124 citations · 190 across the 20 of their papers we have counts for
6 papers · 1 filter
Synaptic Modulation using Interspike Intervals Increases Energy Efficiency of Spiking Neural Networks
Dylan Adams, Magda Zajaczkowska, Ashiq Anjum +2
Despite basic differences between Spiking Neural Networks (SNN) and Artificial Neural Networks (ANN), most research on SNNs involve adapting ANN-based methods for SNNs. Pruning (dr…
Context Meta-Reinforcement Learning via Neuromodulation
Eseoghene Ben-Iwhiwhu, Jeffery Dick, Nicholas A. Ketz +2
Meta-reinforcement learning (meta-RL) algorithms enable agents to adapt quickly to tasks from few samples in dynamic environments. Such a feat is achieved through dynamic represent…
Evolving Inborn Knowledge For Fast Adaptation in Dynamic POMDP Problems
Eseoghene Ben-Iwhiwhu, Pawel Ladosz, Jeffery Dick +3
Rapid online adaptation to changing tasks is an important problem in machine learning and, recently, a focus of meta-reinforcement learning. However, reinforcement learning (RL) al…
Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks
Andrea Soltoggio, Kenneth O. Stanley, Sebastian Risi
Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements…
Online Representation Learning with Single and Multi-layer Hebbian Networks for Image Classification
Yanis Bahroun, Andrea Soltoggio
Unsupervised learning permits the development of algorithms that are able to adapt to a variety of different data sets using the same underlying rules thanks to the autonomous disc…
Short-term plasticity as cause-effect hypothesis testing in distal reward learning
Andrea Soltoggio
Asynchrony, overlaps and delays in sensory-motor signals introduce ambiguity as to which stimuli, actions, and rewards are causally related. Only the repetition of reward episodes…