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Sample records for neuron networks method

  1. Epileptic neuronal networks: methods of identification and clinical relevance.

    PubMed

    Stefan, Hermann; Lopes da Silva, Fernando H

    2013-01-01

    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.

  2. [Neuronal network].

    PubMed

    Langmeier, M; Maresová, D

    2005-01-01

    Function of the central nervous system is based on mutual relations among the nerve cells. Description of nerve cells and their processes, including their contacts was enabled by improvement of optical features of the microscope and by the development of impregnation techniques. It is associated with the name of Antoni van Leeuwenhoek (1632-1723), J. Ev. Purkyne (1787-1869), Camillo Golgi (1843-1926), and Ramón y Cajal (1852-1934). Principal units of the neuronal network are the synapses. The term synapse was introduced into neurophysiology by Charles Scott Sherrington (1857-1952). Majority of the interactions between nerve cells is mediated by neurotransmitters acting at the receptors of the postsynaptic membrane or at the autoreceptors of the presynaptic part of the synapse. Attachment of the vesicles to the presynaptic membrane and the release of the neurotransmitter into the synaptic cleft depend on the intracellular calcium concentration and on the presence of several proteins in the presynaptic element.

  3. 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

  4. Cryopreservation of adherent neuronal networks.

    PubMed

    Ma, Wu; O'Shaughnessy, Thomas; Chang, Eddie

    2006-07-31

    Neuronal networks have been widely used for neurophysiology, drug discovery and toxicity testing. An essential prerequisite for future widespread application of neuronal networks is the development of efficient cryopreservation protocols to facilitate their storage and transportation. Here is the first report on cryopreservation of mammalian adherent neuronal networks. Dissociated spinal cord cells were attached to a poly-d-lysine/laminin surface and allowed to form neuronal networks. Adherent neuronal networks were embedded in a thin film of collagen gel and loaded with trehalose prior to transfer to a freezing medium containing DMSO, FBS and culture medium. This was followed by a slow rate of cooling to -80 degrees C for 24 h and then storage for up to 2 months in liquid nitrogen at -196 degrees C. The three components: DMSO, collagen gel entrapment and trehalose loading combined provided the highest post-thaw viability, relative to individual or two component protocols. The post-thaw cells with this protocol demonstrated similar neuronal and astrocytic markers and morphological structure as those detected in unfrozen cells. Fluorescent dye FM1-43 staining revealed active recycling of synaptic vesicles upon depolarizing stimulation in the post-thaw neuronal networks. These results suggest that a combination of DMSO, collagen gel entrapment and trehalose loading can significantly improve conventional slow-cooling methods in cryopreservation of adherent neuronal networks.

  5. 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.

  6. 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.

  7. [Diagnosis and prognosis of cerebral ischemic disturbances course using a method of artificial neuronal networks].

    PubMed

    Ivanov, Iu S; Semin, G F

    2004-01-01

    Based on the data of examination of 224 patients with different stages of cerebral ischemic disturbances (CID) and 84 age-matched controls, an artificial neuronal network was constructed and tried in differential diagnosis of CID stages according to the data of transcranial ultrasonic dopplerography. Diagnostic efficacy of the network was 80% for sensitivity, 100% for specificity and 82.7% for reliability. A modeling of the influence of the main risk factors for cerebral ischemia and of the reserve state of cerebral hemodynamics for establishing the stage of CID was performed.

  8. 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

  9. 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.

  10. 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

  11. 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

  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.

  13. Whole-brain neural network analysis (connectomics) using cell lineage-based neuron-labeling method.

    PubMed

    Ito, Kei; Ito, Masayoshi

    2014-11-01

    The brain is a computing machine that receives input signals from sensory neurons, calculates best responses to changing environments, and sends output signals to motor muscles. How such computation is materialized remains largely unknown. Understanding the entire wiring network of neural connections in the brain, which is recently called the connectomics (connection + omics), should provide indispensable insights on this problem.To resolve the circuit diagram from the tangled thickets of neural fibers, only a small subset of neurons should be visualized at one time. Previous studies visualized such selective cells by injecting dyes or by detecting specific molecules or gene expression patterns using antibodies and expression driver strains. These approaches were unfortunately not efficient enough for identifying all the brain cells in a comprehensive and systematic manner.Neurons are generated by neural stem cells. The entire neural population can therefore be divided into a finite number of families - or clones - of the cells that are the progeny of each single stem cell. The central brain of the fruit fly Drosophila melanogaster consists of about 15,000 neurons per side and is made by utmost 100 stem cells. By genetically labeling one of such stem cells and tracing the projection patterns of its progeny in the adult brain, we were able to identify the neural projections of almost all the clonal cell groups.To visualize these neural projections, we made serial optical sections of the fly brain using laser confocal microscopy. Because of its relatively small size (0.6-mm wide and less than 0.3-mm thick), the entire fly brain can be imaged using high-resolution objectives with n.a. 1.2. Neuronal fibers are visualized by ectopically expressed cytoplasmic and membrane-bound fluorescent proteins, and the output synaptic sites are visualized with ectopically expressed tag proteins that are fused with the proteins associated with synaptic vesicles. In addition, density

  14. 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.

  15. 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

  16. Observability of Neuronal Network Motifs

    PubMed Central

    Whalen, Andrew J.; Brennan, Sean N.; Sauer, Timothy D.; Schiff, Steven J.

    2014-01-01

    We quantify observability in small (3 node) neuronal networks as a function of 1) the connection topology and symmetry, 2) the measured nodes, and 3) the nodal dynamics (linear and nonlinear). We find that typical observability metrics for 3 neuron motifs range over several orders of magnitude, depending upon topology, and for motifs containing symmetry the network observability decreases when observing from particularly confounded nodes. Nonlinearities in the nodal equations generally decrease the average network observability and full network information becomes available only in limited regions of the system phase space. Our findings demonstrate that such networks are partially observable, and suggest their potential efficacy in reconstructing network dynamics from limited measurement data. How well such strategies can be used to reconstruct and control network dynamics in experimental settings is a subject for future experimental work. PMID:25909092

  17. Network synchronization in hippocampal neurons

    PubMed Central

    Penn, Yaron; Segal, Menahem; Moses, Elisha

    2016-01-01

    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

  18. 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

  19. From the neuron doctrine to neural networks.

    PubMed

    Yuste, Rafael

    2015-08-01

    For over a century, the neuron doctrine--which states that the neuron is the structural and functional unit of the nervous system--has provided a conceptual foundation for neuroscience. This viewpoint reflects its origins in a time when the use of single-neuron anatomical and physiological techniques was prominent. However, newer multineuronal recording methods have revealed that ensembles of neurons, rather than individual cells, can form physiological units and generate emergent functional properties and states. As a new paradigm for neuroscience, neural network models have the potential to incorporate knowledge acquired with single-neuron approaches to help us understand how emergent functional states generate behaviour, cognition and mental disease. PMID:26152865

  20. Population density methods for large-scale modelling of neuronal networks with realistic synaptic kinetics: cutting the dimension down to size.

    PubMed

    Haskell, E; Nykamp, D Q; Tranchina, D

    2001-05-01

    Population density methods provide promising time-saving alternatives to direct Monte Carlo simulations of neuronal network activity, in which one tracks the state of thousands of individual neurons and synapses. A population density method has been found to be roughly a hundred times faster than direct simulation for various test networks of integrate-and-fire model neurons with instantaneous excitatory and inhibitory post-synaptic conductances. In this method, neurons are grouped into large populations of similar neurons. For each population, one calculates the evolution of a probability density function (PDF) which describes the distribution of neurons over state space. The population firing rate is then given by the total flux of probability across the threshold voltage for firing an action potential. Extending the method beyond instantaneous synapses is necessary for obtaining accurate results, because synaptic kinetics play an important role in network dynamics. Embellishments incorporating more realistic synaptic kinetics for the underlying neuron model increase the dimension of the PDF, which was one-dimensional in the instantaneous synapse case. This increase in dimension causes a substantial increase in computation time to find the exact PDF, decreasing the computational speed advantage of the population density method over direct Monte Carlo simulation. We report here on a one-dimensional model of the PDF for neurons with arbitrary synaptic kinetics. The method is more accurate than the mean-field method in the steady state, where the mean-field approximation works best, and also under dynamic-stimulus conditions. The method is much faster than direct simulations. Limitations of the method are demonstrated, and possible improvements are discussed. PMID:11405420

  1. Spiking neuron network Helmholtz machine

    PubMed Central

    Sountsov, Pavel; Miller, Paul

    2015-01-01

    An increasing amount of behavioral and neurophysiological data suggests that the brain performs optimal (or near-optimal) probabilistic inference and learning during perception and other tasks. Although many machine learning algorithms exist that perform inference and learning in an optimal way, the complete description of how one of those algorithms (or a novel algorithm) can be implemented in the brain is currently incomplete. There have been many proposed solutions that address how neurons can perform optimal inference but the question of how synaptic plasticity can implement optimal learning is rarely addressed. This paper aims to unify the two fields of probabilistic inference and synaptic plasticity by using a neuronal network of realistic model spiking neurons to implement a well-studied computational model called the Helmholtz Machine. The Helmholtz Machine is amenable to neural implementation as the algorithm it uses to learn its parameters, called the wake-sleep algorithm, uses a local delta learning rule. Our spiking-neuron network implements both the delta rule and a small example of a Helmholtz machine. This neuronal network can learn an internal model of continuous-valued training data sets without supervision. The network can also perform inference on the learned internal models. We show how various biophysical features of the neural implementation constrain the parameters of the wake-sleep algorithm, such as the duration of the wake and sleep phases of learning and the minimal sample duration. We examine the deviations from optimal performance and tie them to the properties of the synaptic plasticity rule. PMID:25954191

  2. Dynamics of moment neuronal networks.

    PubMed

    Feng, Jianfeng; Deng, Yingchun; Rossoni, Enrico

    2006-04-01

    A theoretical framework is developed for moment neuronal networks (MNNs). Within this framework, the behavior of the system of spiking neurons is specified in terms of the first- and second-order statistics of their interspike intervals, i.e., the mean, the variance, and the cross correlations of spike activity. Since neurons emit and receive spike trains which can be described by renewal--but generally non-Poisson--processes, we first derive a suitable diffusion-type approximation of such processes. Two approximation schemes are introduced: the usual approximation scheme (UAS) and the Ornstein-Uhlenbeck scheme. It is found that both schemes approximate well the input-output characteristics of spiking models such as the IF and the Hodgkin-Huxley models. The MNN framework is then developed according to the UAS scheme, and its predictions are tested on a few examples.

  3. Parallel network simulations with NEURON.

    PubMed

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

    2006-10-01

    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 2,000 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.

  4. [Cognitive processes and neuronal networks].

    PubMed

    Ohayon, M

    1990-10-01

    It is clear that computers are but a poor brain models: the nervous system has many "processors" (neurons) in parallel, whereas von Neuman's machines work sequentially on a single processor. In complex systems, emergent properties cannot be inferred from the behaviour of single elements. Anthills display collective "meaningful" moves, while each ant seems to obey local interactions only. Likewise, large parallel networks of processing elements elicit emergent properties. Like brains, some of them are self-organizing systems. In large parallel processing networks, each unit performs an elementary computation: adding inputs from other units. Large nets display surprising spontaneous computational abilities: associative memories, classes, generalizations may be seen as emergent properties of the network. Symbols are dynamical entities, whose handing is driven by local interactions of activation/inhibition of related representations. In such models, representations (memories) are distributed in the whole network, as stable configurations. Indeed, the basic properties of representation in connectionist models seem closer to human mental objects than the classic Artificial Intelligence concepts. Connectionist models have been used in many fields, namely simulations of real neural networks, pattern recognition and artificial vision, speech recognition, language understanding and knowledge representation, problem solving... Connectionist models have been thus used in neurobiology as well as cognition. One basic structure seems indeed able to account for a range of cognitive functions, from perception to problem solving and high level cognitive tasks. Nevertheless studies about "pathological" networks are yet rare, still an open field... We explore some of these fields. PMID:1965482

  5. [Cognitive processes and neuronal networks].

    PubMed

    Ohayon, M

    1990-10-01

    It is clear that computers are but a poor brain models: the nervous system has many "processors" (neurons) in parallel, whereas von Neuman's machines work sequentially on a single processor. In complex systems, emergent properties cannot be inferred from the behaviour of single elements. Anthills display collective "meaningful" moves, while each ant seems to obey local interactions only. Likewise, large parallel networks of processing elements elicit emergent properties. Like brains, some of them are self-organizing systems. In large parallel processing networks, each unit performs an elementary computation: adding inputs from other units. Large nets display surprising spontaneous computational abilities: associative memories, classes, generalizations may be seen as emergent properties of the network. Symbols are dynamical entities, whose handing is driven by local interactions of activation/inhibition of related representations. In such models, representations (memories) are distributed in the whole network, as stable configurations. Indeed, the basic properties of representation in connectionist models seem closer to human mental objects than the classic Artificial Intelligence concepts. Connectionist models have been used in many fields, namely simulations of real neural networks, pattern recognition and artificial vision, speech recognition, language understanding and knowledge representation, problem solving... Connectionist models have been thus used in neurobiology as well as cognition. One basic structure seems indeed able to account for a range of cognitive functions, from perception to problem solving and high level cognitive tasks. Nevertheless studies about "pathological" networks are yet rare, still an open field... We explore some of these fields.

  6. 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.

  7. 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

  8. 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

  9. 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

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. 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

  16. Stiff substrates enhance cultured neuronal network activity

    PubMed Central

    Zhang, Quan-You; Zhang, Yan-Yan; Xie, Jing; Li, Chen-Xu; Chen, Wei-Yi; Liu, Bai-Lin; Wu, Xiao-an; Li, Shu-Na; Huo, Bo; Jiang, Lin-Hua; Zhao, Hu-Cheng

    2014-01-01

    The mechanical property of extracellular matrix and cell-supporting substrates is known to modulate neuronal growth, differentiation, extension and branching. Here we show that substrate stiffness is an important microenvironmental cue, to which mouse hippocampal neurons respond and integrate into synapse formation and transmission in cultured neuronal network. Hippocampal neurons were cultured on polydimethylsiloxane substrates fabricated to have similar surface properties but a 10-fold difference in Young's modulus. Voltage-gated Ca2+ channel currents determined by patch-clamp recording were greater in neurons on stiff substrates than on soft substrates. Ca2+ oscillations in cultured neuronal network monitored using time-lapse single cell imaging increased in both amplitude and frequency among neurons on stiff substrates. Consistently, synaptic connectivity recorded by paired recording was enhanced between neurons on stiff substrates. Furthermore, spontaneous excitatory postsynaptic activity became greater and more frequent in neurons on stiff substrates. Evoked excitatory transmitter release and excitatory postsynaptic currents also were heightened at synapses between neurons on stiff substrates. Taken together, our results provide compelling evidence to show that substrate stiffness is an important biophysical factor modulating synapse connectivity and transmission in cultured hippocampal neuronal network. Such information is useful in designing instructive scaffolds or supporting substrates for neural tissue engineering. PMID:25163607

  17. 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].

  18. HCN channels in developing neuronal networks

    PubMed Central

    Bender, Roland A.

    2008-01-01

    Developing neuronal networks evolve continuously, requiring that neurons modulate both their intrinsic properties and their responses to incoming synaptic signals. Emerging evidence supports roles for the hyperpolarization-activated cyclic nucleotide-gated (HCN) channels in this neuronal plasticity. HCN channels seem particularly suited for fine-tuning neuronal properties and responses because of their remarkably large and variable repertoire of functions, enabling integration of a wide range of cellular signals. Here, we discuss the involvement of HCN channels in cortical and hippocampal network maturation, and consider potential roles of developmental HCN channel dysregulation in brain disorders such as epilepsy. PMID:18834920

  19. 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.}

  20. 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

  1. Synaptic connectivity in engineered neuronal networks.

    PubMed

    Molnar, Peter; Kang, Jung-Fong; Bhargava, Neelima; Das, Mainak; Hickman, James J

    2014-01-01

    We have developed a method to organize cells in dissociated cultures using engineered chemical clues on a culture surface and determined their connectivity patterns. Although almost all elements of the synaptic transmission machinery can be studied separately in single cell models in dissociated cultures, the complex physiological interactions between these elements are usually lost. Thus, factors affecting synaptic transmission are generally studied in organotypic cultures, brain slices, or in vivo where the cellular architecture generally remains intact. However, by utilizing engineered neuronal networks complex phenomenon such as synaptic transmission or synaptic plasticity can be studied in a simple, functional, cell culture-based system. We have utilized self-assembled monolayers and photolithography to create the surface templates. Embryonic hippocampal cells, plated on the resultant patterns in serum-free medium, followed the surface clues and formed the engineered neuronal networks. Basic whole-cell patch-clamp electrophysiology was applied to characterize the synaptic connectivity in these engineered two-cell networks. The same technology has been used to pattern other cell types such as cardiomyocytes or skeletal muscle fibers.

  2. 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.

  3. Physiology of neuronal-glial networking.

    PubMed

    Verkhratsky, Alexei

    2010-11-01

    Neuronal-glial networks are the substrate for the brain function. Evolution of the nervous system resulted in the appearance of highly specialized neuronal web optimized for rapid information transfer. This neuronal web is embedded into glial syncytium, thereby creating sophisticated neuronal-glial circuitry were both types of neural cells are working in concert, ensuring amplification of brain computational power. In addition neuroglial cells are fundamental for control of brain homeostasis and they represent the intrinsic brain defence system, being thus intimately involved in pathogenesis of neurological diseases.

  4. 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.

  5. Doubly stochastic coherence in complex neuronal networks

    NASA Astrophysics Data System (ADS)

    Gao, Yang; Wang, Jianjun

    2012-11-01

    A system composed of coupled FitzHugh-Nagumo neurons with various topological structures is investigated under the co-presence of two independently additive and multiplicative Gaussian white noises, in which particular attention is paid to the neuronal networks spiking regularity. As the additive noise intensity and the multiplicative noise intensity are simultaneously adjusted to optimal values, the temporal periodicity of the output of the system reaches the maximum, indicating the occurrence of doubly stochastic coherence. The network topology randomness exerts different influences on the temporal coherence of the spiking oscillation for dissimilar coupling strength regimes. At a small coupling strength, the spiking regularity shows nearly no difference in the regular, small-world, and completely random networks. At an intermediate coupling strength, the temporal periodicity in a small-world neuronal network can be improved slightly by adding a small fraction of long-range connections. At a large coupling strength, the dynamical behavior of the neurons completely loses the resonance property with regard to the additive noise intensity or the multiplicative noise intensity, and the spiking regularity decreases considerably with the increase of the network topology randomness. The network topology randomness plays more of a depressed role than a favorable role in improving the temporal coherence of the spiking oscillation in the neuronal network research study.

  6. Map-based neuron networks

    NASA Astrophysics Data System (ADS)

    Ibarz, Borja; Cao, Hongjun; Sanjuán, Miguel A. F.

    2007-02-01

    Ever since the pioneering work of Hodgkin and Huxley, biological neuron models have consisted of ODEs representing the evolution of the transmembrane voltage and the dynamics of ionic conductances. It is only recently that maps — or difference equations — have begun to receive attention as valid conductance neuron models. They can not only be computationally advantageous substitutes of ODE models, but, since they accommodate chaotic dynamics in a natural way, they may reproduce rich collective behaviors that we explore here.

  7. Sloppiness in spontaneously active neuronal networks.

    PubMed

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

    2015-06-01

    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.

  8. Structural properties of the Caenorhabditis elegans neuronal network.

    PubMed

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

    2011-02-03

    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.

  9. Neuronal networks and energy bursts in epilepsy.

    PubMed

    Wu, Y; Liu, D; Song, Z

    2015-02-26

    Epilepsy can be defined as the abnormal activities of neurons. The occurrence, propagation and termination of epileptic seizures rely on the networks of neuronal cells that are connected through both synaptic- and non-synaptic interactions. These complicated interactions contain the modified functions of normal neurons and glias as well as the mediation of excitatory and inhibitory mechanisms with feedback homeostasis. Numerous spread patterns are detected in disparate networks of ictal activities. The cortical-thalamic-cortical loop is present during a general spike wave seizure. The thalamic reticular nucleus (nRT) is the major inhibitory input traversing the region, and the dentate gyrus (DG) controls CA3 excitability. The imbalance between γ-aminobutyric acid (GABA)-ergic inhibition and glutamatergic excitation is the main disorder in epilepsy. Adjustable negative feedback that mediates both inhibitory and excitatory components affects neuronal networks through neurotransmission fluctuation, receptor and transmitter signaling, and through concomitant influences on ion concentrations and field effects. Within a limited dynamic range, neurons slowly adapt to input levels and have a high sensitivity to synaptic changes. The stability of the adapting network depends on the ratio of the adaptation rates of both the excitatory and inhibitory populations. Thus, therapeutic strategies with multiple effects on seizures are required for the treatment of epilepsy, and the therapeutic functions on networks are reviewed here. Based on the high-energy burst theory of epileptic activity, we propose a potential antiepileptic therapeutic strategy to transfer the high energy and extra electricity out of the foci.

  10. 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

  11. 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.

  12. On the Dynamics of Random Neuronal Networks

    NASA Astrophysics Data System (ADS)

    Robert, Philippe; Touboul, Jonathan

    2016-10-01

    We study the mean-field limit and stationary distributions of a pulse-coupled network modeling the dynamics of a large neuronal assemblies. Our model takes into account explicitly the intrinsic randomness of firing times, contrasting with the classical integrate-and-fire model. The ergodicity properties of the Markov process associated to finite networks are investigated. We derive the large network size limit of the distribution of the state of a neuron, and characterize their invariant distributions as well as their stability properties. We show that the system undergoes transitions as a function of the averaged connectivity parameter, and can support trivial states (where the network activity dies out, which is also the unique stationary state of finite networks in some cases) and self-sustained activity when connectivity level is sufficiently large, both being possibly stable.

  13. Fast sigmoidal networks via spiking neurons.

    PubMed

    Maass, W

    1997-02-15

    We show that networks of relatively realistic mathematical models for biological neurons in principle can simulate arbitrary feedforward sigmoidal neural nets in a way that has previously not been considered. This new approach is based on temporal coding by single spikes (respectively by the timing of synchronous firing in pools of neurons) rather than on the traditional interpretation of analog variables in terms of firing rates. The resulting new simulation is substantially faster and hence more consistent with experimental results about the maximal speed of information processing in cortical neural systems. As a consequence we can show that networks of noisy spiking neurons are "universal approximators" in the sense that they can approximate with regard to temporal coding any given continuous function of several variables. This result holds for a fairly large class of schemes for coding analog variables by firing times of spiking neurons. This new proposal for the possible organization of computations in networks of spiking neurons systems has some interesting consequences for the type of learning rules that would be needed to explain the self-organization of such networks. Finally, the fast and noise-robust implementation of sigmoidal neural nets by temporal coding points to possible new ways of implementing feedforward and recurrent sigmoidal neural nets with pulse stream VLSI.

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

    PubMed

    Huang, Yanping; Rao, Rajesh P N

    2016-08-01

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

  15. 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

  16. 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

  17. Inhibition Controls Asynchronous States of Neuronal Networks.

    PubMed

    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.

  18. 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

  19. A unit paradox for artificial neuronal networks.

    PubMed

    Lábos, E

    1993-01-01

    In formal or artificial neuronal networks (FNN; ANN) built of threshold logic gates, a unit step function is used. Recently the step function has been replaced by an S-shaped curve. This replacement is common and many studies are based on this substitution, often combined rarely not with some adaptive or learning procedure. However, the dynamical consequences e. g. in the time course of single unit discharges have not been sufficiently examined. The concepts of unit (neuron) and network in these models of engineering and neurobiology correspond to each other only partially: model units may behave similarly to real nets of real units. This is called here a unit paradox. Propositions are presented to eliminate this apparent contradiction. A further paradox is also mentioned (Section 3). It seems necessary to reconsider the relationship of the various trends of neural network theory.

  20. Neuronal Network Oscillations in Neurodegenerative Diseases.

    PubMed

    Nimmrich, Volker; Draguhn, Andreas; Axmacher, Nikolai

    2015-09-01

    Cognitive and behavioral acts go along with highly coordinated spatiotemporal activity patterns in neuronal networks. Most of these patterns are synchronized by coherent membrane potential oscillations within and between local networks. By entraining multiple neurons into a common time regime, such network oscillations form a critical interface between cellular activity and large-scale systemic functions. Synaptic integrity is altered in neurodegenerative diseases, and it is likely that this goes along with characteristic changes of coordinated network activity. This notion is supported by EEG recordings from human patients and from different animal models of such disorders. However, our knowledge about the pathophysiology of network oscillations in neurodegenerative diseases is surprisingly incomplete, and increased research efforts are urgently needed. One complicating factor is the pronounced diversity of network oscillations between different brain regions and functional states. Pathological changes must, therefore, be analyzed separately in each condition and affected area. However, cumulative evidence from different diseases may result, in the future, in more unifying "oscillopathy" concepts of neurodegenerative diseases. In this review, we report present evidence for pathological changes of network oscillations in Alzheimer's disease (AD), one of the most prominent and challenging neurodegenerative disorders. The heterogeneous findings from AD are contrasted to Parkinson's disease, where motor-related changes in specific frequency bands do already fulfill criteria of a valid biomarker. PMID:25920466

  1. 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.

  2. 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

  3. 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

  4. 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.

  5. 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

  6. 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.

  7. Independent variable timestep integration of individual neurons for network simulations

    PubMed Central

    Lytton, William; Hines, Michael

    2009-01-01

    Realistic neural networks involve the co-existence of stiff, coupled, continuous differential equations arising from the integrations of individual neurons, with the discrete events with delays used for modeling synaptic connections. We present here an integration method, the local variable time-step method (lvardt) that utilizes separate variable step integrators for individual neurons in the network. Cells which are undergoing excitation tend to have small time-steps and cells which are at rest with little synaptic input tend to have large time-steps. A synaptic input to a cell causes re-initialization of only that cell’s integrator without affecting the integration of other cells. We illustrated the use of lvardt on three models: a worst-case synchronizing mutual-inhibition model, a best-case synfire chain model, and a more realistic thalamocortical network model. PMID:15829094

  8. An artificial network model for estimating the network structure underlying partially observed neuronal signals.

    PubMed

    Komatsu, Misako; Namikawa, Jun; Chao, Zenas C; Nagasaka, Yasuo; Fujii, Naotaka; Nakamura, Kiyohiko; Tani, Jun

    2014-01-01

    Many previous studies have proposed methods for quantifying neuronal interactions. However, these methods evaluated the interactions between recorded signals in an isolated network. In this study, we present a novel approach for estimating interactions between observed neuronal signals by theorizing that those signals are observed from only a part of the network that also includes unobserved structures. We propose a variant of the recurrent network model that consists of both observable and unobservable units. The observable units represent recorded neuronal activity, and the unobservable units are introduced to represent activity from unobserved structures in the network. The network structures are characterized by connective weights, i.e., the interaction intensities between individual units, which are estimated from recorded signals. We applied this model to multi-channel brain signals recorded from monkeys, and obtained robust network structures with physiological relevance. Furthermore, the network exhibited common features that portrayed cortical dynamics as inversely correlated interactions between excitatory and inhibitory populations of neurons, which are consistent with the previous view of cortical local circuits. Our results suggest that the novel concept of incorporating an unobserved structure into network estimations has theoretical advantages and could provide insights into brain dynamics beyond what can be directly observed.

  9. [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

  10. 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

  11. Synchronization properties of heterogeneous neuronal networks with mixed excitability type

    NASA Astrophysics Data System (ADS)

    Leone, Michael J.; Schurter, Brandon N.; Letson, Benjamin; Booth, Victoria; Zochowski, Michal; Fink, Christian G.

