  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.
