11 citations · 13 across the 6 of their papers we have counts for
4 papers · 1 filter
Learning to learn online with neuromodulated synaptic plasticity in spiking neural networks
Samuel Schmidgall, Joe Hays
We propose that in order to harness our understanding of neuroscience toward machine learning, we must first have powerful tools for training brain-like models of learning. Althoug…
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…