    2015-03-01

    We study the synchronization of neuronal networks with dynamical heterogeneity, showing that network structures with the same propensity for synchronization (as quantified by master stability function analysis) may develop dramatically different synchronization properties when heterogeneity is introduced with respect to neuronal excitability type. Specifically, we investigate networks composed of neurons with different types of phase response curves (PRCs), which characterize how oscillating neurons respond to excitatory perturbations. Neurons exhibiting type 1 PRC respond exclusively with phase advances, while neurons exhibiting type 2 PRC respond with either phase delays or phase advances, depending on when the perturbation occurs. We find that Watts-Strogatz small world networks transition to synchronization gradually as the proportion of type 2 neurons increases, whereas scale-free networks may transition gradually or rapidly, depending upon local correlations between node degree and excitability type. Random placement of type 2 neurons results in gradual transition to synchronization, whereas placement of type 2 neurons as hubs leads to a much more rapid transition, showing that type 2 hub cells easily "hijack" neuronal networks to synchronization. These results underscore the fact that the degree of synchronization observed in neuronal networks is determined by a complex interplay between network structure and the dynamical properties of individual neurons, indicating that efforts to recover structural connectivity from dynamical correlations must in general take both factors into account.

  12. Synchronization properties of heterogeneous neuronal networks with mixed excitability type.

    PubMed

    Leone, Michael J; Schurter, Brandon N; Letson, Benjamin; Booth, Victoria; Zochowski, Michal; Fink, Christian G

    2015-03-01

    We study the synchronization of neuronal networks with dynamical heterogeneity, showing that network structures with the same propensity for synchronization (as quantified by master stability function analysis) may develop dramatically different synchronization properties when heterogeneity is introduced with respect to neuronal excitability type. Specifically, we investigate networks composed of neurons with different types of phase response curves (PRCs), which characterize how oscillating neurons respond to excitatory perturbations. Neurons exhibiting type 1 PRC respond exclusively with phase advances, while neurons exhibiting type 2 PRC respond with either phase delays or phase advances, depending on when the perturbation occurs. We find that Watts-Strogatz small world networks transition to synchronization gradually as the proportion of type 2 neurons increases, whereas scale-free networks may transition gradually or rapidly, depending upon local correlations between node degree and excitability type. Random placement of type 2 neurons results in gradual transition to synchronization, whereas placement of type 2 neurons as hubs leads to a much more rapid transition, showing that type 2 hub cells easily "hijack" neuronal networks to synchronization. These results underscore the fact that the degree of synchronization observed in neuronal networks is determined by a complex interplay between network structure and the dynamical properties of individual neurons, indicating that efforts to recover structural connectivity from dynamical correlations must in general take both factors into account.

  13. 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

  14. 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

  15. 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.

  16. 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.

  17. Hierarchical networks, power laws, and neuronal avalanches.

    PubMed

    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.

  18. 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.

  19. A network of tufted layer 5 pyramidal neurons.

    PubMed

    Markram, H

    1997-09-01

    Tufted layer 5 (TL5) pyramidal neurons are important projection neurons from the cerebral cortex to subcortical areas. Recent and ongoing experiments aimed at understanding the computational analysis performed by a network of synaptically connected TL5 neurons are reviewed here. The experiments employed dual and triple whole-cell patch clamp recordings from visually identified and preselected neurons in brain slices of somatosensory cortex of young (14- to 16-day-old) rats. These studies suggest that a local network of TL5 neurons within a cortical module of diameter 300 microns consists of a few hundred neurons that are extensively inter-connected with reciprocal feedback from at least first-, second- and third-order target neurons. A statistical analysis of synaptic innervation suggests that this recurrent network is not randomly arranged and hence each neuron could be functionally unique. Synaptic transmission between these neurons is characterized by use-dependent synaptic depression which confers novel properties to this recurrent network of neurons. First, a range of rates of depression for different synaptic connections enable each TL5 neuron to receive a unique mixture of information about the average firing rates and the temporally correlated action potential (AP) activity in the population of presynaptic TL5 neurons. Second, each AP generated by any neuron in the network induces a change (defined as an iteration step) in the functional coupling of the neurons in the network (defined as network configuration). It is proposed that the network configuration is iterated during a stimulus to achieve an optimally orchestrated network response. Hebbian, anti-Hebbian and neuromodulatory-induced modifications of neurotransmitter release probability change the rates of synaptic depression and thereby alter the iteration step size. These data may be important to understand the dynamics of electrical activity within the network.

  20. Inferring network dynamics and neuron properties from population recordings.

    PubMed

    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

  1. 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.

  2. Human embryonic stem cell-derived neuronal cells form spontaneously active neuronal networks in vitro.

    PubMed

    Heikkilä, Teemu J; Ylä-Outinen, Laura; Tanskanen, Jarno M A; Lappalainen, Riikka S; Skottman, Heli; Suuronen, Riitta; Mikkonen, Jarno E; Hyttinen, Jari A K; Narkilahti, Susanna

    2009-07-01

    The production of functional human embryonic stem cell (hESC)-derived neuronal cells is critical for the application of hESCs in treating neurodegenerative disorders. To study the potential functionality of hESC-derived neurons, we cultured and monitored the development of hESC-derived neuronal networks on microelectrode arrays. Immunocytochemical studies revealed that these networks were positive for the neuronal marker proteins beta-tubulin(III) and microtubule-associated protein 2 (MAP-2). The hESC-derived neuronal networks were spontaneously active and exhibited a multitude of electrical impulse firing patterns. Synchronous bursts of electrical activity similar to those reported for hippocampal neurons and rodent embryonic stem cell-derived neuronal networks were recorded from the differentiated cultures until up to 4 months. The dependence of the observed neuronal network activity on sodium ion channels was examined using tetrodotoxin (TTX). Antagonists for the glutamate receptors NMDA [D(-)-2-amino-5-phosphonopentanoic acid] and AMPA/kainate [6-cyano-7-nitroquinoxaline-2,3-dione], and for GABAA receptors [(-)-bicuculline methiodide] modulated the spontaneous electrical activity, indicating that pharmacologically susceptible neuronal networks with functional synapses had been generated. The findings indicate that hESC-derived neuronal cells can generate spontaneously active networks with synchronous communication in vitro, and are therefore suitable for use in developmental and drug screening studies, as well as for regenerative medicine.

  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. Integrated workflows for spiking neuronal network simulations.

    PubMed

    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.

  6. Collective stochastic coherence in recurrent neuronal networks

    NASA Astrophysics Data System (ADS)

    Sancristóbal, Belén; Rebollo, Beatriz; Boada, Pol; Sanchez-Vives, Maria V.; Garcia-Ojalvo, Jordi

    2016-09-01

    Recurrent networks of dynamic elements frequently exhibit emergent collective oscillations, which can show substantial regularity even when the individual elements are considerably noisy. How noise-induced dynamics at the local level coexists with regular oscillations at the global level is still unclear. Here we show that a combination of stochastic recurrence-based initiation with deterministic refractoriness in an excitable network can reconcile these two features, leading to maximum collective coherence for an intermediate noise level. We report this behaviour in the slow oscillation regime exhibited by a cerebral cortex network under dynamical conditions resembling slow-wave sleep and anaesthesia. Computational analysis of a biologically realistic network model reveals that an intermediate level of background noise leads to quasi-regular dynamics. We verify this prediction experimentally in cortical slices subject to varying amounts of extracellular potassium, which modulates neuronal excitability and thus synaptic noise. The model also predicts that this effectively regular state should exhibit noise-induced memory of the spatial propagation profile of the collective oscillations, which is also verified experimentally. Taken together, these results allow us to construe the high regularity observed experimentally in the brain as an instance of collective stochastic coherence.

  7. 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.

  8. 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

  9. Developmental time windows for axon growth influence neuronal network topology.

    PubMed

    Lim, Sol; Kaiser, Marcus

    2015-04-01

    Early brain connectivity development consists of multiple stages: birth of neurons, their migration and the subsequent growth of axons and dendrites. Each stage occurs within a certain period of time depending on types of neurons and cortical layers. Forming synapses between neurons either by growing axons starting at similar times for all neurons (much-overlapped time windows) or at different time points (less-overlapped) may affect the topological and spatial properties of neuronal networks. Here, we explore the extreme cases of axon formation during early development, either starting at the same time for all neurons (parallel, i.e., maximally overlapped time windows) or occurring for each neuron separately one neuron after another (serial, i.e., no overlaps in time windows). For both cases, the number of potential and established synapses remained comparable. Topological and spatial properties, however, differed: Neurons that started axon growth early on in serial growth achieved higher out-degrees, higher local efficiency and longer axon lengths while neurons demonstrated more homogeneous connectivity patterns for parallel growth. Second, connection probability decreased more rapidly with distance between neurons for parallel growth than for serial growth. Third, bidirectional connections were more numerous for parallel growth. Finally, we tested our predictions with C. elegans data. Together, this indicates that time windows for axon growth influence the topological and spatial properties of neuronal networks opening up the possibility to a posteriori estimate developmental mechanisms based on network properties of a developed network.

  10. 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.

  11. 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.

  12. 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.

  13. Hypothalamic leptin-neurotensin-hypocretin neuronal networks in zebrafish.

    PubMed

    Levitas-Djerbi, Talia; Yelin-Bekerman, Laura; Lerer-Goldshtein, Tali; Appelbaum, Lior

    2015-04-01

    Neurotensin (NTS) is a 13 amino acid neuropeptide that is expressed in the hypothalamus. In mammals, NTS-producing neurons that express leptin receptor (LepRb) regulate the function of hypocretin/orexin (HCRT) and dopamine neurons. Thus, the hypothalamic leptin-NTS-HCRT neuronal network orchestrates key homeostatic output, including sleep, feeding, and reward. However, the intricate mechanisms of the circuitry and the unique role of NTS-expressing neurons remain unclear. We studied the NTS neuronal networks in zebrafish and cloned the genes encoding the NTS neuropeptide and receptor (NTSR). Similar to mammals, the ligand is expressed primarily in the hypothalamus, while the receptor is expressed widely throughout the brain in zebrafish. A portion of hypothalamic nts-expressing neurons are inhibitory and some coexpress leptin receptor (lepR1). As in mammals, NTS and HCRT neurons are localized adjacently in the hypothalamus. To track the development and axonal projection of NTS neurons, the NTS promoter was isolated. Transgenesis and double labeling of NTS and HCRT neurons showed that NTS axons project toward HCRT neurons, some of which express ntsr. Moreover, another target of NTS neurons is ntsr-expressing dopaminergeric neurons. These findings suggest structural circuitry between leptin, NTS, and hypocretinergic or dopaminergic neurons and establish the zebrafish as a model to study the role of these neuronal circuits in the regulation of feeding, sleep, and reward.

  14. Leader neurons in leaky integrate and fire neural network simulations.

    PubMed

    Zbinden, Cyrille

    2011-10-01

    In this paper, we highlight the topological properties of leader neurons whose existence is an experimental fact. Several experimental studies show the existence of leader neurons in population bursts of activity in 2D living neural networks (Eytan and Marom, J Neurosci 26(33):8465-8476, 2006; Eckmann et al., New J Phys 10(015011), 2008). A leader neuron is defined as a neuron which fires at the beginning of a burst (respectively network spike) more often than we expect by chance considering its mean firing rate. This means that leader neurons have some burst triggering power beyond a chance-level statistical effect. In this study, we characterize these leader neuron properties. This naturally leads us to simulate neural 2D networks. To build our simulations, we choose the leaky integrate and fire (lIF) neuron model (Gerstner and Kistler 2002; Cessac, J Math Biol 56(3):311-345, 2008), which allows fast simulations (Izhikevich, IEEE Trans Neural Netw 15(5):1063-1070, 2004; Gerstner and Naud, Science 326:379-380, 2009). The dynamics of our lIF model has got stable leader neurons in the burst population that we simulate. These leader neurons are excitatory neurons and have a low membrane potential firing threshold. Except for these two first properties, the conditions required for a neuron to be a leader neuron are difficult to identify and seem to depend on several parameters involved in the simulations themselves. However, a detailed linear analysis shows a trend of the properties required for a neuron to be a leader neuron. Our main finding is: A leader neuron sends signals to many excitatory neurons as well as to few inhibitory neurons and a leader neuron receives only signals from few other excitatory neurons. Our linear analysis exhibits five essential properties of leader neurons each with different relative importance. This means that considering a given neural network with a fixed mean number of connections per neuron, our analysis gives us a way of

  15. Automated Neuron Tracing Methods: An Updated Account.

    PubMed

    Acciai, Ludovica; Soda, Paolo; Iannello, Giulio

    2016-10-01

    The reconstruction of neuron morphology allows to investigate how the brain works, which is one of the foremost challenges in neuroscience. This process aims at extracting the neuronal structures from microscopic imaging data. The great advances in microscopic technologies have made a huge amount of data available at the micro-, or even lower, resolution where manual inspection is time consuming, prone to error and utterly impractical. This has motivated the development of methods to automatically trace the neuronal structures, a task also known as neuron tracing. This paper surveys the latest neuron tracing methods available in the scientific literature as well as a selection of significant older papers to better place these proposals into context. They are categorized into global processing, local processing and meta-algorithm approaches. Furthermore, we point out the algorithmic components used to design each method and we report information on the datasets and the performance metrics used. PMID:27447185

  16. Automated Neuron Tracing Methods: An Updated Account.

    PubMed

    Acciai, Ludovica; Soda, Paolo; Iannello, Giulio

    2016-10-01

    The reconstruction of neuron morphology allows to investigate how the brain works, which is one of the foremost challenges in neuroscience. This process aims at extracting the neuronal structures from microscopic imaging data. The great advances in microscopic technologies have made a huge amount of data available at the micro-, or even lower, resolution where manual inspection is time consuming, prone to error and utterly impractical. This has motivated the development of methods to automatically trace the neuronal structures, a task also known as neuron tracing. This paper surveys the latest neuron tracing methods available in the scientific literature as well as a selection of significant older papers to better place these proposals into context. They are categorized into global processing, local processing and meta-algorithm approaches. Furthermore, we point out the algorithmic components used to design each method and we report information on the datasets and the performance metrics used.

  17. Nanostructured superhydrophobic substrates trigger the development of 3D neuronal networks.

    PubMed

    Limongi, Tania; Cesca, Fabrizia; Gentile, Francesco; Marotta, Roberto; Ruffilli, Roberta; Barberis, Andrea; Dal Maschio, Marco; Petrini, Enrica Maria; Santoriello, Stefania; Benfenati, Fabio; Di Fabrizio, Enzo

    2013-02-11

    The generation of 3D networks of primary neurons is a big challenge in neuroscience. Here, a novel method is presented for a 3D neuronal culture on superhydrophobic (SH) substrates. How nano-patterned SH devices stimulate neurons to build 3D networks is investigated. Scanning electron microscopy and confocal imaging show that soon after plating neurites adhere to the nanopatterned pillar sidewalls and they are subsequently pulled between pillars in a suspended position. These neurons display an enhanced survival rate compared to standard cultures and develop mature networks with physiological excitability. These findings underline the importance of using nanostructured SH surfaces for directing 3D neuronal growth, as well as for the design of biomaterials for neuronal regeneration.

  18. Identifying controlling nodes in neuronal networks in different scales.

    PubMed

    Tang, Yang; Gao, Huijun; Zou, Wei; Kurths, Jürgen

    2012-01-01

    Recent studies have detected hubs in neuronal networks using degree, betweenness centrality, motif and synchronization and revealed the importance of hubs in their structural and functional roles. In addition, the analysis of complex networks in different scales are widely used in physics community. This can provide detailed insights into the intrinsic properties of networks. In this study, we focus on the identification of controlling regions in cortical networks of cats' brain in microscopic, mesoscopic and macroscopic scales, based on single-objective evolutionary computation methods. The problem is investigated by considering two measures of controllability separately. The impact of the number of driver nodes on controllability is revealed and the properties of controlling nodes are shown in a statistical way. Our results show that the statistical properties of the controlling nodes display a concave or convex shape with an increase of the allowed number of controlling nodes, revealing a transition in choosing driver nodes from the areas with a large degree to the areas with a low degree. Interestingly, the community Auditory in cats' brain, which has sparse connections with other communities, plays an important role in controlling the neuronal networks.

  19. 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

  20. 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

  1. Information diversity in structure and dynamics of simulated neuronal networks.

    PubMed

    Mäki-Marttunen, Tuomo; Aćimović, Jugoslava; Nykter, Matti; Kesseli, Juha; Ruohonen, Keijo; Yli-Harja, Olli; Linne, Marja-Leena

    2011-01-01

    Neuronal networks exhibit a wide diversity of structures, which contributes to the diversity of the dynamics therein. The presented work applies an information theoretic framework to simultaneously analyze structure and dynamics in neuronal networks. Information diversity within the structure and dynamics of a neuronal network is studied using the normalized compression distance. To describe the structure, a scheme for generating distance-dependent networks with identical in-degree distribution but variable strength of dependence on distance is presented. The resulting network structure classes possess differing path length and clustering coefficient distributions. In parallel, comparable realistic neuronal networks are generated with NETMORPH simulator and similar analysis is done on them. To describe the dynamics, network spike trains are simulated using different network structures and their bursting behaviors are analyzed. For the simulation of the network activity the Izhikevich model of spiking neurons is used together with the Tsodyks model of dynamical synapses. We show that the structure of the simulated neuronal networks affects the spontaneous bursting activity when measured with bursting frequency and a set of intraburst measures: the more locally connected networks produce more and longer bursts than the more random networks. The information diversity of the structure of a network is greatest in the most locally connected networks, smallest in random networks, and somewhere in between in the networks between order and disorder. As for the dynamics, the most locally connected networks and some of the in-between networks produce the most complex intraburst spike trains. The same result also holds for sparser of the two considered network densities in the case of full spike trains.

  2. 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.

  3. Microfabrication- and microfluidics-based patterning of cultured neuronal network.

    PubMed

    Takayama, Yuzo; Kotake, Naoki; Haga, Tatsuya; Suzuki, Takafumi; Mabuchi, Kunihiko

    2011-01-01

    The cultured neuronal monolayer has been a promising model system for studying the neuronal dynamics, from single cell to network-wide level. Randomness in the reconstituted network structure has, however, hindered regulated signal transmissions from one neuron to another or from one neuronal population to another. Applying microfabrication-based cell patterning techniques is a promising approach to handling these problems. In the present study, we attempt to regulate the direction of axon development and the pathway of signal transmissions in cultured neuronal networks using micro-fabrication and - fluidic techniques. We created a PDMS-based culture device, which consisted of arrays of U-shaped cell trapping microwells, and placed it onto a chemically micropatterned glass substrate. After 6 days in vitro, we confirmed that cortical neurons extended neurites along the medium flow direction and the micropatterned regions. PMID:22255121

  4. Cell specific electrodes for neuronal network reconstruction and monitoring.

    PubMed

    Bendali, Amel; Bouguelia, Sihem; Roupioz, Yoann; Forster, Valérie; Mailley, Pascal; Benosman, Ryad; Livache, Thierry; Sahel, José-Alain; Picaud, Serge

    2014-07-01

    Direct interfacing of neurons with electronic devices has been investigated for both prosthetic and neuro-computing applications. In vitro neuronal networks provide great tools not only for improving neuroprostheses but also to take advantage of their computing abilities. However, it is often difficult to organize neuronal networks according to specific cell distributions. Our aim was to develop a cell-type specific immobilization of neurons on individual electrodes to produce organized in vitro neuronal networks on multi-electrode arrays (MEAs). We demonstrate the selective capture of retinal neurons on antibody functionalized surfaces following the formation of self-assembled monolayers from protein-thiol conjugates by simple contact and protein-polypyrrole deposits by electrochemical functionalization. This neuronal selection was achieved on gold for either cone photoreceptors or retinal ganglion neurons using a PNA lectin or a Thy1 antibody, respectively. Anti-fouling of un-functionalized gold surfaces was optimized to increase the capture efficiencies. The technique was extended to electrode arrays by addressing electropolymerization of pyrrole monomers and pyrrole-protein conjugates to active electrodes. Retinal ganglion cell recording on the array further demonstrated the integrity of these neurons following their selection on polypyrrole-coated electrodes. Therefore, this protein-polypyrrole electrodeposition could provide a new approach to generate organized in vitro neuronal networks.

  5. 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

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

    PubMed

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

    2016-03-22

    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.

  7. [Differential diagnosis using artificial neuronal networks].

    PubMed

    Shchetinin, V G; Komarov, V T

    1998-11-01

    In order to rule out the effects of subjective factors and decrease the number of diagnostic errors, artificial neuron nets are proposed. By means of these nets the subjective and half-empirical heuristics are replaced with rational diagnostic information based on quantitative and logic analysis. The proposed method was used for deriving the decisive regularities ensuring the differential diagnosis between infective endocarditis and active rheumatic fever, infective endocarditis and systemic lupus erythematosus, and systemic lupus erythematosus and active rheumatic fever. Reliability of diagnostic tables representing the decisive regularities in the usual form is confirmed by clinical data.

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

    PubMed

    Kapucu, Fikret E; Tanskanen, Jarno M A; Mikkonen, Jarno E; Ylä-Outinen, Laura; Narkilahti, Susanna; Hyttinen, Jari A K

    2012-01-01

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

  9. Transition of spatiotemporal patterns in neuronal networks with chemical synapses

    NASA Astrophysics Data System (ADS)

    Wang, Rong; Li, Jiajia; Du, Mengmeng; Lei, Jinzhi; Wu, Ying

    2016-11-01

    In mammalian neocortex plane waves, spiral and irregular waves appear alternately. In this paper, we study the transition of spatiotemporal patterns in neuronal networks in which neurons are coupled via two types of chemical synapses: fast excitatory synapse and fast inhibitory synapse. Our results indicate that the fast excitatory synapse connection is easier to induce regular spatiotemporal patterns than fast inhibitory synapse connection, and the mechanism is discussed through bifurcation analysis of a single neuron. We introduce the permutation entropy as a measure of network firing complexity to study the mechanisms of formation and transition of spatiotemporal patterns. Our calculations show that the spatiotemporal pattern transitions are closely connected to a sudden decrease in the firing complexity of neuronal networks, and the neuronal networks with fast excitatory synapses have higher firing complexity than those with fast inhibitory synapses.

  10. 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.

  11. Emergent Oscillations in Networks of Stochastic Spiking Neurons

    PubMed Central

    van Drongelen, Wim; Cowan, Jack D.

    2011-01-01

    Networks of neurons produce diverse patterns of oscillations, arising from the network's global properties, the propensity of individual neurons to oscillate, or a mixture of the two. Here we describe noisy limit cycles and quasi-cycles, two related mechanisms underlying emergent oscillations in neuronal networks whose individual components, stochastic spiking neurons, do not themselves oscillate. Both mechanisms are shown to produce gamma band oscillations at the population level while individual neurons fire at a rate much lower than the population frequency. Spike trains in a network undergoing noisy limit cycles display a preferred period which is not found in the case of quasi-cycles, due to the even faster decay of phase information in quasi-cycles. These oscillations persist in sparsely connected networks, and variation of the network's connectivity results in variation of the oscillation frequency. A network of such neurons behaves as a stochastic perturbation of the deterministic Wilson-Cowan equations, and the network undergoes noisy limit cycles or quasi-cycles depending on whether these have limit cycles or a weakly stable focus. These mechanisms provide a new perspective on the emergence of rhythmic firing in neural networks, showing the coexistence of population-level oscillations with very irregular individual spike trains in a simple and general framework. PMID:21573105

  12. Effect of the heterogeneous neuron and information transmission delay on stochastic resonance of neuronal networks.

    PubMed

    Wang, Qingyun; Zhang, Honghui; Chen, Guanrong

    2012-12-01

    We study the effect of heterogeneous neuron and information transmission delay on stochastic resonance of scale-free neuronal networks. For this purpose, we introduce the heterogeneity to the specified neuron with the highest degree. It is shown that in the absence of delay, an intermediate noise level can optimally assist spike firings of collective neurons so as to achieve stochastic resonance on scale-free neuronal networks for small and intermediate α(h), which plays a heterogeneous role. Maxima of stochastic resonance measure are enhanced as α(h) increases, which implies that the heterogeneity can improve stochastic resonance. However, as α(h) is beyond a certain large value, no obvious stochastic resonance can be observed. If the information transmission delay is introduced to neuronal networks, stochastic resonance is dramatically affected. In particular, the tuned information transmission delay can induce multiple stochastic resonance, which can be manifested as well-expressed maximum in the measure for stochastic resonance, appearing every multiple of one half of the subthreshold stimulus period. Furthermore, we can observe that stochastic resonance at odd multiple of one half of the subthreshold stimulus period is subharmonic, as opposed to the case of even multiple of one half of the subthreshold stimulus period. More interestingly, multiple stochastic resonance can also be improved by the suitable heterogeneous neuron. Presented results can provide good insights into the understanding of the heterogeneous neuron and information transmission delay on realistic neuronal networks.

  13. Growth of large patterned arrays of neurons using plasma methods

    NASA Astrophysics Data System (ADS)

    Brown, I. G.; Bjornstad, K. A.; Blakely, E. A.; Galvin, J. E.; Monteiro, O. R.; Sangyuenyongpipat, S.

    2003-05-01

    To understand how large systems of neurons communicate, we need to develop, among other things, methods for growing patterned networks of large numbers of neurons. Success with this challenge will be important to our understanding of how the brain works, as well as to the development of novel kinds of computer architecture that may parallel the organization of the brain. We have investigated the use of metal ion implantation using a vacuum-arc ion source, and plasma deposition with a filtered vacuum-arc system, as a means of forming regions of selective neuronal attachment on surfaces. Lithographic patterns created by the treating surface with ion species that enhance or inhibit neuronal cell attachment allow subsequent proliferation and/or differentiation of the neurons to form desired patterned neural arrays. In the work described here, we used glass microscope slides as substrates, and some of the experiments made use of simple masks to form patterns of ion beam or plasma deposition treated regions. PC-12 rat neurons were then cultured on the treated substrates coated with Type I Collagen, and the growth and differentiation was monitored. Particularly good selective growth was obtained using plasma deposition of diamond-like carbon films of about one hundred Angstroms thickness. Neuron proliferation and the elaboration of dendrites and axons after the addition of nerve growth factor both showed excellent contrast, with prolific growth and differentiation on the treated surfaces and very low growth on the untreated surfaces.

  14. 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.

  15. 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

  16. 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.

  17. On the Dynamics of the Spontaneous Activity in Neuronal Networks

    PubMed Central

    Bonifazi, Paolo; Ruaro, Maria Elisabetta; Torre, Vincent

    2007-01-01

    Most neuronal networks, even in the absence of external stimuli, produce spontaneous bursts of spikes separated by periods of reduced activity. The origin and functional role of these neuronal events are still unclear. The present work shows that the spontaneous activity of two very different networks, intact leech ganglia and dissociated cultures of rat hippocampal neurons, share several features. Indeed, in both networks: i) the inter-spike intervals distribution of the spontaneous firing of single neurons is either regular or periodic or bursting, with the fraction of bursting neurons depending on the network activity; ii) bursts of spontaneous spikes have the same broad distributions of size and duration; iii) the degree of correlated activity increases with the bin width, and the power spectrum of the network firing rate has a 1/f behavior at low frequencies, indicating the existence of long-range temporal correlations; iv) the activity of excitatory synaptic pathways mediated by NMDA receptors is necessary for the onset of the long-range correlations and for the presence of large bursts; v) blockage of inhibitory synaptic pathways mediated by GABAA receptors causes instead an increase in the correlation among neurons and leads to a burst distribution composed only of very small and very large bursts. These results suggest that the spontaneous electrical activity in neuronal networks with different architectures and functions can have very similar properties and common dynamics. PMID:17502919

  18. Population coding in sparsely connected networks of noisy neurons

    PubMed Central

    Tripp, Bryan P.; Orchard, Jeff

    2012-01-01

    This study examines the relationship between population coding and spatial connection statistics in networks of noisy neurons. Encoding of sensory information in the neocortex is thought to require coordinated neural populations, because individual cortical neurons respond to a wide range of stimuli, and exhibit highly variable spiking in response to repeated stimuli. Population coding is rooted in network structure, because cortical neurons receive information only from other neurons, and because the information they encode must be decoded by other neurons, if it is to affect behavior. However, population coding theory has often ignored network structure, or assumed discrete, fully connected populations (in contrast with the sparsely connected, continuous sheet of the cortex). In this study, we modeled a sheet of cortical neurons with sparse, primarily local connections, and found that a network with this structure could encode multiple internal state variables with high signal-to-noise ratio. However, we were unable to create high-fidelity networks by instantiating connections at random according to spatial connection probabilities. In our models, high-fidelity networks required additional structure, with higher cluster factors and correlations between the inputs to nearby neurons. PMID:22586391

  19. Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON.

