Quenet Brigitte, Horn David
Laboratoire d'Electronique, Ecole Superieure de Physique et Chimie Industrielles, Paris 75005, France.
Neural Comput. 2003 Feb;15(2):309-29. doi: 10.1162/089976603762552933.
We describe and discuss the properties of a binary neural network that can serve as a dynamic neural filter (DNF), which maps regions of input space into spatiotemporal sequences of neuronal activity. Both deterministic and stochastic dynamics are studied, allowing the investigation of the stability of spatiotemporal sequences under noisy conditions. We define a measure of the coding capacity of a DNF and develop an algorithm for constructing a DNF that can serve as a source of given codes. On the basis of this algorithm, we suggest using a minimal DNF capable of generating observed sequences as a measure of complexity of spatiotemporal data. This measure is applied to experimental observations in the locust olfactory system, whose reverberating local field potential provides a natural temporal scale allowing the use of a binary DNF. For random synaptic matrices, a DNF can generate very large cycles, thus becoming an efficient tool for producing spatiotemporal codes. The latter can be stabilized by applying to the parameters of the DNF a learning algorithm with suitable margins.
我们描述并讨论了一种二元神经网络的特性，该网络可作为动态神经滤波器（DNF），将输入空间区域映射为神经元活动的时空序列。我们研究了确定性和随机动力学，从而能够研究噪声条件下时空序列的稳定性。我们定义了DNF编码容量的度量，并开发了一种构建DNF的算法，该DNF可作为给定代码的来源。基于此算法，我们建议使用能够生成观测序列的最小DNF作为时空数据复杂性的度量。此度量应用于蝗虫嗅觉系统的实验观测，其回响局部场电位提供了一个自然的时间尺度，从而能够使用二元DNF。对于随机突触矩阵，DNF可以生成非常大的周期，从而成为产生时空编码的有效工具。通过将具有适当裕度的学习算法应用于DNF的参数，可以使后者稳定下来。
