom sequence generated by the LFSR circuit from time series patterns of stimulated action potentials. Stimulated action potentials were recorded from dissociated cultures of hippocampal neurons grown on a multielectrode array. We could find several M-sequences from a 3-stage LFSR circuit (M3). These results show the possibility of assembling LFSR circuits or its equivalent ones in a neuronal network. However, since the M3 pattern was composed of only four spike intervals, the possibility of an accidental detection was not zero. Then, we detected M-sequences from random spike sequences which were not generated from an LFSR circuit and compare the result with the number of M-sequences from the originally observed raster data. As a result, a significant difference was confirmed: a greater number of "0-1" reversed the 3-stage M-sequences occurred than would have accidentally be detected. This result suggests that some LFSR equivalent circuits are assembled in neuronal networks. PMID:22851966

  14. Detection of M-Sequences from Spike Sequence in Neuronal Networks

    PubMed Central

    Nishitani, Yoshi; Hosokawa, Chie; Mizuno-Matsumoto, Yuko; Miyoshi, Tomomitsu; Sawai, Hajime; Tamura, Shinichi

    2012-01-01

    In circuit theory, it is well known that a linear feedback shift register (LFSR) circuit generates pseudorandom bit sequences (PRBS), including an M-sequence with the maximum period of length. In this study, we tried to detect M-sequences known as a pseudorandom sequence generated by the LFSR circuit from time series patterns of stimulated action potentials. Stimulated action potentials were recorded from dissociated cultures of hippocampal neurons grown on a multielectrode array. We could find several M-sequences from a 3-stage LFSR circuit (M3). These results show the possibility of assembling LFSR circuits or its equivalent ones in a neuronal network. However, since the M3 pattern was composed of only four spike intervals, the possibility of an accidental detection was not zero. Then, we detected M-sequences from random spike sequences which were not generated from an LFSR circuit and compare the result with the number of M-sequences from the originally observed raster data. As a result, a significant difference was confirmed: a greater number of “0–1” reversed the 3-stage M-sequences occurred than would have accidentally be detected. This result suggests that some LFSR equivalent circuits are assembled in neuronal networks. PMID:22851966

  15. Comparison of neuron selection algorithms of wavelet-based neural network

    NASA Astrophysics Data System (ADS)

    Mei, Xiaodan; Sun, Sheng-He

    2001-09-01

    Wavelet networks have increasingly received considerable attention in various fields such as signal processing, pattern recognition, robotics and automatic control. Recently people are interested in employing wavelet functions as activation functions and have obtained some satisfying results in approximating and localizing signals. However, the function estimation will become more and more complex with the growth of the input dimension. The hidden neurons contribute to minimize the approximation error, so it is important to study suitable algorithms for neuron selection. It is obvious that exhaustive search procedure is not satisfying when the number of neurons is large. The study in this paper focus on what type of selection algorithm has faster convergence speed and less error for signal approximation. Therefore, the Genetic algorithm and the Tabu Search algorithm are studied and compared by some experiments. This paper first presents the structure of the wavelet-based neural network, then introduces these two selection algorithms and discusses their properties and learning processes, and analyzes the experiments and results. We used two wavelet functions to test these two algorithms. The experiments show that the Tabu Search selection algorithm's performance is better than the Genetic selection algorithm, TSA has faster convergence rate than GA under the same stopping criterion.

  16. Metastability and inter-band frequency modulation in networks of oscillating spiking neuron populations.

    PubMed

    Bhowmik, David; Shanahan, Murray

    2013-01-01

    Groups of neurons firing synchronously are hypothesized to underlie many cognitive functions such as attention, associative learning, memory, and sensory selection. Recent theories suggest that transient periods of synchronization and desynchronization provide a mechanism for dynamically integrating and forming coalitions of functionally related neural areas, and that at these times conditions are optimal for information transfer. Oscillating neural populations display a great amount of spectral complexity, with several rhythms temporally coexisting in different structures and interacting with each other. This paper explores inter-band frequency modulation between neural oscillators using models of quadratic integrate-and-fire neurons and Hodgkin-Huxley neurons. We vary the structural connectivity in a network of neural oscillators, assess the spectral complexity, and correlate the inter-band frequency modulation. We contrast this correlation against measures of metastable coalition entropy and synchrony. Our results show that oscillations in different neural populations modulate each other so as to change frequency, and that the interaction of these fluctuating frequencies in the network as a whole is able to drive different neural populations towards episodes of synchrony. Further to this, we locate an area in the connectivity space in which the system directs itself in this way so as to explore a large repertoire of synchronous coalitions. We suggest that such dynamics facilitate versatile exploration, integration, and communication between functionally related neural areas, and thereby supports sophisticated cognitive processing in the brain. PMID:23614040

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

    PubMed

    Colliaux, David; Yger, Pierre; Kaneko, Kunihiko

    2015-12-01

    Neuronal adaptation is the intrinsic capacity of the brain to change, by various mechanisms, its dynamical responses as a function of the context. Such a phenomena, widely observed in vivo and in vitro, is known to be crucial in homeostatic regulation of the activity and gain control. The effects of adaptation have already been studied at the single-cell level, resulting from either voltage or calcium gated channels both activated by the spiking activity and modulating the dynamical responses of the neurons. In this study, by disentangling those effects into a linear (sub-threshold) and a non-linear (supra-threshold) part, we focus on the the functional role of those two distinct components of adaptation onto the neuronal activity at various scales, starting from single-cell responses up to recurrent networks dynamics, and under stationary or non-stationary stimulations. The effects of slow currents on collective dynamics, like modulation of population oscillation and reliability of spike patterns, is quantified for various types of adaptation in sparse recurrent networks. PMID:26400658

  18. Combined exposure to simulated microgravity and acute or chronic radiation reduces neuronal network integrity and cell survival

    NASA Astrophysics Data System (ADS)

    Benotmane, Rafi

    During orbital or interplanetary space flights, astronauts are exposed to cosmic radiations and microgravity. This study aimed at assessing the effect of these combined conditions on neuronal network density, cell morphology and survival, using well-connected mouse cortical neuron cultures. To this end, neurons were exposed to acute low and high doses of low LET (X-rays) radiation or to chronic low dose-rate of high LET neutron irradiation (Californium-252), under the simulated microgravity generated by the Random Positioning Machine (RPM, Dutch space). High content image analysis of cortical neurons positive for the neuronal marker βIII-tubulin unveiled a reduced neuronal network integrity and connectivity, and an altered cell morphology after exposure to acute/chronic radiation or to simulated microgravity. Additionally, in both conditions, a defect in DNA-repair efficiency was revealed by an increased number of γH2AX-positive foci, as well as an increased number of Annexin V-positive apoptotic neurons. Of interest, when combining both simulated space conditions, we noted a synergistic effect on neuronal network density, neuronal morphology, cell survival and DNA repair. Furthermore, these observations are in agreement with preliminary gene expression data, revealing modulations in cytoskeletal and apoptosis-related genes after exposure to simulated microgravity. In conclusion, the observed in vitro changes in neuronal network integrity and cell survival induced by space simulated conditions provide us with mechanistic understanding to evaluate health risks and the development of countermeasures to prevent neurological disorders in astronauts over long-term space travels. Acknowledgements: This work is supported partly by the EU-FP7 projects CEREBRAD (n° 295552)

  19. Mutual and intermittent enhancements of synchronization transitions by autaptic and synaptic delay in scale-free neuron networks

    NASA Astrophysics Data System (ADS)

    Wang, Qi; Gong, Yubing; Xie, Huijuan

    2016-05-01

    In neural networks, there exist both synaptic delays among different neurons and autaptic self-feedback delays in a neuron itself. In this paper, we study synchronization transitions induced by synaptic and autaptic delays in scale-free neuron networks, mainly exploring how these two time delays affect synchronization transitions induced by each other. It is found that the synchronization transitions induced by synaptic (autaptic) delay are intermittently enhanced when autaptic (synaptic) delay is varied. There are optimal autaptic strength and synaptic coupling strength by which the synchronization transitions induced by autaptic and synaptic delays become strongest. The underlying mechanisms are briefly discussed in terms of the relationships of autaptic delay, synaptic delay, and inter-burst interval. These results show that synaptic and autaptic delays could contribute to each other and enhance synchronization transitions in the neuronal networks. This implies that autaptic and synaptic delays could play a vital role for the information transmission in neural systems.