    PubMed

    Lytton, William W; Seidenstein, Alexandra H; Dura-Bernal, Salvador; McDougal, Robert A; Schürmann, Felix; Hines, Michael L

    2016-10-01

    Large multiscale neuronal network simulations are of increasing value as more big data are gathered about brain wiring and organization under the auspices of a current major research initiative, such as Brain Research through Advancing Innovative Neurotechnologies. The development of these models requires new simulation technologies. We describe here the current use of the NEURON simulator with message passing interface (MPI) for simulation in the domain of moderately large networks on commonly available high-performance computers (HPCs). We discuss the basic layout of such simulations, including the methods of simulation setup, the run-time spike-passing paradigm, and postsimulation data storage and data management approaches. Using the Neuroscience Gateway, a portal for computational neuroscience that provides access to large HPCs, we benchmark simulations of neuronal networks of different sizes (500-100,000 cells), and using different numbers of nodes (1-256). We compare three types of networks, composed of either Izhikevich integrate-and-fire neurons (I&F), single-compartment Hodgkin-Huxley (HH) cells, or a hybrid network with half of each. Results show simulation run time increased approximately linearly with network size and decreased almost linearly with the number of nodes. Networks with I&F neurons were faster than HH networks, although differences were small since all tested cells were point neurons with a single compartment. PMID:27557104

  20. Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON.

    PubMed

    Lytton, William W; Seidenstein, Alexandra H; Dura-Bernal, Salvador; McDougal, Robert A; Schürmann, Felix; Hines, Michael L

    2016-10-01

    Large multiscale neuronal network simulations are of increasing value as more big data are gathered about brain wiring and organization under the auspices of a current major research initiative, such as Brain Research through Advancing Innovative Neurotechnologies. The development of these models requires new simulation technologies. We describe here the current use of the NEURON simulator with message passing interface (MPI) for simulation in the domain of moderately large networks on commonly available high-performance computers (HPCs). We discuss the basic layout of such simulations, including the methods of simulation setup, the run-time spike-passing paradigm, and postsimulation data storage and data management approaches. Using the Neuroscience Gateway, a portal for computational neuroscience that provides access to large HPCs, we benchmark simulations of neuronal networks of different sizes (500-100,000 cells), and using different numbers of nodes (1-256). We compare three types of networks, composed of either Izhikevich integrate-and-fire neurons (I&F), single-compartment Hodgkin-Huxley (HH) cells, or a hybrid network with half of each. Results show simulation run time increased approximately linearly with network size and decreased almost linearly with the number of nodes. Networks with I&F neurons were faster than HH networks, although differences were small since all tested cells were point neurons with a single compartment.

  1. 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.

  2. Neuronal avalanches of a self-organized neural network with active-neuron-dominant structure.

    PubMed

    Li, Xiumin; Small, Michael

    2012-06-01

    Neuronal avalanche is a spontaneous neuronal activity which obeys a power-law distribution of population event sizes with an exponent of -3/2. It has been observed in the superficial layers of cortex both in vivo and in vitro. In this paper, we analyze the information transmission of a novel self-organized neural network with active-neuron-dominant structure. Neuronal avalanches can be observed in this network with appropriate input intensity. We find that the process of network learning via spike-timing dependent plasticity dramatically increases the complexity of network structure, which is finally self-organized to be active-neuron-dominant connectivity. Both the entropy of activity patterns and the complexity of their resulting post-synaptic inputs are maximized when the network dynamics are propagated as neuronal avalanches. This emergent topology is beneficial for information transmission with high efficiency and also could be responsible for the large information capacity of this network compared with alternative archetypal networks with different neural connectivity.

  3. 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

  4. 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

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

    PubMed

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

    2015-07-01

    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.

  6. 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

  7. Effect of methylprednisolone on mammalian neuronal networks in vitro.

    PubMed

    Wittstock, Matthias; Rommer, Paulus S; Schiffmann, Florian; Jügelt, Konstantin; Stüwe, Simone; Benecke, Reiner; Schiffmann, Dietmar; Zettl, Uwe K

    2015-01-01

    Glucocorticosteroids (GCS) are widely used for the treatment of neurological diseases, e.g. multiple sclerosis. High levels of GCS are toxic to the central nervous system and can produce adverse effects. The effect of methylprednisolone (MP) on mammalian neuronal networks was studied in vitro. We demonstrate a dose-dependent excitatory effect of MP on cultured neuronal networks, followed by a shut-down of electrical activity using the microelectrode array technique.

  8. 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

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

    PubMed

    Lelito, Katherine R; Shafer, Orie T

    2012-04-01

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

  10. 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.

  11. Connectomic constraints on computation in feedforward networks of spiking neurons.

    PubMed

    Ramaswamy, Venkatakrishnan; Banerjee, Arunava

    2014-10-01

    Several efforts are currently underway to decipher the connectome or parts thereof in a variety of organisms. Ascertaining the detailed physiological properties of all the neurons in these connectomes, however, is out of the scope of such projects. It is therefore unclear to what extent knowledge of the connectome alone will advance a mechanistic understanding of computation occurring in these neural circuits, especially when the high-level function of the said circuit is unknown. We consider, here, the question of how the wiring diagram of neurons imposes constraints on what neural circuits can compute, when we cannot assume detailed information on the physiological response properties of the neurons. We call such constraints-that arise by virtue of the connectome-connectomic constraints on computation. For feedforward networks equipped with neurons that obey a deterministic spiking neuron model which satisfies a small number of properties, we ask if just by knowing the architecture of a network, we can rule out computations that it could be doing, no matter what response properties each of its neurons may have. We show results of this form, for certain classes of network architectures. On the other hand, we also prove that with the limited set of properties assumed for our model neurons, there are fundamental limits to the constraints imposed by network structure. Thus, our theory suggests that while connectomic constraints might restrict the computational ability of certain classes of network architectures, we may require more elaborate information on the properties of neurons in the network, before we can discern such results for other classes of networks.

  12. 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

  13. 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

  14. Network activity of mirror neurons depends on experience.

    PubMed

    Ushakov, Vadim L; Kartashov, Sergey I; Zavyalova, Victoria V; Bezverhiy, Denis D; Posichanyuk, Vladimir I; Terentev, Vasliliy N; Anokhin, Konstantin V

    2013-03-01

    In this work, the investigation of network activity of mirror neurons systems in animal brains depending on experience (existence or absence performance of the shown actions) was carried out. It carried out the research of mirror neurons network in the C57/BL6 line mice in the supervision task of swimming mice-demonstrators in Morris water maze. It showed the presence of mirror neurons systems in the motor cortex M1, M2, cingular cortex, hippocampus in mice groups, having experience of the swimming and without it. The conclusion is drawn about the possibility of the new functional network systems formation by means of mirror neurons systems and the acquisition of new knowledge through supervision by the animals in non-specific tasks.

  15. Identifying repeating motifs in the activation of synchronized bursts in cultured neuronal networks.

    PubMed

    Raichman, Nadav; Ben-Jacob, Eshel

    2008-05-15

    Cultured neuronal networks cultivated on micro-electrode arrays are a widely used tool for the investigation of network mechanisms, providing structural framework for long-term recordings of network electrical activity, as well as the network reaction to electrical or chemical stimulations. The typical activity pattern of the culture takes the form of synchronized bursting events (SBEs), in which a large fraction of the recorded neurons simultaneously fire trains of action potentials in short bursts of several hundreds of a millisecond. We developed a method that identifies clusters of bursts that share a similar activation motif throughout the culture based on the fact that the culture morphology remains relatively unchanged for an extended time interval and that neurons fire in a recognizable and precise manner during a burst initiation. Our method compares accuracies in time delays that occurred between the activation of spike-trains of different neurons. Three culture architectures were studied and analyzed: a large network of 2 million cells, a smaller network limited in size of 100,000 cells, and a large network divided into 4 clusters. In each of the morphologies we identified cultures that showed more than one activation motif. Clustered networks showed more motifs on average than uniform cultures. The algorithm was able to show high fidelity to artificial noise. We also compare the results of our method with another method based on a correlation measure.

  16. Small-world networks in neuronal populations: a computational perspective.

    PubMed

    Zippo, Antonio G; Gelsomino, Giuliana; Van Duin, Pieter; Nencini, Sara; Caramenti, Gian Carlo; Valente, Maurizio; Biella, Gabriele E M

    2013-08-01

    The analysis of the brain in terms of integrated neural networks may offer insights on the reciprocal relation between structure and information processing. Even with inherent technical limits, many studies acknowledge neuron spatial arrangements and communication modes as key factors. In this perspective, we investigated the functional organization of neuronal networks by explicitly assuming a specific functional topology, the small-world network. We developed two different computational approaches. Firstly, we asked whether neuronal populations actually express small-world properties during a definite task, such as a learning task. For this purpose we developed the Inductive Conceptual Network (ICN), which is a hierarchical bio-inspired spiking network, capable of learning invariant patterns by using variable-order Markov models implemented in its nodes. As a result, we actually observed small-world topologies during learning in the ICN. Speculating that the expression of small-world networks is not solely related to learning tasks, we then built a de facto network assuming that the information processing in the brain may occur through functional small-world topologies. In this de facto network, synchronous spikes reflected functional small-world network dependencies. In order to verify the consistency of the assumption, we tested the null-hypothesis by replacing the small-world networks with random networks. As a result, only small world networks exhibited functional biomimetic characteristics such as timing and rate codes, conventional coding strategies and neuronal avalanches, which are cascades of bursting activities with a power-law distribution. Our results suggest that small-world functional configurations are liable to underpin brain information processing at neuronal level.

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

    PubMed

    Sadeh, Sadra; Rotter, Stefan

    2015-01-01

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

  18. Orientation Selectivity in Inhibition-Dominated Networks of Spiking Neurons: Effect of Single Neuron Properties and Network Dynamics

    PubMed Central

    Sadeh, Sadra; Rotter, Stefan

    2015-01-01

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

  19. 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.

  20. Breathing and calling: neuronal networks in the Xenopus laevis hindbrain.

    PubMed

    Zornik, Erik; Kelley, Darcy B

    2007-03-20

    Xenopus laevis is an aquatic anuran with a complex vocal repertoire. Unlike terrestrial frogs, vocalizations are independent of respiration, and a single muscle group--the laryngeal dilators--produces underwater calls. We sought to identify the premotor neural network that underlies vocal behaviors. Vocal patterns generated by premotor networks control laryngeal motor neurons in cranial nucleus (n.) IX-X. Glottal motor neurons, active during respiration, are also present in n.IX-X. We used horseradish peroxidase (HRP), Lucifer yellow, and fluorescently conjugated dextrans to characterize the organization of n.IX-X and to trace premotor neuron projections. Premotor nuclei include the inferior reticular formation (Ri) adjacent to n.IX-X and the pretrigeminal nucleus of the dorsal tegmental area of the medulla (DTAM), the primary descending input to n.IX-X. Intramuscular HRP injections revealed a spatially segregated pattern, with glottal motor neurons in anterior n.IX-X and laryngeal motor neurons in the caudal portion of the nucleus. Dextran injections identified commissural n.IX-X neurons that project to the contralateral motor nucleus and DTAM-projecting n.IX-X neurons. Both neuronal types are clustered in anteromedial n.IX-X, closely associated with glottal motor neurons. Ri neurons project to ipsilateral and contralateral DTAM. Projections from DTAM target n.IX-X bilaterally, and all four identified subtypes receive DTAM input. In contrast, Ri neurons receive little input from DTAM. We hypothesize that connectivity between neurons in n.IX-X, Ri and DTAM may provide mechanisms to generate laryngeal and glottal activity patterns and that DTAM may coordinate vocal and respiratory motor pools, perhaps acting to switch between these two mutually exclusive behaviors.

  1. Spiking neural networks for cortical neuronal spike train decoding.

    PubMed

    Fang, Huijuan; Wang, Yongji; He, Jiping

    2010-04-01

    Recent investigation of cortical coding and computation indicates that temporal coding is probably a more biologically plausible scheme used by neurons than the rate coding used commonly in most published work. We propose and demonstrate in this letter that spiking neural networks (SNN), consisting of spiking neurons that propagate information by the timing of spikes, are a better alternative to the coding scheme based on spike frequency (histogram) alone. The SNN model analyzes cortical neural spike trains directly without losing temporal information for generating more reliable motor command for cortically controlled prosthetics. In this letter, we compared the temporal pattern classification result from the SNN approach with results generated from firing-rate-based approaches: conventional artificial neural networks, support vector machines, and linear regression. The results show that the SNN algorithm can achieve higher classification accuracy and identify the spiking activity related to movement control earlier than the other methods. Both are desirable characteristics for fast neural information processing and reliable control command pattern recognition for neuroprosthetic applications. PMID:19922291

  2. Mild hypoxia affects synaptic connectivity in cultured neuronal networks.

    PubMed

    Hofmeijer, Jeannette; Mulder, Alex T B; Farinha, Ana C; van Putten, Michel J A M; le Feber, Joost

    2014-04-01

    Eighty percent of patients with chronic mild cerebral ischemia/hypoxia resulting from chronic heart failure or pulmonary disease have cognitive impairment. Overt structural neuronal damage is lacking and the precise cause of neuronal damage is unclear. As almost half of the cerebral energy consumption is used for synaptic transmission, and synaptic failure is the first abrupt consequence of acute complete anoxia, synaptic dysfunction is a candidate mechanism for the cognitive deterioration in chronic mild ischemia/hypoxia. Because measurement of synaptic functioning in patients is problematic, we use cultured networks of cortical neurons from new born rats, grown over a multi-electrode array, as a model system. These were exposed to partial hypoxia (partial oxygen pressure of 150Torr lowered to 40-50Torr) during 3 (n=14) or 6 (n=8) hours. Synaptic functioning was assessed before, during, and after hypoxia by assessment of spontaneous network activity, functional connectivity, and synaptically driven network responses to electrical stimulation. Action potential heights and shapes and non-synaptic stimulus responses were used as measures of individual neuronal integrity. During hypoxia of 3 and 6h, there was a statistically significant decrease of spontaneous network activity, functional connectivity, and synaptically driven network responses, whereas direct responses and action potentials remained unchanged. These changes were largely reversible. Our results indicate that in cultured neuronal networks, partial hypoxia during 3 or 6h causes isolated disturbances of synaptic connectivity.

  3. 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

  4. PAN hollow fiber membranes elicit functional hippocampal neuronal network.

    PubMed

    Morelli, Sabrina; Piscioneri, Antonella; Salerno, Simona; Tasselli, Franco; Di Vito, Anna; Giusi, Giuseppina; Canonaco, Marcello; Drioli, Enrico; De Bartolo, Loredana

    2012-01-01

    This study focuses on the development of an advanced in vitro biohybrid culture model system based on the use of hollow fibre membranes (HFMs) and hippocampal neurons in order to promote the formation of a high density neuronal network. Polyacrylonitrile (PAN) and modified polyetheretherketone (PEEK-WC) membranes were prepared in hollow fibre configuration. The morphological and metabolic behaviour of hippocampal neurons cultured on PAN HF membranes were compared with those cultured on PEEK-WC HF. The differences of cell behaviour between HFMs were evidenced by the morphometric analysis in terms of axon length and also by the investigation of metabolic activity in terms of neurotrophin secretion. These findings suggested that PAN HFMs induced the in vitro reconstruction of very highly functional and complex neuronal networks. Thus, these biomaterials could potentially be used for the in vitro realization of a functional hippocampal tissue analogue for the study of neurobiological functions and/or neurodegenerative diseases.

  5. 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.

  6. Self-organization and neuronal avalanches in networks of dissociated cortical neurons.

    PubMed

    Pasquale, V; Massobrio, P; Bologna, L L; Chiappalone, M; Martinoia, S

    2008-06-01

    Dissociated cortical neurons from rat embryos cultured onto micro-electrode arrays exhibit characteristic patterns of electrophysiological activity, ranging from isolated spikes in the first days of development to highly synchronized bursts after 3-4 weeks in vitro. In this work we analyzed these features by considering the approach proposed by the self-organized criticality theory: we found that networks of dissociated cortical neurons also generate spontaneous events of spreading activity, previously observed in cortical slices, in the form of neuronal avalanches. Choosing an appropriate time scale of observation to detect such neuronal avalanches, we studied the dynamics by considering the spontaneous activity during acute recordings in mature cultures and following the development of the network. We observed different behaviors, i.e. sub-critical, critical or super-critical distributions of avalanche sizes and durations, depending on both the age and the development of cultures. In order to clarify this variability, neuronal avalanches were correlated with other statistical parameters describing the global activity of the network. Criticality was found in correspondence to medium synchronization among bursts and high ratio between bursting and spiking activity. Then, the action of specific drugs affecting global bursting dynamics (i.e. acetylcholine and bicuculline) was investigated to confirm the correlation between criticality and regulated balance between synchronization and variability in the bursting activity. Finally, a computational model of neuronal network was developed in order to interpret the experimental results and understand which parameters (e.g. connectivity, excitability) influence the distribution of avalanches. In summary, cortical neurons preserve their capability to self-organize in an effective network even when dissociated and cultured in vitro. The distribution of avalanche features seems to be critical in those cultures displaying

  7. Detection of 5-hydroxytryptamine (5-HT) in vitro using a hippocampal neuronal network-based biosensor with extracellular potential analysis of neurons.

    PubMed

    Hu, Liang; Wang, Qin; Qin, Zhen; Su, Kaiqi; Huang, Liquan; Hu, Ning; Wang, Ping

    2015-04-15

    5-hydroxytryptamine (5-HT) is an important neurotransmitter in regulating emotions and related behaviors in mammals. To detect and monitor the 5-HT, effective and convenient methods are demanded in investigation of neuronal network. In this study, hippocampal neuronal networks (HNNs) endogenously expressing 5-HT receptors were employed as sensing elements to build an in vitro neuronal network-based biosensor. The electrophysiological characteristics were analyzed in both neuron and network levels. The firing rates and amplitudes were derived from signal to determine the biosensor response characteristics. The experimental results demonstrate a dose-dependent inhibitory effect of 5-HT on hippocampal neuron activities, indicating the effectiveness of this hybrid biosensor in detecting 5-HT with a response range from 0.01μmol/L to 10μmol/L. In addition, the cross-correlation analysis of HNNs activities suggests 5-HT could weaken HNN connectivity reversibly, providing more specificity of this biosensor in detecting 5-HT. Moreover, 5-HT induced spatiotemporal firing pattern alterations could be monitored in neuron and network levels simultaneously by this hybrid biosensor in a convenient and direct way. With those merits, this neuronal network-based biosensor will be promising to be a valuable and utility platform for the study of neurotransmitter in vitro.

  8. On the design of artificial auto-associative neuronal networks.

    PubMed

    Reimann, Stefan

    1998-06-01

    In this paper, we consider the problem of how to construct an artificial neuronal network such that it reproduces a given set of patterns in an exact manner. It turns out that the structure of the weight matrix of the network represents the structure of the set of patterns it is acting on, not the patterns themselves. Conditions are discussed under which the associative network memorizes a certain subset of these patterns. Our formal approach is based on the simple observation that neural networks are structured sets of neurons. By regarding recurrent neural networks as dynamical systems with symmetry, the category of G-sets and G-morphisms appears as a natural framework for evaluating their structure and functioning analytically.

  9. 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

  10. 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

  11. 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

  12. 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.

  13. Image Informatics Strategies for Deciphering Neuronal Network Connectivity.

    PubMed

    Detrez, Jan R; Verstraelen, Peter; Gebuis, Titia; Verschuuren, Marlies; Kuijlaars, Jacobine; Langlois, Xavier; Nuydens, Rony; Timmermans, Jean-Pierre; De Vos, Winnok H

    2016-01-01

    Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Amongst the neuronal structures that show morphological plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular communication and the associated calcium bursting behaviour. In vitro cultured neuronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardization of both image acquisition and image analysis, it has become possible to extract statistically relevant readouts from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies. PMID:27207365

  14. 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.

  15. 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.

  16. Quantifying phase-amplitude coupling in neuronal network oscillations.

    PubMed

    Onslow, Angela C E; Bogacz, Rafal; Jones, Matthew W

    2011-03-01

    Neuroscience time series data from a range of techniques and species reveal complex, non-linear interactions between different frequencies of neuronal network oscillations within and across brain regions. Here, we briefly review the evidence that these nested, cross-frequency interactions act in concert with linearly covariant (within-frequency) activity to dynamically coordinate functionally related neuronal ensembles during behaviour. Such studies depend upon reliable quantification of cross-frequency coordination, and we compare the properties of three techniques used to measure phase-amplitude coupling (PAC)--Envelope-to-Signal Correlation (ESC), the Modulation Index (MI) and Cross-Frequency Coherence (CFC)--by standardizing their filtering algorithms and systematically assessing their robustness to noise and signal amplitude using artificial signals. Importantly, we also introduce a freely-downloadable method for estimating statistical significance of PAC, a step overlooked in the majority of published studies. We find that varying data length and noise levels leads to the three measures differentially detecting false positives or correctly identifying frequency bands of interaction; these conditions should therefore be taken into careful consideration when selecting PAC analyses. Finally, we demonstrate the utility of the three measures in quantifying PAC in local field potential data simultaneously recorded from rat hippocampus and prefrontal cortex, revealing a novel finding of prefrontal cortical theta phase modulating hippocampal gamma power. Future adaptations that allow detection of time-variant PAC should prove essential in deciphering the roles of cross-frequency coupling in mediating or reflecting nervous system function.

  17. 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

  18. Brain extracellular matrix retains connectivity in neuronal networks.

    PubMed

    Bikbaev, Arthur; Frischknecht, Renato; Heine, Martin

    2015-09-29

    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.

  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.

  20. Control of neuronal network organization by chemical surface functionalization of multi-walled carbon nanotube arrays

    PubMed Central

    Liu, Jie; Appaix, Florence; Bibari, Olivier; Marchand, Gilles; Benabid, Alim-Louis; Sauter-Starace, Fabien; Waard, Michel De

    2011-01-01

    Carbon nanotube substrates are promising candidates for biological applications and devices. Interfacing of these carbon nanotubes with neurons can be controlled by chemical modifications. In this study, we investigated how chemical surface functionalisation of multi-walled carbon nanotube arrays (MWNT-A) influences neuronal adhesion and network organization. Functionalisation of MWNT-A dramatically modifies length of neurite fascicles, cluster interconnection success rate, and percentage of neurites that escape from the clusters. We propose that chemical functionalisation represents a method of choice for developing applications in which neuronal patterning on MWNT-A substrates is a must. PMID:21436508

  1. 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.

  2. Amyloid beta modulation of neuronal network activity in vitro.

    PubMed

    Charkhkar, Hamid; Meyyappan, Susheela; Matveeva, Evgenia; Moll, Jonathan R; McHail, Daniel G; Peixoto, Nathalia; Cliff, Richard O; Pancrazio, Joseph J

    2015-12-10

    In vitro assays offer a means of screening potential therapeutics and accelerating the drug development process. Here, we utilized neuronal cultures on planar microelectrode arrays (MEA) as a functional assay to assess the neurotoxicity of amyloid-β 1-42 (Aβ42), a biomolecule implicated in the Alzheimer׳s disease (AD). In this approach, neurons harvested from embryonic mice were seeded on the substrate-integrated microelectrode arrays. The cultured neurons form a spontaneously active network, and the spiking activity as a functional endpoint could be detected via the MEA. Aβ42 oligomer, but not monomer, significantly reduced network spike rate. In addition, we demonstrated that the ionotropic glutamate receptors, NMDA and AMPA/kainate, play a role in the effects of Aβ42 on neuronal activity in vitro. To examine the utility of the MEA-based assay for AD drug discovery, we tested two model therapeutics for AD, methylene blue (MB) and memantine. Our results show an almost full recovery in the activity within 24h after administration of Aβ42 in the cultures pre-treated with either MB or memantine. Our findings suggest that cultured neuronal networks may be a useful platform in screening potential therapeutics for Aβ induced changes in neurological function.

  3. Amyloid beta modulation of neuronal network activity in vitro.

    PubMed

    Charkhkar, Hamid; Meyyappan, Susheela; Matveeva, Evgenia; Moll, Jonathan R; McHail, Daniel G; Peixoto, Nathalia; Cliff, Richard O; Pancrazio, Joseph J

    2015-12-10

    In vitro assays offer a means of screening potential therapeutics and accelerating the drug development process. Here, we utilized neuronal cultures on planar microelectrode arrays (MEA) as a functional assay to assess the neurotoxicity of amyloid-β 1-42 (Aβ42), a biomolecule implicated in the Alzheimer׳s disease (AD). In this approach, neurons harvested from embryonic mice were seeded on the substrate-integrated microelectrode arrays. The cultured neurons form a spontaneously active network, and the spiking activity as a functional endpoint could be detected via the MEA. Aβ42 oligomer, but not monomer, significantly reduced network spike rate. In addition, we demonstrated that the ionotropic glutamate receptors, NMDA and AMPA/kainate, play a role in the effects of Aβ42 on neuronal activity in vitro. To examine the utility of the MEA-based assay for AD drug discovery, we tested two model therapeutics for AD, methylene blue (MB) and memantine. Our results show an almost full recovery in the activity within 24h after administration of Aβ42 in the cultures pre-treated with either MB or memantine. Our findings suggest that cultured neuronal networks may be a useful platform in screening potential therapeutics for Aβ induced changes in neurological function. PMID:26453830

  4. 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

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

    PubMed

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

    2016-08-02

    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.

  6. 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.

  7. 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.

  8. 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.

  9. Neuron network activity scales exponentially with synapse density.

    PubMed

    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. PMID:19104141

  10. Bifurcations of large networks of two-dimensional integrate and fire neurons.

    PubMed

    Nicola, Wilten; Campbell, Sue Ann

    2013-08-01

    Recently, a class of two-dimensional integrate and fire models has been used to faithfully model spiking neurons. This class includes the Izhikevich model, the adaptive exponential integrate and fire model, and the quartic integrate and fire model. The bifurcation types for the individual neurons have been thoroughly analyzed by Touboul (SIAM J Appl Math 68(4):1045-1079, 2008). However, when the models are coupled together to form networks, the networks can display bifurcations that an uncoupled oscillator cannot. For example, the networks can transition from firing with a constant rate to burst firing. This paper introduces a technique to reduce a full network of this class of neurons to a mean field model, in the form of a system of switching ordinary differential equations. The reduction uses population density methods and a quasi-steady state approximation to arrive at the mean field system. Reduced models are derived for networks with different topologies and different model neurons with biologically derived parameters. The mean field equations are able to qualitatively and quantitatively describe the bifurcations that the full networks display. Extensions and higher order approximations are discussed.

  11. 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

  12. 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

  13. Network architecture underlying maximal separation of neuronal representations.

    PubMed

    Jortner, Ron A

    2012-01-01

    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.

  14. 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.

