# Learning with filopodia and spines: Complementary strong and weak competition lead to specialized, graded, and protected receptive fields

## 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.

## Key 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 has a selectivity comparable to additive STDP and captures input correlations as well as multiplicative models of STDP. We also show how it can protect previously formed memories and perform synaptic consolidation. Overall, our results can be seen as a phenomenological description of how filopodia and spines could cooperate to overcome the individual difficulties faced by strong and weak competition mechanisms.

Changes in the strength of synaptic connections between neurons are thought to be the basis of learning in biological and artificial networks. In animals, these changes can only depend on locally available signals, and are usually modeled with learning rules. Based on recent discoveries on filopodia, a special type of synaptic structure, we propose a new learning rule called Filopodium-Spine spike-timing-dependent-plasticity (FS-STDP). Our rule proposes that filopodia follow strongly-competitive STDP and spines (mature synapses) weakly-competitive STDP. We show that our model overcomes classic difficulties that these learning rules have separately, such as the absence of stability or specificity, and can be seen as a first stage of synaptic consolidation.