Buonomano D V, Merzenich M M
Keck Center for Integrative Neuroscience, University of California at San Francisco 94143.
Science. 1995 Feb 17;267(5200):1028-30. doi: 10.1126/science.7863330.
Neurons exhibit a wide range of properties in addition to postsynaptic potential (PSP) summation and spike generation. Although other neuronal properties such as paired-pulse facilitation (PPF) and slow PSPs are well characterized, their role in information processing remains unclear. It is possible that these properties contribute to temporal processing in the range of hundreds of milliseconds, a range relevant to most complex sensory processing. A continuous-time neural network model based on integrate-and-fire elements that incorporate PPF and slow inhibitory postsynaptic potentials (IPSPs) was developed here. The time constants of the PPF and IPSPs were estimated from empirical data and were identical and constant for all elements in the circuit. When these elements were incorporated into a circuit inspired by neocortical connectivity, the network was able to discriminate different temporal patterns. Generalization emerged spontaneously. These results demonstrate that known time-dependent neuronal properties enable a network to transform temporal information into a spatial code in a self-organizing manner--that is, with no need to assume a spectrum of time delays or to custom-design the circuit.
神经元除了具有突触后电位（PSP）总和与动作电位产生的特性外，还表现出广泛的其他特性。尽管诸如双脉冲易化（PPF）和缓慢PSP等其他神经元特性已得到充分表征，但其在信息处理中的作用仍不清楚。这些特性有可能在数百毫秒的时间范围内对时间处理有所贡献，这一范围与大多数复杂的感觉处理相关。在此开发了一种基于积分发放神经元模型的连续时间神经网络模型，该模型纳入了PPF和缓慢抑制性突触后电位（IPSP）。PPF和IPSP的时间常数根据实验数据估算得出，并且对于电路中的所有元件都是相同且恒定的。当将这些元件纳入受新皮质连接启发的电路中时，该网络能够区分不同的时间模式。泛化能力会自发出现。这些结果表明，已知的时间依赖性神经元特性能够使网络以自组织的方式将时间信息转化为空间编码，也就是说，无需假设一系列时间延迟或进行定制电路设计。
