IEEE Trans Biomed Circuits Syst. 2009 Feb;3(1):32-42. doi: 10.1109/TBCAS.2008.2005781.
Real-time classification of patterns of spike trains is a difficult computational problem that both natural and artificial networks of spiking neurons are confronted with. The solution to this problem not only could contribute to understanding the fundamental mechanisms of computation used in the biological brain, but could also lead to efficient hardware implementations of a wide range of applications ranging from autonomous sensory-motor systems to brain-machine interfaces. Here we demonstrate real-time classification of complex patterns of mean firing rates, using a VLSI network of spiking neurons and dynamic synapses which implement a robust spike-driven plasticity mechanism. The learning rule implemented is a supervised one: a teacher signal provides the output neuron with an extra input spike-train during training, in parallel to the spike-trains that represent the input pattern. The teacher signal simply indicates if the neuron should respond to the input pattern with a high rate or with a low one. The learning mechanism modifies the synaptic weights only as long as the current generated by all the stimulated plastic synapses does not match the output desired by the teacher, as in the perceptron learning rule. We describe the implementation of this learning mechanism and present experimental data that demonstrate how the VLSI neural network can learn to classify patterns of neural activities, also in the case in which they are highly correlated.
实时分类尖峰神经元发放模式是一个困难的计算问题，自然和人工的尖峰神经元网络都需要面对这个问题。这个问题的解决方案不仅有助于理解生物大脑中使用的基本计算机制，而且可以为从自主感觉运动系统到脑机接口的广泛应用提供高效的硬件实现。在这里，我们展示了使用尖峰神经元和动态突触的 VLSI 网络实时分类复杂的平均发放率模式，该网络实现了一种稳健的尖峰驱动可塑性机制。所实现的学习规则是监督式的：在训练期间，教师信号为输出神经元提供与表示输入模式的尖峰序列并行的额外输入尖峰序列。教师信号仅在所有被刺激的可塑性突触产生的电流与教师期望的输出不匹配时才指示神经元应以高速率还是低速率对输入模式作出响应，这与感知机学习规则相同。我们描述了这个学习机制的实现，并给出了实验数据，证明了 VLSI 神经网络如何学习分类神经活动模式，即使在它们高度相关的情况下也是如此。
