Design and Mathematical Modelling of Inter Spike Interval of Temporal Neuromorphic Encoder for Image Recognition

TL;DR

This paper presents a low-power neuromorphic encoder for image recognition that models the relationship between image pixels and spike train intervals, validated through circuit simulation.

Contribution

It introduces a novel mathematical model linking image pixels to inter-spike intervals in a neuromorphic encoder, enhancing power efficiency for image processing.

Findings

Established an exponential relationship between pixels and inter-spike intervals.

Validated the mathematical model with circuit simulation results.

Demonstrated potential for low-power neuromorphic image encoding.

Abstract

Neuromorphic computing systems emulate the electrophysiological behavior of the biological nervous system using mixed-mode analog or digital VLSI circuits. These systems show superior accuracy and power efficiency in carrying out cognitive tasks. The neural network architecture used in neuromorphic computing systems is spiking neural networks (SNNs) analogous to the biological nervous system. SNN operates on spike trains as a function of time. A neuromorphic encoder converts sensory data into spike trains. In this paper, a low-power neuromorphic encoder for image processing is implemented. A mathematical model between pixels of an image and the inter-spike intervals is also formulated. Wherein an exponential relationship between pixels and inter-spike intervals is obtained. Finally, the mathematical equation is validated with circuit simulation.