ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2015
Fast Learning with Weak Synaptic Plasticity.
Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Temporal dependence emerges from a carbodiimide driven acid-anhydride equilibrium coupled to a photocatalytic decarboxylation reaction.Chemical science · 2026Article
- Homeostatic Plasticity Enables Stable yet Tunable Neuronal Assemblies.bioRxiv : the preprint server for biology · 2026Article
- Incremental self-organization of spatio-temporal spike pattern detection.Scientific reports · 2025Article
- Synaptic self-organization of spatio-temporal pattern selectivity.PLoS computational biology · 2023Article
- Effects of neurofeedback training combined with transcranial direct current stimulation on motor imagery: A randomized controlled trial.Frontiers in neuroscience · 2023Article
- Estimated gray matter volume rapidly changes after a short motor task.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
- Abnormal synaptic plasticity and impaired cognition in schizophrenia.World journal of psychiatry · 2022Review
- Brain-inspired global-local learning incorporated with neuromorphic computing.Nature communications · 2022Article
- Brain-inspired Predictive Coding Improves the Performance of Machine Challenging Tasks.Frontiers in computational neuroscience · 2022Article
- Memories in a network with excitatory and inhibitory plasticity are encoded in the spiking irregularity.PLoS computational biology · 2021Article
- Fast and flexible sequence induction in spiking neural networks via rapid excitability changes.eLife · 2019Article
- A neuromechanistic model for rhythmic beat generation.PLoS computational biology · 2019Article
- STDP Allows Close-to-Optimal Spatiotemporal Spike Pattern Detection by Single Coincidence Detector Neurons.Neuroscience · 2018Article
- Predictive Coding in Area V4: Dynamic Shape Discrimination under Partial Occlusion.Neural computation · 2018Article
- Optimal Localist and Distributed Coding of Spatiotemporal Spike Patterns Through STDP and Coincidence Detection.Frontiers in computational neuroscience · 2018Article
- Cortical adrenoceptor expression, function and adaptation under conditions of cannabinoid receptor deletion.Experimental neurology · 2017Article
- Natural Firing Patterns Imply Low Sensitivity of Synaptic Plasticity to Spike Timing Compared with Firing Rate.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2016Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
New sensory stimuli can be learned with a single or a few presentations. Similarly, the responses of cortical neurons to a stimulus have been shown to increase reliably after just a few repetitions. Long-term memory is thought to be mediated by synaptic plasticity, but in vitro experiments in cortical cells typically show very small changes in synaptic strength after a pair of presynaptic and postsynaptic spikes. Thus, it is traditionally thought that fast learning requires stronger synaptic changes, possibly because of neuromodulation. Here we show theoretically that weak synaptic plasticity can, in fact, support fast learning, because of the large number of synapses N onto a cortical neuron. In the fluctuation-driven regime characteristic of cortical neurons in vivo, the size of membrane potential fluctuations grows only as √N, whereas a single output spike leads to potentiation of a number of synapses proportional to N. Therefore, the relative effect of a single spike on synaptic potentiation grows as √N. This leverage effect requires precise spike timing. Thus, the large number of synapses onto cortical neurons allows fast learning with very small synaptic changes. Significance statement: Long-term memory is thought to rely on the strengthening of coactive synapses. This physiological mechanism is generally considered to be very gradual, and yet new sensory stimuli can be learned with just a few presentations. Here we show theoretically that this apparent paradox can be solved when there is a tight balance between excitatory and inhibitory input. In this case, small synaptic modifications applied to the many synapses onto a given neuron disrupt that balance and produce a large effect even for modifications induced by a single stimulus. This effect makes fast learning possible with small synaptic changes and reconciles physiological and behavioral observations.
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