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Talk by Dr. Christopher Trengove

Integrated Simulation of Living Matter Group, Computational Science Research Program, RIKEN, Japan

21 Dec 2011 13:30
21 Dec 2011 14:30

Storage Capacity of a Model of Cortically Embedded Synfire Chains

Synfire chains, sequences of pools linked by feedforward connections, support the propagation of precisely timed spike sequences, or synfire waves. We present a model of synfire chain embedding in a cortical scale recurrent network using conductance-based synapses, balanced chains, and variable transmission delays. The network attains substantially higher storage capacities than previous spiking neuron models and allows all its connections to be used for embedding. The number of waves is regulated by recurrent background noise. We computationally explored the storage capacity limit, and use a mean field analysis to describe the equilibrium state. Simulations confirm the mean field analysis over broad ranges of pool size and connectivity per neuron; the number of pools embedded in the system trades off against the firing rate and the number of waves. An optimal inhibition level balances the conflicting requirements of stable synfire propagation and limited response to background noise. A simplified analysis shows that the present conductance-based synapses achieve higher contrast between the responses to synfire input and background noise compared to current-based synapses, and also reveals the role of transmission delays, both within and between links. Regulation of wave numbers depends on the use of variable intra-link transmission delays. An outstanding question which this work does not address is how and when the asynchronous irregular ground state becomes unstable in the model. Variability of delays, whether within or between links, seems to be important for stability. However, the appearances of instability in our simulations may be due to an artifact of conductance-based delta-synapses.