Rao R P, Sejnowski T J
Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195-2350, USA.
Neural Comput. 2001 Oct;13(10):2221-37. doi: 10.1162/089976601750541787.
A spike-timing-dependent Hebbian mechanism governs the plasticity of recurrent excitatory synapses in the neocortex: synapses that are activated a few milliseconds before a postsynaptic spike are potentiated, while those that are activated a few milliseconds after are depressed. We show that such a mechanism can implement a form of temporal difference learning for prediction of input sequences. Using a biophysical model of a cortical neuron, we show that a temporal difference rule used in conjunction with dendritic backpropagating action potentials reproduces the temporally asymmetric window of Hebbian plasticity observed physio-logically. Furthermore, the size and shape of the window vary with the distance of the synapse from the soma. Using a simple example, we show how a spike-timing-based temporal difference learning rule can allow a network of neocortical neurons to predict an input a few milliseconds before the input's expected arrival.
在突触后尖峰前几毫秒被激活的突触会增强，而在突触后尖峰后几毫秒被激活的突触则会减弱。我们表明，这样一种机制可以实现一种用于预测输入序列的时间差学习形式。使用一个皮层神经元的生物物理模型，我们表明，与树突反向传播动作电位结合使用的时间差规则再现了生理上观察到的赫布可塑性的时间不对称窗口。此外，该窗口的大小和形状随突触与胞体的距离而变化。通过一个简单的例子，我们展示了基于尖峰时间的时间差学习规则如何允许新皮层神经元网络在输入预期到达前几毫秒预测该输入。
