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Found 3 papers, 1 papers with code
A Review of Neuroscience-Inspired Machine Learning
As a result, it accommodates hardware and scientific modeling, e. g. learning with physical systems and non-differentiable behavior.
Signal Propagation: A Framework for Learning and Inference In a Forward Pass
To further support relevance to biological and hardware learning, we use sigprop to train continuous time neural networks with Hebbian updates, and train spiking neural networks with only the voltage or with biologically and hardware compatible surrogate functions.
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