Work

Information Entropy of Biometric Data in a Recurrent Neural Network with Low Connectivity

Year: 2025

Type: article

Abstract: In this paper, we explore the storage capacity and maximal information content of a random recurrent neural network characterized by a very low connectivity. A specific set of patterns is embedded into the network according to the Hebb prescription, a fundamental principle in neural learning. We thoroughly examine how various properties of the network, such as its connectivity and the level of synaptic noise, influence its performance and information retention capabilities, which is evaluated through an entropy measure. Our theoretical analyses are complemented by extensive simulations, and the results are validated through comparisons with the retrieval of real biometric patterns, including retinal vessel maps and fingerprints. This comprehensive approach provides deeper insights into the functionality and limitations of finite-connectivity neural networks and their applicability to the retrieval of complex, structured patterns.

Source: Entropy

DOI: 10.3390/e27111125

Authors: David Dominguez-Carreta, Mario González-Rodríguez, Francisco B. Rodriguez, Angel Sánchez, Rubem Erichsen

Institutions: Universidad Autónoma de Madrid, Universidad de Las Américas, Universidad Rey Juan Carlos, Universidade Federal do Rio Grande do Sul

Language: English

Topic: Neural Networks and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funders: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, European Regional Development Fund, Agencia Estatal de Investigación