Computational Neuroscience Journal Club

Add to your list(s) Download to your calendar using vCal

• Dylan Festa (University of Cambridge)
• Tuesday 27 October 2015, 16:00-17:00
• Cambridge University Engineering Department, CBL, BE-438 (http://learning.eng.cam.ac.uk/Public/Directions).

If you have a question about this talk, please contact Guillaume Hennequin.

Dylan Festa will cover:

• Dynamics of Multistable States during Ongoing and Evoked Cortical Activity
• L Mazzucato, A Fontanini and G La Camera
• J Neurosci (2015)
• http://www.jneurosci.org/content/35/21/8214.abstract

Single-trial analyses of ensemble activity in alert animals demonstrate that cortical circuits dynamics evolve through temporal sequences of metastable states. Metastability has been studied for its potential role in sensory coding, memory, and decision-making. Yet, very little is known about the network mechanisms responsible for its genesis. It is often assumed that the onset of state sequences is triggered by an external stimulus. Here we show that state sequences can be observed also in the absence of overt sensory stimulation. Analysis of multielectrode recordings from the gustatory cortex of alert rats revealed ongoing sequences of states, where single neurons spontaneously attain several firing rates across different states. This single-neuron multistability represents a challenge to existing spiking network models, where typically each neuron is at most bistable. We present a recurrent spiking network model that accounts for both the spontaneous generation of state sequences and the multistability in single-neuron firing rates. Each state results from the activation of neural clusters with potentiated intracluster connections, with the firing rate in each cluster depending on the number of active clusters. Simulations show that the model’s ensemble activity hops among the different states, reproducing the ongoing dynamics observed in the data. When probed with external stimuli, the model predicts the quenching of single-neuron 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 evoked network dynamics in a single mechanistic model.

This talk is part of the Computational Neuroscience series.

This talk is included in these lists:

• All Talks (aka the CURE list)
• Biology
• Biology
• CBL important
• CamBridgeSens
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge Neuroscience Seminars
• Cambridge University Engineering Department, CBL, BE-438 (http://learning.eng.cam.ac.uk/Public/Directions)
• Cambridge talks
• Chris Davis' list
• Computational Neuroscience
• Computational and Biological Learning Seminar Series
• Featured lists
• Guy Emerson's list
• Hanchen DaDaDash
• Inference Group Journal Clubs
• Inference Group Summary
• Information Engineering Division seminar list
• Interested Talks
• Life Science
• Life Science Interface Seminars
• Life Sciences
• Life Sciences
• ME Seminar
• Neuroscience
• Neuroscience Seminars
• Neuroscience Seminars
• Quantum Matter Journal Club
• Required lists for MLG
• Stem Cells & Regenerative Medicine
• TQS Journal Clubs
• Trust & Technology Initiative - interesting events
• bld31
• custom
• dh539
• my_list
• ndk22's list
• ob366-ai4er
• other talks
• rp587
• se456's list
• yk373's list

Note that ex-directory lists are not shown.