论文标题
椎板特异性神经元特性促进了前馈网络中稳定的稳定信号传播
Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks
论文作者
论文摘要
前馈网络（FFN）是神经系统中无处不在的结构，已经研究以了解可靠的信号和信息传输的机制。在许多FFN中，一层中的神经元具有与突触前/突触后层中不同的固有属性，但是这如何影响网络级别信息处理仍未开发。在这里，我们表明，由椎板特异性细胞特性引起的层到层异质性有助于FFN中的信号和信息传输。具体而言，我们发现，由输入驱动的尖峰信号上的每一层神经元进行的信号转换，由前层引入的解调信号失真。这种机制可以提高通过传播尖峰信号携带的信息传输，从而支持深层FFN中可靠的尖峰信号和信息传输。我们的研究表明，神经回路中的不同细胞类型，执行不同的计算功能，促进整体信息处理。
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.
