Energy efficient sparse connectivity from imbalanced synaptic plasticity rules

It is believed that energy efficiency is an important constraint in brain evolution. As synaptic transmission dominates energy consumption, energy can be saved by ensuring that only a few synapses are active. It is therefore likely that the formation of sparse codes and sparse connectivity are funda...

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Main Authors: Sacramento, João, Wichert, Andreas, van Rossum, Mark C.W.
Format: Article
Published: Public Library of Science 2015
Online Access:https://eprints.nottingham.ac.uk/49632/
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author Sacramento, João
Wichert, Andreas
van Rossum, Mark C.W.
author_facet Sacramento, João
Wichert, Andreas
van Rossum, Mark C.W.
author_sort Sacramento, João
building Nottingham Research Data Repository
collection Online Access
description It is believed that energy efficiency is an important constraint in brain evolution. As synaptic transmission dominates energy consumption, energy can be saved by ensuring that only a few synapses are active. It is therefore likely that the formation of sparse codes and sparse connectivity are fundamental objectives of synaptic plasticity. In this work we study how sparse connectivity can result from a synaptic learning rule of excitatory synapses. Information is maximised when potentiation and depression are balanced according to the mean presynaptic activity level and the resulting fraction of zero-weight synapses is around 50%. However, an imbalance towards depression increases the fraction of zero-weight synapses without significantly affecting performance. We show that imbalanced plasticity corresponds to imposing a regularising constraint on the L1-norm of the synaptic weight vector, a procedure that is well-known to induce sparseness. Imbalanced plasticity is biophysically plausible and leads to more efficient synaptic configurations than a previously suggested approach that prunes synapses after learning. Our framework gives a novel interpretation to the high fraction of silent synapses found in brain regions like the cerebellum.
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spelling nottingham-496322020-05-04T17:11:22Z https://eprints.nottingham.ac.uk/49632/ Energy efficient sparse connectivity from imbalanced synaptic plasticity rules Sacramento, João Wichert, Andreas van Rossum, Mark C.W. It is believed that energy efficiency is an important constraint in brain evolution. As synaptic transmission dominates energy consumption, energy can be saved by ensuring that only a few synapses are active. It is therefore likely that the formation of sparse codes and sparse connectivity are fundamental objectives of synaptic plasticity. In this work we study how sparse connectivity can result from a synaptic learning rule of excitatory synapses. Information is maximised when potentiation and depression are balanced according to the mean presynaptic activity level and the resulting fraction of zero-weight synapses is around 50%. However, an imbalance towards depression increases the fraction of zero-weight synapses without significantly affecting performance. We show that imbalanced plasticity corresponds to imposing a regularising constraint on the L1-norm of the synaptic weight vector, a procedure that is well-known to induce sparseness. Imbalanced plasticity is biophysically plausible and leads to more efficient synaptic configurations than a previously suggested approach that prunes synapses after learning. Our framework gives a novel interpretation to the high fraction of silent synapses found in brain regions like the cerebellum. Public Library of Science 2015-06-05 Article PeerReviewed Sacramento, João, Wichert, Andreas and van Rossum, Mark C.W. (2015) Energy efficient sparse connectivity from imbalanced synaptic plasticity rules. PLoS Computational Biology, 11 (6). e1004265. ISSN 1553-7358 http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004265 doi:10.1371/journal.pcbi.1004265 doi:10.1371/journal.pcbi.1004265
spellingShingle Sacramento, João
Wichert, Andreas
van Rossum, Mark C.W.
Energy efficient sparse connectivity from imbalanced synaptic plasticity rules
title Energy efficient sparse connectivity from imbalanced synaptic plasticity rules
title_full Energy efficient sparse connectivity from imbalanced synaptic plasticity rules
title_fullStr Energy efficient sparse connectivity from imbalanced synaptic plasticity rules
title_full_unstemmed Energy efficient sparse connectivity from imbalanced synaptic plasticity rules
title_short Energy efficient sparse connectivity from imbalanced synaptic plasticity rules
title_sort energy efficient sparse connectivity from imbalanced synaptic plasticity rules
url https://eprints.nottingham.ac.uk/49632/
https://eprints.nottingham.ac.uk/49632/
https://eprints.nottingham.ac.uk/49632/