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Feedforward networks (FFN) are ubiquitous structures in neural systems andhave been studied to understand mechanisms of reliable signal and informationtransmission. In many FFNs, neurons in one layer have intrinsic properties thatare distinct from those in their pre-/postsynaptic layers, but how this affectsnetwork-level information processing remains unexplored. Here we show thatlayer-to-layer heterogeneity arising from lamina-specific cellular propertiesfacilitates signal and information transmission in FFNs. Specifically, we foundthat signal transformations, made by each layer of neurons on an input-drivenspike signal, demodulate signal distortions introduced by preceding layers.This mechanism boosts information transfer carried by a propagating spikesignal and thereby supports reliable spike signal and information transmissionin a deep FFN. Our study suggests that distinct cell types in neural circuits,performing different computational functions, facilitate information processingon the whole.

Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks