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

TL;DR

This study demonstrates that lamina-specific neuronal properties in feedforward networks enhance the robustness and stability of signal propagation by demodulating distortions and boosting information transfer across layers.

Contribution

It reveals how layer-specific neuronal heterogeneity promotes reliable signal transmission in feedforward neural networks, a previously unexplored aspect.

Findings

Lamina-specific properties facilitate signal demodulation.

Heterogeneity boosts information transfer in FFNs.

Distinct cell types support overall neural computation.

Abstract

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