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

    PubMed

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

    2015-01-01

    Models of brain stem ventral respiratory column (VRC) circuits typically emphasize populations of neurons, each active during a particular phase of the respiratory cycle. We have proposed that "tonic" pericolumnar expiratory (t-E) neurons tune breathing during baroreceptor-evoked reductions and central chemoreceptor-evoked enhancements of inspiratory (I) drive. The aims of this study were to further characterize the coordinated activity of t-E neurons and test the hypothesis that peripheral chemoreceptors also modulate drive via inhibition of t-E neurons and disinhibition of their inspiratory neuron targets. Spike trains of 828 VRC neurons were acquired by multielectrode arrays along with phrenic nerve signals from 22 decerebrate, vagotomized, neuromuscularly blocked, artificially ventilated adult cats. Forty-eight of 191 t-E neurons fired synchronously with another t-E neuron as indicated by cross-correlogram central peaks; 32 of the 39 synchronous pairs were elements of groups with mutual pairwise correlations. Gravitational clustering identified fluctuations in t-E neuron synchrony. A network model supported the prediction that inhibitory populations with spike synchrony reduce target neuron firing probabilities, resulting in offset or central correlogram troughs. In five animals, stimulation of carotid chemoreceptors evoked changes in the firing rates of 179 of 240 neurons. Thirty-two neuron pairs had correlogram troughs consistent with convergent and divergent t-E inhibition of I cells and disinhibitory enhancement of drive. Four of 10 t-E neurons that responded to sequential stimulation of peripheral and central chemoreceptors triggered 25 cross-correlograms with offset features. The results support the hypothesis that multiple afferent systems dynamically tune inspiratory drive in part via coordinated t-E neurons. PMID:25343784

  1. NOA: a novel Network Ontology Analysis method

    PubMed Central

    Wang, Jiguang; Huang, Qiang; Liu, Zhi-Ping; Wang, Yong; Wu, Ling-Yun; Chen, Luonan; Zhang, Xiang-Sun

    2011-01-01

    Gene ontology analysis has become a popular and important tool in bioinformatics study, and current ontology analyses are mainly conducted in individual gene or a gene list. However, recent molecular network analysis reveals that the same list of genes with different interactions may perform different functions. Therefore, it is necessary to consider molecular interactions to correctly and specifically annotate biological networks. Here, we propose a novel Network Ontology Analysis (NOA) method to perform gene ontology enrichment analysis on biological networks. Specifically, NOA first defines link ontology that assigns functions to interactions based on the known annotations of joint genes via optimizing two novel indexes ‘Coverage’ and ‘Diversity’. Then, NOA generates two alternative reference sets to statistically rank the enriched functional terms for a given biological network. We compare NOA with traditional enrichment analysis methods in several biological networks, and find that: (i) NOA can capture the change of functions not only in dynamic transcription regulatory networks but also in rewiring protein interaction networks while the traditional methods cannot and (ii) NOA can find more relevant and specific functions than traditional methods in different types of static networks. Furthermore, a freely accessible web server for NOA has been developed at http://www.aporc.org/noa/. PMID:21543451

  2. Method and system for mesh network embedded devices

    NASA Technical Reports Server (NTRS)

    Wang, Ray (Inventor)

    2009-01-01

    A method and system for managing mesh network devices. A mesh network device with integrated features creates an N-way mesh network with a full mesh network topology or a partial mesh network topology.

  3. A coarse-grained framework for spiking neuronal networks: between homogeneity and synchrony.

    PubMed

    Zhang, Jiwei; Zhou, Douglas; Cai, David; Rangan, Aaditya V

    2014-08-01

    Homogeneously structured networks of neurons driven by noise can exhibit a broad range of dynamic behavior. This dynamic behavior can range from homogeneity to synchrony, and often incorporates brief spurts of collaborative activity which we call multiple-firing-events (MFEs). These multiple-firing-events depend on neither structured architecture nor structured input, and are an emergent property of the system. Although these MFEs likely play a major role in the neuronal avalanches observed in culture and in vivo, the mechanisms underlying these MFEs cannot easily be captured using current population-dynamics models. In this work we introduce a coarse-grained framework which illustrates certain dynamics responsible for the generation of MFEs. By using a new kind of ensemble-average, this coarse-grained framework can not only address the nucleation of MFEs, but can also faithfully capture a broad range of dynamic regimes ranging from homogeneity to synchrony. PMID:24338105

  4. Two-photon imaging of spatially extended neuronal network dynamics with high temporal resolution

    PubMed Central

    Lillis, Kyle P.; Eng, Alfred; White, John A.; Mertz, Jerome

    2008-01-01

    We describe a simple two-photon fluorescence imaging strategy, called targeted path scanning (TPS), to monitor the dynamics of spatially extended neuronal networks with high spatiotemporal resolution. Our strategy combines the advantages of mirror-based scanning, minimized dead time, ease of implementation, and compatibility with high-resolution low-magnification objectives. To demonstrate the performance of TPS, we monitor the calcium dynamics distributed across an entire juvenile rat hippocampus (>1.5mm), at scan rates of 100Hz, with single cell resolution and single action potential sensitivity. Our strategy for fast, efficient two-photon microscopy over spatially extended regions provides a particularly attractive solution for monitoring neuronal population activity in thick tissue, without sacrificing the signal to noise ratio or high spatial resolution associated with standard two-photon microscopy. Finally, we provide the code to make our technique generally available. PMID:18539336

  5. Intelligent detection of impulse noise using multilayer neural network with multi-valued neurons

    NASA Astrophysics Data System (ADS)

    Aizenberg, Igor; Wallace, Glen

    2012-03-01

    In this paper, we solve the impulse noise detection problem using an intelligent approach. We use a multilayer neural network based on multi-valued neurons (MLMVN) as an intelligent impulse noise detector. MLMVN was already used for point spread function identification and intelligent edge enhancement. So it is very attractive to apply it for solving another image processing problem. The main result, which is presented in the paper, is the proven ability of MLMVN to detect impulse noise on different images after a learning session with the data taken just from a single noisy image. Hence MLMVN can be used as a robust impulse detector. It is especially efficient for salt and pepper noise detection and outperforms all competitive techniques. It also shows comparable results in detection of random impulse noise. Moreover, for random impulse noise detection, MLMVN with the output neuron with a periodic activation function is used for the first time.

  6. A multiscale method for a robust detection of the default mode network

    NASA Astrophysics Data System (ADS)

    Baquero, Katherine; Gómez, Francisco; Cifuentes, Christian; Guldenmund, Pieter; Demertzi, Athena; Vanhaudenhuyse, Audrey; Gosseries, Olivia; Tshibanda, Jean-Flory; Noirhomme, Quentin; Laureys, Steven; Soddu, Andrea; Romero, Eduardo

    2013-11-01

    The Default Mode Network (DMN) is a resting state network widely used for the analysis and diagnosis of mental disorders. It is normally detected in fMRI data, but for its detection in data corrupted by motion artefacts or low neuronal activity, the use of a robust analysis method is mandatory. In fMRI it has been shown that the signal-to-noise ratio (SNR) and the detection sensitivity of neuronal regions is increased with di erent smoothing kernels sizes. Here we propose to use a multiscale decomposition based of a linear scale-space representation for the detection of the DMN. Three main points are proposed in this methodology: rst, the use of fMRI data at di erent smoothing scale-spaces, second, detection of independent neuronal components of the DMN at each scale by using standard preprocessing methods and ICA decomposition at scale-level, and nally, a weighted contribution of each scale by the Goodness of Fit measurement. This method was applied to a group of control subjects and was compared with a standard preprocesing baseline. The detection of the DMN was improved at single subject level and at group level. Based on these results, we suggest to use this methodology to enhance the detection of the DMN in data perturbed with artefacts or applied to subjects with low neuronal activity. Furthermore, the multiscale method could be extended for the detection of other resting state neuronal networks.

