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NEUROSYNAPTIC PROCESSING CORE WITH SPIKE TIME DEPENDENT PLASTICITY (STDP) LEARNING FOR A SPIKING NEURAL NETWORK

机译:尖峰神经网络的具有尖峰时间依赖可塑性(STDP)学习的神经突触处理核心

摘要

There is provided a neurosynaptic processing core with spike time dependent plasticity (STDP) learning for a spiking neural network, including: a spiking neuron block including a pre-synaptic block and a post-synaptic block; a synapse block communicatively coupled to the spiking neuron block; a STDP learning block communicatively coupled to the spiking neuron block and the synapse block, the STDP learning block including a pre-synaptic event accumulator including a pre-synaptic spike event memory block and a pre-synaptic spike parameter modifier; a post-synaptic event accumulator including a post-synaptic spike event memory block and a post-synaptic spike parameter modifier, a weight change accumulator, and a weight change parameter modifier; a learning error modulator; and a synaptic weight modifier configured to modify a synaptic weight parameter based on a weight change parameter and a learning error corresponding to the synaptic weight parameter. There is also provided a corresponding method of operating and a corresponding method of forming the neurosynaptic processing core.
机译:本发明提供了一种用于尖峰神经网络的具有尖峰时间依赖性可塑性(STDP)学习的神经突触处理核心,包括:包括突触前阻滞和突触后阻滞的尖峰神经元阻滞;可通信地耦合到脉冲神经元块的突触块;与所述尖峰神经元块和所述突触块通信耦合的STDP学习块,所述STDP学习块包括突触前事件累加器,所述突触前事件累加器包括突触前尖峰事件记忆块和突触前尖峰参数修改器;一种突触后事件累加器,包括突触后尖峰事件记忆块和突触后尖峰参数修改器、权重变化累加器和权重变化参数修改器;学习误差调制器;以及突触权重修改器,其被配置为基于权重改变参数和对应于该突触权重参数的学习误差来修改突触权重参数。本发明还提供了相应的操作方法和形成神经突触处理核心的相应方法。

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