Cargando…
Effectiveness of Biologically Inspired Neural Network Models in Learning and Patterns Memorization
Purpose: In this work, we propose an implementation of the Bienenstock–Cooper–Munro (BCM) model, obtained by a combination of the classical framework and modern deep learning methodologies. The BCM model remains one of the most promising approaches to modeling the synaptic plasticity of neurons, but...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9141587/ https://www.ncbi.nlm.nih.gov/pubmed/35626566 http://dx.doi.org/10.3390/e24050682 |
_version_ | 1784715381190950912 |
---|---|
author | Squadrani, Lorenzo Curti, Nico Giampieri, Enrico Remondini, Daniel Blais, Brian Castellani, Gastone |
author_facet | Squadrani, Lorenzo Curti, Nico Giampieri, Enrico Remondini, Daniel Blais, Brian Castellani, Gastone |
author_sort | Squadrani, Lorenzo |
collection | PubMed |
description | Purpose: In this work, we propose an implementation of the Bienenstock–Cooper–Munro (BCM) model, obtained by a combination of the classical framework and modern deep learning methodologies. The BCM model remains one of the most promising approaches to modeling the synaptic plasticity of neurons, but its application has remained mainly confined to neuroscience simulations and few applications in data science. Methods: To improve the convergence efficiency of the BCM model, we combine the original plasticity rule with the optimization tools of modern deep learning. By numerical simulation on standard benchmark datasets, we prove the efficiency of the BCM model in learning, memorization capacity, and feature extraction. Results: In all the numerical simulations, the visualization of neuronal synaptic weights confirms the memorization of human-interpretable subsets of patterns. We numerically prove that the selectivity obtained by BCM neurons is indicative of an internal feature extraction procedure, useful for patterns clustering and classification. The introduction of competitiveness between neurons in the same BCM network allows the network to modulate the memorization capacity of the model and the consequent model selectivity. Conclusions: The proposed improvements make the BCM model a suitable alternative to standard machine learning techniques for both feature selection and classification tasks. |
format | Online Article Text |
id | pubmed-9141587 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91415872022-05-28 Effectiveness of Biologically Inspired Neural Network Models in Learning and Patterns Memorization Squadrani, Lorenzo Curti, Nico Giampieri, Enrico Remondini, Daniel Blais, Brian Castellani, Gastone Entropy (Basel) Article Purpose: In this work, we propose an implementation of the Bienenstock–Cooper–Munro (BCM) model, obtained by a combination of the classical framework and modern deep learning methodologies. The BCM model remains one of the most promising approaches to modeling the synaptic plasticity of neurons, but its application has remained mainly confined to neuroscience simulations and few applications in data science. Methods: To improve the convergence efficiency of the BCM model, we combine the original plasticity rule with the optimization tools of modern deep learning. By numerical simulation on standard benchmark datasets, we prove the efficiency of the BCM model in learning, memorization capacity, and feature extraction. Results: In all the numerical simulations, the visualization of neuronal synaptic weights confirms the memorization of human-interpretable subsets of patterns. We numerically prove that the selectivity obtained by BCM neurons is indicative of an internal feature extraction procedure, useful for patterns clustering and classification. The introduction of competitiveness between neurons in the same BCM network allows the network to modulate the memorization capacity of the model and the consequent model selectivity. Conclusions: The proposed improvements make the BCM model a suitable alternative to standard machine learning techniques for both feature selection and classification tasks. MDPI 2022-05-12 /pmc/articles/PMC9141587/ /pubmed/35626566 http://dx.doi.org/10.3390/e24050682 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Squadrani, Lorenzo Curti, Nico Giampieri, Enrico Remondini, Daniel Blais, Brian Castellani, Gastone Effectiveness of Biologically Inspired Neural Network Models in Learning and Patterns Memorization |
title | Effectiveness of Biologically Inspired Neural Network Models in Learning and Patterns Memorization |
title_full | Effectiveness of Biologically Inspired Neural Network Models in Learning and Patterns Memorization |
title_fullStr | Effectiveness of Biologically Inspired Neural Network Models in Learning and Patterns Memorization |
title_full_unstemmed | Effectiveness of Biologically Inspired Neural Network Models in Learning and Patterns Memorization |
title_short | Effectiveness of Biologically Inspired Neural Network Models in Learning and Patterns Memorization |
title_sort | effectiveness of biologically inspired neural network models in learning and patterns memorization |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9141587/ https://www.ncbi.nlm.nih.gov/pubmed/35626566 http://dx.doi.org/10.3390/e24050682 |
work_keys_str_mv | AT squadranilorenzo effectivenessofbiologicallyinspiredneuralnetworkmodelsinlearningandpatternsmemorization AT curtinico effectivenessofbiologicallyinspiredneuralnetworkmodelsinlearningandpatternsmemorization AT giampierienrico effectivenessofbiologicallyinspiredneuralnetworkmodelsinlearningandpatternsmemorization AT remondinidaniel effectivenessofbiologicallyinspiredneuralnetworkmodelsinlearningandpatternsmemorization AT blaisbrian effectivenessofbiologicallyinspiredneuralnetworkmodelsinlearningandpatternsmemorization AT castellanigastone effectivenessofbiologicallyinspiredneuralnetworkmodelsinlearningandpatternsmemorization |