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REWARD-BASED UPDATING OF SYNPATIC WEIGHTS WITH A SPIKING NEURAL NETWORK
REWARD-BASED UPDATING OF SYNPATIC WEIGHTS WITH A SPIKING NEURAL NETWORK
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机译：尖峰神经网络基于奖励的突触权重更新
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摘要
Techniques and mechanisms to update a synaptic weight of a spiking neural network which is trained to provide a decision of a decision-making sequence. In an embodiment, a synapse of the spiking neural network is associated with a weight which is to be given to communications via that given synapse. The spiking neural network generates output signaling, indicating a decision to the decision-making process, which is evaluated to determine whether, according to predefined test criteria, the decision-making process is successful or unsuccessful. One or more nodes of the spiking neural network receive a reward/penalty signal which is based on the evaluation. In response to the reward/penalty signal indicating a reward event or a penalty event, a synaptic weight value is updated. In another embodiment, input signaling provided to the spiking neural network represents a sub-sequence of two or more most recent states in a sequence of states.
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