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Information transmission is a key element for information processing in thebrain. A number of mechanisms have been proposed for transferring volleys ofspikes between layers of a feedforward neural circuit. Many of these mechanismsuse synchronous activity to provide windows in time when spikes may betransferred more easily from layer to layer. Recently, we have demonstratedthat a pulse-gating mechanism can transfer graded information between layers ina feedforward neuronal network. Our transfer mechanism resulted in atime-translationally invariant firing rate and synaptic current waveforms ofarbitrary amplitude, thus providing exact, graded information transfer betweenlayers. In this paper, we apply a Fokker-Planck approach to understand how thistranslational invariance manifests itself in a high-dimensional, non-linearfeedforward integrate-and-fire network. We show that there is goodcorrespondence in spiking probabilities between the Fokker-Planck solutions andour previous mean-field solutions. We identify an approximate line attractor instate space as the essential structure underlying the time-translationalinvariance of our solutions. This approach has enabled us to better understandthe role played by the synaptic coupling, gating pulse and the membranepotential probability density function in information transfer.

A Fokker-Planck approach to graded information propagation in pulse-gated feedforward neuronal networks