Synchronized stepwise control of firing and learning thresholds in a spiking randomly connected neural network toward hardware implementation.

ABSTRACT: Spiking randomly connected neural network (RNN) hardware is promising as ultimately low power devices for temporal data processing at the edge. Although the potential of RNNs for temporal data processing has been demonstrated, randomness of the network architecture often causes performance degradation. To mitigate such degradation, self-organization mechanism using intrinsic plasticity (IP) and synaptic plasticity (SP) should be implemented in the spiking RNN. Therefore, we propose hardware-oriented models of these functions. To implement the function of IP, a variable firing threshold is introduced to each excitatory neuron in the RNN that changes stepwise in accordance with its activity. We also define other thresholds for SP that synchronize with the firing threshold, which determine th

SUBMITTER: Nomura K 

PROVIDER: S-EPMC11599226 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature