100 citations · 109 across the 3 of their papers we have counts for
5 papers
Continuous Learning in a Single-Incremental-Task Scenario with Spike Features
Ruthvik Vaila, John Chiasson, Vishal Saxena
Deep Neural Networks (DNNs) have two key deficiencies, their dependence on high precision computing and their inability to perform sequential learning, that is, when a DNN is train…
Deep Convolutional Spiking Neural Networks for Image Classification
Ruthvik Vaila, John Chiasson, Vishal Saxena
Spiking neural networks are biologically plausible counterparts of the artificial neural networks, artificial neural networks are usually trained with stochastic gradient descent a…
A Compact CMOS Memristor Emulator Circuit and its Applications
Vishal Saxena
Conceptual memristors have recently gathered wider interest due to their diverse application in non-von Neumann computing, machine learning, neuromorphic computing, and chaotic cir…
Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition
Xinyu Wu, Vishal Saxena, Kehan Zhu
A neuromorphic chip that combines CMOS analog spiking neurons and memristive synapses offers a promising solution to brain-inspired computing, as it can provide massive neural netw…
A CMOS Spiking Neuron for Dense Memristor-Synapse Connectivity for Brain-Inspired Computing
Xinyu Wu, Vishal Saxena, Kehan Zhu
Neuromorphic systems that densely integrate CMOS spiking neurons and nano-scale memristor synapses open a new avenue of brain-inspired computing. Existing silicon neurons have mold…