Learning as a phenomenon occurring in a critical state

TL;DR

This paper proposes a model of brain activity at a critical state that can learn and remember logical rules like XOR, demonstrating that criticality and slow plastic adaptation are key to effective learning.

Contribution

The study introduces a novel neural model at criticality capable of learning complex logical rules, including XOR, with insights into the role of plasticity and network topology.

Findings

Model reproduces experimentally observed critical brain state.

Successfully learns logical rules including XOR.

Learning performance depends on plasticity strength, not task complexity.

Abstract

Recent physiological measurements have provided clear evidence about scale-free avalanche brain activity and EEG spectra, feeding the classical enigma of how such a chaotic system can ever learn or respond in a controlled and reproducible way. Models for learning, like neural networks or perceptrons, have traditionally avoided strong fluctuations. Conversely, we propose that brain activity having features typical of systems at a critical point, represents a crucial ingredient for learning. We present here a study which provides novel insights toward the understanding of the problem. Our model is able to reproduce quantitatively the experimentally observed critical state of the brain and, at the same time, learns and remembers logical rules including the exclusive OR (XOR), which has posed difficulties to several previous attempts. We implement the model on a network with topological properties close to the functionality network in real brains. Learning occurs via plastic adaptation of synaptic strengths and exhibits universal features. We find that the learning performance and the average time required to learn are controlled by the strength of plastic adaptation, in a way independent of the specific task assigned to the system. Even complex rules can be learned provided that the plastic adaptation is sufficiently slow.