Critical neural networks with short and long term plasticity

TL;DR

This paper introduces a neural network model combining short-term and long-term plasticity, reproducing critical avalanche behavior and enabling learning of binary rules like XOR, with implications for understanding brain dynamics and learning.

Contribution

It presents a novel model integrating short-term and long-term plasticity mechanisms, capturing avalanche statistics and learning capabilities in neural systems.

Findings

Avalanche size and duration distributions match experimental data.

Neuronal activity shows 1/f power spectrum decay.

System can learn binary rules such as XOR.

Abstract

In recent years self organised critical neuronal models have provided insights regarding the origin of the experimentally observed avalanching behaviour of neuronal systems. It has been shown that dynamical synapses, as a form of short-term plasticity, can cause critical neuronal dynamics. Whereas long-term plasticity, such as hebbian or activity dependent plasticity, have a crucial role in shaping the network structure and endowing neural systems with learning abilities. In this work we provide a model which combines both plasticity mechanisms, acting on two different time-scales. The measured avalanche statistics are compatible with experimental results for both the avalanche size and duration distribution with biologically observed percentages of inhibitory neurons. The time-series of neuronal activity exhibits temporal bursts leading to 1/f decay in the power spectrum. The presence of long-term plasticity gives the system the ability to learn binary rules such as XOR, providing the foundation of future research on more complicated tasks such as pattern recognition.