2011
NeurIPS
NeurIPS 2011
Variational Learning for Recurrent Spiking Networks
Abstract
We derive a plausible learning rule updating the synaptic efficacies for feedforward, feedback and lateral connections between observed and latent neurons. Operating in the context of a generative model for distributions of spike sequences, the learning mechanism is derived from variational inference principles. The synaptic plasticity rules found are interesting in that they are strongly reminiscent of experimentally found results on Spike Time Dependent Plasticity, and in that they differ for excitatory and inhibitory neurons. A simulation confirms the method's applicability to learning both stationary and temporal spike patterns.
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- Variational Inference
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Hot Topic Early Bird
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Authors
Topics
Artificial Intelligence > Core AI > Memory
Machine Learning > Core Methods > Representation Learning
Machine Learning > Learning Types > Self-Supervised Learning
Machine Learning > Learning Types > Unsupervised Learning
Machine Learning > Optimization & Theory > Bayesian Inference
Deep Learning > Architectures > Neural Networks
Deep Learning > Models > Variational Inference
Machine Learning > Bayesian & Probabilistic > Variational Inference
Artificial Intelligence > Bayesian & Probabilistic > Variational Inference
Machine Learning > Core Methods > Neural Networks
Interdisciplinary > Science > Neuroscience