loading
Papers

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Elena Agliari ; Adriano Barra ; Andrea Galluzzi ; Daniele Tantari and Flavia Tavani

Affiliation: Sapienza Università di Roma, Italy

ISBN: 978-989-758-054-3

Keyword(s): Statistical Mechanics, Spin-glasses, Random Graphs.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Self-Organization and Emergence ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: Neural networks are nowadays both powerful operational tools (e.g., for pattern recognition, data mining, error correction codes) and complex theoretical models on the focus of scientific investigation. As for the research branch, neural networks are handled and studied by psychologists, neurobiologists, engineers, mathematicians and theoretical physicists. In particular, in theoretical physics, the key instrument for the quantitative analysis of neural networks is statistical mechanics. From this perspective, here, we review attractor networks: starting from ferromagnets and spin-glass models, we discuss the underlying philosophy and we recover the strand paved by Hopfield, Amit-Gutfreund-Sompolinky. As a sideline, in this walk we derive an alternative (with respect to the original Hebb proposal) way to recover the Hebbian paradigm, stemming from mixing ferromagnets with spin-glasses. Further, as these notes are thought of for an Engineering audience, we highlight also the mappings b etween ferromagnets and operational amplifiers, hoping that such a bridge plays as a concrete prescription to capture the beauty of robotics from the statistical mechanical perspective. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.173.234.140

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Agliari, E.; Barra, A.; Galluzzi, A.; Tantari, D. and Tavani, F. (2014). A Walk in the Statistical Mechanical Formulation of Neural Networks - Alternative Routes to Hebb Prescription.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014) ISBN 978-989-758-054-3, pages 210-217. DOI: 10.5220/0005077902100217

@conference{ncta14,
author={Elena Agliari. and Adriano Barra. and Andrea Galluzzi. and Daniele Tantari. and Flavia Tavani.},
title={A Walk in the Statistical Mechanical Formulation of Neural Networks - Alternative Routes to Hebb Prescription},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)},
year={2014},
pages={210-217},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005077902100217},
isbn={978-989-758-054-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)
TI - A Walk in the Statistical Mechanical Formulation of Neural Networks - Alternative Routes to Hebb Prescription
SN - 978-989-758-054-3
AU - Agliari, E.
AU - Barra, A.
AU - Galluzzi, A.
AU - Tantari, D.
AU - Tavani, F.
PY - 2014
SP - 210
EP - 217
DO - 10.5220/0005077902100217

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.