  7. GABAB receptor deficiency causes failure of neuronal homeostasis in hippocampal networks.

    PubMed

    Vertkin, Irena; Styr, Boaz; Slomowitz, Edden; Ofir, Nir; Shapira, Ilana; Berner, David; Fedorova, Tatiana; Laviv, Tal; Barak-Broner, Noa; Greitzer-Antes, Dafna; Gassmann, Martin; Bettler, Bernhard; Lotan, Ilana; Slutsky, Inna

    2015-06-23

    Stabilization of neuronal activity by homeostatic control systems is fundamental for proper functioning of neural circuits. Failure in neuronal homeostasis has been hypothesized to underlie common pathophysiological mechanisms in a variety of brain disorders. However, the key molecules regulating homeostasis in central mammalian neural circuits remain obscure. Here, we show that selective inactivation of GABAB, but not GABA(A), receptors impairs firing rate homeostasis by disrupting synaptic homeostatic plasticity in hippocampal networks. Pharmacological GABA(B) receptor (GABA(B)R) blockade or genetic deletion of the GB(1a) receptor subunit disrupts homeostatic regulation of synaptic vesicle release. GABA(B)Rs mediate adaptive presynaptic enhancement to neuronal inactivity by two principle mechanisms: First, neuronal silencing promotes syntaxin-1 switch from a closed to an open conformation to accelerate soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) complex assembly, and second, it boosts spike-evoked presynaptic calcium flux. In both cases, neuronal inactivity removes tonic block imposed by the presynaptic, GB(1a)-containing receptors on syntaxin-1 opening and calcium entry to enhance probability of vesicle fusion. We identified the GB(1a) intracellular domain essential for the presynaptic homeostatic response by tuning intermolecular interactions among the receptor, syntaxin-1, and the Ca(V)2.2 channel. The presynaptic adaptations were accompanied by scaling of excitatory quantal amplitude via the postsynaptic, GB(1b)-containing receptors. Thus, GABA(B)Rs sense chronic perturbations in GABA levels and transduce it to homeostatic changes in synaptic strength. Our results reveal a novel role for GABA(B)R as a key regulator of population firing stability and propose that disruption of homeostatic synaptic plasticity may underlie seizure's persistence in the absence of functional GABA(B)Rs. PMID:26056260

  8. GABAB receptor deficiency causes failure of neuronal homeostasis in hippocampal networks

    PubMed Central

    Vertkin, Irena; Styr, Boaz; Slomowitz, Edden; Ofir, Nir; Shapira, Ilana; Berner, David; Fedorova, Tatiana; Laviv, Tal; Barak-Broner, Noa; Greitzer-Antes, Dafna; Gassmann, Martin; Bettler, Bernhard; Lotan, Ilana; Slutsky, Inna

    2015-01-01

    Stabilization of neuronal activity by homeostatic control systems is fundamental for proper functioning of neural circuits. Failure in neuronal homeostasis has been hypothesized to underlie common pathophysiological mechanisms in a variety of brain disorders. However, the key molecules regulating homeostasis in central mammalian neural circuits remain obscure. Here, we show that selective inactivation of GABAB, but not GABAA, receptors impairs firing rate homeostasis by disrupting synaptic homeostatic plasticity in hippocampal networks. Pharmacological GABAB receptor (GABABR) blockade or genetic deletion of the GB1a receptor subunit disrupts homeostatic regulation of synaptic vesicle release. GABABRs mediate adaptive presynaptic enhancement to neuronal inactivity by two principle mechanisms: First, neuronal silencing promotes syntaxin-1 switch from a closed to an open conformation to accelerate soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) complex assembly, and second, it boosts spike-evoked presynaptic calcium flux. In both cases, neuronal inactivity removes tonic block imposed by the presynaptic, GB1a-containing receptors on syntaxin-1 opening and calcium entry to enhance probability of vesicle fusion. We identified the GB1a intracellular domain essential for the presynaptic homeostatic response by tuning intermolecular interactions among the receptor, syntaxin-1, and the CaV2.2 channel. The presynaptic adaptations were accompanied by scaling of excitatory quantal amplitude via the postsynaptic, GB1b-containing receptors. Thus, GABABRs sense chronic perturbations in GABA levels and transduce it to homeostatic changes in synaptic strength. Our results reveal a novel role for GABABR as a key regulator of population firing stability and propose that disruption of homeostatic synaptic plasticity may underlie seizure's persistence in the absence of functional GABABRs. PMID:26056260

  9. Running rewires the neuronal network of adult-born dentate granule cells.

    PubMed

    Vivar, Carmen; Peterson, Benjamin D; van Praag, Henriette

    2016-05-01

    Exercise improves cognition in humans and animals. Running increases neurogenesis in the dentate gyrus of the hippocampus, a brain area important for learning and memory. It is unclear how running modifies the circuitry of new dentate gyrus neurons to support their role in memory function. Here we combine retroviral labeling with rabies virus mediated trans-synaptic retrograde tracing to define and quantify new neuron afferent inputs in young adult male C57Bl/6 mice, housed with or without a running wheel for one month. Exercise resulted in a shift in new neuron networks that may promote sparse encoding and pattern separation. Neurogenesis increased in the dorsal, but not the ventral, dentate gyrus by three-fold, whereas afferent traced cell labeling doubled in number. Regional analysis indicated that running differentially affected specific inputs. Within the hippocampus the ratio of innervation from inhibitory interneurons and glutamatergic mossy cells to new neurons was reduced. Distal traced cells were located in sub-cortical and cortical regions, including perirhinal, entorhinal and sensory cortices. Innervation from entorhinal cortex (EC) was augmented, in proportion to the running-induced enhancement of adult neurogenesis. Within EC afferent input and short-term synaptic plasticity from lateral entorhinal cortex, considered to convey contextual information to the hippocampus was increased. Furthermore, running upregulated innervation from regions important for spatial memory and theta rhythm generation, including caudo-medial entorhinal cortex and subcortical medial septum, supra- and medial mammillary nuclei. Altogether, running may facilitate contextual, spatial and temporal information encoding by increasing adult hippocampal neurogenesis and by reorganization of new neuron circuitry. PMID:26589333

  10. Optimal Information Representation and Criticality in an Adaptive Sensory Recurrent Neuronal Network

    PubMed Central

    Shriki, Oren; Yellin, Dovi

    2016-01-01

    Recurrent connections play an important role in cortical function, yet their exact contribution to the network computation remains unknown. The principles guiding the long-term evolution of these connections are poorly understood as well. Therefore, gaining insight into their computational role and into the mechanism shaping their pattern would be of great importance. To that end, we studied the learning dynamics and emergent recurrent connectivity in a sensory network model based on a first-principle information theoretic approach. As a test case, we applied this framework to a model of a hypercolumn in the visual cortex and found that the evolved connections between orientation columns have a "Mexican hat" profile, consistent with empirical data and previous modeling work. Furthermore, we found that optimal information representation is achieved when the network operates near a critical point in its dynamics. Neuronal networks working near such a phase transition are most sensitive to their inputs and are thus optimal in terms of information representation. Nevertheless, a mild change in the pattern of interactions may cause such networks to undergo a transition into a different regime of behavior in which the network activity is dominated by its internal recurrent dynamics and does not reflect the objective input. We discuss several mechanisms by which the pattern of interactions can be driven into this supercritical regime and relate them to various neurological and neuropsychiatric phenomena. PMID:26882372

  11. Diversity and time delays induce resonance in a modular neuronal network

    NASA Astrophysics Data System (ADS)

    Jia, Y. B.; Yang, X. L.; Kurths, J.

    2014-12-01

    This paper focuses on the resonance dynamics of a modular neuronal network consisting of several small-world subnetworks. The considered network is composed of delay-coupled FitzHugh-Nagumo (FHN) neurons, whose characteristic parameters present diversity in the form of quenched noise. Our numerical results indicate that when such a network is subjected to an external subthreshold periodic signal, its collective response is optimized for an intermediate level of diversity, namely, a resonant behavior can be induced by an appropriate level of diversity. How the probabilities of intramodule and intermodule connections, as well as the number of subnetworks influence the diversity-induced resonance are also discussed. Further, conclusive evidences demonstrate the nontrivial role of time-delayed coupling on the diversity-induced resonance properties. Especially, multiple resonance is obviously detected when time delays are located at integer multiples of the oscillation period of the signal. Moreover, the phenomenon of fine-tuned delays in inducing multiple resonance remains when diversity is within an intermediate range. Our findings have implications that neural systems may profit from their generic diversity and delayed coupling to optimize the response to external stimulus.

