Learning with filopodia and spines: Complementary strong and weak competition lead to specialized, graded, and protected receptive fields
Albert Albesa-González, Claudia Clopath, Daniele Marinazzo, Blake A Richards, Daniele Marinazzo, Blake A Richards, Daniele Marinazzo, Daniele Marinazzo
TL;DR
The paper introduces a new model of synaptic learning that combines filopodia and spines to improve memory stability and specificity.
Contribution
A novel learning rule called FS-STDP that integrates strong and weak competition mechanisms of filopodia and spines.
Findings
The model captures input correlations as effectively as multiplicative STDP models.
The learning rule protects memories and enables synaptic consolidation.
Filopodia and spines work together to overcome individual limitations of strong and weak competition.
Abstract
Filopodia are thin synaptic protrusions that have been long known to play an important role in early development. Recently, they have been found to be more abundant in the adult cortex than previously thought, and more plastic than spines (button-shaped mature synapses). Inspired by these findings, we introduce a new model of synaptic plasticity that jointly describes learning of filopodia and spines. The model assumes that filopodia exhibit strongly competitive learning dynamics -similarly to additive spike-timing-dependent plasticity (STDP). At the same time it proposes that, if filopodia undergo sufficient potentiation, they consolidate into spines. Spines follow weakly competitive learning, classically associated with multiplicative, soft-bounded models of STDP. This makes spines more stable and sensitive to the fine structure of input correlations. We show that our learning rule…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Neuroscience and Neuropharmacology Research · Neural dynamics and brain function