Adair R K
Department of Physics, Yale University, New Haven, CT 06520-8121, USA.
Proc Natl Acad Sci U S A. 2001 Jun 19;98(13):7253-8. doi: 10.1073/pnas.121171598. Epub 2001 Jun 12.
I describe physiologically plausible "voter-coincidence" neural networks such that secondary "coincidence" neurons fire on the simultaneous receipt of sufficiently large sets of input pulses from primary sets of neurons. The networks operate such that the firing rate of the secondary, output neurons increases (or decreases) sharply when the mean firing rate of primary neurons increases (or decreases) to a much smaller degree. In certain sensory systems, signals that are generally smaller than the noise levels of individual primary detectors, are manifest in very small increases in the firing rates of sets of afferent neurons. For such systems, this kind of network can act to generate relatively large changes in the firing rate of secondary "coincidence" neurons. These differential amplification systems can be cascaded to generate sharp, "yes-no" spike signals that can direct behavioral responses.
我描述了生理上合理的“投票者巧合”神经网络，使得次级“巧合”神经元在同时接收到来自初级神经元组的足够大的输入脉冲集时放电。这些网络的运行方式是，当初级神经元的平均放电率以小得多的程度增加（或减少）时，次级输出神经元的放电率会急剧增加（或减少）。在某些感觉系统中，通常小于单个初级探测器噪声水平的信号，表现为传入神经元组放电率的非常小的增加。对于这样的系统，这种网络可以起到在次级“巧合”神经元的放电率中产生相对较大变化的作用。这些差分放大系统可以级联起来，以产生尖锐的“是-否”尖峰信号，从而指导行为反应。
