Learning and discrimination through STDP in a top-down modulated associative memory

TL;DR

This paper presents a spiking neuron network model that uses STDP and top-down modulation to learn and discriminate multimodal associations, demonstrating stable activity patterns even without initial learning.

Contribution

The model integrates top-down modulation with STDP in spiking neurons to enhance associative learning and discrimination capabilities.

Findings

The model successfully associates activity patterns with different stimuli.

Stable activity patterns emerge even without initial training.

The approach mimics neurobiological mechanisms of learning.

Abstract

This article underlines the learning and discrimination capabilities of a model of associative memory based on artificial networks of spiking neurons. Inspired from neuropsychology and neurobiology, the model implements top-down modulations, as in neocortical layer V pyramidal neurons, with a learning rule based on synaptic plasticity (STDP), for performing a multimodal association learning task. A temporal correlation method of analysis proves the ability of the model to associate specific activity patterns to different samples of stimulation. Even in the absence of initial learning and with continuously varying weights, the activity patterns become stable enough for discrimination.