Spatiotemporal activities of a pulse-coupled biological neural network

The present work is on the spatiotemporal activities and effects of chaotic neurons in a pulse-coupled biological neural network. The biological neural network used is that of Caenorhabditis elegans. Because of its similarity to human neural network, it can be used to understand the simple dynamics of human brain. Within the network the neurons are found to exhibit chaotic nature, even though their parameters are that of normal neurons. It is observed that when the strength of synaptic conductance is increased, initially the bursting synchronization, entropy of the network and the average firing rate decrease slightly and then increase. Since chaotic dynamics of neuron plays an important role in human brain functions, the neurons of the network are intentionally made chaotic and the dynamics is studied. As the neurons of the network are made chaotic, ‘near-death’-like surges of neuron activity before ending firing is observed throughout the network. Also, the brain dynamics changes from alert to rest state. When most of the neurons of the network are made chaotic, their activities become independent of the coupling strength.

[1]  Chris G. Antonopoulos,et al.  Dynamical complexity in the C.elegans neural network , 2015, 1510.07260.

[2]  Jun Ma,et al.  Cooperative dynamics in neuronal networks , 2013 .

[3]  B. Ermentrout.  Neural networks as spatio-temporal pattern-forming systems , 1998 .

[4]  R Erichsen,et al.  Multistability in networks of Hindmarsh-Rose neurons. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  L. Chawla,et al.  Surges of electroencephalogram activity at the time of death: a case series. , 2009, Journal of palliative medicine.

[6]  Antonio Torralba,et al.  Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence , 2016, Scientific Reports.

[7]  J. Donoghue,et al.  Collective dynamics in human and monkey sensorimotor cortex: predicting single neuron spikes , 2009, Nature Neuroscience.

[8]  R. E. Amritkar,et al.  General mechanism for amplitude death in coupled systems. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  Rose P. Ignatius,et al.  Nonlinear feedback coupling in Hindmarsh–Rose neurons , 2017 .

[10]  Jun Tang,et al.  Astrocyte calcium wave induces seizure-like behavior in neuron network , 2017 .

[11]  Antonio Politi,et al.  Irregular collective behavior of heterogeneous neural networks. , 2010, Physical review letters.

[12]  Roxanne Ward,et al.  Electroencephalographic Recordings During Withdrawal of Life-Sustaining Therapy Until 30 Minutes After Declaration of Death , 2016, Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques.

[13]  Nancy Kopell,et al.  Synchronization of Strongly Coupled Excitatory Neurons: Relating Network Behavior to Biophysics , 2003, Journal of Computational Neuroscience.

[14]  Wei Li,et al.  Analysis on the synchronized network of Hindmarsh-Rose neuronal models , 2015 .

[15]  Takanori Kochiyama,et al.  Spatiotemporal neural network dynamics for the processing of dynamic facial expressions , 2015, Scientific Reports.

[16]  Hengtong Wang,et al.  Spatiotemporal activities of neural network exposed to external electric fields , 2016 .

[17]  Jun Tang,et al.  Autaptic Modulation of Electrical Activity in a Network of Neuron-Coupled Astrocyte , 2017, Complex..

[18]  Wuyin Jin,et al.  Pattern selection and self-organization induced by random boundary initial values in a neuronal network , 2016 .

[19]  Eugene M. Izhikevich,et al.  Which model to use for cortical spiking neurons? , 2004, IEEE Transactions on Neural Networks.

[20]  Jürgen Kurths,et al.  Generalizing the transition from amplitude to oscillation death in coupled oscillators. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  D. Abrams,et al.  Chimera states: coexistence of coherence and incoherence in networks of coupled oscillators , 2014, 1403.6204.

[22]  Ma Jun,et al.  A review for dynamics of collective behaviors of network of neurons , 2015 .

[23]  Philippe Faure,et al.  Is there chaos in the brain? II. Experimental evidence and related models. , 2003, Comptes rendus biologies.

[24]  Wilten Nicola,et al.  Network bursting using experimentally constrained single compartment CA3 hippocampal neuron models with adaptation , 2011, Journal of Computational Neuroscience.

[25]  Lav R. Varshney,et al.  Structural Properties of the Caenorhabditis elegans Neuronal Network , 2009, PLoS Comput. Biol..

[26]  Marcus Kaiser,et al.  Nonoptimal Component Placement, but Short Processing Paths, due to Long-Distance Projections in Neural Systems , 2006, PLoS Comput. Biol..

[27]  Angelo Di Garbo,et al.  Calcium signalling in astrocytes and modulation of neural activity , 2007, Biosyst..

[28]  An Wang,et al.  Computer Simulation of Noise Effects of the Neighborhood of Stimulus Threshold for a Mathematical Model of Homeostatic Regulation of Sleep-Wake Cycles , 2017, Complex..

[29]  Alan D. Dorval,et al.  Probability distributions of the logarithm of inter-spike intervals yield accurate entropy estimates from small datasets , 2008, Journal of Neuroscience Methods.

[30]  Victoria Booth,et al.  Interaction of Cellular and Network Mechanisms in Spatiotemporal Pattern Formation in Neuronal Networks , 2009, The Journal of Neuroscience.

[31]  Victoria Booth,et al.  Cellularly-Driven Differences in Network Synchronization Propensity Are Differentially Modulated by Firing Frequency , 2011, PLoS Comput. Biol..

[32]  Renate Wackerbauer,et al.  Transient spatiotemporal chaos in the Morris-Lecar neuronal ring network. , 2014, Chaos.

[33]  Robert A. Kosinski,et al.  Dynamics of the Model of the Caenorhabditis Elegans Neural Network , 2007 .

[34]  Chris G. Antonopoulos,et al.  Chimera-like States in Modular Neural Networks , 2015, Scientific Reports.

[35]  Eugene M. Izhikevich,et al.  Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.

[36]  J. Sprott.  Chaotic dynamics on large networks. , 2008, Chaos.

[37]  W. Freeman,et al.  Role of chaotic dynamics in neural plasticity. , 1994, Progress in brain research.

[38]  Tanmoy Banerjee,et al.  Amplitude death and synchronized states in nonlinear time-delay systems coupled through mean-field diffusion. , 2013, Chaos.

[39]  Rolf Kötter,et al.  Online retrieval, processing, and visualization of primate connectivity data from the CoCoMac Database , 2007, Neuroinformatics.

[40]  A. Destexhe.  Stability of periodic oscillations in a network of neurons with time delay , 1994 .

[41]  Eugene M Izhikevich,et al.  Hybrid spiking models , 2010, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[42]  Huaguang Gu,et al.  Transition from double coherence resonances to single coherence resonance in a neuronal network with phase noise. , 2015, Chaos.

[43]  D. Hansel,et al.  Role of delays in shaping spatiotemporal dynamics of neuronal activity in large networks. , 2005, Physical review letters.

[44]  Jun Ma,et al.  Lévy noise improves the electrical activity in a neuron under electromagnetic radiation , 2017, PloS one.

[45]  G. Rangarajan,et al.  Stability of synchronized dynamics and pattern formation in coupled systems: Review of some recent results , 2006 .

[46]  J. Hizanidis,et al.  Multi-chimera states in the Leaky Integrate-and-Fire model , 2015, 1509.08668.