Learning with filopodia and spines

Abstract

Filopodia are thin synaptic protrusions that have been long known to play an important role in early development. It has recently been found that they are 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 additive learning, which is highly competitive and volatile. At the same time, it proposes that if filopodia undergo sufficient potentiation they consolidate into spines, and start following multiplicative learning dynamics. 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 spike-timing-dependent plasticity (STDP) and represents input correlations as well as multiplicative STDP. We also show how it can protect previously formed memories and act as a synaptic consolidation mechanism. Overall, our results provide a mechanistic explanation of how filopodia and spines could cooperate to overcome the difficulties that these separate forms of learning (additive and multiplicative) each have. Author SummaryChanges in the strength of synaptic connections between neurons are 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. Our rule proposes that filopodia follow additive STDP and spines (mature synapses) multiplicative STDP. We show that our model overcomes classic difficulties that these learning rules have separately, like the absence of stability or specificity, and can also be seen as a first stage of synaptic consolidation.