ArticleScientific reports2017
Nonspecific synaptic plasticity improves the recognition of sparse patterns degraded by local noise.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Modeling nitric oxide diffusion and plasticity modulation in cerebellar learning.APL bioengineering · 2025Article
- Endocannabinoid receptors contribute significantly to multiple forms of long-term depression in the rat dentate gyrus.Learning & memory (Cold Spring Harbor, N.Y.) · 2020Article
- Perceptron Learning and Classification in a Modeled Cortical Pyramidal Cell.Frontiers in computational neuroscience · 2020Article
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5 authors.
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Abstract
Many forms of synaptic plasticity require the local production of volatile or rapidly diffusing substances such as nitric oxide. The nonspecific plasticity these neuromodulators may induce at neighboring non-active synapses is thought to be detrimental for the specificity of memory storage. We show here that memory retrieval may benefit from this non-specific plasticity when the applied sparse binary input patterns are degraded by local noise. Simulations of a biophysically realistic model of a cerebellar Purkinje cell in a pattern recognition task show that, in the absence of noise, leakage of plasticity to adjacent synapses degrades the recognition of sparse static patterns. However, above a local noise level of 20%, the model with nonspecific plasticity outperforms the standard, specific model. The gain in performance is greatest when the spatial distribution of noise in the input matches the range of diffusion-induced plasticity. Hence non-specific plasticity may offer a benefit in noisy environments or when the pressure to generalize is strong.
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