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SUMMARY:Computational Neuroscience Journal Club - Dylan Festa (University 
 of Cambridge)
DTSTART:20151027T160000Z
DTEND:20151027T170000Z
UID:TALK62116@talks.cam.ac.uk
CONTACT:Guillaume Hennequin
DESCRIPTION:Dylan Festa will cover:\n\n* Dynamics of Multistable States du
 ring Ongoing and Evoked Cortical Activity\n* L Mazzucato\, A Fontanini and
  G La Camera\n* J Neurosci (2015)\n* http://www.jneurosci.org/content/35/2
 1/8214.abstract\n\nSingle-trial analyses of ensemble activity in alert ani
 mals demonstrate that cortical circuits dynamics evolve through temporal s
 equences of metastable states. Metastability has been studied for its pote
 ntial role in sensory coding\, memory\, and decision-making. Yet\, very li
 ttle is known about the network mechanisms responsible for its genesis. It
  is often assumed that the onset of state sequences is triggered by an ext
 ernal stimulus. Here we show that state sequences can be observed also in 
 the absence of overt sensory stimulation. Analysis of multielectrode recor
 dings from the gustatory cortex of alert rats revealed ongoing sequences o
 f states\, where single neurons spontaneously attain several firing rates 
 across different states. This single-neuron multistability represents a ch
 allenge to existing spiking network models\, where typically each neuron i
 s at most bistable. We present a recurrent spiking network model that acco
 unts for both the spontaneous generation of state sequences and the multis
 tability in single-neuron firing rates. Each state results from the activa
 tion of neural clusters with potentiated intracluster connections\, with t
 he firing rate in each cluster depending on the number of active clusters.
  Simulations show that the model's ensemble activity hops among the differ
 ent states\, reproducing the ongoing dynamics observed in the data. When p
 robed with external stimuli\, the model predicts the quenching of single-n
 euron multistability into bistability and the reduction of trial-by-trial 
 variability. Both predictions were confirmed in the data. Together\, these
  results provide a theoretical framework that captures both ongoing and ev
 oked network dynamics in a single mechanistic model.\n
LOCATION:Cambridge University Engineering Department\, CBL\, BE-438 (http:
 //learning.eng.cam.ac.uk/Public/Directions)
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