White J, Dickinson T A, Walt D R, Kauer J S
Department of Neuroscience, Tufts University School of Medicine, Boston, MA 02111, USA.
Biol Cybern. 1998 Apr;78(4):245-51. doi: 10.1007/s004220050430.
Odorant sensitivity and discrimination in the olfactory system appear to involve extensive neural processing of the primary sensory inputs from the olfactory epithelium. To test formally the functional consequences of such processing, we implemented in an artificial chemosensing system a new analytical approach that is based directly on neural circuits of the vertebrate olfactory system. An array of fiber-optic chemosensors, constructed with response properties similar to those of olfactory sensory neurons, provide time-varying inputs to a computer simulation of the olfactory bulb (OB). The OB simulation produces spatiotemporal patterns of neuronal firing that vary with vapor type. These patterns are then recognized by a delay line neural network (DLNN). In the final output of these two processing steps, vapor identity is encoded by the spatial patterning of activity across units in the DLNN, and vapor intensity is encoded by response latency. The OB-DLNN combination thus separates identity and intensity information into two distinct codes carried by the same output units, enabling discrimination among organic vapors over a range of input signal intensities. In addition to providing a well-defined system for investigating olfactory information processing, this biologically based neuronal network performs better than standard feed-forward neural networks in discriminating vapors when small amounts of training data are used.
嗅觉系统中的气味敏感性和辨别能力似乎涉及对来自嗅觉上皮的初级感觉输入进行广泛的神经处理。为了正式测试这种处理的功能后果，我们在一个人工化学传感系统中实施了一种直接基于脊椎动物嗅觉系统神经回路的新分析方法。一系列具有与嗅觉感觉神经元相似响应特性的光纤化学传感器，为嗅球（OB）的计算机模拟提供随时间变化的输入。OB模拟产生随蒸汽类型变化的神经元放电时空模式。然后，这些模式由延迟线神经网络（DLNN）识别。在这两个处理步骤的最终输出中，蒸汽身份由DLNN中各单元活动的空间模式编码，蒸汽强度由响应潜伏期编码。因此，OB-DLNN组合将身份和强度信息分离为同一输出单元携带的两种不同编码，从而能够在一系列输入信号强度范围内区分有机蒸汽。除了提供一个用于研究嗅觉信息处理的明确系统外，这种基于生物学的神经网络在使用少量训练数据时，在区分蒸汽方面比标准前馈神经网络表现更好。