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

    PubMed

    Yang, Yiqun; Yeo, Chai Kiat

    2015-07-01

    From a single-cell animal like paramecium to vertebrates like ape, the nervous system plays an important role in responding to the variations of the environment. Compared to animals, the nervous system in the human body possesses more intricate organization and utility. The nervous system anatomy has been understood progressively, yet the explanation at the cell level regarding complete information transmission is still lacking. Along the signal pathway toward the brain, an external stimulus first activates action potentials in the sensing neuron and these electric pulses transmit along the spinal nerve or cranial nerve to the neurons in the brain. Second, calcium elevation is triggered in the branch of astrocyte at the tripartite synapse. Third, the local calcium wave expands to the entire territory of the astrocyte. Finally, the calcium wave propagates to the neighboring astrocyte via gap junction channel. In our study, we integrate the existing mathematical model and biological experiments in each step of the signal transduction to establish a conceptual network model for the human nervous system. The network is composed of four layers and the communication protocols of each layer could be adapted to entities with different characterizations. We verify our simulation results against the available biological experiments and mathematical models and provide a test case of the integrated network. As the production of conscious episode in the human nervous system is still under intense research, our model serves as a useful tool to facilitate, complement and verify current and future study in human cognition. PMID:25706505

  13. Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons.

    PubMed

    Probst, Dimitri; Petrovici, Mihai A; Bytschok, Ilja; Bill, Johannes; Pecevski, Dejan; Schemmel, Johannes; Meier, Karlheinz

    2015-01-01

    The means by which cortical neural networks are able to efficiently solve inference problems remains an open question in computational neuroscience. Recently, abstract models of Bayesian computation in neural circuits have been proposed, but they lack a mechanistic interpretation at the single-cell level. In this article, we describe a complete theoretical framework for building networks of leaky integrate-and-fire neurons that can sample from arbitrary probability distributions over binary random variables. We test our framework for a model inference task based on a psychophysical phenomenon (the Knill-Kersten optical illusion) and further assess its performance when applied to randomly generated distributions. As the local computations performed by the network strongly depend on the interaction between neurons, we compare several types of couplings mediated by either single synapses or interneuron chains. Due to its robustness to substrate imperfections such as parameter noise and background noise correlations, our model is particularly interesting for implementation on novel, neuro-inspired computing architectures, which can thereby serve as a fast, low-power substrate for solving real-world inference problems. PMID:25729361

  14. Computational Statistical Methods for Social Network Models

    PubMed Central

    Hunter, David R.; Krivitsky, Pavel N.; Schweinberger, Michael

    2013-01-01

    We review the broad range of recent statistical work in social network models, with emphasis on computational aspects of these methods. Particular focus is applied to exponential-family random graph models (ERGM) and latent variable models for data on complete networks observed at a single time point, though we also briefly review many methods for incompletely observed networks and networks observed at multiple time points. Although we mention far more modeling techniques than we can possibly cover in depth, we provide numerous citations to current literature. We illustrate several of the methods on a small, well-known network dataset, Sampson’s monks, providing code where possible so that these analyses may be duplicated. PMID:23828720

  15. Improved system identification using artificial neural networks and analysis of individual differences in responses of an identified neuron.

    PubMed

    Costalago Meruelo, Alicia; Simpson, David M; Veres, Sandor M; Newland, Philip L

    2016-03-01

    Mathematical modelling is used routinely to understand the coding properties and dynamics of responses of neurons and neural networks. Here we analyse the effectiveness of Artificial Neural Networks (ANNs) as a modelling tool for motor neuron responses. We used ANNs to model the synaptic responses of an identified motor neuron, the fast extensor motor neuron, of the desert locust in response to displacement of a sensory organ, the femoral chordotonal organ, which monitors movements of the tibia relative to the femur of the leg. The aim of the study was threefold: first to determine the potential value of ANNs as tools to model and investigate neural networks, second to understand the generalisation properties of ANNs across individuals and to different input signals and third, to understand individual differences in responses of an identified neuron. A metaheuristic algorithm was developed to design the ANN architectures. The performance of the models generated by the ANNs was compared with those generated through previous mathematical models of the same neuron. The results suggest that ANNs are significantly better than LNL and Wiener models in predicting specific neural responses to Gaussian White Noise, but not significantly different when tested with sinusoidal inputs. They are also able to predict responses of the same neuron in different individuals irrespective of which animal was used to develop the model, although notable differences between some individuals were evident. PMID:26717237

  16. An improved sampling method of complex network

    NASA Astrophysics Data System (ADS)

    Gao, Qi; Ding, Xintong; Pan, Feng; Li, Weixing

    2014-12-01

    Sampling subnet is an important topic of complex network research. Sampling methods influence the structure and characteristics of subnet. Random multiple snowball with Cohen (RMSC) process sampling which combines the advantages of random sampling and snowball sampling is proposed in this paper. It has the ability to explore global information and discover the local structure at the same time. The experiments indicate that this novel sampling method could keep the similarity between sampling subnet and original network on degree distribution, connectivity rate and average shortest path. This method is applicable to the situation where the prior knowledge about degree distribution of original network is not sufficient.

  17. EEG markers for characterizing anomalous activities of cerebral neurons in NAT (neuronal activity topography) method.

    PubMed

    Musha, Toshimitsu; Matsuzaki, Haruyasu; Kobayashi, Yohei; Okamoto, Yoshiwo; Tanaka, Mieko; Asada, Takashi

    2013-08-01

    A pair of markers, sNAT and vNAT, is derived from the electroencephalogram (EEG) power spectra (PS) recorded for 5 min with 21 electrodes (4-20 Hz) arranged according to the 10-20 standard. These markers form a new diagnosis tool "NAT" aiming at characterizing various brain disorders. Each signal sequence is divided into segments of 0.64 s and its discrete PS consists of eleven frequency components from 4.68 (3 × 1.56) Hz through 20.34 (13 × 1.56) Hz. PS is normalized to its mean and the bias of PS components on each frequency component across the 21 signal channels is reset to zero. The marker sNAT consists of ten frequency components on 21 channels, characterizing neuronal hyperactivity or hypoactivity as compared with NLc (normal controls). The marker vNAT consists of ten ratios between adjacent PS components denoting the over- or undersynchrony of collective neuronal activities as compared with NLc. The likelihood of a test subject to a specified brain disease is defined in terms of the normalized distance to the template NAT state of the disease in the NAT space. Separation of MCI-AD patients (developing AD in 12-18 months) from NLc is made with a false alarm rate of 15%. Locations with neuronal hypoactivity and undersynchrony of AD patients agree with locations of rCBF reduction measured by SPECT. The 2-D diagram composed of the binary likelihoods between ADc and NLc in the two representations of sNAT and vNAT enables tracing the NAT state of a test subject approaching the AD area, and the follow-up of the treatment effects. PMID:23559020

  18. Brain without mind: Computer simulation of neural networks with modifiable neuronal interactions

    NASA Astrophysics Data System (ADS)

    Clark, John W.; Rafelski, Johann; Winston, Jeffrey V.

