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STDP-BASED LEARNING METHOD FOR A NETWORK HAVING DUAL ACCUMULATOR NEURONS

机译:具有双累加器神经元的网络的基于STDP的学习方法

摘要

A method for unsupervised learning of a multilevel hierarchical network of artificial neurons wherein each neuron is interconnected with artificial synapses to neurons of a lower hierarchical level and to neurons of an upper hierarchical level. The method includes at a neuron the steps of integrating inference spikes from the interconnected neurons of the lower hierarchical level both in a first and a second accumulators using the same synaptic weights; when the first accumulator reaches a first threshold, generating a learning spike, resetting the first accumulator, triggering synaptic conductance modification in accordance with a spike-timing dependent plasticity rule and delivering the learning spike as an inhibitory signal to other neurons in the same hierarchical level; when the second accumulator reaches a second threshold, generating an inference spike, delivering the generated inference spike to the interconnected neurons of the upper hierarchical level, resetting the second accumulator and possibly delivering the inference spike as an inhibitory signal to other neurons in the same hierarchical level.
机译:一种用于无监督学习人工神经元的多层网络的方法,其中,每个神经元都与人工突触相互连接,该神经突触连接到较低层次的神经元和较高层次的神经元。该方法包括在神经元处使用相同的突触权重在第一累加器和第二累加器中整合来自较低层次级别的互连神经元的推断尖峰的步骤;当第一累加器达到第一阈值时,产生学习尖峰,重置第一累加器,根据依赖于尖峰时间的可塑性规则触发突触电导修饰,并将学习尖峰作为抑制信号传递给相同层次级别的其他神经元;当第二累加器达到第二阈值时,生成推理尖峰,将生成的推理尖峰传递到较高层次的互连神经元,重置第二累加器,并可能将推理尖峰作为抑制信号传递到同一层次中的其他神经元水平。

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