This paper presents a biologically plausible method for converting real-valued input into spike trains for processing with spiking neural networks. The proposed method mimics the adaptive behaviour of retinal ganglion cells and allows input neurons to adapt their response to changes in the statistics of the input. Thus, rather than passively receiving values and forwarding them to the hidden and output layers, the input layer acts as a self-regulating filter which emphasises deviations from the average while allowing the input neurons to become effectively desensitised to the average itself. Another merit of the proposed method is that it requires only one input neuron per variable, rather than an entire population of neurons as in the case of the commonly used conversion method based on Gaussian receptive fields. In addition, since the statistics of the input emerge naturally over time, it becomes unnecessary to pre-process the data before feeding it to the network. This enables spiking neural networks to process raw, non-normalised streaming data. A proof-of-concept experiment is performed to demonstrate that the proposed method operates as expected.
翻译:本文展示了将实际价值输入转换成用于神经网络冲刺处理的峰值列中的一种生物上可行的方法。 提议的方法模仿视网膜交织细胞的适应行为, 允许输入神经元适应输入输入数据的变化。 因此, 输入层不是被动接收数值并将其传送到隐藏和输出层, 而是作为一个自我调节过滤器, 强调与平均值不同, 同时允许输入神经元有效地向普通人本身消敏。 所提议方法的另一个优点是, 它只需要每个变量输入一个神经神经元, 而不是整个神经元群, 正如在Gausian 接受字段的常用转换方法中那样。 此外, 由于输入数据的统计数据会随着时间的自然出现, 因此没有必要在输入到网络之前预先处理数据。 这样可以让神经网络处理原始的、非正常的流数据。 测试进行了测试, 以证明拟议方法按预期的方式运行 。