29 citations · 104 across the 23 of their papers we have counts for
5 papers · 1 filter
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…
Self-Replicating Neural Programs
Samuel Schmidgall
In this work, a neural network is trained to replicate the code that trains it using only its own output as input. A paradigm for evolutionary self-replication in neural programs i…
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…
Self-Constructing Neural Networks Through Random Mutation
Samuel Schmidgall
The search for neural architecture is producing many of the most exciting results in artificial intelligence. It has increasingly become apparent that task-specific neural architec…