One dimensional approximations of neuronal dynamics reveal computational strategy.
PLoS Comput Biol
; 19(1): e1010784, 2023 01.
Article
em En
| MEDLINE
| ID: mdl-36607933
ABSTRACT
The relationship between neuronal activity and computations embodied by it remains an open question. We develop a novel methodology that condenses observed neuronal activity into a quantitatively accurate, simple, and interpretable model and validate it on diverse systems and scales from single neurons in C. elegans to fMRI in humans. The model treats neuronal activity as collections of interlocking 1-dimensional trajectories. Despite their simplicity, these models accurately predict future neuronal activity and future decisions made by human participants. Moreover, the structure formed by interconnected trajectories-a scaffold-is closely related to the computational strategy of the system. We use these scaffolds to compare the computational strategy of primates and artificial systems trained on the same task to identify specific conditions under which the artificial agent learns the same strategy as the primate. The computational strategy extracted using our methodology predicts specific errors on novel stimuli. These results show that our methodology is a powerful tool for studying the relationship between computation and neuronal activity across diverse systems.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Caenorhabditis elegans
/
Modelos Neurológicos
Tipo de estudo:
Prognostic_studies
Limite:
Animals
/
Humans
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article