by DE ARCANGELIS L., HERRMANN H.J.
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 that provides unique 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, 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.
Reference:
Learning as a phenomenon occurring in a critical state (DE ARCANGELIS L., HERRMANN H.J.), In PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, volume 107 n°9, 2010. (Articolo in rivista)
Bibtex Entry:
@article{dea10,
author = {DE ARCANGELIS L., and HERRMANN H.J.,},
pages = {3977-3981},
title = {Learning as a phenomenon occurring in a critical state},
volume = {107 n°9},
note = {Articolo in rivista},
issn = {1091-6490},
journal = {PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA},
year = {2010},
wosId = {WOS:000275131100009},
scopusId = {2-s2.0-77749239921},
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 that provides
unique 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, 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.}
}