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SUMMARY:Chaos and entropy production in spiking networks - Rainer Engelken
DTSTART:20141110T103000Z
DTEND:20141110T113000Z
UID:TALK56128@talks.cam.ac.uk
CONTACT:Guillaume Hennequin
DESCRIPTION:*Please note that the venue is different from the usual one*\n
 \nThe prevailing explanation for the irregularity of spike sequences in th
 e cerebral cortex is a dynamic balance of excitatory and inhibitory synapt
 ic inputs - the so-called balanced state. Recently it was found that the s
 tability of the balanced state dynamics depends strongly on the detailed u
 nderlying dynamics of individual neurons.\n\nPrevious studies of the dynam
 ics of the balanced state used random (Erdös-Reni) networks. We extended 
 this to arbitrary topologies. An analytical expression for the Jacobian en
 ables us to calculate the full Lyapunov spectrum. Using a neuron model in 
 which action potential onset rapidness is adjustable\, we simulated the ne
 twork dynamics in numerically exact event-based simulations and calculated
  Lyapunov spectrum\, Kolmogorov-Sinai entropy production rate and attracto
 r dimension for a variety of network topologies.\n\nWe found that the impo
 rtance of the internal single neuron dynamics for the network stability pe
 rsists in different topologies. While the entropy production and attractor
  dimension in clustered and ring networks was very similar to random netwo
 rks\, these dynamical properties were changed substantially when introduci
 ng second order network motifs or a small world topology. We also extended
  the model from constant to stochastic spiking external input and studied 
 its transition. We found that input spike trains suppress chaos in balance
 d neural circuits.\n\nOur study shows the importance of single neuron dyna
 mics for network chaos and provides a novel avenue to study the role of se
 nsory streams in shaping the dynamics of large networks.
LOCATION:Engineering Department\, Baker Building (5th floor)\, MIL meeting
  room
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