    1985-07-01

    Aspects of brain function are examined in terms of a nonlinear dynamical system of highly interconnected neuron-like binary decision elements. The model neurons operate synchronously in discrete time, according to deterministic or probabilistic equations of motion. Plasticity of the nervous system, which underlies such cognitive collective phenomena as adaptive development, learning, and memory, is represented by temporal modification of interneuronal connection strengths depending on momentary or recent neural activity. A formal basis is presented for the construction of local plasticity algorithms, or connection-modification routines, spanning a large class. To build an intuitive understanding of the behavior of discrete-time network models, extensive computer simulations have been carried out (a) for nets with fixed, quasirandom connectivity and (b) for nets with connections that evolve under one or another choice of plasticity algorithm. From the former experiments, insights are gained concerning the spontaneous emergence of order in the form of cyclic modes of neuronal activity. In the course of the latter experiments, a simple plasticity routine (“brainwashing,” or “anti-learning”) was identified which, applied to nets with initially quasirandom connectivity, creates model networks which provide more felicitous starting points for computer experiments on the engramming of content-addressable memories and on learning more generally. The potential relevance of this algorithm to developmental neurobiology and to sleep states is discussed. The model considered is at the same time a synthesis of earlier synchronous neural-network models and an elaboration upon them; accordingly, the present article offers both a focused review of the dynamical properties of such systems and a selection of new findings derived from computer simulation.

  19. High codimensional bifurcation analysis to a six-neuron BAM neural network.

    PubMed

    Liu, Yanwei; Li, Shanshan; Liu, Zengrong; Wang, Ruiqi

    2016-04-01

    In this article, the high codimension bifurcations of a six-neuron BAM neural network system with multiple delays are addressed. We first deduce the existence conditions under which the origin of the system is a Bogdanov-Takens singularity with multiplicities two or three. By choosing the connection coefficients as bifurcation parameters and using the formula derived from the normal form theory and the center manifold, the normal forms of Bogdanov-Takens and triple zero bifurcations are presented. Some numerical examples are shown to support our main results. PMID:27066152

  20. Effect of spike-timing-dependent plasticity on coherence resonance and synchronization transitions by time delay in adaptive neuronal networks

    NASA Astrophysics Data System (ADS)

    Xie, Huijuan; Gong, Yubing; Wang, Qi

    2016-06-01

    In this paper, we numerically study how time delay induces multiple coherence resonance (MCR) and synchronization transitions (ST) in adaptive Hodgkin-Huxley neuronal networks with spike-timing dependent plasticity (STDP). It is found that MCR induced by time delay STDP can be either enhanced or suppressed as the adjusting rate Ap of STDP changes, and ST by time delay varies with the increase of Ap, and there is optimal Ap by which the ST becomes strongest. It is also found that there are optimal network randomness and network size by which ST by time delay becomes strongest, and when Ap increases, the optimal network randomness and optimal network size increase and related ST is enhanced. These results show that STDP can either enhance or suppress MCR and optimal STDP can enhance ST induced by time delay in the adaptive neuronal networks. These findings provide a new insight into STDP's role for the information processing and transmission in neural systems.

  1. New Electrochemical Methods for Studying Nanoparticle Electrocatalysis and Neuronal Exocytosis

    NASA Astrophysics Data System (ADS)

    Cox, Jonathan T.

    This dissertation presents the construction and application of micro and nanoscale electrodes for electroanalytical analysis. The studies presented herein encompass two main areas: electrochemical catalysis, and studies of the dynamics of single cell exocytosis. The first portion of this dissertation engages the use of Pt nanoelectrodes to study the stability and electrocatalytic properties of materials. A single nanoparticle electrode (SNPE) was fabricated by immobilizing a single Au nanoparticle on a Pt disk nanoelectrode via an amine-terminated silane cross linker. In this manner we were able to effectively study the electrochemistry and electrocatalytic activity of single Au nanoparticles and found that the electrocatalytic activity is dependent on nanoparticle size. This study can further the understanding of the structure-function relationship in nanoparticle based electrocatalysis. Further work was conducted to probe the stability of Pt nanoelectrodes under conditions of potential cycling. Pt based catalysts are known to deteriorate under such conditions due to losses in electrochemical surface area and Pt dissolution. By using Pt disk nanoelectrodes we were able to study Pt dissolution via steady-state voltammetry. We observed an enhanced dissolution rate and higher charge density on nanoelectrodes than that previously found on macro scale electrodes. The goal of the second portion of this dissertation is to develop new analytical methods to study the dynamics of exocytosis from single cells. The secretion of neurotransmitters plays a key role in neuronal communication, and our studies highlight how bipolar electrochemistry can be employed to enhance detection of neurotransmitters from single cells. First, we developed a theory to quantitatively characterize the voltammetric behavior of bipolar carbon fiber microelectrodes and secondly applied those principles to single cell detection. We showed that by simply adding an additional redox mediator to the back

  2. Spatiotemporal memory is an intrinsic property of networks of dissociated cortical neurons.

    PubMed

    Ju, Han; Dranias, Mark R; Banumurthy, Gokulakrishna; VanDongen, Antonius M J

    2015-03-01

    The ability to process complex spatiotemporal information is a fundamental process underlying the behavior of all higher organisms. However, how the brain processes information in the temporal domain remains incompletely understood. We have explored the spatiotemporal information-processing capability of networks formed from dissociated rat E18 cortical neurons growing in culture. By combining optogenetics with microelectrode array recording, we show that these randomly organized cortical microcircuits are able to process complex spatiotemporal information, allowing the identification of a large number of temporal sequences and classification of musical styles. These experiments uncovered spatiotemporal memory processes lasting several seconds. Neural network simulations indicated that both short-term synaptic plasticity and recurrent connections are required for the emergence of this capability. Interestingly, NMDA receptor function is not a requisite for these short-term spatiotemporal memory processes. Indeed, blocking the NMDA receptor with the antagonist APV significantly improved the temporal processing ability of the networks, by reducing spontaneously occurring network bursts. These highly synchronized events have disastrous effects on spatiotemporal information processing, by transiently erasing short-term memory. These results show that the ability to process and integrate complex spatiotemporal information is an intrinsic property of generic cortical networks that does not require specifically designed circuits. PMID:25740531

  3. Emergent Dynamics from Spiking Neuron Networks through Symmetry Breaking of Connectivity

    PubMed Central

    Woodman, M. Marmaduke; Jirsa, Viktor K.

    2013-01-01

    Low-dimensional attractive manifolds with flows prescribing the evolution of state variables are commonly used to capture the lawful behavior of behavioral and cognitive variables. Neural network dynamics underlie many of the mechanistic explanations of function and demonstrate the existence of such low-dimensional attractive manifolds. In this study, we focus on exploring the network mechanisms due to asymmetric couplings giving rise to the emergence of arbitrary flows in low dimensional spaces. Here we use a spiking neural network model, specifically the theta neuron model and simple synaptic dynamics, to show how a qualitatively identical set of basic behaviors arises from different combinations of couplings with broken symmetry, in fluctuations of both firing rate and spike timing. We further demonstrate how such network dynamics can be combined to create more complex processes. These results suggest that 1) asymmetric coupling is not always a variance to be averaged over, 2) different networks may produce the same dynamics by different dynamical routes and 3) complex dynamics may be formed by simpler dynamics through a combination of couplings. PMID:23691200

  4. Multiclass Classification by Adaptive Network of Dendritic Neurons with Binary Synapses Using Structural Plasticity

    PubMed Central

    Hussain, Shaista; Basu, Arindam

    2016-01-01

    The development of power-efficient neuromorphic devices presents the challenge of designing spike pattern classification algorithms which can be implemented on low-precision hardware and can also achieve state-of-the-art performance. In our pursuit of meeting this challenge, we present a pattern classification model which uses a sparse connection matrix and exploits the mechanism of nonlinear dendritic processing to achieve high classification accuracy. A rate-based structural learning rule for multiclass classification is proposed which modifies a connectivity matrix of binary synaptic connections by choosing the best “k” out of “d” inputs to make connections on every dendritic branch (k < < d). Because learning only modifies connectivity, the model is well suited for implementation in neuromorphic systems using address-event representation (AER). We develop an ensemble method which combines several dendritic classifiers to achieve enhanced generalization over individual classifiers. We have two major findings: (1) Our results demonstrate that an ensemble created with classifiers comprising moderate number of dendrites performs better than both ensembles of perceptrons and of complex dendritic trees. (2) In order to determine the moderate number of dendrites required for a specific classification problem, a two-step solution is proposed. First, an adaptive approach is proposed which scales the relative size of the dendritic trees of neurons for each class. It works by progressively adding dendrites with fixed number of synapses to the network, thereby allocating synaptic resources as per the complexity of the given problem. As a second step, theoretical capacity calculations are used to convert each neuronal dendritic tree to its optimal topology where dendrites of each class are assigned different number of synapses. The performance of the model is evaluated on classification of handwritten digits from the benchmark MNIST dataset and compared with other