  15. Targeting single neuronal networks for gene expression and cell labeling in vivo.

    PubMed

    Marshel, James H; Mori, Takuma; Nielsen, Kristina J; Callaway, Edward M

    2010-08-26

    To understand fine-scale structure and function of single mammalian neuronal networks, we developed and validated a strategy to genetically target and trace monosynaptic inputs to a single neuron in vitro and in vivo. The strategy independently targets a neuron and its presynaptic network for specific gene expression and fine-scale labeling, using single-cell electroporation of DNA to target infection and monosynaptic retrograde spread of a genetically modifiable rabies virus. The technique is highly reliable, with transsynaptic labeling occurring in every electroporated neuron infected by the virus. Targeting single neocortical neuronal networks in vivo, we found clusters of both spiny and aspiny neurons surrounding the electroporated neuron in each case, in addition to intricately labeled distal cortical and subcortical inputs. This technique, broadly applicable for probing and manipulating single neuronal networks with single-cell resolution in vivo, may help shed new light on fundamental mechanisms underlying circuit development and information processing by neuronal networks throughout the brain.

  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. Observed network dynamics from altering the balance between excitatory and inhibitory neurons in cultured networks

    NASA Astrophysics Data System (ADS)

    Chen, Xin; Dzakpasu, Rhonda

    2010-09-01

    Complexity in the temporal organization of neural systems may be a reflection of the diversity of their neural constituents. These constituents, excitatory and inhibitory neurons, comprise a well-defined ratio in vivo and form the substrate for rhythmic oscillatory activity. To begin to elucidate the dynamical implications that underlie this balance, we construct neural circuits not ordinarily found in nature and study the resulting temporal patterns. We culture several networks of neurons composed of varying fractions of excitatory and inhibitory cells and use a multielectrode array to study their temporal dynamics as this balance is modulated. We use the electrode burst as the temporal imprimatur to signify the presence of network activity. Burst durations, interburst intervals, and the number of spikes participating within a burst are used to illustrate the vivid differences in the temporal organization between the various cultured networks. When the network consists largely of excitatory neurons, no network temporal structure is apparent. However, the addition of inhibitory neurons evokes a temporal order. Calculation of the temporal autocorrelation shows that when the number of inhibitory neurons is a major fraction of the network, a striking network pattern materializes when none was previously present.

  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. 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.

  1. 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....

  2. Neuronal networks provide rapid neuroprotection against spreading toxicity.

    PubMed

    Samson, Andrew J; Robertson, Graham; Zagnoni, Michele; Connolly, Christopher N

    2016-01-01

    Acute secondary neuronal cell death, as seen in neurodegenerative disease, cerebral ischemia (stroke) and traumatic brain injury (TBI), drives spreading neurotoxicity into surrounding, undamaged, brain areas. This spreading toxicity occurs via two mechanisms, synaptic toxicity through hyperactivity, and excitotoxicity following the accumulation of extracellular glutamate. To date, there are no fast-acting therapeutic tools capable of terminating secondary spreading toxicity within a time frame relevant to the emergency treatment of stroke or TBI patients. Here, using hippocampal neurons (DIV 15-20) cultured in microfluidic devices in order to deliver a localized excitotoxic insult, we replicate secondary spreading toxicity and demonstrate that this process is driven by GluN2B receptors. In addition to the modeling of spreading toxicity, this approach has uncovered a previously unknown, fast acting, GluN2A-dependent neuroprotective signaling mechanism. This mechanism utilizes the innate capacity of surrounding neuronal networks to provide protection against both forms of spreading neuronal toxicity, synaptic hyperactivity and direct glutamate excitotoxicity. Importantly, network neuroprotection against spreading toxicity can be effectively stimulated after an excitotoxic insult has been delivered, and may identify a new therapeutic window to limit brain damage. PMID:27650924

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

    PubMed Central

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

    2015-01-01

    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

  4. Neuronal networks provide rapid neuroprotection against spreading toxicity

    PubMed Central

    Samson, Andrew J.; Robertson, Graham; Zagnoni, Michele; Connolly, Christopher N.

    2016-01-01

    Acute secondary neuronal cell death, as seen in neurodegenerative disease, cerebral ischemia (stroke) and traumatic brain injury (TBI), drives spreading neurotoxicity into surrounding, undamaged, brain areas. This spreading toxicity occurs via two mechanisms, synaptic toxicity through hyperactivity, and excitotoxicity following the accumulation of extracellular glutamate. To date, there are no fast-acting therapeutic tools capable of terminating secondary spreading toxicity within a time frame relevant to the emergency treatment of stroke or TBI patients. Here, using hippocampal neurons (DIV 15–20) cultured in microfluidic devices in order to deliver a localized excitotoxic insult, we replicate secondary spreading toxicity and demonstrate that this process is driven by GluN2B receptors. In addition to the modeling of spreading toxicity, this approach has uncovered a previously unknown, fast acting, GluN2A-dependent neuroprotective signaling mechanism. This mechanism utilizes the innate capacity of surrounding neuronal networks to provide protection against both forms of spreading neuronal toxicity, synaptic hyperactivity and direct glutamate excitotoxicity. Importantly, network neuroprotection against spreading toxicity can be effectively stimulated after an excitotoxic insult has been delivered, and may identify a new therapeutic window to limit brain damage. PMID:27650924

  5. Neuronal networks provide rapid neuroprotection against spreading toxicity.

    PubMed

    Samson, Andrew J; Robertson, Graham; Zagnoni, Michele; Connolly, Christopher N

    2016-09-21

    Acute secondary neuronal cell death, as seen in neurodegenerative disease, cerebral ischemia (stroke) and traumatic brain injury (TBI), drives spreading neurotoxicity into surrounding, undamaged, brain areas. This spreading toxicity occurs via two mechanisms, synaptic toxicity through hyperactivity, and excitotoxicity following the accumulation of extracellular glutamate. To date, there are no fast-acting therapeutic tools capable of terminating secondary spreading toxicity within a time frame relevant to the emergency treatment of stroke or TBI patients. Here, using hippocampal neurons (DIV 15-20) cultured in microfluidic devices in order to deliver a localized excitotoxic insult, we replicate secondary spreading toxicity and demonstrate that this process is driven by GluN2B receptors. In addition to the modeling of spreading toxicity, this approach has uncovered a previously unknown, fast acting, GluN2A-dependent neuroprotective signaling mechanism. This mechanism utilizes the innate capacity of surrounding neuronal networks to provide protection against both forms of spreading neuronal toxicity, synaptic hyperactivity and direct glutamate excitotoxicity. Importantly, network neuroprotection against spreading toxicity can be effectively stimulated after an excitotoxic insult has been delivered, and may identify a new therapeutic window to limit brain damage.

  6. Derivation of a neural field model from a network of theta neurons.

    PubMed

    Laing, Carlo R

    2014-07-01

    Neural field models are used to study macroscopic spatiotemporal patterns in the cortex. Their derivation from networks of model neurons normally involves a number of assumptions, which may not be correct. Here we present an exact derivation of a neural field model from an infinite network of theta neurons, the canonical form of a type I neuron. We demonstrate the existence of a "bump" solution in both a discrete network of neurons and in the corresponding neural field model.

  7. 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

  8. 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

  9. 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

  10. 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.

  11. 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

  12. Neural control of heart rate: the role of neuronal networking.

    PubMed

    Kember, G; Armour, J A; Zamir, M

    2011-05-21

    Neural control of heart rate, particularly its sympathetic component, is generally thought to reside primarily in the central nervous system, though accumulating evidence suggests that intrathoracic extracardiac and intrinsic cardiac ganglia are also involved. We propose an integrated model in which the control of heart rate is achieved via three neuronal "levels" representing three control centers instead of the conventional one. Most importantly, in this model control is effected through networking between neuronal populations within and among these layers. The results obtained indicate that networking serves to process demands for systemic blood flow before transducing them to cardiac motor neurons. This provides the heart with a measure of protection against the possibility of "overdrive" implied by the currently held centrally driven system. The results also show that localized networking instabilities can lead to sporadic low frequency oscillations that have the characteristics of the well-known Mayer waves. The sporadic nature of Mayer waves has been unexplained so far and is of particular interest in clinical diagnosis.

  13. 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.

  14. 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

  15. 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

  16. 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.

  17. Use of cortical neuronal networks for in vitro material biocompatibility testing.

    PubMed

    Charkhkar, Hamid; Frewin, Christopher; Nezafati, Maysam; Knaack, Gretchen L; Peixoto, Nathalia; Saddow, Stephen E; Pancrazio, Joseph J

    2014-03-15

    Neural interfaces aim to restore neurological function lost during disease or injury. Novel implantable neural interfaces increasingly capitalize on novel materials to achieve microscale coupling with the nervous system. Like any biomedical device, neural interfaces should consist of materials that exhibit biocompatibility in accordance with the international standard ISO10993-5, which describes in vitro testing involving fibroblasts where cytotoxicity serves as the main endpoint. In the present study, we examine the utility of living neuronal networks as functional assays for in vitro material biocompatibility, particularly for materials that comprise implantable neural interfaces. Embryonic mouse cortical tissue was cultured to form functional networks where spontaneous action potentials, or spikes, can be monitored non-invasively using a substrate-integrated microelectrode array. Taking advantage of such a platform, we exposed established positive and negative control materials to the neuronal networks in a consistent method with ISO 10993-5 guidance. Exposure to the negative controls, gold and polyethylene, did not significantly change the neuronal activity whereas the positive controls, copper and polyvinyl chloride (PVC), resulted in reduction of network spike rate. We also compared the functional assay with an established cytotoxicity measure using L929 fibroblast cells. Our findings indicate that neuronal networks exhibit enhanced sensitivity to positive control materials. In addition, we assessed functional neurotoxicity of tungsten, a common microelectrode material, and two conducting polymer formulations that have been used to modify microelectrode properties for in vivo recording and stimulation. These data suggest that cultured neuronal networks are a useful platform for evaluating the functional toxicity of materials intended for implantation in the nervous system.

  18. Spectral Entropy Based Neuronal Network Synchronization Analysis Based on Microelectrode Array Measurements

    PubMed Central

    Kapucu, Fikret E.; Välkki, Inkeri; Mikkonen, Jarno E.; Leone, Chiara; Lenk, Kerstin; Tanskanen, Jarno M. A.; Hyttinen, Jari A. K.

    2016-01-01

    Synchrony and asynchrony are essential aspects of the functioning of interconnected neuronal cells and networks. New information on neuronal synchronization can be expected to aid in understanding these systems. Synchronization provides insight in the functional connectivity and the spatial distribution of the information processing in the networks. Synchronization is generally studied with time domain analysis of neuronal events, or using direct frequency spectrum analysis, e.g., in specific frequency bands. However, these methods have their pitfalls. Thus, we have previously proposed a method to analyze temporal changes in the complexity of the frequency of signals originating from different network regions. The method is based on the correlation of time varying spectral entropies (SEs). SE assesses the regularity, or complexity, of a time series by quantifying the uniformity of the frequency spectrum distribution. It has been previously employed, e.g., in electroencephalogram analysis. Here, we revisit our correlated spectral entropy method (CorSE), providing evidence of its justification, usability, and benefits. Here, CorSE is assessed with simulations and in vitro microelectrode array (MEA) data. CorSE is first demonstrated with a specifically tailored toy simulation to illustrate how it can identify synchronized populations. To provide a form of validation, the method was tested with simulated data from integrate-and-fire model based computational neuronal networks. To demonstrate the analysis of real data, CorSE was applied on in vitro MEA data measured from rat cortical cell cultures, and the results were compared with three known event based synchronization measures. Finally, we show the usability by tracking the development of networks in dissociated mouse cortical cell cultures. The results show that temporal correlations in frequency spectrum distributions reflect the network relations of neuronal populations. In the simulated data, CorSE unraveled the

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

    PubMed Central

    2012-01-01

    Background Traditional electroencephalography provides a critical assessment of pain responses. The perception of pain, however, may involve a series of signal transmission pathways in higher cortical function. Recent studies have shown that a mathematical method, the neuronal avalanche model, may be applied to evaluate higher-order network dynamics. The neuronal avalanche is a cascade of neuronal activity, the size distribution of which can be approximated by a power law relationship manifested by the slope of a straight line (i.e., the α value). We investigated whether the neuronal avalanche could be a useful index for nociceptive assessment. Findings Neuronal activity was recorded with a 4 × 8 multichannel electrode array in the primary somatosensory cortex (S1) and anterior cingulate cortex (ACC). Under light anesthesia, peripheral pinch stimulation increased the slope of the α value in both the ACC and S1, whereas brush stimulation increased the α value only in the S1. The increase in α values was blocked in both regions under deep anesthesia. The increase in α values in the ACC induced by peripheral pinch stimulation was blocked by medial thalamic lesion, but the increase in α values in the S1 induced by brush and pinch stimulation was not affected. Conclusions The neuronal avalanche model shows a critical state in the cortical network for noxious-related signal processing. The α value may provide an index of brain network activity that distinguishes the responses to somatic stimuli from the control state. These network dynamics may be valuable for the evaluation of acute nociceptive processes and may be applied to chronic pathological pain conditions. PMID:22537828

  20. 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.

  1. 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

  2. A Neuronal Network Model for Pitch Selectivity and Representation.

    PubMed

    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

  3. 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.

  4. Sparse Gamma Rhythms Arising through Clustering in Adapting Neuronal Networks

    PubMed Central

    Kilpatrick, Zachary P.; Ermentrout, Bard

    2011-01-01

    Gamma rhythms (30–100 Hz) are an extensively studied synchronous brain state responsible for a number of sensory, memory, and motor processes. Experimental evidence suggests that fast-spiking interneurons are responsible for carrying the high frequency components of the rhythm, while regular-spiking pyramidal neurons fire sparsely. We propose that a combination of spike frequency adaptation and global inhibition may be responsible for this behavior. Excitatory neurons form several clusters that fire every few cycles of the fast oscillation. This is first shown in a detailed biophysical network model and then analyzed thoroughly in an idealized model. We exploit the fact that the timescale of adaptation is much slower than that of the other variables. Singular perturbation theory is used to derive an approximate periodic solution for a single spiking unit. This is then used to predict the relationship between the number of clusters arising spontaneously in the network as it relates to the adaptation time constant. We compare this to a complementary analysis that employs a weak coupling assumption to predict the first Fourier mode to destabilize from the incoherent state of an associated phase model as the external noise is reduced. Both approaches predict the same scaling of cluster number with respect to the adaptation time constant, which is corroborated in numerical simulations of the full system. Thus, we develop several testable predictions regarding the formation and characteristics of gamma rhythms with sparsely firing excitatory neurons. PMID:22125486

  5. 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.

  6. 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

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. 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

  12. 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.

  13. 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

  14. Euchromatin histone methyltransferase 1 regulates cortical neuronal network development

    PubMed Central

    Bart Martens, Marijn; Frega, Monica; Classen, Jessica; Epping, Lisa; Bijvank, Elske; Benevento, Marco; van Bokhoven, Hans; Tiesinga, Paul; Schubert, Dirk; Nadif Kasri, Nael

    2016-01-01

    Heterozygous mutations or deletions in the human Euchromatin histone methyltransferase 1 (EHMT1) gene cause Kleefstra syndrome, a neurodevelopmental disorder that is characterized by autistic-like features and severe intellectual disability (ID). Neurodevelopmental disorders including ID and autism may be related to deficits in activity-dependent wiring of brain circuits during development. Although Kleefstra syndrome has been associated with dendritic and synaptic defects in mice and Drosophila, little is known about the role of EHMT1 in the development of cortical neuronal networks. Here we used micro-electrode arrays and whole-cell patch-clamp recordings to investigate the impact of EHMT1 deficiency at the network and single cell level. We show that EHMT1 deficiency impaired neural network activity during the transition from uncorrelated background action potential firing to synchronized network bursting. Spontaneous bursting and excitatory synaptic currents were transiently reduced, whereas miniature excitatory postsynaptic currents were not affected. Finally, we show that loss of function of EHMT1 ultimately resulted in less regular network bursting patterns later in development. These data suggest that the developmental impairments observed in EHMT1-deficient networks may result in a temporal misalignment between activity-dependent developmental processes thereby contributing to the pathophysiology of Kleefstra syndrome. PMID:27767173

  15. Towards neuronal organoids: a method for long-term culturing of high-density hippocampal neurons.

    PubMed

    Todd, George K; Boosalis, Casey A; Burzycki, Aaron A; Steinman, Michael Q; Hester, Lynda D; Shuster, Pete W; Patterson, Randen L

    2013-01-01

    One of the goals in neuroscience is to obtain tractable laboratory cultures that closely recapitulate in vivo systems while still providing ease of use in the lab. Because neurons can exist in the body over a lifetime, long-term culture systems are necessary so as to closely mimic the physiological conditions under laboratory culture conditions. Ideally, such a neuronal organoid culture would contain multiple cell types, be highly differentiated, and have a high density of interconnected cells. However, before these types of cultures can be created, certain problems associated with long-term neuronal culturing must be addressed. We sought to develop a new protocol which may further prolong the duration and integrity of E18 rat hippocampal cultures. We have developed a protocol that allows for culturing of E18 hippocampal neurons at high densities for more than 120 days. These cultured hippocampal neurons are (i) well differentiated with high numbers of synapses, (ii) anchored securely to their substrate, (iii) have high levels of functional connectivity, and (iv) form dense multi-layered cellular networks. We propose that our culture methodology is likely to be effective for multiple neuronal subtypes-particularly those that can be grown in Neurobasal/B27 media. This methodology presents new avenues for long-term functional studies in neurons.

  16. 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

  17. 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.

  18. 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

  19. Synchronization of the small-world neuronal network with unreliable synapses

    NASA Astrophysics Data System (ADS)

    Li, Chunguang; Zheng, Qunxian

    2010-09-01

    As is well known, synchronization phenomena are ubiquitous in neuronal systems. Recently a lot of work concerning the synchronization of the neuronal network has been accomplished. In these works, the synapses are usually considered reliable, but experimental results show that, in biological neuronal networks, synapses are usually unreliable. In our previous work, we have studied the synchronization of the neuronal network with unreliable synapses; however, we have not paid attention to the effect of topology on the synchronization of the neuronal network. Several recent studies have found that biological neuronal networks have typical properties of small-world networks, characterized by a short path length and high clustering coefficient. In this work, mainly based on the small-world neuronal network (SWNN) with inhibitory neurons, we study the effect of network topology on the synchronization of the neuronal network with unreliable synapses. Together with the network topology, the effects of the GABAergic reversal potential, time delay and noise are also considered. Interestingly, we found a counter-intuitive phenomenon for the SWNN with specific shortcut adding probability, that is, the less reliable the synapses, the better the synchronization performance of the SWNN. We also consider the effects of both local noise and global noise in this work. It is shown that these two different types of noise have distinct effects on the synchronization: one is negative and the other is positive.

  20. Pharmacological characterization of cultivated neuronal networks: relevance to synaptogenesis and synaptic connectivity.

    PubMed

    Verstraelen, Peter; Pintelon, Isabel; Nuydens, Rony; Cornelissen, Frans; Meert, Theo; Timmermans, Jean-Pierre

    2014-07-01

    Mental disorders, such as schizophrenia or Alzheimer's disease, are associated with impaired synaptogenesis and/or synaptic communication. During development, neurons assemble into neuronal networks, the primary supracellular mediators of information processing. In addition to the orchestrated activation of genetic programs, spontaneous electrical activity and associated calcium signaling have been shown to be critically involved in the maturation of such neuronal networks. We established an in vitro model that recapitulates the maturation of neuronal networks, including spontaneous electrical activity. Upon plating, mouse primary hippocampal neurons grow neurites and interconnect via synapses to form a dish-wide neuronal network. Via live cell calcium imaging, we identified a limited period of time in which the spontaneous activity synchronizes across neurons, indicative of the formation of a functional network. After establishment of network activity, the neurons grow dendritic spines, the density of which was used as a morphological readout for neuronal maturity and connectivity. Hence, quantification of neurite outgrowth, synapse density, spontaneous neuronal activity, and dendritic spine density allowed to study neuronal network maturation from the day of plating until the presence of mature neuronal networks. Via acute pharmacological intervention, we show that synchronized network activity is mediated by the NMDA-R. The balance between kynurenic and quinolinic acid, both neuro-active intermediates in the tryptophan/kynurenine pathway, was shown to be decisive for the maintenance of network activity. Chronic modulation of the neurotrophic support influenced the network formation and revealed the extreme sensitivity of calcium imaging to detect subtle alterations in neuronal physiology. Given the reproducible cultivation in a 96-well setup in combination with fully automated analysis of the calcium recordings, this approach can be used to build a high

  1. 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

  2. Constrained Synaptic Connectivity in Functional Mammalian Neuronal Networks Grown on Patterned Surfaces

    NASA Astrophysics Data System (ADS)

    Bourdieu, Laurent; Wyart, Claire; Ybert, Christophe; Herr, Catherine; Chatenay, Didier

    2002-03-01

    The use of ordered neuronal networks in vitro is a promising approach to study the development and the activity of neuronal assemblies. However in previous attempts, sufficient growth control and physiological maturation of neurons could not be achieved. We describe an original protocol in which polylysine patterns confine the adhesion of cellular bodies to prescribed spots and the neuritic growth to thin lines. Hippocampal neurons are maintained healthy in serum free medium up to five weeks in vitro. Electrophysiology and immunochemistry show that neurons exhibit mature excitatory and inhibitory synapses and calcium imaging reveals spontaneous bursting activity of neurons in isolated networks. Neurons in these geometrical networks form functional synapses preferentially to their first neighbors. We have therefore established a simple and robust protocol to constrain both the location of neuronal cell bodies and their pattern of connectivity.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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

  8. Granger causality-based synaptic weights estimation for analyzing neuronal networks.

    PubMed

    Shao, Pei-Chiang; Huang, Jian-Jia; Shann, Wei-Chang; Yen, Chen-Tung; Tsai, Meng-Li; Yen, Chien-Chang

    2015-06-01

    Granger causality (GC) analysis has emerged as a powerful analytical method for estimating the causal relationship among various types of neural activity data. However, two problems remain not very clear and further researches are needed: (1) The GC measure is designed to be nonnegative in its original form, lacking of the trait for differentiating the effects of excitations and inhibitions between neurons. (2) How is the estimated causality related to the underlying synaptic weights? Based on the GC, we propose a computational algorithm under a best linear predictor assumption for analyzing neuronal networks by estimating the synaptic weights among them. Under this assumption, the GC analysis can be extended to measure both excitatory and inhibitory effects between neurons. The method was examined by three sorts of simulated networks: those with linear, almost linear, and nonlinear network structures. The method was also illustrated to analyze real spike train data from the anterior cingulate cortex (ACC) and the striatum (STR). The results showed, under the quinpirole administration, the significant existence of excitatory effects inside the ACC, excitatory effects from the ACC to the STR, and inhibitory effects inside the STR.

  9. 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

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

    PubMed Central

    Hutt, Axel; Mierau, Andreas; Lefebvre, Jérémie

    2016-01-01

    Oscillatory brain activity is believed to play a central role in neural coding. Accumulating evidence shows that features of these oscillations are highly dynamic: power, frequency and phase fluctuate alongside changes in behavior and task demands. The role and mechanism supporting this variability is however poorly understood. We here analyze a network of recurrently connected spiking neurons with time delay displaying stable synchronous dynamics. Using mean-field and stability analyses, we investigate the influence of dynamic inputs on the frequency of firing rate oscillations. We show that afferent noise, mimicking inputs to the neurons, causes smoothing of the system’s response function, displacing equilibria and altering the stability of oscillatory states. Our analysis further shows that these noise-induced changes cause a shift of the peak frequency of synchronous oscillations that scales with input intensity, leading the network towards critical states. We lastly discuss the extension of these principles to periodic stimulation, in which externally applied driving signals can trigger analogous phenomena. Our results reveal one possible mechanism involved in shaping oscillatory activity in the brain and associated control principles. PMID:27669018

  11. Assessment of strawberry aroma through solid-phase microextraction-gas chromatography and artificial neuron network methods. Variety classification versus growing years.

    PubMed

    de Boishebert, Virginie; Urruty, Louise; Giraudel, Jean-Luc; Montury, Michel

    2004-05-01

    In a previous work, the SPME-GC-MS method (chemical analysis) coupled with KSOM-ANN treatment of the results (statistical algorithm) has proved to be efficient to classify 70 strawberry samples harvested in the same year, through the 17 varieties to which they belonged, in a two-dimensional map. As an extension, the present study confirms that these results were not dependent on the year of strawberry production and discusses what effects were observed between results obtained in different years. Samples of different strawberry varieties were harvested during the three campaigns of 2000, 2001, and 2002 and analyzed independently. The chemical data matrix obtained in each case allowed the verification of the proposal that the same discriminative effect could be obtained independently of the year of production by using maps of different sizes. Therefore, 30 measures obtained from samples of 9 varieties in 2000, 54 measures from 13 varieties in 2001, and 80 measures from 20 varieties in 2002 were correctly classified by using 20, 35, and 56 hexagon maps, respectively. In a second analysis based on the 2002 production, the chemical differences between variety aromatic features were noted through the increasing size of the map used. Finally, results relative to 7 varieties cultivated in 2001 and 2002 and stored under exactly the same conditions were computed together for elaborating a single map. An interesting effect of double classification according to the year and the varieties was observed.

  12. Assessment of strawberry aroma through solid-phase microextraction-gas chromatography and artificial neuron network methods. Variety classification versus growing years.

    PubMed

    de Boishebert, Virginie; Urruty, Louise; Giraudel, Jean-Luc; Montury, Michel

    2004-05-01

    In a previous work, the SPME-GC-MS method (chemical analysis) coupled with KSOM-ANN treatment of the results (statistical algorithm) has proved to be efficient to classify 70 strawberry samples harvested in the same year, through the 17 varieties to which they belonged, in a two-dimensional map. As an extension, the present study confirms that these results were not dependent on the year of strawberry production and discusses what effects were observed between results obtained in different years. Samples of different strawberry varieties were harvested during the three campaigns of 2000, 2001, and 2002 and analyzed independently. The chemical data matrix obtained in each case allowed the verification of the proposal that the same discriminative effect could be obtained independently of the year of production by using maps of different sizes. Therefore, 30 measures obtained from samples of 9 varieties in 2000, 54 measures from 13 varieties in 2001, and 80 measures from 20 varieties in 2002 were correctly classified by using 20, 35, and 56 hexagon maps, respectively. In a second analysis based on the 2002 production, the chemical differences between variety aromatic features were noted through the increasing size of the map used. Finally, results relative to 7 varieties cultivated in 2001 and 2002 and stored under exactly the same conditions were computed together for elaborating a single map. An interesting effect of double classification according to the year and the varieties was observed. PMID:15113143

  13. Delay-induced bifurcation in a tri-neuron fractional neural network

    NASA Astrophysics Data System (ADS)

    Huang, Chengdai; Cao, Jinde; Ma, Zhongjun

    2016-11-01

    This paper investigates the issue of stability and bifurcation for a delayed fractional neural network with three neurons by applying the sum of time delays as the bifurcation parameter. Based on fractional Laplace transform and the method of stability switches, some explicit conditions for describing the stability interval and emergence of Hopf bifurcation are derived. The analysis indicates that time delay can effectively enhance the stability of fractional neural networks. In addition, it is found that the stability interval can be varied by regulating the fractional order if all the parameters are fixed including time delay. Finally, numerical examples are presented to validate the derived theoretical results.

  14. 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

  15. 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.

  16. 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

  17. 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.

  18. 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

  19. 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.

  20. 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…

  1. 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.

  2. 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

  3. On a Kinetic Fitzhugh-Nagumo Model of Neuronal Network

    NASA Astrophysics Data System (ADS)

    Mischler, S.; Quiñinao, C.; Touboul, J.

    2016-03-01

    We investigate existence and uniqueness of solutions of a McKean-Vlasov evolution PDE representing the macroscopic behaviour of interacting Fitzhugh-Nagumo neurons. This equation is hypoelliptic, nonlocal and has unbounded coefficients. We prove existence of a solution to the evolution equation and non trivial stationary solutions. Moreover, we demonstrate uniqueness of the stationary solution in the weakly nonlinear regime. Eventually, using a semigroup factorisation method, we show exponential nonlinear stability in the small connectivity regime.

