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Coherent neural spiking and local field potentials are believed to besignatures of the binding and transfer of information in the brain. Coherentactivity has now been measured experimentally in many regions of mammaliancortex. Synfire chains are one of the main theoretical constructs that havebeen appealed to to describe coherent spiking phenomena. However, for sometime, it has been known that synchronous activity in feedforward networksasymptotically either approaches an attractor with fixed waveform andamplitude, or fails to propagate. This has limited their ability to explaingraded neuronal responses. Recently, we have shown that pulse-gated synfirechains are capable of propagating graded information coded in mean populationcurrent or firing rate amplitudes. In particular, we showed that it is possibleto use one synfire chain to provide gating pulses and a second, pulse-gatedsynfire chain to propagate graded information. We called these circuitssynfire-gated synfire chains (SGSCs). Here, we present SGSCs in which gradedinformation can rapidly cascade through a neural circuit, and show acorrespondence between this type of transfer and a mean-field model in whichgating pulses overlap in time. We show that SGSCs are robust in the presence ofvariability in population size, pulse timing and synaptic strength. Finally, wedemonstrate the computational capabilities of SGSC-based information coding byimplementing a self-contained, spike-based, modular neural circuit that istriggered by, then reads in streaming input, processes the input, then makes adecision based on the processed information and shuts itself down.

Graded, Dynamically Routable Information Processing with Synfire-Gated Synfire Chains