11 citations · 13 across the 6 of their papers we have counts for
4 papers · 1 filter
Learning To Estimate Regions Of Attraction Of Autonomous Dynamical Systems Using Physics-Informed Neural Networks
Cody Scharzenberger, Joe Hays
When learning to perform motor tasks in a simulated environment, neural networks must be allowed to explore their action space to discover new potentially viable solutions. However…
Stable Lifelong Learning: Spiking neurons as a solution to instability in plastic neural networks
Samuel Schmidgall, Joe Hays
Synaptic plasticity poses itself as a powerful method of self-regulated unsupervised learning in neural networks. A recent resurgence of interest has developed in utilizing Artific…
Evolutionary Self-Replication as a Mechanism for Producing Artificial Intelligence
Samuel Schmidgall, Joseph Hays
Can reproduction alone in the context of survival produce intelligence in our machines? In this work, self-replication is explored as a mechanism for the emergence of intelligent b…
SpikePropamine: Differentiable Plasticity in Spiking Neural Networks
Samuel Schmidgall, Julia Ashkanazy, Wallace Lawson +1
The adaptive changes in synaptic efficacy that occur between spiking neurons have been demonstrated to play a critical role in learning for biological neural networks. Despite this…