Oscillator Circuit for Spike Neural Network with Sigmoid Like Activation Function and Firing Rate Coding
This work addresses the development of neuromorphic devices and artificial intelligence by providing a specific circuit design for spike neural networks, but it appears incremental as it builds on existing concepts without broad SOTA claims.
The study tackled the design of an oscillator circuit for spike neural networks, achieving a sigmoid-like activation function and firing rate coding through a circuit with a switching element and capacitors, where the oscillation frequency strongly depends on a control resistor.
The study presents an oscillator circuit for a spike neural network with the possibility of firing rate coding and sigmoid-like activation function. The circuit contains a switching element with an S-shaped current-voltage characteristic and two capacitors; one of the capacitors is shunted by a control resistor. The circuit is characterised by a strong dependence of the frequency of relaxation oscillations on the magnitude of the control resistor. The dependence has a sigmoid-like form and we present an analytical method for dependence calculation. Finally, we describe the concept of the spike neural network architecture with firing rate coding based on the presented circuit for creating neuromorphic devices and artificial intelligence.