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Neuromorphic encoding system design with chaos based CMOS analog neuron

Neuromorphic computing is a novel paradigm that inspired from the dynamic behavior of the biological brain. The encoding capability plays a vital role in information processing, especially for neural network based systems. In this paper, a compact, low power, and robust spiking-time-dependent encoder is designed with an accommodative Leaky Integrate and Fire (LIF) model based neuron cluster and a chaotic circuit with ring oscillators. Novel and fundamental methodologies, which represent data by using spike timing dependent encoding, has been developed. The information in signal amplitude has been mapped into a spike time sequence efficiently by time encoding, which represents the input data and offers perfect recovery for band limited stimuli. Time dependent temporal scales have been adopted to pattern the neural activities across multiple timescales and encode the sensory information. Furthermore, chaotic circuit based Pseudorandom Time Series Generator (PTSG) is designed to generate sampling clock. High resolution is provided with chaotic based sampling in the proposed encoding circuit. Detailed post layout simulation results and analysis of the designed circuit are presented.