Robust development of synfire chains from multiple plasticity mechanisms

Biological neural networks are shaped by a large number of plasticity mechanisms operating at different time scales. How these mechanisms work together to sculpt such networks into effective information processing circuits is still poorly understood. Here we study the spontaneous development of synfire chains in a self-organizing recurrent neural network (SORN) model that combines a number of different plasticity mechanisms including spike-timing-dependent plasticity, structural plasticity, as well as homeostatic forms of plasticity. We find that the network develops an abundance of feed-forward motifs giving rise to synfire chains. The chains develop into ring-like structures, which we refer to as “synfire rings.” These rings emerge spontaneously in the SORN network and allow for stable propagation of activity on a fast time scale. A single network can contain multiple non-overlapping rings suppressing each other. On a slower time scale activity switches from one synfire ring to another maintaining firing rate homeostasis. Overall, our results show how the interaction of multiple plasticity mechanisms might give rise to the robust formation of synfire chains in biological neural networks.

[1]  Henning Sprekeler,et al.  Inhibitory synaptic plasticity: spike timing-dependence and putative network function , 2013, Front. Neural Circuits.

[2]  Kristen M Harris,et al.  Coordination of size and number of excitatory and inhibitory synapses results in a balanced structural plasticity along mature hippocampal CA1 dendrites during LTP , 2011, Hippocampus.

[3]  Eugene M. Izhikevich,et al.  Polychronization: Computation with Spikes , 2006, Neural Computation.

[4]  R. Silver,et al.  Synaptic connections between layer 4 spiny neurone‐ layer 2/3 pyramidal cell pairs in juvenile rat barrel cortex: physiology and anatomy of interlaminar signalling within a cortical column , 2002, The Journal of physiology.

[5]  H. Kasai,et al.  Principles of Long-Term Dynamics of Dendritic Spines , 2008, The Journal of Neuroscience.

[6]  W. Gerstner,et al.  Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity , 2006, The Journal of Neuroscience.

[7]  P. J. Sjöström,et al.  Correction: Highly Nonrandom Features of Synaptic Connectivity in Local Cortical Circuits , 2005, PLoS Biology.

[8]  Paul Miller,et al.  Excitatory, Inhibitory, and Structural Plasticity Produce Correlated Connectivity in Random Networks Trained to Solve Paired-Stimulus Tasks , 2011, Front. Comput. Neurosci..

[9]  Richard Hans Robert Hahnloser,et al.  An ultra-sparse code underliesthe generation of neural sequences in a songbird , 2002, Nature.

[10]  C. Petersen,et al.  The Excitatory Neuronal Network of the C2 Barrel Column in Mouse Primary Somatosensory Cortex , 2009, Neuron.

[11]  Richard Hans Robert Hahnloser,et al.  Spike-Time-Dependent Plasticity and Heterosynaptic Competition Organize Networks to Produce Long Scale-Free Sequences of Neural Activity , 2010, Neuron.

[12]  R. Yuste,et al.  Dynamics of Spontaneous Activity in Neocortical Slices , 2001, Neuron.

[13]  L. Abbott,et al.  Synaptic plasticity: taming the beast , 2000, Nature Neuroscience.

[14]  Joseph K Jun,et al.  Development of Neural Circuitry for Precise Temporal Sequences through Spontaneous Activity, Axon Remodeling, and Synaptic Plasticity , 2007, PloS one.

[15]  H. Abarbanel,et al.  Spike-timing-dependent plasticity of inhibitory synapses in the entorhinal cortex. , 2006, Journal of neurophysiology.

[16]  Erratum to Coordination of Size and Number of Excitatory and Inhibitory Synapses Results in a Balanced Structural Plasticity Along Mature Hippocampal CA1 Dendrites During LTP [Hippocampus, 21, (2011) 354-373] DOI 10.1002/hipo.20768] , 2013 .

[17]  Henning Sprekeler,et al.  Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks , 2011, Science.

