ArticlePLoS computational biology2015
Energy Efficient Sparse Connectivity from Imbalanced Synaptic Plasticity Rules.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Brain-inspired energy efficient technologies for next-generation artificial intelligence.Biological cybernetics · 2026Review
- Two-factor synaptic consolidation reconciles robustness with pruning and homeostatic scaling.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Arbor-TVB: a novel multi-scale co-simulation framework with a case study on neural-level seizure generation and whole-brain propagation.Frontiers in computational neuroscience · 2025Article
- Competitive plasticity to reduce the energetic costs of learning.PLoS computational biology · 2024Article
- Article
- Postsynaptic Potential Energy as Determinant of Synaptic Plasticity.Frontiers in computational neuroscience · 2022Article
- Traceable stimulus-dependent rapid molecular changes in dendritic spines in the brain.Scientific reports · 2020Article
- Energy efficient synaptic plasticity.eLife · 2020Article
- Hebbian Wiring Plasticity Generates Efficient Network Structures for Robust Inference with Synaptic Weight Plasticity.Frontiers in neural circuits · 2016Article
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3 authors.
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Abstract
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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