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Synaptic motor adaptation: A three-factor learning rule for adaptive robotic control in spiking neural networks
Legged robots operating in real-world environments must possess the ability to rapidly adapt to unexpected conditions, such as changing terrains and varying payloads.
Learning to learn online with neuromodulated synaptic plasticity in spiking neural networks
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.
Learning To Estimate Regions Of Attraction Of Autonomous Dynamical Systems Using Physics-Informed Neural Networks
We aim to address this problem by training a neural network, which we will refer to as a "safety network", to estimate the region of attraction (ROA) of a controlled autonomous dynamical system.
Stable Lifelong Learning: Spiking neurons as a solution to instability in plastic neural networks
A recent resurgence of interest has developed in utilizing Artificial Neural Networks (ANNs) together with synaptic plasticity for intra-lifetime learning.
SpikePropamine: Differentiable Plasticity in Spiking Neural Networks
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.