  5. Multiclass Classification by Adaptive Network of Dendritic Neurons with Binary Synapses Using Structural Plasticity.

    PubMed

    Hussain, Shaista; Basu, Arindam

    2016-01-01

    The development of power-efficient neuromorphic devices presents the challenge of designing spike pattern classification algorithms which can be implemented on low-precision hardware and can also achieve state-of-the-art performance. In our pursuit of meeting this challenge, we present a pattern classification model which uses a sparse connection matrix and exploits the mechanism of nonlinear dendritic processing to achieve high classification accuracy. A rate-based structural learning rule for multiclass classification is proposed which modifies a connectivity matrix of binary synaptic connections by choosing the best "k" out of "d" inputs to make connections on every dendritic branch (k < < d). Because learning only modifies connectivity, the model is well suited for implementation in neuromorphic systems using address-event representation (AER). We develop an ensemble method which combines several dendritic classifiers to achieve enhanced generalization over individual classifiers. We have two major findings: (1) Our results demonstrate that an ensemble created with classifiers comprising moderate number of dendrites performs better than both ensembles of perceptrons and of complex dendritic trees. (2) In order to determine the moderate number of dendrites required for a specific classification problem, a two-step solution is proposed. First, an adaptive approach is proposed which scales the relative size of the dendritic trees of neurons for each class. It works by progressively adding dendrites with fixed number of synapses to the network, thereby allocating synaptic resources as per the complexity of the given problem. As a second step, theoretical capacity calculations are used to convert each neuronal dendritic tree to its optimal topology where dendrites of each class are assigned different number of synapses. The performance of the model is evaluated on classification of handwritten digits from the benchmark MNIST dataset and compared with other spike

  6. Stability of the synchronized network of Hindmarsh-Rose neuronal models with nearest and global couplings

    NASA Astrophysics Data System (ADS)

    Dtchetgnia Djeundam, S. R.; Yamapi, R.; Filatrella, G.; Kofane, T. C.

    2015-05-01

    We analytically and numerically investigate a set of N identical and non-identical Hindmarsh-Rose neuronal models with nearest-neighbor and global couplings. The stability boundary of the synchronized states is analyzed using the Master Stability Function approach for the case of identical oscillators (complete synchronization) and the Kuramoto order parameter for the disordered case (phase synchronization). We find that, through a linear coupling modeling electrical synapses, complete synchronization occurs in a system of many nearest-neighbor or globally coupled identical oscillators, and in the case of non-identical neurons it is stable even in the presence of a spread of the parameters. We find that the Hindmarsh-Rose neuronal models can synchronize when coupled through the action of potential variable or through the interaction by rapid flows of ions through the membrane. The degree of connectivity of the network favors synchronization: in the global coupling case, the threshold for the in-phase state stabilizes when the number of dynamical units increases. The transition from disordered to the ordered state is a second order dynamical phase transition, although very sharp.

  7. Classifying heterogeneity of spontaneous up-states: a method for revealing variations in firing probability, engaged neurons and Fano factor.

    PubMed

    Gullo, Francesca; Maffezzoli, Andrea; Dossi, Elena; Lecchi, Marzia; Wanke, Enzo

    2012-01-30

    The dynamics of spontaneous and sensory-evoked up-states have been recently compared, in multi-site recordings in vivo and found to have similarities and differences. Also in vitro, this is evident because we here describe a novel computational method to classify into statistically different states the spontaneous reverberating activity recorded from long-term (12-18 days-in vitro) cultured cortical neurons (from 60-site multi-electrode arrays, MEA). State classification was performed by spike number time histograms (SNTH, or other burst features) of excitatory and inhibitory neuron clusters and revealed that in novel identified states the number of engaged neurons or up-state duration can change. To improve the characterization of each state we also computed the firing spike histograms (FSH) which revealed a new facet of the firing probability of clusters. In exemplary functional experiments we show that: (i) up to 6-7 states can be safely categorized during several hours of recordings without observing spike rate changes, (ii) they disappear after a short pharmacological stimulation being replaced with novel states active and living up to 6-8 h, (iii) antagonists in the nM range can split the activity of a homogeneous network into the chronological coexistence of 2 states, one completely different and one not significantly different from control state. In conclusion, we believe that this novel procedure better characterizes the number of functional states of a network and opens up the possibility of predicting the elementary "vocabulary" used by small networks of neurons. PMID:22037594

  8. The Emergence of Synaesthesia in a Neuronal Network Model via Changes in Perceptual Sensitivity and Plasticity

    PubMed Central

    Ward, Jamie

    2016-01-01

    Synaesthesia is an unusual perceptual experience in which an inducer stimulus triggers a percept in a different domain in addition to its own. To explore the conditions under which synaesthesia evolves, we studied a neuronal network model that represents two recurrently connected neural systems. The interactions in the network evolve according to learning rules that optimize sensory sensitivity. We demonstrate several scenarios, such as sensory deprivation or heightened plasticity, under which synaesthesia can evolve even though the inputs to the two systems are statistically independent and the initial cross-talk interactions are zero. Sensory deprivation is the known causal mechanism for acquired synaesthesia and increased plasticity is implicated in developmental synaesthesia. The model unifies different causes of synaesthesia within a single theoretical framework and repositions synaesthesia not as some quirk of aberrant connectivity, but rather as a functional brain state that can emerge as a consequence of optimising sensory information processing. PMID:27392215

  9. The Emergence of Synaesthesia in a Neuronal Network Model via Changes in Perceptual Sensitivity and Plasticity.

    PubMed

    Shriki, Oren; Sadeh, Yaniv; Ward, Jamie

    2016-07-01

    Synaesthesia is an unusual perceptual experience in which an inducer stimulus triggers a percept in a different domain in addition to its own. To explore the conditions under which synaesthesia evolves, we studied a neuronal network model that represents two recurrently connected neural systems. The interactions in the network evolve according to learning rules that optimize sensory sensitivity. We demonstrate several scenarios, such as sensory deprivation or heightened plasticity, under which synaesthesia can evolve even though the inputs to the two systems are statistically independent and the initial cross-talk interactions are zero. Sensory deprivation is the known causal mechanism for acquired synaesthesia and increased plasticity is implicated in developmental synaesthesia. The model unifies different causes of synaesthesia within a single theoretical framework and repositions synaesthesia not as some quirk of aberrant connectivity, but rather as a functional brain state that can emerge as a consequence of optimising sensory information processing. PMID:27392215

  10. Adaptive coupling optimized spiking coherence and synchronization in Newman-Watts neuronal networks

    NASA Astrophysics Data System (ADS)

    Gong, Yubing; Xu, Bo; Wu, Ya'nan

    2013-09-01

    In this paper, we have numerically studied the effect of adaptive coupling on the temporal coherence and synchronization of spiking activity in Newman-Watts Hodgkin-Huxley neuronal networks. It is found that random shortcuts can enhance the spiking synchronization more rapidly when the increment speed of adaptive coupling is increased and can optimize the temporal coherence of spikes only when the increment speed of adaptive coupling is appropriate. It is also found that adaptive coupling strength can enhance the synchronization of spikes and can optimize the temporal coherence of spikes when random shortcuts are appropriate. These results show that adaptive coupling has a big influence on random shortcuts related spiking activity and can enhance and optimize the temporal coherence and synchronization of spiking activity of the network. These findings can help better understand the roles of adaptive coupling for improving the information processing and transmission in neural systems.

