An adaptive threshold neuron for recurrent spiking neural networks with nanodevice hardware implementation

Abstract

Recurrent spiking neural networks have garnered interest due to their energy efficiency; however, they suffer from lower accuracy compared to conventional neural networks. Here, the authors present an alternative neuron model and its efficient hardware implementation, demonstrating high classification accuracy across a range of datasets.