Training deep neural networks (DNNs) is computationally intensive but ar...
Spiking Neural Networks (SNNs) have emerged as a hardware efficient
arch...
In this work, we aim to devise an end-to-end spiking implementation for
...
Spiking Neural Networks (SNNs) have shown great potential in solving dee...
C. elegans shows chemotaxis using klinokinesis where the worm senses the...
In this paper we present a Spiking Neural Network (SNN) for autonomous
n...
The in-memory computing paradigm with emerging memory devices has been
r...
The human brain comprises about a hundred billion neurons connected thro...
Liquid State Machine (LSM) is a brain-inspired architecture used for sol...
To enable a dense integration of model synapses in a spiking neural netw...
Spiking neural networks (SNNs) are being explored in an attempt to mimic...
Spiking Neural Network (SNN) naturally inspires hardware implementation ...
Spiking Neural Networks (SNN) are more closely related to brain-like
com...