Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices,
and models that contrast the pervasive von Neumann computer architecture. This …

Spiking Neural Network (SNN), the next generation of Neural Network, is supposed to be
more energy-saving than the previous generation represented by Convolution Neural …

Due to the ability to implement customized topology, FPGA is increasingly used to deploy
SNNs in both embedded and high-performance applications. In this paper, we survey state …

The human brain's ability to efficiently detect patterns from the continuous streaming sensory
stimuli has been a source of constant intrigue for naturalists, and has set the course for the …

From it's initiation, the field of artificial intelligence has been inspired primarily by the human
brain. Recent advances and collaboration of computational neuroscience and artificial …

This chapter explores the development and application of Spiking Neural Networks (SNNs)
on Field-Programmable Gate Arrays (FPGAs), tracing their evolution since the debut of …

The fields of neuroscience and artificial intelligence have a long and entwined history. In
recent times, however, communication and collaboration between the two fields has become …

This chapter of the book proposes a new information theory for temporal data compression
through spike-time encoding for the purpose of reducing the amount of raw data from time …

In this thesis, I studied the feasibility of implementing Spiking Neural Networks (SNNs) in
reconfigurable hardware. The main research is focused on efficient computation models for …