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.

突触整合是神经元信息处理的一个突出方面。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突触兴奋性输入求和模型，并通过表征突触后神经元的一些功能特性来说明其用途。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响应：随机共振和相干共振。该模型需要少量参数，对于隔离依赖于输入总和而几乎没有树突处理的集成机制的作用特别有用。