  11. GABA depolarizes immature neurons and inhibits network activity in the neonatal neocortex in vivo.

    PubMed

    Kirmse, Knut; Kummer, Michael; Kovalchuk, Yury; Witte, Otto W; Garaschuk, Olga; Holthoff, Knut

    2015-01-01

    A large body of evidence from in vitro studies suggests that GABA is depolarizing during early postnatal development. However, the mode of GABA action in the intact developing brain is unknown. Here we examine the in vivo effects of GABA in cells of the upper cortical plate using a combination of electrophysiological and Ca(2+)-imaging techniques. We report that at postnatal days (P) 3-4, GABA depolarizes the majority of immature neurons in the occipital cortex of anaesthetized mice. At the same time, GABA does not efficiently activate voltage-gated Ca(2+) channels and fails to induce action potential firing. Blocking GABA(A) receptors disinhibits spontaneous network activity, whereas allosteric activation of GABA(A) receptors has the opposite effect. In summary, our data provide evidence that in vivo GABA acts as a depolarizing neurotransmitter imposing an inhibitory control on network activity in the neonatal (P3-4) neocortex. PMID:26177896

  12. A network of spiking neurons for computing sparse representations in an energy efficient way

    PubMed Central

    Hu, Tao; Genkin, Alexander; Chklovskii, Dmitri B.

    2013-01-01

    Computing sparse redundant representations is an important problem both in applied mathematics and neuroscience. In many applications, this problem must be solved in an energy efficient way. Here, we propose a hybrid distributed algorithm (HDA), which solves this problem on a network of simple nodes communicating via low-bandwidth channels. HDA nodes perform both gradient-descent-like steps on analog internal variables and coordinate-descent-like steps via quantized external variables communicated to each other. Interestingly, such operation is equivalent to a network of integrate-and-fire neurons, suggesting that HDA may serve as a model of neural computation. We compare the numerical performance of HDA with existing algorithms and show that in the asymptotic regime the representation error of HDA decays with time, t, as 1/t. We show that HDA is stable against time-varying noise, specifically, the representation error decays as 1/t for Gaussian white noise. PMID:22920853

  13. A decaying factor accounts for contained activity in neuronal networks with no need of hierarchical or modular organization

    NASA Astrophysics Data System (ADS)

    Amancio, Diego R.; Oliveira, Osvaldo N., Jr.; Costa, Luciano da F.

    2012-11-01

    The mechanisms responsible for containing activity in systems represented by networks are crucial in various phenomena, for example, in diseases such as epilepsy that affect the neuronal networks and for information dissemination in social networks. The first models to account for contained activity included triggering and inhibition processes, but they cannot be applied to social networks where inhibition is clearly absent. A recent model showed that contained activity can be achieved with no need of inhibition processes provided that the network is subdivided into modules (communities). In this paper, we introduce a new concept inspired in the Hebbian theory, through which containment of activity is achieved by incorporating a dynamics based on a decaying activity in a random walk mechanism preferential to the node activity. Upon selecting the decay coefficient within a proper range, we observed sustained activity in all the networks tested, namely, random, Barabási-Albert and geographical networks. The generality of this finding was confirmed by showing that modularity is no longer needed if the dynamics based on the integrate-and-fire dynamics incorporated the decay factor. Taken together, these results provide a proof of principle that persistent, restrained network activation might occur in the absence of any particular topological structure. This may be the reason why neuronal activity does not spread out to the entire neuronal network, even when no special topological organization exists. .

  14. Patterned neuronal networks using nanodiamonds and the effect of varying nanodiamond properties on neuronal adhesion and outgrowth

    NASA Astrophysics Data System (ADS)

    Edgington, R. J.; Thalhammer, A.; Welch, J. O.; Bongrain, A.; Bergonzo, P.; Scorsone, E.; Jackman, R. B.; Schoepfer, R.

    2013-10-01

    Objective. Detonation nanodiamond monolayer coatings are exceptionally biocompatible substrates for in vitro cell culture. However, the ability of nanodiamond coatings of different origin, size, surface chemistry and morphology to promote neuronal adhesion, and the ability to pattern neurons with nanodiamonds have yet to be investigated. Approach. Various nanodiamond coatings of different type are investigated for their ability to promote neuronal adhesion with respect to surface coating parameters and neurite extension. Nanodiamond tracks are patterned using photolithography and reactive ion etching. Main results. Universal promotion of neuronal adhesion is observed on all coatings tested and analysis shows surface roughness to not be a sufficient metric to describe biocompatibility, but instead nanoparticle size and curvature shows a significant correlation with neurite extension. Furthermore, neuronal patterning is achieved with high contrast using patterned nanodiamond coatings down to at least 10 µm. Significance. The results of nanoparticle size and curvature being influential upon neuronal adhesion has great implications towards biomaterial design, and the ability to pattern neurons using nanodiamond tracks shows great promise for applications both in vitro and in vivo.

  15. Tau loss attenuates neuronal network hyperexcitability in mouse and Drosophila genetic models of epilepsy

    PubMed Central

    Holth, Jerrah K.; Bomben, Valerie C.; Reed, J. Graham; Inoue, Taeko; Younkin, Linda; Younkin, Steven G.; Pautler, Robia G.; Botas, Juan; Noebels, Jeffrey L.

    2013-01-01

    Neuronal network hyperexcitability underlies the pathogenesis of seizures and is a component of some degenerative neurological disorders such as Alzheimer’s disease (AD). Recently, the microtubule binding protein tau has been implicated in the regulation of network synchronization. Genetic removal of Mapt, the gene encoding tau, in AD models overexpressing amyloid-beta (Aβ) decreases hyperexcitability and normalizes the excitation/inhibition imbalance. Whether this effect of tau removal is specific to Aβ mouse models remains to be determined. Here we examined tau as an excitability modifier in the non-AD nervous system using genetic deletion of tau in mouse and Drosophila models of hyperexcitability. Kcna1−/− mice lack Kv1.1 delayed rectifier currents and exhibit severe spontaneous seizures, early lethality, and megencephaly. Young Kcna1−/− mice retained wild-type levels of Aβ, tau, and tau phospho-Thr231. Decreasing tau in Kcna1−/− mice reduced hyperexcitability and alleviated seizure-related comorbidities. Tau reduction decreased Kcna1−/− video-EEG recorded seizure frequency and duration as well as normalized Kcna1−/− hippocampal network hyperexcitability in vitro. Additionally, tau reduction increased Kcna1−/− survival and prevented megencephaly and hippocampal hypertrophy, as determined by MRI. Bang-sensitive Drosophila mutants display paralysis and seizures in response to mechanical stimulation, providing a complementary excitability assay for epistatic interactions. We found that tau reduction significantly decreased seizure sensitivity in two independent bang-sensitive mutant models, kcc and eas. Our results indicate that tau plays a general role in regulating intrinsic neuronal network hyperexcitability independently of Aβ overexpression and suggest that reducing tau function could be a viable target for therapeutic intervention in seizure disorders and antiepileptogenesis. PMID:23345237

  16. Run-time interoperability between neuronal network simulators based on the MUSIC framework.

    PubMed

    Djurfeldt, Mikael; Hjorth, Johannes; Eppler, Jochen M; Dudani, Niraj; Helias, Moritz; Potjans, Tobias C; Bhalla, Upinder S; Diesmann, Markus; Kotaleski, Jeanette Hellgren; Ekeberg, Orjan

    2010-03-01

    MUSIC is a standard API allowing large scale neuron simulators to exchange data within a parallel computer during runtime. A pilot implementation of this API has been released as open source. We provide experiences from the implementation of MUSIC interfaces for two neuronal network simulators of different kinds, NEST and MOOSE. A multi-simulation of a cortico-striatal network model involving both simulators is performed, demonstrating how MUSIC can promote inter-operability between models written for different simulators and how these can be re-used to build a larger model system. Benchmarks show that the MUSIC pilot implementation provides efficient data transfer in a cluster computer with good scaling. We conclude that MUSIC fulfills the design goal that it should be simple to adapt existing simulators to use MUSIC. In addition, since the MUSIC API enforces independence of the applications, the multi-simulation could be built from pluggable component modules without adaptation of the components to each other in terms of simulation time-step or topology of connections between the modules. PMID:20195795