  4. Dynamical properties of neural network model for working memory with Hodgkin-Huxley neurons

    NASA Astrophysics Data System (ADS)

    Omori, Toshiaki; Horiguchi, Tsuyoshi

    2004-03-01

    We propose a neural network model of working memory with one-compartmental neurons and investigate its dynamical properties. We assume that the model consists of excitatory neurons and inhibitory neurons; all the neurons are connected to each other. The excitatory neurons are distinguished as several groups of selective neurons and one group of non-selective neurons. The selective neurons are assumed to form subpopulations in which each selective neuron belongs to only one of subpopulations. The non-selective neurons are assumed not to form any subpopulation. Synaptic strengths between neurons within a subpopulation are assumed to be potentiated. By the numerical simulations, persistent firing of neurons in a subpopulation emerges; the persistent firing corresponds to the retention of memory as one of the functions of working memory. We find that the strength of external input and the strength of N-methyl- D-aspartate synapse are important factors for dynamical behaviors of the network; for example, if we enhance the strength of the external input to a subpopulation while the persistent firing is occurring in other subpopulation, the persistent firing occurs in the subpopulation or is sustained against the external input. These results reveal that the neural network as for the function of the working memory is controlled by the neuromodulation and the external stimuli within the proposed model. We also find that the persistent time of firing of the selective neurons shows a kind of phase transition as a function of the degree of potentiation of synapses.

  5. C-Mantec: a novel constructive neural network algorithm incorporating competition between neurons.

    PubMed

    Subirats, José L; Franco, Leonardo; Jerez, José M

    2012-02-01

    C-Mantec is a novel neural network constructive algorithm that combines competition between neurons with a stable modified perceptron learning rule. The neuron learning is governed by the thermal perceptron rule that ensures stability of the acquired knowledge while the architecture grows and while the neurons compete for new incoming information. Competition makes it possible that even after new units have been added to the network, existing neurons still can learn if the incoming information is similar to their stored knowledge, and this constitutes a major difference with existing constructing algorithms. The new algorithm is tested on two different sets of benchmark problems: a Boolean function set used in logic circuit design and a well studied set of real world problems. Both sets were used to analyze the size of the constructed architectures and the generalization ability obtained and to compare the results with those from other standard and well known classification algorithms. The problem of overfitting is also analyzed, and a new built-in method to avoid its effects is devised and successfully applied within an active learning paradigm that filter noisy examples. The results show that the new algorithm generates very compact neural architectures with state-of-the-art generalization capabilities.

  6. 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.

  7. 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.

  8. 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…

  9. Relationship between inter-stimulus-intervals and intervals of autonomous activities in a neuronal network.

    PubMed

    Ito, Hidekatsu; Minoshima, Wataru; Kudoh, Suguru N

    2015-08-01

    To investigate relationships between neuronal network activity and electrical stimulus, we analyzed autonomous activity before and after electrical stimulus. Recordings of autonomous activity were performed using dissociated culture of rat hippocampal neurons on a multi-electrodes array (MEA) dish. Single stimulus and pared stimuli were applied to a cultured neuronal network. Single stimulus was applied every 1 min, and paired stimuli was performed by two sequential stimuli every 1 min. As a result, the patterns of synchronized activities of a neuronal network were changed after stimulus. Especially, long range synchronous activities were induced by paired stimuli. When 1 s inter-stimulus-intervals (ISI) and 1.5 s ISI paired stimuli are applied to a neuronal network, relatively long range synchronous activities expressed in case of 1.5 s ISI. Temporal synchronous activity of neuronal network is changed according to inter-stimulus-intervals (ISI) of electrical stimulus. In other words, dissociated neuronal network can maintain given information in temporal pattern and a certain type of an information maintenance mechanism was considered to be implemented in a semi-artificial dissociated neuronal network. The result is useful toward manipulation technology of neuronal activity in a brain system.

  10. 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.

  11. 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.

  12. 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.

  13. The neuron network forecasting dynamics of the ozone layer

    NASA Astrophysics Data System (ADS)

    Lankin, J.; Sakash, I.

    One of the major tasks today is preserving the Earth's ozonosphere, - because the ozone layer serves as a screen, filtering a part of harmful solar ultraviolet radiation. During the recent decades, some scientists of different countries have paid their attention to significant decrease of ozone density in the atmosphere above certain points of the Earth's surface. If the average ozone concentration in the atmosphere is about 300-350 D. u., its annual drop above the Antarctic continent follows to the values of concentration even of 90 D. u. Such -- and similar -- publications have triggered widespread public interest (tied to the global environmental crisis); the phenomenon has been named "an ozone hole" and followed by an explosion of scientific interest to ozonospheric studies. In order to simulate hourly TOC oscillations, the ground observation data (City of Tomsk, Russia - 56 N and 84 E) has been used. The following models of ÒÎÑ have been designed: hourly (hourly CCO values are been forecasted for March 30 and 31, 1996-99), diurnal (June 14 -- October 31, 1998), monthly (September 1975 -- August 1977) and annual (1975-1982) ones. The study performed has proven a high degree of effectiveness of using neuron networks for simulating the dynamics of the Earth's ozonosphere. A real opportunity to design local prognostic models using neuronets is revealed. In future, some three-dimentional global models of ozonosphere and atmosphere may be established. However, there a number of cases of significant variations of the ozone concentration time rows for different seasons and time spans. Studying and predicting such changes is of the great interest in terms of better understanding the specifics of developing the atmospheric processes and improving the technology of the forecast. The study is focused upon revealing the precursors of the spontaneous "ozone hole" (areas of the lowered concentration of ozone) formation in the stratosphere and based on neuron network

  14. 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.

  15. 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.

  16. Voltage-sensitive dye recording from networks of cultured neurons

    NASA Astrophysics Data System (ADS)

    Chien, Chi-Bin

    This thesis describes the development and testing of a sensitive apparatus for recording electrical activity from microcultures of rat superior cervical ganglion (SCG) neurons by using voltage-sensitive fluorescent dyes.The apparatus comprises a feedback-regulated mercury arc light source, an inverted epifluorescence microscope, a novel fiber-optic camera with discrete photodiode detectors, and low-noise preamplifiers. Using an NA 0.75 objective and illuminating at 10 W/cm2 with the 546 nm mercury line, a typical SCG neuron stained with the styryl dye RH423 gives a detected photocurrent of 1 nA; the light source and optical detectors are quiet enough that the shot noise in this photocurrent--about.03% rms--dominates. The design, theory, and performance of this dye-recording apparatus are discussed in detail.Styryl dyes such as RH423 typically give signals of 1%/100 mV on these cells; the signals are linear in membrane potential, but do not appear to arise from a purely electrochromic mechanism. Given this voltage sensitivity and the noise level of the apparatus, it should be possible to detect both action potentials and subthreshold synaptic potentials from SCG cell bodies. In practice, dye recording can easily detect action potentials from every neuron in an SCG microculture, but small synaptic potentials are obscured by dye signals from the dense network of axons.In another microculture system that does not have such long and complex axons, this dye-recording apparatus should be able to detect synaptic potentials, making it possible to noninvasively map the synaptic connections in a microculture, and thus to study long-term synaptic plasticity.

  17. Delay-induced multiple stochastic resonances on scale-free neuronal networks.

    PubMed

    Wang, Qingyun; Perc, Matjaz; Duan, Zhisheng; Chen, Guanrong

    2009-06-01

    We study the effects of periodic subthreshold pacemaker activity and time-delayed coupling on stochastic resonance over scale-free neuronal networks. As the two extreme options, we introduce the pacemaker, respectively, to the neuron with the highest degree and to one of the neurons with the lowest degree within the network, but we also consider the case when all neurons are exposed to the periodic forcing. In the absence of delay, we show that an intermediate intensity of noise is able to optimally assist the pacemaker in imposing its rhythm on the whole ensemble, irrespective to its placing, thus providing evidences for stochastic resonance on the scale-free neuronal networks. Interestingly thereby, if the forcing in form of a periodic pulse train is introduced to all neurons forming the network, the stochastic resonance decreases as compared to the case when only a single neuron is paced. Moreover, we show that finite delays in coupling can significantly affect the stochastic resonance on scale-free neuronal networks. In particular, appropriately tuned delays can induce multiple stochastic resonances independently of the placing of the pacemaker, but they can also altogether destroy stochastic resonance. Delay-induced multiple stochastic resonances manifest as well-expressed maxima of the correlation measure, appearing at every multiple of the pacemaker period. We argue that fine-tuned delays and locally active pacemakers are vital for assuring optimal conditions for stochastic resonance on complex neuronal networks.

  18. 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.

  19. Optimal Balance of the Striatal Medium Spiny Neuron Network

    PubMed Central

    Ponzi, Adam; Wickens, Jeffery R.

    2013-01-01

    Slowly varying activity in the striatum, the main Basal Ganglia input structure, is important for the learning and execution of movement sequences. Striatal medium spiny neurons (MSNs) form cell assemblies whose population firing rates vary coherently on slow behaviourally relevant timescales. It has been shown that such activity emerges in a model of a local MSN network but only at realistic connectivities of and only when MSN generated inhibitory post-synaptic potentials (IPSPs) are realistically sized. Here we suggest a reason for this. We investigate how MSN network generated population activity interacts with temporally varying cortical driving activity, as would occur in a behavioural task. We find that at unrealistically high connectivity a stable winners-take-all type regime is found where network activity separates into fixed stimulus dependent regularly firing and quiescent components. In this regime only a small number of population firing rate components interact with cortical stimulus variations. Around connectivity a transition to a more dynamically active regime occurs where all cells constantly switch between activity and quiescence. In this low connectivity regime, MSN population components wander randomly and here too are independent of variations in cortical driving. Only in the transition regime do weak changes in cortical driving interact with many population components so that sequential cell assemblies are reproducibly activated for many hundreds of milliseconds after stimulus onset and peri-stimulus time histograms display strong stimulus and temporal specificity. We show that, remarkably, this activity is maximized at striatally realistic connectivities and IPSP sizes. Thus, we suggest the local MSN network has optimal characteristics – it is neither too stable to respond in a dynamically complex temporally extended way to cortical variations, nor is it too unstable to respond in a consistent repeatable way. Rather, it is optimized to

  20. Coarse-grained event tree analysis for quantifying Hodgkin-Huxley neuronal network dynamics.

    PubMed

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

    2012-02-01

    We present an event tree analysis of studying the dynamics of the Hodgkin-Huxley (HH) neuronal networks. Our study relies on a coarse-grained projection to event trees and to the event chains that comprise these trees by using a statistical collection of spatial-temporal sequences of relevant physiological observables (such as sequences of spiking multiple neurons). This projection can retain information about network dynamics that covers multiple features, swiftly and robustly. We demonstrate that for even small differences in inputs, some dynamical regimes of HH networks contain sufficiently higher order statistics as reflected in event chains within the event tree analysis. Therefore, this analysis is effective in discriminating small differences in inputs. Moreover, we use event trees to analyze the results computed from an efficient library-based numerical method proposed in our previous work, where a pre-computed high resolution data library of typical neuronal trajectories during the interval of an action potential (spike) allows us to avoid resolving the spikes in detail. In this way, we can evolve the HH networks using time steps one order of magnitude larger than the typical time steps used for resolving the trajectories without the library, while achieving comparable statistical accuracy in terms of average firing rate and power spectra of voltage traces. Our numerical simulation results show that the library method is efficient in the sense that the results generated by using this numerical method with much larger time steps contain sufficiently high order statistical structure of firing events that are similar to the ones obtained using a regular HH solver. We use our event tree analysis to demonstrate these statistical similarities.

  1. Multilaminar networks of cortical neurons integrate common inputs from sensory thalamus.

    PubMed

    Morgenstern, Nicolás A; Bourg, Jacques; Petreanu, Leopoldo

    2016-08-01

    Neurons in the thalamorecipient layers of sensory cortices integrate thalamic and recurrent cortical input. Cortical neurons form fine-scale, functionally cotuned networks, but whether interconnected cortical neurons within a column process common thalamocortical inputs is unknown. We tested how local and thalamocortical connectivity relate to each other by analyzing cofluctuations of evoked responses in cortical neurons after photostimulation of thalamocortical axons. We found that connected pairs of pyramidal neurons in layer (L) 4 of mouse visual cortex share more inputs from the dorsal lateral geniculate nucleus than nonconnected pairs. Vertically aligned connected pairs of L4 and L2/3 neurons were also preferentially contacted by the same thalamocortical axons. Our results provide a circuit mechanism for the observed amplification of sensory responses by L4 circuits. They also show that sensory information is concurrently processed in L4 and L2/3 by columnar networks of interconnected neurons contacted by the same thalamocortical axons.

  2. Temporal coding in a silicon network of integrate-and-fire neurons.

    PubMed

    Liu, Shih-Chii; Douglas, Rodney

    2004-09-01

    Spatio-temporal processing of spike trains by neuronal networks depends on a variety of mechanisms distributed across synapses, dendrites, and somata. In natural systems, the spike trains and the processing mechanisms cohere though their common physical instantiation. This coherence is lost when the natural system is encoded for simulation on a general purpose computer. By contrast, analog VLSI circuits are, like neurons, inherently related by their real-time physics, and so, could provide a useful substrate for exploring neuronlike event-based processing. Here, we describe a hybrid analog-digital VLSI chip comprising a set of integrate-and-fire neurons and short-term dynamical synapses that can be configured into simple network architectures with some properties of neocortical neuronal circuits. We show that, despite considerable fabrication variance in the properties of individual neurons, the chip offers a viable substrate for exploring real-time spike-based processing in networks of neurons.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. Macroscopic self-oscillations and aging transition in a network of synaptically coupled quadratic integrate-and-fire neurons

    NASA Astrophysics Data System (ADS)

    Ratas, Irmantas; Pyragas, Kestutis

    2016-09-01

    We analyze the dynamics of a large network of coupled quadratic integrate-and-fire neurons, which represent the canonical model for class I neurons near the spiking threshold. The network is heterogeneous in that it includes both inherently spiking and excitable neurons. The coupling is global via synapses that take into account the finite width of synaptic pulses. Using a recently developed reduction method based on the Lorentzian ansatz, we derive a closed system of equations for the neuron's firing rate and the mean membrane potential, which are exact in the infinite-size limit. The bifurcation analysis of the reduced equations reveals a rich scenario of asymptotic behavior, the most interesting of which is the macroscopic limit-cycle oscillations. It is shown that the finite width of synaptic pulses is a necessary condition for the existence of such oscillations. The robustness of the oscillations against aging damage, which transforms spiking neurons into nonspiking neurons, is analyzed. The validity of the reduced equations is confirmed by comparing their solutions with the solutions of microscopic equations for the finite-size networks.

  8. Development of an artificial neuronal network with post-mitotic rat fetal hippocampal cells by polyethylenimine.

    PubMed

    Liu, Bingfang; Ma, Jun; Gao, Erjing; He, Yu; Cui, Fuzhai; Xu, Qunyuan

    2008-03-14

    The selection of appropriate surface materials that promote cellular adhesion and growth is an important consideration when designing a simplified neuronal network in vitro. In the past, extracellular matrix proteins such as laminin (LN) or positively charged substances such as poly-l-lysine (PLL) have been used. In this study, we examined the ability of another positively charged polymer, polyethyleneimine (PEI), to promote neuronal adhesion, growth and the formation of a functional neuronal network in vitro. PEI, PLL and LN were used to produce grid-shape patterns on glass coverslips by micro-contact printing. Post-mitotic neurons from the rat fetal hippocampus were cultured on the different polymers and the viability and morphology of these neurons under serum-free culture conditions were observed using fluorescent microscopy and atomic force microscopy (AFM). We show that neurons cultured on the PEI- and PLL-coated surfaces adhered to and extended neurites along the grid-shape patterns, whereas neurons cultured on the LN-coated coverslips clustered into clumps of cells. In addition, we found that the neurons on the PEI and PLL-coated grids survived for more than 2 weeks in serum-free conditions, whereas most neurons cultured on the LN-coated grids died after 1 week. Using AFM, we observed some neurosynapse-like structures near the neuronal soma on PEI-coated coverslips. These findings indicate that PEI is a suitable surface for establishing a functional neuronal network in vitro.

  9. Long-term optical stimulation of channelrhodopsin-expressing neurons to study network plasticity

    PubMed Central

    Lignani, Gabriele; Ferrea, Enrico; Difato, Francesco; Amarù, Jessica; Ferroni, Eleonora; Lugarà, Eleonora; Espinoza, Stefano; Gainetdinov, Raul R.; Baldelli, Pietro; Benfenati, Fabio

    2013-01-01

    Neuronal plasticity produces changes in excitability, synaptic transmission, and network architecture in response to external stimuli. Network adaptation to environmental conditions takes place in time scales ranging from few seconds to days, and modulates the entire network dynamics. To study the network response to defined long-term experimental protocols, we setup a system that combines optical and electrophysiological tools embedded in a cell incubator. Primary hippocampal neurons transduced with lentiviruses expressing channelrhodopsin-2/H134R were subjected to various photostimulation protocols in a time window in the order of days. To monitor the effects of light-induced gating of network activity, stimulated transduced neurons were simultaneously recorded using multi-electrode arrays (MEAs). The developed experimental model allows discerning short-term, long-lasting, and adaptive plasticity responses of the same neuronal network to distinct stimulation frequencies applied over different temporal windows. PMID:23970852

  10. 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.

  11. Constrained synaptic connectivity in functional mammalian neuronal networks grown on patterned surfaces.

    PubMed

    Wyart, Claire; Ybert, Christophe; Bourdieu, Laurent; Herr, Catherine; Prinz, Christelle; Chatenay, Didier

    2002-06-30

    The use of ordered neuronal networks in vitro is a promising approach to study the development and the activity of small neuronal assemblies. However, in previous attempts, sufficient growth control and physiological maturation of neurons could not be achieved. Here we describe an original protocol in which polylysine patterns confine the adhesion of cellular bodies to prescribed spots and the neuritic growth to thin lines. Hippocampal neurons in these networks are maintained healthy in serum free medium up to 5 weeks in vitro. Electrophysiology and immunochemistry show that neurons exhibit mature excitatory and inhibitory synapses and calcium imaging reveals spontaneous activity of neurons in isolated networks. We demonstrate that neurons in these geometrical networks form functional synapses preferentially to their first neighbors. We have, therefore, established a simple and robust protocol to constrain both the location of neuronal cell bodies and their pattern of connectivity. Moreover, the long term maintenance of the geometry and the physiology of the networks raises the possibility of new applications for systematic screening of pharmacological agents and for electronic to neuron devices.

  12. 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

  13. Dynamic range in small-world networks of Hodgkin-Huxley neurons with chemical synapses

    NASA Astrophysics Data System (ADS)

    Batista, C. A. S.; Viana, R. L.; Lopes, S. R.; Batista, A. M.

    2014-09-01

    According to Stevens' law the relationship between stimulus and response is a power-law within an interval called the dynamic range. The dynamic range of sensory organs is found to be larger than that of a single neuron, suggesting that the network structure plays a key role in the behavior of both the scaling exponent and the dynamic range of neuron assemblies. In order to verify computationally the relationships between stimulus and response for spiking neurons, we investigate small-world networks of neurons described by the Hodgkin-Huxley equations connected by chemical synapses. We found that the dynamic range increases with the network size, suggesting that the enhancement of the dynamic range observed in sensory organs, with respect to single neurons, is an emergent property of complex network dynamics.

  14. 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

  15. 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

  16. 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.

  17. Targeting Neuronal Networks with Combined Drug and Stimulation Paradigms Guided by Neuroimaging to Treat Brain Disorders.

    PubMed

    Faingold, Carl L; Blumenfeld, Hal

    2015-10-01

    Improved therapy of brain disorders can be achieved by focusing on neuronal networks, utilizing combined pharmacological and stimulation paradigms guided by neuroimaging. Neuronal networks that mediate normal brain functions, such as hearing, interact with other networks, which is important but commonly neglected. Network interaction changes often underlie brain disorders, including epilepsy. "Conditional multireceptive" (CMR) brain areas (e.g., brainstem reticular formation and amygdala) are critical in mediating neuroplastic changes that facilitate network interactions. CMR neurons receive multiple inputs but exhibit extensive response variability due to milieu and behavioral state changes and are exquisitely sensitive to agents that increase or inhibit GABA-mediated inhibition. Enhanced CMR neuronal responsiveness leads to expression of emergent properties--nonlinear events--resulting from network self-organization. Determining brain disorder mechanisms requires animals that model behaviors and neuroanatomical substrates of human disorders identified by neuroimaging. However, not all sites activated during network operation are requisite for that operation. Other active sites are ancillary, because their blockade does not alter network function. Requisite network sites exhibit emergent properties that are critical targets for pharmacological and stimulation therapies. Improved treatment of brain disorders should involve combined pharmacological and stimulation therapies, guided by neuroimaging, to correct network malfunctions by targeting specific network neurons.

  18. 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

  19. An algorithmic method for reducing conductance-based neuron models.

    PubMed

    Sorensen, Michael E; DeWeerth, Stephen P

    2006-08-01

    Although conductance-based neural models provide a realistic depiction of neuronal activity, their complexity often limits effective implementation and analysis. Neuronal model reduction methods provide a means to reduce model complexity while retaining the original model's realism and relevance. Such methods, however, typically include ad hoc components that require that the modeler already be intimately familiar with the dynamics of the original model. We present an automated, algorithmic method for reducing conductance-based neuron models using the method of equivalent potentials (Kelper et al., Biol Cybern 66(5):381-387, 1992) Our results demonstrate that this algorithm is able to reduce the complexity of the original model with minimal performance loss, and requires minimal prior knowledge of the model's dynamics. Furthermore, by utilizing a cost function based on the contribution of each state variable to the total conductance of the model, the performance of the algorithm can be significantly improved.

  20. 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

  1. 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.

  2. 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

  3. 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

  4. Self-organization of repetitive spike patterns in developing neuronal networks in vitro.

    PubMed

    Sun, Jyh-Jang; Kilb, Werner; Luhmann, Heiko J

    2010-10-01

    The appearance of spontaneous correlated activity is a fundamental feature of developing neuronal networks in vivo and in vitro. To elucidate whether the ontogeny of correlated activity is paralleled by the appearance of specific spike patterns we used a template-matching algorithm to detect repetitive spike patterns in multi-electrode array recordings from cultures of dissociated mouse neocortical neurons between 6 and 15 days in vitro (div). These experiments demonstrated that the number of spiking neurons increased significantly between 6 and 15 div, while a significantly synchronized network activity appeared at 9 div and became the main discharge pattern in the subsequent div. Repetitive spike patterns with a low complexity were first observed at 8 div. The number of repetitive spike patterns in each dataset as well as their complexity and recurrence increased during development in vitro. The number of links between neurons implicated in repetitive spike patterns, as well as their strength, showed a gradual increase during development. About 8% of the spike sequences contributed to more than one repetitive spike patterns and were classified as core patterns. These results demonstrate for the first time that defined neuronal assemblies, as represented by repetitive spike patterns, appear quite early during development in vitro, around the time synchronized network burst become the dominant network pattern. In summary, these findings suggest that dissociated neurons can self-organize into complex neuronal networks that allow reliable flow and processing of neuronal information already during early phases of development.

  5. 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.

  6. 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

  7. 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.

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

    PubMed

    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. PMID:27176338

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

    PubMed

    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.

  10. 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

  11. 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

  12. Sulfated glycans in network rewiring and plasticity after neuronal injuries.

    PubMed

    Kadomatsu, Kenji; Sakamoto, Kazuma

    2014-01-01

    Biopolymers in the human body belong to three major classes: polynucleotides (DNA, RNA), polypeptides (proteins) and polysaccharides (glycans). Although striking progress in our understanding of neurobiology has been achieved through a focus on polypeptides as the main players, important biological functions are also expected to be attributable to glycans. Nonetheless, the significance of glycans remains largely unexplored. In this review, we focus on the roles of sulfated glycans. Axonal regeneration/sprouting after injuries does not easily occur in the adult mammalian central nervous system. This is due to the low intrinsic potential of regeneration and the emerging inhibitory molecules. The latter include the sulfated long glycans chondroitin sulfate (CS) and keratan sulfate (KS). Enzymatic ablation of CS or KS, and genetic ablation of KS promote functional recovery after spinal cord injury. Interestingly, the combination of CS and KS ablations exhibits neither additive nor synergistic effects. Thus, KS and CS work in the same pathway in inhibition of axonal regeneration/sprouting. Furthermore, CS has been implicated in neural plasticity as a functional component of the perineuronal nets surrounding inhibitory interneurons. Elucidation of the mechanisms of action for KS and CS will pave the way to treatments to promote network rewiring and plasticity after neuronal injuries.

  13. 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

  14. Transient epileptiform signaling during neuronal network development: regulation by external stimulation and bimodal GABAergic activity.

    PubMed

    Zemianek, Jill M; Shultz, Abraham M; Lee, Sangmook; Guaraldi, Mary; Yanco, Holly A; Shea, Thomas B

    2013-04-01

    A predominance of excitatory activity, with protracted appearance of inhibitory activity, accompanies cortical neuronal development. It is unclear whether or not inhibitory neuronal activity is solicited exclusively by excitatory neurons or whether the transient excitatory activity displayed by developing GABAergic neurons contributes to an excitatory threshold that fosters their conversion to inhibitory activity. We addressed this possibility by culturing murine embryonic neurons on multi-electrode arrays. A wave of individual 0.2-0.4 mV signals ("spikes") appeared between approx. 20-30 days in culture, then declined. A transient wave of high amplitude (>0.5 mV) epileptiform activity coincided with the developmental decline in spikes. Bursts (clusters of ≥3 low-amplitude spikes within 0.7s prior to returning to baseline) persisted following this decline. Addition of the GABAergic antagonist bicuculline initially had no effect on signaling, consistent with delayed development of GABAergic synapses. This was followed by a period in which bicuculline inhibited overall signaling, confirming that GABAergic neurons initially display excitatory activity in ex vivo networks. Following the transient developmental wave of epileptiform signaling, bicuculline induced a resurgence of epileptiform signaling, indicating that GABAergic neurons at this point displayed inhibitory activity. The appearance of transition after the developmental and decline of epileptiform activity, rather than immediately after the developmental decline in lower-amplitude spikes, suggests that the initial excitatory activity of GABAergic neurons contributes to their transition into inhibitory neurons, and that inhibitory GABAergic activity is essential for network development. Prior studies indicate that a minority (25%) of neurons in these cultures were GABAergic, suggesting that inhibitory neurons regulate multiple excitatory neurons. A similar robust increase in signaling following cessation of

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

    PubMed

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

    2013-01-01

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

  16. 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.

  17. 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

  18. 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.

  19. Physical and biological regulation of neuron regenerative growth and network formation on recombinant dragline silks.

