# Resource-dependent heterosynaptic spike-timing-dependent plasticity in recurrent networks with and without synaptic degeneration

## TL;DR

This paper introduces a new STDP learning rule that allows stable learning in recurrent networks and compensates for synaptic degeneration.

## Contribution

A novel resource-dependent STDP rule that enables stable learning and compensates for synaptic degeneration in recurrent networks.

## Key findings

- A resource-based STDP rule balances potentiation and depression for synaptic homeostasis.
- The learning rule supports stable activity in recurrent networks without runaway potentiation.
- The rule inherently compensates for synaptic degeneration.

## Abstract

Many computational models that incorporate spike-timing-dependent plasticity (STDP) have shown the ability to learn from stimuli, supporting theories that STDP is a sufficient basis for learning and memory. However, to prevent runaway activity and potentiation, particularly within recurrent networks, additional global mechanisms are commonly necessary. A STDP-based learning rule, which involves local resource-dependent potentiation and heterosynaptic depression, is shown to enable stable learning in recurrent spiking networks. A balance between potentiation and depression facilitates synaptic homeostasis, and learned synaptic characteristics align with experimental observations. Furthermore, this resource-based STDP learning rule demonstrates an innate compensatory mechanism for synaptic degeneration.