论文标题
简单神经元中突触整合的最小模型
A minimal model for synaptic integration in simple neurons
论文作者
论文摘要
突触整合是神经元信息处理的重要方面。调节突触输入的详细机制决定了任何给定神经元的计算特性。我们研究了一个简单的模型，用于总结突触中的兴奋性输入，并通过表征突触后神经元的某些功能特性来说明其使用。在这方面，我们研究了模型定义的突触后神经元对两个众所周知的噪声驱动过程的反应：随机和连贯的共振。该模型需要少量参数，并且对于隔离依赖于少量树枝状处理的输入的求和的集成机制的作用特别有用。
Synaptic integration is a prominent aspect of neuronal information processing. The detailed mechanisms that modulate synaptic inputs determine the computational properties of any given neuron. We study a simple model for the summation of excitatory inputs from synapses and illustrate its use by characterizing some functional properties of postsynaptic neurons. In this regard, we study the response of postsynaptic neurons as defined by the model to two well known noise driven processes: stochastic and coherence resonance. The model requires a small number of parameters and is especially useful to isolate the role of integration mechanisms that rely on summation of inputs with little dendritic processing.