    PubMed

    An, Bo; Tang-Schomer, Min D; Huang, Wenwen; He, Jiuyang; Jones, Justin A; Lewis, Randolph V; Kaplan, David L

    2015-04-01

    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

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

    PubMed

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

    2015-02-01

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

  1. 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

  2. Matrix stiffness modulates formation and activity of neuronal networks of controlled architectures.

    PubMed

    Lantoine, Joséphine; Grevesse, Thomas; Villers, Agnès; Delhaye, Geoffrey; Mestdagh, Camille; Versaevel, Marie; Mohammed, Danahe; Bruyère, Céline; Alaimo, Laura; Lacour, Stéphanie P; Ris, Laurence; Gabriele, Sylvain

    2016-05-01

    The ability to construct easily in vitro networks of primary neurons organized with imposed topologies is required for neural tissue engineering as well as for the development of neuronal interfaces with desirable characteristics. However, accumulating evidence suggests that the mechanical properties of the culture matrix can modulate important neuronal functions such as growth, extension, branching and activity. Here we designed robust and reproducible laminin-polylysine grid micropatterns on cell culture substrates that have similar biochemical properties but a 100-fold difference in Young's modulus to investigate the role of the matrix rigidity on the formation and activity of cortical neuronal networks. We found that cell bodies of primary cortical neurons gradually accumulate in circular islands, whereas axonal extensions spread on linear tracks to connect circular islands. Our findings indicate that migration of cortical neurons is enhanced on soft substrates, leading to a faster formation of neuronal networks. Furthermore, the pre-synaptic density was two times higher on stiff substrates and consistently the number of action potentials and miniature synaptic currents was enhanced on stiff substrates. Taken together, our results provide compelling evidence to indicate that matrix stiffness is a key parameter to modulate the growth dynamics, synaptic density and electrophysiological activity of cortical neuronal networks, thus providing useful information on scaffold design for neural tissue engineering.

  3. Efficiency characterization of a large neuronal network: A causal information approach

    NASA Astrophysics Data System (ADS)

    Montani, Fernando; Deleglise, Emilia B.; Rosso, Osvaldo A.

    2014-05-01

    When inhibitory neurons constitute about 40% of neurons they could have an important antinociceptive role, as they would easily regulate the level of activity of other neurons. We consider a simple network of cortical spiking neurons with axonal conduction delays and spike timing dependent plasticity, representative of a cortical column or hypercolumn with a large proportion of inhibitory neurons. Each neuron fires following a Hodgkin-Huxley like dynamics and it is interconnected randomly to other neurons. The network dynamics is investigated estimating Bandt and Pompe probability distribution function associated to the interspike intervals and taking different degrees of interconnectivity across neurons. More specifically we take into account the fine temporal “structures” of the complex neuronal signals not just by using the probability distributions associated to the interspike intervals, but instead considering much more subtle measures accounting for their causal information: the Shannon permutation entropy, Fisher permutation information and permutation statistical complexity. This allows us to investigate how the information of the system might saturate to a finite value as the degree of interconnectivity across neurons grows, inferring the emergent dynamical properties of the system.

  4. 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.

  5. Multiple network interface core apparatus and method

    DOEpatents

    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.

  6. 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.

  7. 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.

  8. Long cycles and special categories of formal neuronal units and networks.

    PubMed

    Lábos, E

    1996-01-01

    Composite categories of formal neurons or units (FN, FU) and their nets (FNN) are displayed. Long state cycles based on previously described methods in FNN-s are presented too. These results make easier any orientation among the immense number of possible cases of such nets. The taxonomical entities (classes, categories) give also cues for the synthesis of long and complicated cycles. The networks belong to the most complicated kind of net synthesis, thus realisations of other cases are expected to be easier. The observed wiring versus dynamics relationships give an insight into the real network control mechanisms and points to additional mechanisms for real nets. The work is a founded on previous results of the author.

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

    PubMed Central

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

    2012-01-01

    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

  10. 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.

  11. 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.

  12. 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.

  13. Random networks of spiking neurons: instability in the Xenopus tadpole moto-neural pattern.

    PubMed

    Fulvi Mari, C

    2000-07-01

    A large network of integrate-and-fire neurons is studied analytically when the synaptic weights are independently randomly distributed according to a Gaussian distribution with arbitrary mean and variance. The relevant order parameters are identified, and it is shown that such network is statistically equivalent to an ensemble of independent integrate-and-fire neurons with each input signal given by the sum of a self-interaction deterministic term and a Gaussian colored noise. The model is able to reproduce the quasisynchronous oscillations, and the dropout of their frequency, of the central nervous system neurons of the swimming Xenopus tadpole. Predictions from the model are proposed for future experiments.

  14. Relation between single neuron and population spiking statistics and effects on network activity.

    PubMed

    Câteau, Hideyuki; Reyes, Alex D

    2006-02-10

    To simplify theoretical analyses of neural networks, individual neurons are often modeled as Poisson processes. An implicit assumption is that even if the spiking activity of each neuron is non-Poissonian, the composite activity obtained by summing many spike trains limits to a Poisson process. Here, we show analytically and through simulations that this assumption is invalid. Moreover, we show with Fokker-Planck equations that the behavior of feedforward networks is reproduced accurately only if the tendency of neurons to fire periodically is incorporated by using colored noise whose autocorrelation has a negative component.

  15. 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

  16. Neurons within the same network independently achieve conserved output by differentially balancing variable conductance magnitudes.

    PubMed

    Ransdell, Joseph L; Nair, Satish S; Schulz, David J

    2013-06-12

    Biological and theoretical evidence suggest that individual neurons may achieve similar outputs by differentially balancing variable underlying ionic conductances. Despite the substantial amount of data consistent with this idea, a direct biological demonstration that cells with conserved output, particularly within the same network, achieve these outputs via different solutions has been difficult to achieve. Here we demonstrate definitively that neurons from native neural networks with highly similar output achieve this conserved output by differentially tuning underlying conductance magnitudes. Multiple motor neurons of the crab (Cancer borealis) cardiac ganglion have highly conserved output within a preparation, despite showing a 2-4-fold range of conductance magnitudes. By blocking subsets of these currents, we demonstrate that the remaining conductances become unbalanced, causing disparate output as a result. Therefore, as strategies to understand neuronal excitability become increasingly sophisticated, it is important that such variability in excitability of neurons, even among those within the same individual, is taken into account. PMID:23761890

  17. Reverberation of excitation in neuronal networks interconnected through voltage-gated gap junction channels

    PubMed Central

    Maciunas, Kestutis; Snipas, Mindaugas; Paulauskas, Nerijus

    2016-01-01

    We combined Hodgkin–Huxley equations and gating models of gap junction (GJ) channels to simulate the spread of excitation in two-dimensional networks composed of neurons interconnected by voltage-gated GJs. Each GJ channel contains two fast and slow gates, each exhibiting current–voltage (I-V) rectification and gating properties that depend on transjunctional voltage (Vj). The data obtained show how junctional conductance (gj), which is necessary for synchronization of the neuronal network, depends on its size and the intrinsic firing rate of neurons. A phase shift between action potentials (APs) of neighboring neurons creates bipolar, short-lasting Vj spikes of approximately ±100 mV that induce Vj gating, leading to a small decay of gj, which can accumulate into larger decays during bursting activity of neurons. We show that I-V rectification of GJs in local regions of the two-dimensional network of neurons can lead to unidirectional AP transfer and consequently to reverberation of excitation. This reverberation can be initiated by a single electrical pulse and terminated by a low-amplitude pulse applied in a specific window of reverberation cycle. Thus, the model accounts for the influence of dynamically modulatable electrical synapses in shaping the function of a neuronal network and the formation of reverberation, which, as proposed earlier, may be important for the development of short-term memory and its consolidation into long-term memory. PMID:26880752

  18. Rare and spatially segregated release sites mediate a synaptic interaction between two identified network neurons.

    PubMed

    Cabirol-Pol, Marie-Jeanne; Combes, Denis; Fénelon, Valérie S; Simmers, John; Meyrand, Pierre

    2002-02-01

    Laser-scanning confocal microscopy (LSCM), electron microcopy (EM), and cellular electrophysiology were used in combination to study the structural basis of an inhibitory synapse between two identified neurons of the same network. To achieve this, we examined the chemical inhibitory synapse between identified neurons belonging to the lobster (Homarus gammarus) pyloric network: the pyloric dilator (PD) and the lateral pyloric (LP) neurons. In order to visualize simultaneously these two neurons, we used intrasomatic injection of Lucifer Yellow (LY) in one and rhodamine/horseradish peroxydase (HRP) in the other. Under LSCM, we found only two zones of close apposition in a restricted part of the neuritic tree of the two network neurons. Then, within these two zones, the synaptic release sites were searched using EM. To this end, photoconversion of LY with immunogold and development of HRP with DAB were performed on the previously observed preparations. Structural evidence was found for only one release site per zone. To confirm this result, and because the zones of contact were always segregated in a restricted part of the dendrites, we used laser photoablation to selectively delete, either pre- or postsynaptically, the branches on which the release sites were located. In both cases, such restrictive ablation completely abolished the functional interaction between these neurons. Our results therefore demonstrate that an inhibitory synapse that is essential for the operation of a neural network relies on only very few sites of contact localized in a highly restricted part of each neuron's dendritic arbor. PMID:11793361

  19. 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

  20. 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.

  1. Neural networks with multiple general neuron models: a hybrid computational intelligence approach using Genetic Programming.

    PubMed

    Barton, Alan J; Valdés, Julio J; Orchard, Robert

    2009-01-01

    Classical neural networks are composed of neurons whose nature is determined by a certain function (the neuron model), usually pre-specified. In this paper, a type of neural network (NN-GP) is presented in which: (i) each neuron may have its own neuron model in the form of a general function, (ii) any layout (i.e network interconnection) is possible, and (iii) no bias nodes or weights are associated to the connections, neurons or layers. The general functions associated to a neuron are learned by searching a function space. They are not provided a priori, but are rather built as part of an Evolutionary Computation process based on Genetic Programming. The resulting network solutions are evaluated based on a fitness measure, which may, for example, be based on classification or regression errors. Two real-world examples are presented to illustrate the promising behaviour on classification problems via construction of a low-dimensional representation of a high-dimensional parameter space associated to the set of all network solutions.

  2. Establishment of a Human Neuronal Network Assessment System by Using a Human Neuron/Astrocyte Co-Culture Derived from Fetal Neural Stem/Progenitor Cells.

    PubMed

    Fukushima, Kazuyuki; Miura, Yuji; Sawada, Kohei; Yamazaki, Kazuto; Ito, Masashi

    2016-01-01

    Using human cell models mimicking the central nervous system (CNS) provides a better understanding of the human CNS, and it is a key strategy to improve success rates in CNS drug development. In the CNS, neurons function as networks in which astrocytes play important roles. Thus, an assessment system of neuronal network functions in a co-culture of human neurons and astrocytes has potential to accelerate CNS drug development. We previously demonstrated that human hippocampus-derived neural stem/progenitor cells (HIP-009 cells) were a novel tool to obtain human neurons and astrocytes in the same culture. In this study, we applied HIP-009 cells to a multielectrode array (MEA) system to detect neuronal signals as neuronal network functions. We observed spontaneous firings of HIP-009 neurons, and validated functional formation of neuronal networks pharmacologically. By using this assay system, we investigated effects of several reference compounds, including agonists and antagonists of glutamate and γ-aminobutyric acid receptors, and sodium, potassium, and calcium channels, on neuronal network functions using firing and burst numbers, and synchrony as readouts. These results indicate that the HIP-009/MEA assay system is applicable to the pharmacological assessment of drug candidates affecting synaptic functions for CNS drug development.

  3. 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...

  4. 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...

  5. 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

  6. Highly ordered large-scale neuronal networks of individual cells - toward single cell to 3D nanowire intracellular interfaces.

    PubMed

    Kwiat, Moria; Elnathan, Roey; Pevzner, Alexander; Peretz, Asher; Barak, Boaz; Peretz, Hagit; Ducobni, Tamir; Stein, Daniel; Mittelman, Leonid; Ashery, Uri; Patolsky, Fernando

    2012-07-25

    The use of artificial, prepatterned neuronal networks in vitro is a promising approach for studying the development and dynamics of small neural systems in order to understand the basic functionality of neurons and later on of the brain. The present work presents a high fidelity and robust procedure for controlling neuronal growth on substrates such as silicon wafers and glass, enabling us to obtain mature and durable neural networks of individual cells at designed geometries. It offers several advantages compared to other related techniques that have been reported in recent years mainly because of its high yield and reproducibility. The procedure is based on surface chemistry that allows the formation of functional, tailormade neural architectures with a micrometer high-resolution partition, that has the ability to promote or repel cells attachment. The main achievements of this work are deemed to be the creation of a large scale neuronal network at low density down to individual cells, that develop intact typical neurites and synapses without any glia-supportive cells straight from the plating stage and with a relatively long term survival rate, up to 4 weeks. An important application of this method is its use on 3D nanopillars and 3D nanowire-device arrays, enabling not only the cell bodies, but also their neurites to be positioned directly on electrical devices and grow with registration to the recording elements underneath.

  7. Fundamental short-term memory of semi-artificial neuronal network.

    PubMed

    Ito, Hidekatsu; Kudoh, Suguru N

    2013-01-01

    Spatiotemporal pattern of neuronal network activity is a key component of brain information processing. Cultured rat hippocampal neurons on the multielectrodes array dish are suitable for analyzing and manipulating network dynamics and its developmental changes. We applied paired electrical inputs at various inter-stimulus intervals (ISi) and analyzed the spatio-temporal pattern of evoked responses. We found that the pattern of evoked electrical activity was affected by existence of a prior input in the case that ISi of paired stimuli was within 2 s. These results suggest that a semi-artificial neuronal network on a culture dish has a fundamental component of short-term memory, and the origin of this hysteresis is transition among the internal states of the network, undertaken by synaptic transmissions.

  8. Recognition of partially occluded and rotated images with a network of spiking neurons.

    PubMed

    Shin, Joo-Heon; Smith, David; Swiercz, Waldemar; Staley, Kevin; Rickard, J Terry; Montero, Javier; Kurgan, Lukasz A; Cios, Krzysztof J

    2010-11-01

    In this paper, we introduce a novel system for recognition of partially occluded and rotated images. The system is based on a hierarchical network of integrate-and-fire spiking neurons with random synaptic connections and a novel organization process. The network generates integrated output sequences that are used for image classification. The proposed network is shown to provide satisfactory predictive performance given that the number of the recognition neurons and synaptic connections are adjusted to the size of the input image. Comparison of synaptic plasticity activity rule (SAPR) and spike timing dependant plasticity rules, which are used to learn connections between the spiking neurons, indicates that the former gives better results and thus the SAPR rule is used. Test results show that the proposed network performs better than a recognition system based on support vector machines.

  9. 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.

  10. REST/NRSF-mediated intrinsic homeostasis protects neuronal networks from hyperexcitability.

    PubMed

    Pozzi, Davide; Lignani, Gabriele; Ferrea, Enrico; Contestabile, Andrea; Paonessa, Francesco; D'Alessandro, Rosalba; Lippiello, Pellegrino; Boido, Davide; Fassio, Anna; Meldolesi, Jacopo; Valtorta, Flavia; Benfenati, Fabio; Baldelli, Pietro

    2013-11-13

    Intrinsic homeostasis enables neuronal circuits to maintain activity levels within an appropriate range by modulating neuronal voltage-gated conductances, but the signalling pathways involved in this process are largely unknown. We characterized the process of intrinsic homeostasis induced by sustained electrical activity in cultured hippocampal neurons based on the activation of the Repressor Element-1 Silencing Transcription Factor/Neuron-Restrictive Silencer Factor (REST/NRSF). We showed that 4-aminopyridine-induced hyperactivity enhances the expression of REST/NRSF, which in turn, reduces the expression of voltage-gated Na(+) channels, thereby decreasing the neuronal Na(+) current density. This mechanism plays an important role in the downregulation of the firing activity at the single-cell level, re-establishing a physiological spiking activity in the entire neuronal network. Conversely, interfering with REST/NRSF expression impaired this homeostatic response. Our results identify REST/NRSF as a critical factor linking neuronal activity to the activation of intrinsic homeostasis and restoring a physiological level of activity in the entire neuronal network. PMID:24149584

  11. 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.

  12. 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

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

    PubMed

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

    2014-03-01

    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, pH(i), [Ca(2+)](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.

  14. 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

  15. Synaptic signal streams generated by ex vivo neuronal networks contain non-random, complex patterns.

    PubMed

    Lee, Sangmook; Zemianek, Jill M; Shultz, Abraham; Vo, Anh; Maron, Ben Y; Therrien, Mikaela; Courtright, Christina; Guaraldi, Mary; Yanco, Holly A; Shea, Thomas B

    2014-11-01

    Cultured embryonic neurons develop functional networks that transmit synaptic signals over multiple sequentially connected neurons as revealed by multi-electrode arrays (MEAs) embedded within the culture dish. Signal streams of ex vivo networks contain spikes and bursts of varying amplitude and duration. Despite the random interactions inherent in dissociated cultures, neurons are capable of establishing functional ex vivo networks that transmit signals among synaptically connected neurons, undergo developmental maturation, and respond to exogenous stimulation by alterations in signal patterns. These characteristics indicate that a considerable degree of organization is an inherent property of neurons. We demonstrate herein that (1) certain signal types occur more frequently than others, (2) the predominant signal types change during and following maturation, (3) signal predominance is dependent upon inhibitory activity, and (4) certain signals preferentially follow others in a non-reciprocal manner. These findings indicate that the elaboration of complex signal streams comprised of a non-random distribution of signal patterns is an emergent property of ex vivo neuronal networks.

  16. [Acquiring new information in a neuronal network: from Hebb's concept to homeostatic plasticity].

    PubMed

    Le Roux, Nicolas; Amar, Muriel; Fossier, Philippe

    2008-01-01

    Synaptic plasticity is the cellular mechanism underlying the phenomena of learning and memory. Much of the research on synaptic plasticity is based on the postulate of Hebb (1949) who proposed that, when a neuron repeatedly takes part in the activation of another neuron, the efficacy of the connections between these neurons is increased. Plasticity has been extensively studied, and often demonstrated through the processes of LTP (Long Term Potentiation) and LTD (Long Term Depression), which represent an increase and a decrease of the efficacy of long-term synaptic transmission. This review summarizes current knowledge concerning the cellular mechanisms of LTP and LTD, whether at the level of excitatory synapses, which have been the most studied, or at the level of inhibitory synapses. However, if we consider neuronal networks rather than the individual synapses, the consequences of synaptic plasticity need to be considered on a large scale to determine if the activity of networks are changed or not. Homeostatic plasticity takes into account the mechanisms which control the efficacy of synaptic transmission for all the synaptic inputs of a neuron. Consequently, this new concept deals with the coordinated activity of excitatory and inhibitory networks afferent to a neuron which maintain a controlled level of excitability during the acquisition of new information related to the potentiation or to the depression of synaptic efficacy. We propose that the protocols of stimulation used to induce plasticity at the synaptic level set up a "homeostatic potentiation" or a "homeostatic depression" of excitation and inhibition at the level of the neuronal networks. The coordination between excitatory and inhibitory circuits allows the neuronal networks to preserve a level of stable activity, thus avoiding episodes of hyper- or hypo-activity during the learning and memory phases.

  17. Early development of the gonadotropin-releasing hormone neuronal network in transgenic zebrafish.

    PubMed

    Zhao, Yali; Lin, Meng-Chin A; Farajzadeh, Matthew; Wayne, Nancy L

    2013-01-01

    Understanding development of gonadotropin-releasing hormone (GnRH) neuronal circuits is fundamental to our understanding of reproduction, but not yet well understood. Most studies have been focused on GnRH neurons located in the hypothalamus and preoptic area (POA), which directly regulate the pituitary-gonadal axis. In zebrafish (Danio rerio), two forms of GnRH have been identified: GnRH2 and GnRH3. GnRH3 neurons in this species plays two roles: hypophysiotropic and neuromodulatory, depending on their location. GnRH3 neurons in the ventral telencephalon, POA, and hypothalamus control pituitary-gonadal function; in other areas (e.g., terminal nerve), they are neuromodulatory and without direct action on reproduction. To investigate the biology of GnRH neurons, a stable line of transgenic zebrafish was generated in which the GnRH3 promoter drives expression of a bright variant of green fluorescent protein (Emerald GFP, or EMD). This provides unprecedented sensitivity in detecting and imaging GnRH3 neurons during early embryogenesis in the transparent embryo. Using timelapse confocal imaging to monitor the time course of GnRH3:EMD expression in the live embryo, we describe the emergence and development of GnRH3 neurons in the olfactory region, hypothalamus, POA, and trigeminal ganglion. By 50 h post fertilization, these diverse groups of GnRH3 neurons project broadly in the central and peripheral nervous systems and make anatomical connections with each other. Immunohistochemistry of synaptic vesicle protein 2 (a marker of synaptic transmission) in this transgenic model suggests synaptic formation is occurring during early development of the GnRH3 neural network. Electrophysiology reveals early emergence of responsiveness to the stimulatory effects of kisspeptin in terminal nerve GnRH3 neurons. Overall, our findings reveal that the GnRH3 neuronal system is comprised of multiple populations of neurons as a complicated network. PMID:24009601

  18. Early Development of the Gonadotropin-Releasing Hormone Neuronal Network in Transgenic Zebrafish

    PubMed Central

    Zhao, Yali; Lin, Meng-Chin A.; Farajzadeh, Matthew; Wayne, Nancy L.

    2013-01-01

    Understanding development of gonadotropin-releasing hormone (GnRH) neuronal circuits is fundamental to our understanding of reproduction, but not yet well understood. Most studies have been focused on GnRH neurons located in the hypothalamus and preoptic area (POA), which directly regulate the pituitary-gonadal axis. In zebrafish (Danio rerio), two forms of GnRH have been identified: GnRH2 and GnRH3. GnRH3 neurons in this species plays two roles: hypophysiotropic and neuromodulatory, depending on their location. GnRH3 neurons in the ventral telencephalon, POA, and hypothalamus control pituitary-gonadal function; in other areas (e.g., terminal nerve), they are neuromodulatory and without direct action on reproduction. To investigate the biology of GnRH neurons, a stable line of transgenic zebrafish was generated in which the GnRH3 promoter drives expression of a bright variant of green fluorescent protein (Emerald GFP, or EMD). This provides unprecedented sensitivity in detecting and imaging GnRH3 neurons during early embryogenesis in the transparent embryo. Using timelapse confocal imaging to monitor the time course of GnRH3:EMD expression in the live embryo, we describe the emergence and development of GnRH3 neurons in the olfactory region, hypothalamus, POA, and trigeminal ganglion. By 50 h post fertilization, these diverse groups of GnRH3 neurons project broadly in the central and peripheral nervous systems and make anatomical connections with each other. Immunohistochemistry of synaptic vesicle protein 2 (a marker of synaptic transmission) in this transgenic model suggests synaptic formation is occurring during early development of the GnRH3 neural network. Electrophysiology reveals early emergence of responsiveness to the stimulatory effects of kisspeptin in terminal nerve GnRH3 neurons. Overall, our findings reveal that the GnRH3 neuronal system is comprised of multiple populations of neurons as a complicated network. PMID:24009601

  19. A functional glutamatergic neurone network in the medial septum and diagonal band area.

    PubMed

    Manseau, F; Danik, M; Williams, S

    2005-08-01

    The medial septum and diagonal band complex (MS/DB) is important for learning and memory and is known to contain cholinergic and GABAergic neurones. Glutamatergic neurones have also been recently described in this area but their function remains unknown. Here we show that local glutamatergic neurones can be activated using 4-aminopyridine (4-AP) and the GABA(A) receptor antagonist bicuculline in regular MS/DB slices, or mini-MS/DB slices. The spontaneous glutamatergic responses were mediated by AMPA receptors and, to a lesser extend, NMDA receptors, and were characterized by large, sometimes repetitive activity that elicited bursts of action potentials postsynaptically. Similar repetitive AMPA receptor-mediated bursts were generated by glutamatergic neurone activation within the MS/DB in disinhibited organotypic MS/DB slices, suggesting that the glutamatergic responses did not originate from extrinsic glutamatergic synapses. It is interesting that glutamatergic neurones were part of a synchronously active network as large repetitive AMPA receptor-mediated bursts were generated concomitantly with extracellular field potentials in intact half-septum preparations in vitro. Glutamatergic neurones appeared important to MS/DB activation as strong glutamatergic responses were present in electrophysiologically identified putative cholinergic, GABAergic and glutamatergic neurones. In agreement with this, we found immunohistochemical evidence that vesicular glutamate-2 (VGLUT2)-positive puncta were in proximity to choline acetyltransferase (ChAT)-, glutamic acid decarboxylase 67 (GAD67)- and VGLUT2-positive neurones. Finally, MS/DB glutamatergic neurones could be activated under more physiological conditions as a cholinergic agonist was found to elicit rhythmic AMPA receptor-mediated EPSPs at a theta relevant frequency of 6-10 Hz. We propose that glutamatergic neurones within the MS/DB can excite cholinergic and GABAergic neurones, and that they are part of a connected

  20. A functional glutamatergic neurone network in the medial septum and diagonal band area

    PubMed Central

    Manseau, F; Danik, M; Williams, S

    2005-01-01

    The medial septum and diagonal band complex (MS/DB) is important for learning and memory and is known to contain cholinergic and GABAergic neurones. Glutamatergic neurones have also been recently described in this area but their function remains unknown. Here we show that local glutamatergic neurones can be activated using 4-aminopyridine (4-AP) and the GABAA receptor antagonist bicuculline in regular MS/DB slices, or mini-MS/DB slices. The spontaneous glutamatergic responses were mediated by AMPA receptors and, to a lesser extend, NMDA receptors, and were characterized by large, sometimes repetitive activity that elicited bursts of action potentials postsynaptically. Similar repetitive AMPA receptor-mediated bursts were generated by glutamatergic neurone activation within the MS/DB in disinhibited organotypic MS/DB slices, suggesting that the glutamatergic responses did not originate from extrinsic glutamatergic synapses. It is interesting that glutamatergic neurones were part of a synchronously active network as large repetitive AMPA receptor-mediated bursts were generated concomitantly with extracellular field potentials in intact half-septum preparations in vitro. Glutamatergic neurones appeared important to MS/DB activation as strong glutamatergic responses were present in electrophysiologically identified putative cholinergic, GABAergic and glutamatergic neurones. In agreement with this, we found immunohistochemical evidence that vesicular glutamate-2 (VGLUT2)-positive puncta were in proximity to choline acetyltransferase (ChAT)-, glutamic acid decarboxylase 67 (GAD67)- and VGLUT2-positive neurones. Finally, MS/DB glutamatergic neurones could be activated under more physiological conditions as a cholinergic agonist was found to elicit rhythmic AMPA receptor-mediated EPSPs at a theta relevant frequency of 6–10 Hz. We propose that glutamatergic neurones within the MS/DB can excite cholinergic and GABAergic neurones, and that they are part of a connected

  1. The formation mechanism of defects, spiral wave in the network of neurons.

    PubMed

    Wu, Xinyi; Ma, Jun

    2013-01-01

    A regular network of neurons is constructed by using the Morris-Lecar (ML) neuron with the ion channels being considered, and the potential mechnism of the formation of a spiral wave is investigated in detail. Several spiral waves are initiated by blocking the target wave with artificial defects and/or partial blocking (poisoning) in ion channels. Furthermore, possible conditions for spiral wave formation and the effect of partial channel blocking are discussed completely. Our results are summarized as follows. 1) The emergence of a target wave depends on the transmembrane currents with diversity, which mapped from the external forcing current and this kind of diversity is associated with spatial heterogeneity in the media. 2) Distinct spiral wave could be induced to occupy the network when the target wave is broken by partially blocking the ion channels of a fraction of neurons (local poisoned area), and these generated spiral waves are similar with the spiral waves induced by artificial defects. It is confirmed that partial channel blocking of some neurons in the network could play a similar role in breaking a target wave as do artificial defects; 3) Channel noise and additive Gaussian white noise are also considered, and it is confirmed that spiral waves are also induced in the network in the presence of noise. According to the results mentioned above, we conclude that appropriate poisoning in ion channels of neurons in the network acts as 'defects' on the evolution of the spatiotemporal pattern, and accounts for the emergence of a spiral wave in the network of neurons. These results could be helpful to understand the potential cause of the formation and development of spiral waves in the cortex of a neuronal system.

