29 citations · 70 across the 17 of their papers we have counts for
5 papers · 1 filter
Brain-inspired learning in artificial neural networks: a review
Samuel Schmidgall, Jascha Achterberg, Thomas Miconi +4
Artificial neural networks (ANNs) have emerged as an essential tool in machine learning, achieving remarkable success across diverse domains, including image and speech generation,…
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…
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…
Adaptive Reinforcement Learning through Evolving Self-Modifying Neural Networks
Samuel Schmidgall
The adaptive learning capabilities seen in biological neural networks are largely a product of the self-modifying behavior emerging from online plastic changes in synaptic connecti…