Journal of Computational Neuroscience

, Volume 34, Issue 2, pp 185-209

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

High-capacity embedding of synfire chains in a cortical network model

  • Chris TrengoveAffiliated withIntegrated Simulation of Living Matter Group, RIKEN, Computational Science Research ProgramLaboratory for Perceptual Dynamics, RIKEN, Brain Science InstituteLaboratory for Computational Neurophysics, RIKEN, Brain Science InstitutePerceptual Dynamics Laboratory, University of Leuven Email author 
  • , Cees van LeeuwenAffiliated withLaboratory for Perceptual Dynamics, RIKEN, Brain Science InstitutePerceptual Dynamics Laboratory, University of Leuven
  • , Markus DiesmannAffiliated withLaboratory for Computational Neurophysics, RIKEN, Brain Science InstituteInstitute of Neuroscience and Medicine, Computational and Systems Neuroscience (INM-6), Research Center Juelich


Synfire chains, sequences of pools linked by feedforward connections, support the propagation of precisely timed spike sequences, or synfire waves. An important question remains, how synfire chains can efficiently be embedded in cortical architecture. 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 embedding capacities than previous spiking neuron models and allows all its connections to be used for embedding. The number of waves in the model is regulated by recurrent background noise. We computationally explore the embedding capacity limit, and use a mean field analysis to describe the equilibrium state. Simulations confirm the mean field analysis over broad ranges of pool sizes and connectivity levels; 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, while regulation of wave numbers is traced to the use of variable transmission delays.


Recurrent network dynamics Feedforward network Synchrony Synaptic conductance Synfire chain Storage capacity