  2. Dendritic integration in pyramidal neurons during network activity and disease.

    PubMed

    Palmer, Lucy M

    2014-04-01

    Neurons have intricate dendritic morphologies which come in an array of shapes and sizes. Not only do they give neurons their unique appearance, but dendrites also endow neurons with the ability to receive and transform synaptic inputs. We now have a wealth of information about the functioning of dendrites which suggests that the integration of synaptic inputs is highly dependent on both dendritic properties and neuronal input patterns. It has been shown that dendrites can perform non-linear processing, actively transforming synaptic input into Na(+) spikes, Ca(2+) plateau spikes and NMDA spikes. These membrane non-linearities can have a large impact on the neuronal output and have been shown to be regulated by numerous factors including synaptic inhibition. Many neuropathological diseases involve changes in how dendrites receive and package synaptic input by altering dendritic spine characteristics, ion channel expression and the inhibitory control of dendrites. This review focuses on the role of dendrites in integrating and transforming input and what goes wrong in the case of neuropathological diseases.

  3. Rate-synchrony relationship between input and output of spike trains in neuronal networks

    NASA Astrophysics Data System (ADS)

    Wang, Sentao; Zhou, Changsong

    2010-01-01

    Neuronal networks interact via spike trains. How the spike trains are transformed by neuronal networks is critical for understanding the underlying mechanism of information processing in the nervous system. Both the rate and synchrony of the spikes can affect the transmission, while the relationship between them has not been fully understood. Here we investigate the mapping between input and output spike trains of a neuronal network in terms of firing rate and synchrony. With large enough input rate, the working mode of the neurons is gradually changed from temporal integrators into coincidence detectors when the synchrony degree of input spike trains increases. Since the membrane potentials of the neurons can be depolarized to near the firing threshold by uncorrelated input spikes, small input synchrony can cause great output synchrony. On the other hand, the synchrony in the output may be reduced when the input rate is too small. The case of the feedforward network can be regarded as iterative process of such an input-output relationship. The activity in deep layers of the feedforward network is in an all-or-none manner depending on the input rate and synchrony.

  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. Inhibition of microRNA 128 promotes excitability of cultured cortical neuronal networks

    PubMed Central

    McSweeney, K. Melodi; Gussow, Ayal B.; Bradrick, Shelton S.; Dugger, Sarah A.; Gelfman, Sahar; Wang, Quanli; Petrovski, Slavé; Frankel, Wayne N.; Boland, Michael J.; Goldstein, David B.

    2016-01-01

    Cultured neuronal networks monitored with microelectrode arrays (MEAs) have been used widely to evaluate pharmaceutical compounds for potential neurotoxic effects. A newer application of MEAs has been in the development of in vitro models of neurological disease. Here, we directly evaluated the utility of MEAs to recapitulate in vivo phenotypes of mature microRNA-128 (miR-128) deficiency, which causes fatal seizures in mice. We show that inhibition of miR-128 results in significantly increased neuronal activity in cultured neuronal networks derived from primary mouse cortical neurons. These results support the utility of MEAs in developing in vitro models of neuroexcitability disorders, such as epilepsy, and further suggest that MEAs provide an effective tool for the rapid identification of microRNAs that promote seizures when dysregulated. PMID:27516621

  7. The immunoglobulin-like genetic predetermination of the brain: the protocadherins, blueprint of the neuronal network

    NASA Astrophysics Data System (ADS)

    Hilschmann, N.; Barnikol, H. U.; Barnikol-Watanabe, S.; Götz, H.; Kratzin, H.; Thinnes, F. P.

    2001-01-01

    The morphogenesis of the brain is governed by synaptogenesis. Synaptogenesis in turn is determined by cell adhesion molecules, which bridge the synaptic cleft and, by homophilic contact, decide which neurons are connected and which are not. Because of their enormous diversification in specificities, protocadherins (pcdhα, pcdhβ, pcdhγ), a new class of cadherins, play a decisive role. Surprisingly, the genetic control of the protocadherins is very similar to that of the immunoglobulins. There are three sets of variable (V) genes followed by a corresponding constant (C) gene. Applying the rules of the immunoglobulin genes to the protocadherin genes leads, despite of this similarity, to quite different results in the central nervous system. The lymphocyte expresses one single receptor molecule specifically directed against an outside stimulus. In contrast, there are three specific recognition sites in each neuron, each expressing a different protocadherin. In this way, 4,950 different neurons arising from one stem cell form a neuronal network, in which homophilic contacts can be formed in 52 layers, permitting an enormous number of different connections and restraints between neurons. This network is one module of the central computer of the brain. Since the V-genes are generated during evolution and V-gene translocation during embryogenesis, outside stimuli have no influence on this network. The network is an inborn property of the protocadherin genes. Every circuit produced, as well as learning and memory, has to be based on this genetically predetermined network. This network is so universal that it can cope with everything, even the unexpected. In this respect the neuronal network resembles the recognition sites of the immunoglobulins.

  8. Characterization and interpretation of electrical signals from random networks of cultured neurons.

    PubMed

    Bove, M; Genta, G; Verreschi, G; Grattarola, M

    1996-04-01

    The main purpose of this paper is to describe several software tools specifically adapted to characterize the electrophysiological behavior of a population of neurons coupled to a planar array of microtransducers by analyzing the recorded signals. Both graphic and signal processing tools are considered. They include: image analysis of the neuronal network topology, peak detection procedure, multi-graphics visualization, spike classification, cross-correlation, interspike and interbursting interval analysis. These tools are utilized to characterize changes induced in the electrophysiological behavior of populations of cultured neurons by modifications to the medium-culture. PMID:8773310

  9. 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

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

    PubMed Central

    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. DOI: http://dx.doi.org/10.7554/eLife.04378.001 PMID:25556699

  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.

  13. 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.

  14. 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.

  15. 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

  16. The influence of hubs in the structure of a neuronal network during an epileptic seizure

    NASA Astrophysics Data System (ADS)

    Rodrigues, Abner Cardoso; Cerdeira, Hilda A.; Machado, Birajara Soares

    2016-02-01

    In this work, we propose changes in the structure of a neuronal network with the intention to provoke strong synchronization to simulate episodes of epileptic seizure. Starting with a network of Izhikevich neurons we slowly increase the number of connections in selected nodes in a controlled way, to produce (or not) hubs. We study how these structures alter the synchronization on the spike firings interval, on individual neurons as well as on mean values, as a function of the concentration of connections for random and non-random (hubs) distribution. We also analyze how the post-ictal signal varies for the different distributions. We conclude that a network with hubs is more appropriate to represent an epileptic state.

  17. Impacts of clustering on noise-induced spiking regularity in the excitatory neuronal networks of subnetworks.

    PubMed

    Li, Huiyan; Sun, Xiaojuan; Xiao, Jinghua

    2015-01-01

    In this paper, we investigate how clustering factors influent spiking regularity of the neuronal network of subnetworks. In order to do so, we fix the averaged coupling probability and the averaged coupling strength, and take the cluster number M, the ratio of intra-connection probability and inter-connection probability R, the ratio of intra-coupling strength and inter-coupling strength S as controlled parameters. With the obtained simulation results, we find that spiking regularity of the neuronal networks has little variations with changing of R and S when M is fixed. However, cluster number M could reduce the spiking regularity to low level when the uniform neuronal network's spiking regularity is at high level. Combined the obtained results, we can see that clustering factors have little influences on the spiking regularity when the entire energy is fixed, which could be controlled by the averaged coupling strength and the averaged connection probability.

  18. The evolution to global burst synchronization in a modular neuronal network

    NASA Astrophysics Data System (ADS)

    Yang, Xiaoli; Wang, Manman

    2016-05-01

    In this paper, we investigated the development of global burst synchronization in a modular neuronal network at the mesoscale level. The modular network consists of some subnetworks, each of them presenting a scale-free property. Numerical results have demonstrated that, upon increasing the coupling strength, all neurons in the modular network initially burst in a desynchronous pattern; then the burst synchronization within each subnetwork is followed at the mesoscale; finally, the global burst synchronization at the macroscale is formed by the bursting activities on each subnetwork moving forward in harmony. This implies the network behavior possesses two distinct mesoscopic and macroscopic properties for some coupling strengths, i.e. the mesoscopic dynamics of burst synchronization on subnetworks is different from the macroscopic property of desynchronous activity on the whole network. It is also found that global burst synchronization can be promoted by large interconnection probability and hindered by small interconnection probability.

  19. Drifting States and Synchronization Induced Chaos in Autonomous Networks of Excitable Neurons

    PubMed Central

    Echeveste, Rodrigo; Gros, Claudius

    2016-01-01

    The study of balanced networks of excitatory and inhibitory neurons has led to several open questions. On the one hand it is yet unclear whether the asynchronous state observed in the brain is autonomously generated, or if it results from the interplay between external drivings and internal dynamics. It is also not known, which kind of network variabilities will lead to irregular spiking and which to synchronous firing states. Here we show how isolated networks of purely excitatory neurons generically show asynchronous firing whenever a minimal level of structural variability is present together with a refractory period. Our autonomous networks are composed of excitable units, in the form of leaky integrators spiking only in response to driving currents, remaining otherwise quiet. For a non-uniform network, composed exclusively of excitatory neurons, we find a rich repertoire of self-induced dynamical states. We show in particular that asynchronous drifting states may be stabilized in purely excitatory networks whenever a refractory period is present. Other states found are either fully synchronized or mixed, containing both drifting and synchronized components. The individual neurons considered are excitable and hence do not dispose of intrinsic natural firing frequencies. An effective network-wide distribution of natural frequencies is however generated autonomously through self-consistent feedback loops. The asynchronous drifting state is, additionally, amenable to an analytic solution. We find two types of asynchronous activity, with the individual neurons spiking regularly in the pure drifting state, albeit with a continuous distribution of firing frequencies. The activity of the drifting component, however, becomes irregular in the mixed state, due to the periodic driving of the synchronized component. We propose a new tool for the study of chaos in spiking neural networks, which consists of an analysis of the time series of pairs of consecutive interspike

  20. In-vitro exposure of neuronal networks to the GSM-1800 signal.

    PubMed

    Moretti, Daniela; Garenne, André; Haro, Emmanuelle; Poulletier de Gannes, Florence; Lagroye, Isabelle; Lévêque, Philippe; Veyret, Bernard; Lewis, Noëlle

    2013-12-01

    The central nervous system is the most likely target of mobile telephony radiofrequency (RF) field exposure in terms of biological effects. Several electroencephalography (EEG) studies have reported variations in the alpha-band power spectrum during and/or after RF exposure, in resting EEG and during sleep. In this context, the observation of the spontaneous electrical activity of neuronal networks under RF exposure can be an efficient tool to detect the occurrence of low-level RF effects on the nervous system. Our research group has developed a dedicated experimental setup in the GHz range for the simultaneous exposure of neuronal networks and monitoring of electrical activity. A transverse electromagnetic (TEM) cell was used to expose the neuronal networks to GSM-1800 signals at a SAR level of 3.2 W/kg. Recording of the neuronal electrical activity and detection of the extracellular spikes and bursts under exposure were performed using microelectrode arrays (MEAs). This work provides the proof of feasibility and preliminary results of the integrated investigation regarding exposure setup, culture of the neuronal network, recording of the electrical activity, and analysis of the signals obtained under RF exposure. In this pilot study on 16 cultures, there was a 30% reversible decrease in firing rate (FR) and bursting rate (BR) during a 3 min exposure to RF. Additional experiments are needed to further characterize this effect.

  1. 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

  2. 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

  3. 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.

  4. Hybrid Multiphoton Volumetric Functional Imaging of Large Scale Bioengineered Neuronal Networks

    PubMed Central

    Paluch, Shir; Dvorkin, Roman; Brosh, Inbar; Shoham, Shy

    2014-01-01

    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 bio-engineered 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/sec of structures with mm-scale dimensions containing a network of over 1000 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. PMID:24898000

  5. Artificial neuron-glia networks learning approach based on cooperative coevolution.

    PubMed

    Mesejo, Pablo; Ibáñez, Oscar; Fernández-Blanco, Enrique; Cedrón, Francisco; Pazos, Alejandro; Porto-Pazos, Ana B

    2015-06-01

    Artificial Neuron-Glia Networks (ANGNs) are a novel bio-inspired machine learning approach. They extend classical Artificial Neural Networks (ANNs) by incorporating recent findings and suppositions about the way information is processed by neural and astrocytic networks in the most evolved living organisms. Although ANGNs are not a consolidated method, their performance against the traditional approach, i.e. without artificial astrocytes, was already demonstrated on classification problems. However, the corresponding learning algorithms developed so far strongly depends on a set of glial parameters which are manually tuned for each specific problem. As a consequence, previous experimental tests have to be done in order to determine an adequate set of values, making such manual parameter configuration time-consuming, error-prone, biased and problem dependent. Thus, in this paper, we propose a novel learning approach for ANGNs that fully automates the learning process, and gives the possibility of testing any kind of reasonable parameter configuration for each specific problem. This new learning algorithm, based on coevolutionary genetic algorithms, is able to properly learn all the ANGNs parameters. Its performance is tested on five classification problems achieving significantly better results than ANGN and competitive results with ANN approaches.

  6. Neural network method applied to particle image velocimetry

    NASA Astrophysics Data System (ADS)

    Grant, Ian; Pan, X.

    1993-12-01

    realised. An important class of neural network is the multi-layer perceptron. The neurons are distributed on surfaces and linked by weighted interconnections. In the present paper we demonstrate how this type of net can developed into a competitive, adaptive filter which will identify PIV image pairs in a number of commonly occurring flow types. Previous work by the authors in particle tracking analysis (1, 2) has shown the efficiency of statistical windowing techniques in flows without systematic (in time or space) variations. The effectiveness of the present neural net is illustrated by applying it to digital simulations ofturbulent and rotating flows. Work reported by Cenedese et al (3) has taken a different approach in examining the potential for neural net methods applied to PIV.

  7. [Changes in synaptic potentials and axonal delays as factors causing epileptic instability of neuronal networks].

    PubMed

    Trabka, W; Trabka, J; Mikrut, Z

    1988-01-01

    Synaptic weights, axonal delays and excitability thresholds are three basic parameters influencing the function of neuronal network as a dynamic system. Changes of the general level of the signal flow, without changes in relation between excitatory and inhibitory connections can cause pathologic oscillations in the network. The conductivity in neuronal nets depends on electrochemical processes and may be influenced by the pH-status, ion concentrations, among other parameters. Also different pathological processes can change it. Even slight changes of axonal delays, as shown above, can cause in certain situations unstable, epileptic oscillations of the net.

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

    SciTech Connect

    Roxin, Alex; Brunel, Nicolas; Hansel, David

    2005-06-17

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

  9. 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

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

    PubMed

    Naze, Sebastien; Bernard, Christophe; Jirsa, Viktor

    2015-05-01

    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

  11. Stimulus-induced transition of clustering firings in neuronal networks with information transmission delay

    NASA Astrophysics Data System (ADS)

    Wang, Qingyun; Zhang, Honghui; Chen, Guanrong

    2013-07-01

    We study the evolution of spatiotemporal dynamics and transition of clustering firing synchronization on spiking Hodgkin-Huxley neuronal networks as information transmission delay and the periodic stimulus are varied. In particular, it is shown that the tuned information transmission delay can induce a clustering anti-phase synchronization transition with the pacemaker, where two equal clusters can alternatively synchronize in anti-phase firing. More interestingly, we show that the periodic stimulus can drive the delay-induced clustering anti-phase firing synchronization bifurcate to the collective perfect synchronization, which is routed by the complex process including collective chaotic firings and clustering out-of-phase synchronization of the neuronal networks. In addition, the periodic stimulus induced clustering firings of the spiking neuronal networks are robust to the connectivity probability of small world networks. Furthermore, the different stimulus frequency induced complexity is also investigated. We hope that the results of this paper can provide insights that could facilitate the understanding of the joint impact of information transmission delays and periodic stimulus on controlling dynamical behaviors of realistic neuronal networks.

  12. 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

  13. Neuronal avalanches imply maximum dynamic range in cortical networks at criticality.

    PubMed

    Shew, Woodrow L; Yang, Hongdian; Petermann, Thomas; Roy, Rajarshi; Plenz, Dietmar

    2009-12-01

    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.

  14. 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

  15. 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.

  16. Population density methods for stochastic neurons with realistic synaptic kinetics: firing rate dynamics and fast computational methods.

    PubMed

    Apfaltrer, Felix; Ly, Cheng; Tranchina, Daniel

    2006-12-01

    An outstanding problem in computational neuroscience is how to use population density function (PDF) methods to model neural networks with realistic synaptic kinetics in a computationally efficient manner. We explore an application of two-dimensional (2-D) PDF methods to simulating electrical activity in networks of excitatory integrate-and-fire neurons. We formulate a pair of coupled partial differential-integral equations describing the evolution of PDFs for neurons in non-refractory and refractory pools. The population firing rate is given by the total flux of probability across the threshold voltage. We use an operator-splitting method to reduce computation time. We report on speed and accuracy of PDF results and compare them to those from direct, Monte-Carlo simulations. We compute temporal frequency response functions for the transduction from the rate of postsynaptic input to population firing rate, and examine its dependence on background synaptic input rate. The behaviors in the1-D and 2-D cases--corresponding to instantaneous and non-instantaneous synaptic kinetics, respectively--differ markedly from those for a somewhat different transduction: from injected current input to population firing rate output (Brunel et al. 2001; Fourcaud & Brunel 2002). We extend our method by adding inhibitory input, consider a 3-D to 2-D dimension reduction method, demonstrate its limitations, and suggest directions for future study. PMID:17162461

  17. Pattern selection and self-organization induced by random boundary initial values in a neuronal network

    NASA Astrophysics Data System (ADS)

    Ma, Jun; Xu, Ying; Wang, Chunni; Jin, Wuyin

    2016-11-01

    Regular spatial patterns could be observed in spatiotemporal systems far from equilibrium states. Artificial networks with different topologies are often designed to reproduce the collective behaviors of nodes (or neurons) which the local kinetics of node is described by kinds of oscillator models. It is believed that the self-organization of network much depends on the bifurcation parameters and topology connection type. Indeed, the boundary effect is every important on the pattern formation of network. In this paper, a regular network of Hindmarsh-Rose neurons is designed in a two-dimensional square array with nearest-neighbor connection type. The neurons on the boundary are excited with random stimulus. It is found that spiral waves, even a pair of spiral waves could be developed in the network under appropriate coupling intensity. Otherwise, the spatial distribution of network shows irregular states. A statistical variable is defined to detect the collective behavior by using mean field theory. It is confirmed that regular pattern could be developed when the synchronization degree is low. The potential mechanism could be that random perturbation on the boundary could induce coherence resonance-like behavior thus spiral wave could be developed in the network.

  18. Computational models of neuron-astrocyte interactions lead to improved efficacy in the performance of neural networks.

    PubMed

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

    2012-01-01

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

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

    PubMed Central

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

    2012-01-01

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

  20. Accounting for network effects in neuronal responses using L1 regularized point process models.

    PubMed

    Kelly, Ryan C; Kass, Robert E; Smith, Matthew A; Lee, Tai Sing

    2010-01-01

    Activity of a neuron, even in the early sensory areas, is not simply a function of its local receptive field or tuning properties, but depends on global context of the stimulus, as well as the neural context. This suggests the activity of the surrounding neurons and global brain states can exert considerable influence on the activity of a neuron. In this paper we implemented an L1 regularized point process model to assess the contribution of multiple factors to the firing rate of many individual units recorded simultaneously from V1 with a 96-electrode "Utah" array. We found that the spikes of surrounding neurons indeed provide strong predictions of a neuron's response, in addition to the neuron's receptive field transfer function. We also found that the same spikes could be accounted for with the local field potentials, a surrogate measure of global network states. This work shows that accounting for network fluctuations can improve estimates of single trial firing rate and stimulus-response transfer functions. PMID:22162918

  1. GENERATING OSCILLATORY BURSTS FROM A NETWORK OF REGULAR SPIKING NEURONS WITHOUT INHIBITION

    PubMed Central

    Shao, Jing; Lai, Dihui; Meyer, Ulrike; Luksch, Harald; Wessel, Ralf

    2010-01-01

    Avian nucleus isthmi pars parvocellularis (Ipc) neurons are reciprocally connected with the layer 10 (L10) neurons in the optic tectum and respond with oscillatory bursts to visual stimulation. Our in vitro experiments show that both neuron types respond with regular spiking to somatic current injection and that the feedforward and feedback synaptic connections are excitatory, but of different strength and time course. To elucidate mechanisms of oscillatory bursting in this network of regularly spiking neurons, we investigated an experimentally constrained model of coupled leaky integrate-and-fire neurons with spike-rate adaptation. The model reproduces the observed Ipc oscillatory bursting in response to simulated visual stimulation. A scan through the model parameter volume reveals that Ipc oscillatory burst generation can be caused by strong and brief feedforward synaptic conductance changes. The mechanism is sensitive to the parameter values of spike-rate adaptation. In conclusion, we show that a network of regular-spiking neurons with feedforward excitation and spike-rate adaptation can generate oscillatory bursting in response to a constant input. PMID:19572191

  2. 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

  3. Imagined gait modulates neuronal network dynamics in the human pedunculopontine nucleus.

    PubMed

    Tattersall, Timothy L; Stratton, Peter G; Coyne, Terry J; Cook, Raymond; Silberstein, Paul; Silburn, Peter A; Windels, François; Sah, Pankaj

    2014-03-01

    The pedunculopontine nucleus (PPN) is a part of the mesencephalic locomotor region and is thought to be important for the initiation and maintenance of gait. Lesions of the PPN induce gait deficits, and the PPN has therefore emerged as a target for deep brain stimulation for the control of gait and postural disability. However, the role of the PPN in gait control is not understood. Using extracellular single-unit recordings in awake patients, we found that neurons in the PPN discharged as synchronous functional networks whose activity was phase locked to alpha oscillations. Neurons in the PPN responded to limb movement and imagined gait by dynamically changing network activity and decreasing alpha phase locking. Our results indicate that different synchronous networks are activated during initial motor planning and actual motion, and suggest that changes in gait initiation in Parkinson's disease may result from disrupted network activity in the PPN.

  4. 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

  5. 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

  6. Effects of hybrid synapses on the vibrational resonance in small-world neuronal networks.

    PubMed

    Yu, Haitao; Wang, Jiang; Sun, Jianbing; Yu, Haifeng

    2012-09-01

    We investigate the effect of vibrational resonance in small-world neuronal networks with hybrid chemical and electrical synapses. It is shown that, irrespective of the probability of chemical synapses, an optimal amplitude of high-frequency component of the signal can optimize the dynamical response of neuron populations to the low-frequency component, which encodes the information. This effect of vibrational resonance of neuronal systems depends extensively on the network structure and parameters, which determine the ability of neuronal networks to enhance the outreach of localized subthreshold low-frequency signal. In particular, chemical synaptic coupling is more efficient than the electrical coupling for the transmission of local input signal due to its selective coupling. Moreover, there exists an optimal small-world topology characterized by an optimal value of rewiring probability, warranting the largest peak value of the system response. Considering that two-frequency signals are ubiquity in brain dynamics, we expect the presented results could have important implications for signal processing in neuronal systems. PMID:23020444

  7. 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

  8. 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

  9. Computational connectionism within neurons: A model of cytoskeletal automata subserving neural networks

    NASA Astrophysics Data System (ADS)

    Rasmussen, Steen; Karampurwala, Hasnain; Vaidyanath, Rajesh; Jensen, Klaus S.; Hameroff, Stuart

    1990-06-01

    “Neural network” models of brain function assume neurons and their synaptic connections to be the fundamental units of information processing, somewhat like switches within computers. However, neurons and synapses are extremely complex and resemble entire computers rather than switches. The interiors of the neurons (and other eucaryotic cells) are now known to contain highly ordered parallel networks of filamentous protein polymers collectively termed the cytoskeleton. Originally assumed to provide merely structural “bone-like” support, cytoskeletal structures such as microtubules are now recognized to organize cell interiors dynamically. The cytoskeleton is the internal communication network for the eucaryotic cell, both by means of simple transport and by means of coordinating extremely complicated events like cell division, growth and differentiation. The cytoskeleton may therefore be viewed as the cell's “nervous system”. Consequently the neuronal cytoskeleton may be involved in molecular level information processing which subserves higher, collective neuronal functions ultimately relating to cognition. Numerous models of information processing within the cytoskeleton (in particular, microtubules) have been proposed. We have utilized cellular automata as a means to model and demonstrate the potential for information processing in cytoskeletal microtubules. In this paper, we extend previous work and simulate associative learning in a cytoskeletal network as well as assembly and disassembly of microtubules. We also discuss possible relevance and implications of cytoskeletal information processing to cognition.

  10. 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

  11. Effects of hybrid synapses on the vibrational resonance in small-world neuronal networks.

    PubMed

    Yu, Haitao; Wang, Jiang; Sun, Jianbing; Yu, Haifeng

    2012-09-01

    We investigate the effect of vibrational resonance in small-world neuronal networks with hybrid chemical and electrical synapses. It is shown that, irrespective of the probability of chemical synapses, an optimal amplitude of high-frequency component of the signal can optimize the dynamical response of neuron populations to the low-frequency component, which encodes the information. This effect of vibrational resonance of neuronal systems depends extensively on the network structure and parameters, which determine the ability of neuronal networks to enhance the outreach of localized subthreshold low-frequency signal. In particular, chemical synaptic coupling is more efficient than the electrical coupling for the transmission of local input signal due to its selective coupling. Moreover, there exists an optimal small-world topology characterized by an optimal value of rewiring probability, warranting the largest peak value of the system response. Considering that two-frequency signals are ubiquity in brain dynamics, we expect the presented results could have important implications for signal processing in neuronal systems.

  12. [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

  13. Effect of the Topology and Delayed Interactions in Neuronal Networks Synchronization

    PubMed Central

    Pérez, Toni; Garcia, Guadalupe C.; Eguíluz, Víctor M.; Vicente, Raúl; Pipa, Gordon; Mirasso, Claudio

    2011-01-01

    As important as the intrinsic properties of an individual nervous cell stands the network of neurons in which it is embedded and by virtue of which it acquires great part of its responsiveness and functionality. In this study we have explored how the topological properties and conduction delays of several classes of neural networks affect the capacity of their constituent cells to establish well-defined temporal relations among firing of their action potentials. This ability of a population of neurons to produce and maintain a millisecond-precise coordinated firing (either evoked by external stimuli or internally generated) is central to neural codes exploiting precise spike timing for the representation and communication of information. Our results, based on extensive simulations of conductance-based type of neurons in an oscillatory regime, indicate that only certain topologies of networks allow for a coordinated firing at a local and long-range scale simultaneously. Besides network architecture, axonal conduction delays are also observed to be another important factor in the generation of coherent spiking. We report that such communication latencies not only set the phase difference between the oscillatory activity of remote neural populations but determine whether the interconnected cells can set in any coherent firing at all. In this context, we have also investigated how the balance between the network synchronizing effects and the dispersive drift caused by inhomogeneities in natural firing frequencies across neurons is resolved. Finally, we show that the observed roles of conduction delays and frequency dispersion are not particular to canonical networks but experimentally measured anatomical networks such as the macaque cortical network can display the same type of behavior. PMID:21637767

  14. 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

  15. 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

  16. Interfacing in silico and in vitro neuronal networks.

    PubMed

    Bruzzone, Arianna; Pasquale, Valentina; Nowak, Przemyslaw; Tessadori, Jacopo; Massobrio, Paolo; Chiappalone, Michela

    2015-01-01

    In this study, an in vitro cortical culture was stimulated according to the activity exhibited by its in silico counterpart. We connected the latter, an artificial spiking neural network (SNN), to the former, a biological network (BNN), via an open-loop (i.e. unidirectional) configuration, with the ultimate goal of establishing a closed-loop bidirectional connection in future studies. We detected network bursts in the SNN and utilized them as triggers for stimulus delivery to the BNN. We analyzed evoked BNN responses to evaluate whether the BNN activity was entrained by the SNN by correlating stimuli and BNN evoked network burst rates at different timescales. We found that this correlation was nearly constant at all timescales, but its magnitude depended on the efficacy of the stimulation source in entraining BNN activity.

