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.

Weaknesses: Although I believe the intrinsic difference of neurons could benefit for information transmission, I have some conceptual questions. I think properly answer these questions in the Discussion or briefly mention some of them in author feedback could improve the impact of this work in general. Whether a network is an integrator of a differentiator is highly determined by the value of \beta. Is it possible with an intermediate value of \beta, the network's output is proportional to the input, i.e., the network simply relay the input but neither differentiating or integrating. In this case, we probably only need one layer to transmit the input without the cascade of an integrator and a differentiator.

The reviewers agree that this paper merits acceptance, though they do raise a number of important issues regarding the generality of these results and the sensitivity to parameter choices. Please update the paper to address these concerns, and if possible, include a parameter sweep/sensitivity plot in the supplementary material. Though the reviewers did not say it, I think Section 2: Related Work needs to be substantially expanded to situate and motivate this paper.

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.