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SUMMARY:LCN Seminar: Chaos and Entropy Production in Spiking Networks
DTSTART:20141211T133000
DTSTAMP:20260916T063933Z
UID:cbb11ebf1ec733e24f226ba0b84e4f27c56f6b11236b45e3b3679c6f
CATEGORIES:Conferences - Seminars
DESCRIPTION:Rainer ENGELKEN\nTheoretical Neuroscience\, Max Planck Institu
 te for Dynamics and Network Dynamics Group \nThe prevailing explanation fo
 r the irregularity of spike sequences in the cerebral cortex is a dynamic 
 balance of excitatory and inhibitory synaptic inputs - the socalled balanc
 ed state.\nRecently it was found that the stability of the balanced state 
 dynamics depends strongly on the detailed underlying dynamics of individua
 l neurons.\nPrevious studies of the dynamics of the balanced state used ra
 ndom (Erdös-Reni) networks. We extended this to arbitrary topologies. An 
 analytical expression for the Jacobian enables us to calculate the fullLya
 punov spectrum. Using a neuron model in which action potential onset rapid
 ness is adjustable\, we simulated the network dynamics in numerically exac
 t event-based simulations and calculated Lyapunov spectrum\, Kolmogorov-Si
 nai entropy production rate and attractor dimension for a variety of netwo
 rk topologies.\nWe found that the importance of the internal single neuron
  dynamics for the network stability persists in different topologies. Whil
 e the entropy production and attractor dimension in clustered and ring net
 works was very similar to random networks\, these dynamical properties wer
 e changed substantially when introducing second order network motifs or a 
 small world topology.\nWe also extended the model from constant to stochas
 tic spiking external input and studied its transition. We found that input
  spike trains suppress chaos in balanced neural circuits.\nOur study shows
  the importance of single neuron dynamics for network chaos and provides a
  novel avenue to study the role of sensory streams in shaping the dynamics
  of large networks.
LOCATION:AAC132 http://plan.epfl.ch/?room=AAC132
STATUS:CONFIRMED
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