We propose an asynchronous spiking chaotic neuron circuit for temporal coding networks based on coincidence detection and spatio-temporal chaotic dynamics. In the proposed circuit, we take into account the coincidence detection among input spikes, continuous-time interspike intervals, relative and absolute refractoriness, an analog internal state value, a continuous output function, an output spike generation delay, and synaptic weight. We implement the neuron circuit with a 0.5 μm CMOS semiconductor process by means of analog circuitry. Moreover, we make most of the model parameters externally controllable so that we can control the behavior of the neuron. As a consequence, by properly setting its circuit parameters, the neuron can function as either a coincidence detector or an integrator. We show the measurement results from the prototype chip. In particular, we show coincidence detection of the input spikes in a short time-window. In addition, we illustrate complex responses of the neuron circuit, providing bifurcation diagrams of the internal state value and the interspike intervals of the output spikes. The proposed neuron circuit is useful for exploring a possible spatio-temporal coding paradigm by constructing a neural network based on the coincidence detection and the spatio-temporal chaotic dynamics.
ASJC Scopus subject areas
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence