Getz W M
Department of Entomology, University of California, Berkeley 94720.
Bull Math Biol. 1991;53(6):805-23. doi: 10.1007/BF02461485.
Several critical issues associated with the processing of olfactory stimuli in animals (but focusing on insects) are discussed with a view to designing a neural network which can process olfactory stimuli. This leads to the construction of a neural network that can learn and identify the quality (direction cosines) of an input vector or extract information from a sequence of correlated input vectors, where the latter corresponds to sampling a time varying olfactory stimulus (or other generically similar pattern recognition problems). The network is constructed around a discrete time content-addressable memory (CAM) module which basically satisfies the Hopfield equations with the addition of a unit time delay feedback. This modification improves the convergence properties of the network and is used to control a switch which activates the learning or template formation process when the input is "unknown". The network dynamics are embedded within a sniff cycle which includes a larger time delay (i.e. an integer ts greater than 1) that is also used to control the template formation switch. In addition, this time delay is used to modify the input into the CAM module so that the more dominant of two mingling odors or an odor increasing against a background of odors is more readily identified. The performance of the network is evaluated using Monte Carlo simulations and numerical results are presented.
本文讨论了与动物（但重点是昆虫）嗅觉刺激处理相关的几个关键问题，旨在设计一个能够处理嗅觉刺激的神经网络。这导致构建了一个神经网络，它可以学习并识别输入向量的质量（方向余弦），或者从一系列相关输入向量中提取信息，其中后者对应于对随时间变化的嗅觉刺激（或其他一般类似的模式识别问题）进行采样。该网络围绕一个离散时间内容可寻址存储器（CAM）模块构建，该模块在添加单位时间延迟反馈的情况下基本满足霍普菲尔德方程。这种修改改善了网络的收敛特性，并用于控制一个开关，当输入“未知”时，该开关会激活学习或模板形成过程。网络动态嵌入在一个嗅探周期内，该周期包括一个更大的时间延迟（即大于1的整数ts），该延迟也用于控制模板形成开关。此外，该时间延迟用于修改输入到CAM模块中的内容，以便更易于识别两种混合气味中占主导地位的气味，或者在气味背景下增加的气味。使用蒙特卡罗模拟对网络性能进行了评估，并给出了数值结果。
