detalle del documento
IDENTIFICACIÓN
oai:arXiv.org:2409.09125
Tema
Quantum Physics Computer Science - Machine Learnin... Computer Science - Neural and Evol... Quantitative Biology - Neurons and...Autor
Hernandes, Vinicius Greplova, EliskaCategoría
Computer Science
Año
2024
fecha de cotización
18/9/2024
Palabras clave
parameters learning neural machine quantumResumen
Understanding of how biological neural networks process information is one of the biggest open scientific questions of our time.
Advances in machine learning and artificial neural networks have enabled the modeling of neuronal behavior, but classical models often require a large number of parameters, complicating interpretability.
Quantum computing offers an alternative approach through quantum machine learning, which can achieve efficient training with fewer parameters.
In this work, we introduce a quantum generative model framework for generating synthetic data that captures the spatial and temporal correlations of biological neuronal activity.
Our model demonstrates the ability to achieve reliable outcomes with fewer trainable parameters compared to classical methods.
These findings highlight the potential of quantum generative models to provide new tools for modeling and understanding neuronal behavior, offering a promising avenue for future research in neuroscience.
;Comment: 33 pages, 14 figures, code: https://gitlab.com/QMAI/papers/spiqgan
Hernandes, Vinicius,Greplova, Eliska, 2024, Exploring Biological Neuronal Correlations with Quantum Generative Models
Shared genetic aetiology of Alzheimer’s disease and age-related macular degeneration by APOC1 and APOE genes
gene diagnostic based analysis amd ad apoe apoc1 identified genes pleiotropy