[18]  K. D. Punta,et al.  An ultra-sparse code underlies the generation of neural sequences in a songbird , 2002 .

[19]  R. Rosenfeld.  Nature , 2009, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

[20]  Yoram Ben-Shaul,et al.  Temporally precise cortical firing patterns are associated with distinct action segments. , 2006, Journal of neurophysiology.

[21]  Gordon Pipa,et al.  Emerging Bayesian Priors in a Self-Organizing Recurrent Network , 2011, ICANN.

[22]  D. Georgescauld.  Local Cortical Circuits, An Electrophysiological Study , 1983 .

[23]  Niraj S. Desai,et al.  Plasticity in the intrinsic excitability of cortical pyramidal neurons , 1999, Nature Neuroscience.

[24]  Jochen Triesch,et al.  Nonlinear Dynamics Analysis of a Self-Organizing Recurrent Neural Network: Chaos Waning , 2014, PloS one.

[25]  Professor Moshe Abeles,et al.  Local Cortical Circuits , 1982, Studies of Brain Function.

[26]  G. Buzsáki,et al.  Sequential structure of neocortical spontaneous activity in vivo , 2007, Proceedings of the National Academy of Sciences.

[27]  G. Bi,et al.  Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type , 1998, The Journal of Neuroscience.

[28]  Heidi Johansen-Berg,et al.  Structural Plasticity: Rewiring the Brain , 2007, Current Biology.

[29]  W. Gerstner,et al.  Connectivity reflects coding: a model of voltage-based STDP with homeostasis , 2010, Nature Neuroscience.

[30]  Markus Diesmann,et al.  Limits to the Development of Feed-Forward Structures in Large Recurrent Neuronal Networks , 2011, Front. Comput. Neurosci..

[31]  H. Markram,et al.  Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs , 1997, Science.

[32]  Christos Dimitrakakis,et al.  Network Self-Organization Explains the Statistics and Dynamics of Synaptic Connection Strengths in Cortex , 2013, PLoS Comput. Biol..

[33]  Sebastian Wernicke,et al.  A Faster Algorithm for Detecting Network Motifs , 2005, WABI.

[34]  Tohru Ikeguchi,et al.  STDP Provides the Substrate for Igniting Synfire Chains by Spatiotemporal Input Patterns , 2008, Neural Computation.

[35]  E. Bienenstock,et al.  The self-organized growth of synfire patterns , 2006 .

[36]  Isaac Meilijson,et al.  Distributed synchrony in a cell assembly of spiking neurons , 2001, Neural Networks.

[37]  Adam Prügel-Bennett,et al.  Learning Synfire Chains: Turning Noise into Signal , 1996, Int. J. Neural Syst..

[38]  Christopher B. Barrett.  Limits of Development , 2015 .

[39]  Niraj S. Desai,et al.  Activity-dependent scaling of quantal amplitude in neocortical neurons , 1998, Nature.

[40]  Sen Song,et al.  Highly Nonrandom Features of Synaptic Connectivity in Local Cortical Circuits , 2005, PLoS biology.

[41]  D. Linden,et al.  The other side of the engram: experience-driven changes in neuronal intrinsic excitability , 2003, Nature Reviews Neuroscience.

[42]  E. Vaadia,et al.  Spatiotemporal structure of cortical activity: properties and behavioral relevance. , 1998, Journal of neurophysiology.

[43]  Gordon Pipa,et al.  SORN: A Self-Organizing Recurrent Neural Network , 2009, Front. Comput. Neurosci..

[44]  Peter A. Appleby,et al.  Triphasic spike-timing-dependent plasticity organizes networks to produce robust sequences of neural activity , 2012, Front. Comput. Neurosci..

[45]  Alessandro E. P. Villa,et al.  Emergence of Preferred Firing Sequences in Large Spiking Neural Networks during Simulated Neuronal Development , 2008, Int. J. Neural Syst..