  17. High fidelity neuronal networks formed by plasma masking with a bilayer membrane: analysis of neurodegenerative and neuroprotective processes.

    PubMed

    Hardelauf, Heike; Sisnaiske, Julia; Taghipour-Anvari, Amir Ali; Jacob, Peter; Drabiniok, Evelyn; Marggraf, Ulrich; Frimat, Jean-Philippe; Hengstler, Jan G; Neyer, Andreas; van Thriel, Christoph; West, Jonathan

    2011-08-21

    Spatially defined neuronal networks have great potential to be used in a wide spectrum of neurobiology assays. We present an original technique for the precise and reproducible formation of neuronal networks. A PDMS membrane comprising through-holes aligned with interconnecting microchannels was used during oxygen plasma etching to dry mask a protein rejecting poly(ethylene glycol) (PEG) adlayer. Patterns were faithfully replicated to produce an oxidized interconnected array pattern which supported protein adsorption. Differentiated human SH-SY5Y neuron-like cells adhered to the array nodes with the micron-scale interconnecting tracks guiding neurite outgrowth to produce neuronal connections and establish a network. A 2.0 μm track width was optimal for high-level network formation and node compliance. These spatially standardized neuronal networks were used to analyse the dynamics of acrylamide-induced neurite degeneration and the protective effects of co-treatment with calpeptin or brain derived neurotrophic factor (BDNF).

  18. 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

  19. Multiple Spatial Coherence Resonances and Spatial Patterns in a Noise-Driven Heterogeneous Neuronal Network

    NASA Astrophysics Data System (ADS)

    Li, Yu-Ye; Ding, Xue-Li

    2014-12-01

    Heterogeneity of the neurons and noise are inevitable in the real neuronal network. In this paper, Gaussian white noise induced spatial patterns including spiral waves and multiple spatial coherence resonances are studied in a network composed of Morris—Lecar neurons with heterogeneity characterized by parameter diversity. The relationship between the resonances and the transitions between ordered spiral waves and disordered spatial patterns are achieved. When parameter diversity is introduced, the maxima of multiple resonances increases first, and then decreases as diversity strength increases, which implies that the coherence degrees induced by noise are enhanced at an intermediate diversity strength. The synchronization degree of spatial patterns including ordered spiral waves and disordered patterns is identified to be a very low level. The results suggest that the nervous system can profit from both heterogeneity and noise, and the multiple spatial coherence resonances are achieved via the emergency of spiral waves instead of synchronization patterns.

  20. Closed-loop neuro-robotic experiments to test computational properties of neuronal networks.

    PubMed

    Tessadori, Jacopo; Chiappalone, Michela

    2015-03-02

    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.

  1. Noise induced complexity: patterns and collective phenomena in a small-world neuronal network.

    PubMed

    Zheng, Yanhong; Wang, Qingyun; Danca, Marius-F

    2014-04-01

    The effects of noise on patterns and collective phenomena are studied in a small-world neuronal network with the dynamics of each neuron being described by a two-dimensional Rulkov map neuron. It is shown that for intermediate noise levels, noise-induced ordered patterns emerge spatially, which supports the spatiotemporal coherence resonance. However, the inherent long range couplings of small-world networks can effectively disrupt the internal spatial scale of the media at small fraction of long-range couplings. The temporal order, characterized by the autocorrelation of a firing rate function, can be greatly enhanced by the introduction of small-world connectivity. There exists an optimal fraction of randomly rewired links, where the temporal order and synchronization can be optimized.

  2. 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.

  3. 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

  4. 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

  5. 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

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. 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...

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

    DOE PAGES

    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

  12. 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…

  13. Self-organized criticality occurs in non-conservative neuronal networks during `up' states

    NASA Astrophysics Data System (ADS)

    Millman, Daniel; Mihalas, Stefan; Kirkwood, Alfredo; Niebur, Ernst

    2010-10-01

    During sleep, under anaesthesia and in vitro, cortical neurons in sensory, motor, association and executive areas fluctuate between so-called up and down states, which are characterized by distinct membrane potentials and spike rates. Another phenomenon observed in preparations similar to those that exhibit up and down states-such as anaesthetized rats, brain slices and cultures devoid of sensory input, as well as awake monkey cortex-is self-organized criticality (SOC). SOC is characterized by activity `avalanches' with a branching parameter near unity and size distribution that obeys a power law with a critical exponent of about -3/2. Recent work has demonstrated SOC in conservative neuronal network models, but critical behaviour breaks down when biologically realistic `leaky' neurons are introduced. Here, we report robust SOC behaviour in networks of non-conservative leaky integrate-and-fire neurons with short-term synaptic depression. We show analytically and numerically that these networks typically have two stable activity levels, corresponding to up and down states, that the networks switch spontaneously between these states and that up states are critical and down states are subcritical.

  14. 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

  15. Critical phenomena and noise-induced phase transitions in neuronal networks.

    PubMed

    Lee, K-E; Lopes, M A; Mendes, J F F; Goltsev, A V

    2014-01-01

    We study numerically and analytically first- and second-order phase transitions in neuronal networks stimulated by shot noise (a flow of random spikes bombarding neurons). Using an exactly solvable cortical model of neuronal networks on classical random networks, we find critical phenomena accompanying the transitions and their dependence on the shot noise intensity. We show that a pattern of spontaneous neuronal activity near a critical point of a phase transition is a characteristic property that can be used to identify the bifurcation mechanism of the transition. We demonstrate that bursts and avalanches are precursors of a first-order phase transition, paroxysmal-like spikes of activity precede a second-order phase transition caused by a saddle-node bifurcation, while irregular spindle oscillations represent spontaneous activity near a second-order phase transition caused by a supercritical Hopf bifurcation. Our most interesting result is the observation of the paroxysmal-like spikes. We show that a paroxysmal-like spike is a single nonlinear event that appears instantly from a low background activity with a rapid onset, reaches a large amplitude, and ends up with an abrupt return to lower activity. These spikes are similar to single paroxysmal spikes and sharp waves observed in electroencephalographic (EEG) measurements. Our analysis shows that above the saddle-node bifurcation, sustained network oscillations appear with a large amplitude but a small frequency in contrast to network oscillations near the Hopf bifurcation that have a small amplitude but a large frequency. We discuss an amazing similarity between excitability of the cortical model stimulated by shot noise and excitability of the Morris-Lecar neuron stimulated by an applied current.

  16. Toward on-chip functional neuronal networks: computational study on the effect of synaptic connectivity on neural activity.

    PubMed

    Foroushani, Armin Najarpour; Ghafar-Zadeh, Ebrahim

    2014-01-01

    This paper presents a new unified computational-experimental approach to study the role of the synaptic activity on the activity of neurons in the small neuronal networks (NNs). In a neuronal tissue/organ, this question is investigated with higher complexities by recording action potentials from population of neurons in order to find the relationship between connectivity and the recorded activities. In this approach, we study the dynamics of very small cortical neuronal networks, which can be experimentally synthesized on chip with constrained connectivity. Multi-compartmental Hodgkin-Huxley model is used in NEURON software to reproduce cells by extracting the experimental data from the synthesized NNs. We thereafter demonstrate how the type of synaptic activity affects the network response to specific spike train using the simulation results.

  17. 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

  18. 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)

  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. Implementation of an artificial neuronal network to predict shunt necessity in carotid surgery.

    PubMed

    Aleksic, Marko; Luebke, Thomas; Heckenkamp, Joerg; Gawenda, Michael; Reichert, Viktor; Brunkwall, Jan

    2008-09-01

    In carotid surgery, it could be useful to know which patient will tolerate carotid cross-clamping in order to minimize the risks of perioperative strokes. In this clinical study, an artificial neuronal network (ANN) was applied and compared with conventional statistical methods to assess the value of various parameters to predict shunt necessity. Eight hundred and fifty patients undergoing carotid endarterectomy for a high-grade internal carotid artery stenosis under local anesthesia were analyzed regarding shunt necessity using a standard feed-forward, backpropagation ANN (NeuroSolutions); NeuroDimensions, Gainesville, FL) with three layers (one input layer, one hidden layer, one output layer). Among the input neurons, preoperative clinical (n = 9) and intraoperative hemodynamic (n = 3) parameters were examined separately. The accuracy of prediction was compared to the results of a regression analysis using the same variables. In 173 patients (20%) a shunt was used because hemispheric deficits or unconsciousness occurred during cross-clamping. With the ANN, not needing a shunt was predicted by preoperative and intraoperative parameters with an accuracy of 96% and 91%, respectively, where the regression analysis showed an accuracy of 98% and 96%, respectively. Those patients who needed a shunt were identified by preoperative parameters in 9% and by intraoperative parameters in 56% when the ANN was used. Regression analysis predicted shunt use correctly in 10% using preoperative parameters and 41% using intraoperative parameters. Intraoperative hemodynamic parameters are more suitable than preoperative parameters to indicate shunt necessity where the application of an ANN provides slightly better results compared to regression analysis. However, the overall accuracy is too low to renounce perioperative neuromonitoring methods like local anesthesia.

  1. 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

  2. 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

  3. 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.

  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. 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.

  7. Flat and tubular membrane systems for the reconstruction of hippocampal neuronal network.

    PubMed

    Morelli, Sabrina; Piscioneri, Antonella; Salerno, Simona; Rende, Maria; Campana, Carla; Tasselli, Franco; di Vito, Anna; Giusi, Giuseppina; Canonaco, Marcello; Drioli, Enrico; De Bartolo, Loredana

    2012-04-01

    The selection of appropriate biomaterials that promote cellular adhesion and growth is particularly important for the in vitro reconstruction of neuronal network. This study focused on the development of new polymeric membranes in flat and tubular (hollow-fibre) configurations as novel biomaterials for neuronal outgrowth. Two membrane systems constituted by modified polyetheretherketone (PEEK-WC) and polyacrylonitrile (PAN) membranes were developed and used for the culture of hamster hippocampal neurons. We demonstrated that all investigated membranes supported the adhesion and growth of hippocampal neurons enhancing neuronal differentiation and neurite alignment. The differences in cell behaviours between cells cultured on flat and hollow-fibre (HF) membranes were highlighted by the quantitative analysis of neuronal marker fluorescence intensity, morphometric analysis, RT-PCR analysis and also by metabolic activity measurements. In particular, the PAN HF membranes showed ideal growth culture conditions, guaranteeing adequate levels of metabolic features. Primary hippocampal cells cultured on PAN HF membranes were able to recreate in vitro a 3D neural tissue-like structure that, mimicking the hippocampal tissue, could be used as a tool for the study of natural and pathological neurobiological events.

  8. 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.

  9. 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.

  10. 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

  11. ELECTRON MICROSCOPE AND EXPERIMENTAL INVESTIGATIONS OF THE NEUROFILAMENTOUS NETWORK IN DEITERS' NEURONS

    PubMed Central

    Metuzals, J.; Mushynski, W. E.

    1974-01-01

    The assembly of filamentous elements and their relations to the plasma membrane and to the nuclear pores have been studied in Deiters' neurons of rabbit brain. Electron microscopy of thin sections and of ectoplasm spread preparations have been integrated with physicochemical experiments and differential interference microscopy of freshly isolated cells. A neurofilamentous network extends as a continuous, three-dimensional, semilattice structure throughout the ectoplasm, the "plasma roads," and the perinuclear zone of the perikaryon. This space network consists of ∼90-Å wide neurofilaments arranged in fascicles which are interconnected by an exchange of neurofilaments. The neurofilaments consist of intercoiled ∼20-Å wide unit-filaments and are associated through cross-associating filaments with other neurofilaments of the fascicle and with microfilaments. The ∼20–50-Å wide microfilaments display intimate associations with the plasma membrane and with the nuclear pores. Electron microscopy of thin sections from glycerinated and heavy meromyosin-treated Deiters' neurons shows that actin-like filaments are present in the pre- and postsynaptic regions of synapses terminating on these neurons. It is proposed that the neurofilamentous space network serves a transducing function by linking plasma membrane activities with the genetic machinery of the neuron. PMID:4599504

  12. Prediction by neural network methods compared for energy control problems

    SciTech Connect

    Surkan, A.J.; Skurikhin, A.N.

    1996-10-01

    Daily recordings of energy consumption are sequenced to construct a time series that is used to test the accuracy of a variety of neural networks in making one- and two-day demand predictions. Five neural network methods were applied to the same data for a problem of predicting daily energy usage. These included four which were gradient-based, namely, back propagation (BACKPROP), quick-propagation (QUICKPROP), cascade correlation (CASCOR), memory neuron network (MNN), and a correlation-based method called ALOPEX. The observed time series is the only information source used for making predictions. Other supplementary parallel series of independent data can produce improvements. It was demonstrated that these neural network learning algorithms can train networks to predict consistently, one-day-ahead, over one year of set-aside test patterns and reduce the average error to below 19%. Reported is a comparison of applicability of neural networks by programmed simulations of the widely used gradient-based algorithms along with newer algorithms MNN and ALOPEX. The diverse capabilities of these various networks give insights which are a useful basis for selecting further studies.

  13. A Theory of the Epigenesis of Neuronal Networks by Selective Stabilization of Synapses

    PubMed Central

    Changeux, Jean-Pierre; Courrége, Philippe; Danchin, Antoine

    1973-01-01

    A formalism is introduced to represent the connective organization of an evolving neuronal network and the effects of environment on this organization by stabilization or degeneration of labile synapses associated with functioning. Learning, or the acquisition of an associative property, is related to a characteristic variability of the connective organization: the interaction of the environment with the genetic program is printed as a particular pattern of such organization through neuronal functioning. An application of the theory to the development of the neuromuscular junction is proposed and the basic selective aspect of learning emphasized. PMID:4517949

  14. Building artificial networks of neuronal cells with light-assisted polymer surface functionalization

    NASA Astrophysics Data System (ADS)

    Nicolau, Dan V.; Taniguchi, Hideo; Taguchi, Takahisa; Yoshikawa, Susumu

    1997-11-01

    The surface of photosensible diazo-naphto-quinone/novolak film was chemically manipulated through UV exposure and subsequent functionalization processes to obtain surfaces with different chemistries (DNQ, carboxylic, and peptidic groups). The neuronal cell attachment is controlled by three pairs of antagonistic surface variables: charged/uncharged species, amino/carboxylic groups and hydrophilic/hydrophobic balance, in which the former promote the adhesion. The study proves that microlithographic techniques in connection with surface functionalization with specific neuro-peptides can be used to build artificial networks with neuronal cells.

  15. Neuronal oscillations form parietal/frontal networks during contour integration.

    PubMed

    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.

  16. Neuronal oscillations form parietal/frontal networks during contour integration.

    PubMed

    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

  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. Postnatal developmental changes in activation profiles of the respiratory neuronal network in the rat ventral medulla

    PubMed Central

    Oku, Yoshitaka; Masumiya, Haruko; Okada, Yasumasa

    2007-01-01

    Two putative respiratory rhythm generators (RRGs), the para-facial respiratory group (pFRG) and the pre-Bötzinger complex (preBötC), have been identified in the neonatal rodent brainstem. To elucidate their functional roles during the neonatal period, we evaluated developmental changes of these RRGs by optical imaging using a voltage-sensitive dye. Optical signals, recorded from the ventral medulla of brainstem–spinal cord preparations of neonatal (P0–P4) rats (n = 44), were analysed by a cross correlation method. With development during the first few postnatal days, the respiratory-related activity in the pFRG reduced and shifted from a preinspiratory (P0–P1) to an inspiratory (P2–P4) pattern, whereas preBötC activity remained unchanged. The μ-opioid agonist [d-Ala(2),N-Me-Phe(4),Gly(5)-ol]-enkephalin (DAMGO) augmented preinspiratory activity in the pFRG, while the μ-opioid antagonist naloxone induced changes in spatiotemporal activation profiles that closely mimicked the developmental changes. These results are consistent with the recently proposed hypothesis by Janczewski and Feldman that the pFRG is activated to compensate for the depression of the preBötC by perinatal opiate surge. We conclude that significant reorganization of the respiratory neuronal network, characterized by a reduction of preinspiratory activity in the pFRG, occurs at P1–P2 in rats. The changes in spatiotemporal activation profiles of the pFRG neurones may reflect changes in the mode of coupling of the two respiratory rhythm generators. PMID:17884928

  19. 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

  20. Complex Rotation Quantum Dynamic Neural Networks (CRQDNN) using Complex Quantum Neuron (CQN): Applications to time series prediction.

    PubMed

    Cui, Yiqian; Shi, Junyou; Wang, Zili

    2015-11-01

    Quantum Neural Networks (QNN) models have attracted great attention since it innovates a new neural computing manner based on quantum entanglement. However, the existing QNN models are mainly based on the real quantum operations, and the potential of quantum entanglement is not fully exploited. In this paper, we proposes a novel quantum neuron model called Complex Quantum Neuron (CQN) that realizes a deep quantum entanglement. Also, a novel hybrid networks model Complex Rotation Quantum Dynamic Neural Networks (CRQDNN) is proposed based on Complex Quantum Neuron (CQN). CRQDNN is a three layer model with both CQN and classical neurons. An infinite impulse response (IIR) filter is embedded in the Networks model to enable the memory function to process time series inputs. The Levenberg-Marquardt (LM) algorithm is used for fast parameter learning. The networks model is developed to conduct time series predictions. Two application studies are done in this paper, including the chaotic time series prediction and electronic remaining useful life (RUL) prediction.

  1. 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

  2. Neurons in the amygdala play an important role in the neuronal network mediating a clonic form of audiogenic seizures both before and after audiogenic kindling.

    PubMed

    Raisinghani, Manish; Faingold, Carl L

    2005-01-25

    Previous studies showed that neuronal network nuclei for behaviorally different forms of audiogenic seizure (AGS) exhibit similarities and important differences. The amygdala is involved differentially in tonic AGS as compared to clonic AGS networks. The role of the lateral amygdala (LAMG) undergoes major changes after AGS repetition (AGS kindling) in tonic forms of AGS. The present study examined the role of LAMG in a clonic form of AGS [genetically epilepsy-prone rats (GEPR-3s)] before and after AGS kindling using bilateral microinjection and chronic neuronal recordings. AGS kindling in GEPR-3s results in facial and forelimb (F&F) clonus, and this behavior could be blocked following bilateral microinjection of a NMDA antagonist (2-amino-7-phosphonoheptanoate) without affecting generalized clonus. Higher AP7 doses blocked both generalized clonus and F&F clonus. LAMG neurons in GEPR-3s exhibited only onset type neuronal responses both before and after AGS kindling, unlike LAMG neurons in normal rats and a tonic form of AGS. A significantly greater LAMG neuronal firing rate occurred after AGS kindling at high acoustic intensities. The latency of LAMG neuronal firing increased significantly after AGS kindling. Burst firing occurred during wild running and generalized clonic behaviors before and after AGS kindling. Burst firing also occurred during F&F clonus after AGS kindling. These findings indicate that LAMG neurons play a critical role in the neuronal network for generalized clonus as well as F&F clonus in GEPR-3s, both before and after AGS kindling, which contrasts markedly with the role of LAMG in tonic AGS.

  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. 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

  5. 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.

  6. Information capacity and transmission are maximized in balanced cortical networks with neuronal avalanches.

    PubMed

    Shew, Woodrow L; Yang, Hongdian; Yu, Shan; Roy, Rajarshi; Plenz, Dietmar

    2011-01-01

    The repertoire of neural activity patterns that a cortical network can produce constrains the ability of the network to transfer and process information. Here, we measured activity patterns obtained from multisite local field potential recordings in cortex cultures, urethane-anesthetized rats, and awake macaque monkeys. First, we quantified the information capacity of the pattern repertoire of ongoing and stimulus-evoked activity using Shannon entropy. Next, we quantified the efficacy of information transmission between stimulus and response using mutual information. By systematically changing the ratio of excitation/inhibition (E/I) in vitro and in a network model, we discovered that both information capacity and information transmission are maximized at a particular intermediate E/I, at which ongoing activity emerges as neuronal avalanches. Next, we used our in vitro and model results to correctly predict in vivo information capacity and interactions between neuronal groups during ongoing activity. Close agreement between our experiments and model suggest that neuronal avalanches and peak information capacity arise because of criticality and are general properties of cortical networks with balanced E/I.

  7. Using phase resetting to predict 1:1 and 2:2 locking in two neuron networks in which firing order is not always preserved.

    PubMed

    Maran, Selva K; Canavier, Carmen C

    2008-02-01

    Our goal is to understand how nearly synchronous modes arise in heterogenous networks of neurons. In heterogenous networks, instead of exact synchrony, nearly synchronous modes arise, which include both 1:1 and 2:2 phase-locked modes. Existence and stability criteria for 2:2 phase-locked modes in reciprocally coupled two neuron circuits were derived based on the open loop phase resetting curve (PRC) without the assumption of weak coupling. The PRC for each component neuron was generated using the change in synaptic conductance produced by a presynaptic action potential as the perturbation. Separate derivations were required for modes in which the firing order is preserved and for those in which it alternates. Networks composed of two model neurons coupled by reciprocal inhibition were examined to test the predictions. The parameter regimes in which both types of nearly synchronous modes are exhibited were accurately predicted both qualitatively and quantitatively provided that the synaptic time constant is short with respect to the period and that the effect of second order resetting is considered. In contrast, PRC methods based on weak coupling could not predict 2:2 modes and did not predict the 1:1 modes with the level of accuracy achieved by the strong coupling methods. The strong coupling prediction methods provide insight into what manipulations promote near-synchrony in a two neuron network and may also have predictive value for larger networks, which can also manifest changes in firing order. We also identify a novel route by which synchrony is lost in mildly heterogenous networks.

  8. 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

  9. 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.

  10. Quantifying biomarkers of cognitive dysfunction and neuronal network hyperexcitability in mouse models of Alzheimer's disease: depletion of calcium-dependent proteins and inhibitory hippocampal remodeling.

    PubMed

    Palop, Jorge J; Mucke, Lennart; Roberson, Erik D

    2011-01-01

    High levels of Aβ impair neuronal function at least in part by disrupting normal synaptic transmission and causing dysfunction of neural networks. This network dysfunction includes abnormal synchronization of neuronal activity resulting in epileptiform activity. Over time, this aberrant network activity can lead to the depletion of calcium-dependent proteins, such as calbindin, Fos, and Arc, and compensatory inhibitory remodeling of hippocampal circuits, including GABAergic sprouting and ectopic expression of the inhibitory neuropeptide Y (NPY) in dentate granule cells. Here we present detailed protocols for detecting and quantifying these alterations in mouse models of Alzheimer's disease (AD) by immunohistochemistry. These methods are useful as surrogate measures for detecting chronic aberrant network activity in models of AD and epilepsy. In addition, since we have found that the severity of these changes relates to the degree of Aβ-dependent cognitive impairments, the protocols are useful for quantifying biomarkers of cognitive impairment in mouse models of AD.

  11. Electrical stimulation therapies for CNS disorders and pain are mediated by competition between different neuronal networks in the brain.

    PubMed

    Faingold, Carl L

    2008-11-01

    CNS neuronal networks are known to control normal physiological functions, including locomotion and respiration. Neuronal networks also mediate the pathophysiology of many CNS disorders. Stimulation therapies, including localized brain and vagus nerve stimulation, electroshock, and acupuncture, are proposed to activate "therapeutic" neuronal networks. These therapeutic networks are dormant prior to stimulatory treatments, but when the dormant networks are activated they compete with pathophysiological neuronal networks, disrupting their function. This competition diminishes the disease symptoms, providing effective therapy for otherwise intractable CNS disorders, including epilepsy, Parkinson's disease, chronic pain, and depression. Competition between stimulation-activated therapeutic networks and pathophysiological networks is a major mechanism mediating the therapeutic effects of stimulation. This network interaction is hypothesized to involve competition for "control" of brain regions that contain high proportions of conditional multireceptive (CMR) neurons. CMR regions, including brainstem reticular formation, amygdala, and cerebral cortex, have extensive connections to numerous brain areas, allowing these regions to participate potentially in many networks. The participation of CMR regions in any network is often variable, depending on the conditions affecting the organism, including vigilance states, drug treatment, and learning. This response variability of CMR neurons is due to the high incidence of excitatory postsynaptic potentials that are below threshold for triggering action potentials. These subthreshold responses can be brought to threshold by blocking inhibition or enhancing excitation via the paradigms used in stimulation therapies. Participation of CMR regions in a network is also strongly affected by pharmacological treatments (convulsant or anesthetic drugs) and stimulus parameters (strength and repetition rate). Many studies indicate that

  12. 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

  13. 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.

  14. 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

  15. Activity and High-Order Effective Connectivity Alterations in Sanfilippo C Patient-Specific Neuronal Networks.

    PubMed

    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

    2015-10-13

    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.

  16. A compressible scaffold for minimally invasive delivery of large intact neuronal networks.

    PubMed

    Béduer, Amélie; Braschler, Thomas; Peric, Oliver; Fantner, Georg E; Mosser, Sébastien; Fraering, Patrick C; Benchérif, Sidi; Mooney, David J; Renaud, Philippe

    2015-01-28

    Millimeter to centimeter-sized injectable neural scaffolds based on macroporous cryogels are presented. The polymer-scaffolds are made from alginate and carboxymethyl-cellulose by a novel simple one-pot cryosynthesis. They allow surgical sterility by means of autoclaving, and present native laminin as an attachment motive for neural adhesion and neurite development. They are designed to protect an extended, living neuronal network during compression to a small fraction of the original volume in order to enable minimally invasive delivery. The scaffolds behave as a mechanical meta-material: they are soft at the macroscopic scale, enabling injection through narrow-bore tubing and potentially good cellular scaffold integration in soft target tissues such as the brain. At the same time, the scaffold material has a high local Young modulus, allowing protection of the neuronal network during injection. Based on macroscopic and nanomechanical characterization, the generic geometrical and mechanical design rules are presented, enabling macroporous cellular scaffold injectability.

  17. 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.

  18. Surface coating as a key parameter in engineering neuronal network structures in vitro.

    PubMed

    Sun, Yi; Huang, Zhuo; Liu, Wenwen; Yang, Kaixuan; Sun, Kang; Xing, Shige; Wang, Dong; Zhang, Wei; Jiang, Xingyu

    2012-12-01

    By quantitatively comparing a variety of macromolecular surface coating agents, we discovered that surface coating strongly modulates the adhesion and morphogenesis of primary hippocampal neurons and serves as a switch of somata clustering and neurite fasciculation in vitro. The kinetics of neuronal adhesion on poly-lysine-coated surfaces is much faster than that on laminin and Matrigel-coated surfaces, and the distribution of adhesion is more homogenous on poly-lysine. Matrigel and laminin, on the other hand, facilitate neuritogenesis more than poly-lysine does. Eventually, on Matrigel-coated surfaces of self-assembled monolayers, neurons tend to undergo somata clustering and neurite fasciculation. By replacing coating proteins with cerebral astrocytes, and patterning neurons on astrocytes through self-assembled monolayers, microfluidics and micro-contact printing, we found that astrocyte promotes soma adhesion and astrocyte processes guide neurites. There, astrocytes could be a versatile substrate in engineering neuronal networks in vitro. Besides, quantitative measurements of cellular responses on various coatings would be valuable information for the neurobiology community in the choice of the most appropriate coating strategy.

  19. 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.

  20. Functional neuronal network activity differs with cognitive dysfunction in childhood-onset systemic lupus erythematosus

    PubMed Central

    2013-01-01

    differential activation of functional neuronal networks during fMRI tasks probing working memory, VCA, and attention. Results suggest a compensatory mechanism allows maintenance of attentional performance under NCD. This mechanism appears to break down for the VCA and working memory challenges presented in this study. The observation that neuronal network activation is related to the formal neuropsychological testing performance makes fMRI a candidate imaging biomarker for cSLE-associated NCD. PMID:23497727

  1. 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)