A VLSI spiking feedback neural network with negative thresholding and its application to associative memory

An integrate-and-fire-type spiking feedback network is discussed in this paper. In our spiking neuron model, analog information expressing processing results is given by the relative relation of spike firing. Therefore, for spiking feedback networks, all neurons should fire (pseudo-)periodically. However, an integrate-and-fire-type neuron generates no spike unless its internal potential exceeds the threshold. To solve this problem, we propose negative thresholding operation. In this paper, this operation is achieved by a global excitatory unit. This unit operates immediately after receiving the first spike input. We have designed a CMOS spiking feedback network VLSI circuit with the global excitatory unit for Hopfield-type associative memory. The circuit simulation results show that the network achieves correct association operation.