  17. Transient global amnesia: Only in already disrupted neuronal integrity of memory network?

    PubMed

    Moon, Yeonsil; Oh, Jeeyoung; Kwon, Kyoung Ja; Han, Seol-Heui

    2016-09-15

    Transient global amnesia is a well-described clinical syndrome; however, the pathophysiology is perplexing. Structural imaging indicates that punctuate hippocampal lesions are the representative pathophysiology, although functional neuroimaging studies have reported that the various regions comprising the episodic memory network are involved. We hypothesized that the neuronal integrity of the memory network might correlate with amnesia symptoms when there is any insult that can affect the hippocampus. Diffusion tensor images of 5 patients with variable diffusion-weighted imaging findings with or without transient global amnesia symptoms were analyzed. Diffusion tensor image analyses were performed using DTI studio software. A patient with a typical restricted diffusion involving the right hippocampus, but without memory symptoms, had more abundant cingulum fibers. However, the serial cingulum fibers of patients having experienced multiple attacks did not show a decremental tendency. The volume of fibers in the affected side was lower than that of the opposite side. This report suggests that memory-related symptoms of transient global amnesia are related to the disrupted neuronal integrity of cingulum fibers. PMID:27538630

  18. Speed of feedforward and recurrent processing in multilayer networks of integrate-and-fire neurons.

    PubMed

    Panzeri, S; Rolls, E T; Battaglia, F; Lavis, R

    2001-11-01

    The speed of processing in the visual cortical areas can be fast, with for example the latency of neuronal responses increasing by only approximately 10 ms per area in the ventral visual system sequence V1 to V2 to V4 to inferior temporal visual cortex. This has led to the suggestion that rapid visual processing can only be based on the feedforward connections between cortical areas. To test this idea, we investigated the dynamics of information retrieval in multiple layer networks using a four-stage feedforward network modelled with continuous dynamics with integrate-and-fire neurons, and associative synaptic connections between stages with a synaptic time constant of 10 ms. Through the implementation of continuous dynamics, we found latency differences in information retrieval of only 5 ms per layer when local excitation was absent and processing was purely feedforward. However, information latency differences increased significantly when non-associative local excitation was included. We also found that local recurrent excitation through associatively modified synapses can contribute significantly to processing in as little as 15 ms per layer, including the feedforward and local feedback processing. Moreover, and in contrast to purely feed-forward processing, the contribution of local recurrent feedback was useful and approximately this rapid even when retrieval was made difficult by noise. These findings suggest that cortical information processing can benefit from recurrent circuits when the allowed processing time per cortical area is at least 15 ms long. PMID:11762898

  19. Working memory in ALS patients: preserved performance but marked changes in underlying neuronal networks.

    PubMed

    Zaehle, Tino; Becke, Andreas; Naue, Nicole; Machts, Judith; Abdulla, Susanne; Petri, Susanne; Kollewe, Katja; Dengler, Reinhard; Heinze, Hans-Jochen; Vielhaber, Stefan; Müller, Notger G

    2013-01-01

    Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease which affects the motor system but also other frontal brain regions. In this study we investigated changes in functional neuronal networks including posterior brain regions that are not directly affected by the neurodegenerative process. To this end, we analyzed the contralateral delay activity (CDA), an ERP component considered an online marker of memory storage in posterior cortex, while 23 ALS patients and their controls performed a delayed-matching-to-sample working memory (WM) task. The task required encoding of stimuli in the cued hemifield whilst ignoring stimuli in the other hemifield. Despite their unimpaired behavioral performance patients displayed several changes in the neuronal markers of the memory processes. Their CDA amplitude was smaller; it showed less load-dependent modulation and lacked the reduction observed when controls performed the same task three months later. The smaller CDA in the patients could be attributed to more ipsilateral cortical activity which may indicate that ALS patients unnecessarily processed the irrelevant stimuli as well. The latter is presumably related to deterioration of the frontal cortex in the patient group which was indicated by slight deficits in tests of their executive functions that increased over time. The frontal pathology presumably affected their top-down control of memory storage in remote regions in the posterior brain. In sum, the present results demonstrate functional changes in neuronal networks, i.e. neuroplasticity, in ALS that go well beyond the known structural changes. They also show that at least in WM tasks, in which strategic top-down control demands are relatively low, the frontal deficit can be compensated for by intact low level processes in posterior brain regions. PMID:23951274

  20. Working Memory in ALS Patients: Preserved Performance but Marked Changes in Underlying Neuronal Networks

    PubMed Central

    Zaehle, Tino; Becke, Andreas; Naue, Nicole; Machts, Judith; Abdulla, Susanne; Petri, Susanne; Kollewe, Katja; Dengler, Reinhard; Heinze, Hans-Jochen; Vielhaber, Stefan; Müller, Notger G.

    2013-01-01

    Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease which affects the motor system but also other frontal brain regions. In this study we investigated changes in functional neuronal networks including posterior brain regions that are not directly affected by the neurodegenerative process. To this end, we analyzed the contralateral delay activity (CDA), an ERP component considered an online marker of memory storage in posterior cortex, while 23 ALS patients and their controls performed a delayed-matching-to-sample working memory (WM) task. The task required encoding of stimuli in the cued hemifield whilst ignoring stimuli in the other hemifield. Despite their unimpaired behavioral performance patients displayed several changes in the neuronal markers of the memory processes. Their CDA amplitude was smaller; it showed less load-dependent modulation and lacked the reduction observed when controls performed the same task three months later. The smaller CDA in the patients could be attributed to more ipsilateral cortical activity which may indicate that ALS patients unnecessarily processed the irrelevant stimuli as well. The latter is presumably related to deterioration of the frontal cortex in the patient group which was indicated by slight deficits in tests of their executive functions that increased over time. The frontal pathology presumably affected their top-down control of memory storage in remote regions in the posterior brain. In sum, the present results demonstrate functional changes in neuronal networks, i.e. neuroplasticity, in ALS that go well beyond the known structural changes. They also show that at least in WM tasks, in which strategic top-down control demands are relatively low, the frontal deficit can be compensated for by intact low level processes in posterior brain regions. PMID:23951274

  1. On-Chip Multichannel Action Potential Recording System for Electrical Measurement of Single Neurites of Neuronal Network

    NASA Astrophysics Data System (ADS)

    Suzuki, Ikurou; Hattori, Akihiro; Yasuda, Kenji

    2007-11-01

    We have developed a multielectrode array recording system for single-neurite-firing measurement using an artificially constructed neuronal network on a chip, which has a 10 μm diameter array with electrodes spaced at 50 μm, for noninvasive 64-channel 100 kHz multirecording and the stimulation of a plurality of neurites extending from a single neuron. To improve the signal/noise ratio, the ground plane was set on the multi-electrode-array plane and platinum black was set on each of the 10 μm electrodes. Using this system, we performed a multisite recording of neurites of a single neuron of a rat hippocampal network in cases of both spontaneous firing and evoked responses to electrical stimulations, and estimated the velocity of action potential propagation among neurites of a single neuron from six recording sites. This demonstrated the potential use of our low-noise chip and our high-speed measurement system for the analysis of neuronal network activities at the single-neuron level.

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

    NASA Astrophysics Data System (ADS)

    Huang, Xu-Hui; Hu, Gang

    2014-10-01

    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.

  3. Model-based analysis and control of a network of basal ganglia spiking neurons in the normal and Parkinsonian states

    NASA Astrophysics Data System (ADS)

    Liu, Jianbo; Khalil, Hassan K.; Oweiss, Karim G.

    2011-08-01

    Controlling the spatiotemporal firing pattern of an intricately connected network of neurons through microstimulation is highly desirable in many applications. We investigated in this paper the feasibility of using a model-based approach to the analysis and control of a basal ganglia (BG) network model of Hodgkin-Huxley (HH) spiking neurons through microstimulation. Detailed analysis of this network model suggests that it can reproduce the experimentally observed characteristics of BG neurons under a normal and a pathological Parkinsonian state. A simplified neuronal firing rate model, identified from the detailed HH network model, is shown to capture the essential network dynamics. Mathematical analysis of the simplified model reveals the presence of a systematic relationship between the network's structure and its dynamic response to spatiotemporally patterned microstimulation. We show that both the network synaptic organization and the local mechanism of microstimulation can impose tight constraints on the possible spatiotemporal firing patterns that can be generated by the microstimulated network, which may hinder the effectiveness of microstimulation to achieve a desired objective under certain conditions. Finally, we demonstrate that the feedback control design aided by the mathematical analysis of the simplified model is indeed effective in driving the BG