Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks
This work addresses the challenge of robust signal propagation in neural circuits, which is incremental as it builds on prior studies of feedforward networks.
The study tackled the problem of how layer-to-layer heterogeneity in feedforward networks affects signal transmission, finding that lamina-specific cellular properties demodulate signal distortions and boost information transfer, supporting reliable spike signal propagation in deep networks.
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.