Shaping dynamical neural computations using spatiotemporal constraints.
Biochem Biophys Res Commun
; 728: 150302, 2024 Oct 08.
Article
en En
| MEDLINE
| ID: mdl-38968771
ABSTRACT
Dynamics play a critical role in computation. The principled evolution of states over time enables both biological and artificial networks to represent and integrate information to make decisions. In the past few decades, significant multidisciplinary progress has been made in bridging the gap between how we understand biological versus artificial computation, including how insights gained from one can translate to the other. Research has revealed that neurobiology is a key determinant of brain network architecture, which gives rise to spatiotemporally constrained patterns of activity that underlie computation. Here, we discuss how neural systems use dynamics for computation, and claim that the biological constraints that shape brain networks may be leveraged to improve the implementation of artificial neural networks. To formalize this discussion, we consider a natural artificial analog of the brain that has been used extensively to model neural computation the recurrent neural network (RNN). In both the brain and the RNN, we emphasize the common computational substrate atop which dynamics occur-the connectivity between neurons-and we explore the unique computational advantages offered by biophysical constraints such as resource efficiency, spatial embedding, and neurodevelopment.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Encéfalo
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Redes Neurales de la Computación
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Modelos Neurológicos
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Neuronas
Límite:
Animals
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Humans
Idioma:
En
Revista:
Biochem Biophys Res Commun
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Biochem. biophys. res. commun
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Biochemical and biophysical research communications
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Estados Unidos