A virtual rodent predicts the structure of neural activity across behaviours.
Nature
; 632(8025): 594-602, 2024 Aug.
Article
em En
| MEDLINE
| ID: mdl-38862024
ABSTRACT
Animals have exquisite control of their bodies, allowing them to perform a diverse range of behaviours. How such control is implemented by the brain, however, remains unclear. Advancing our understanding requires models that can relate principles of control to the structure of neural activity in behaving animals. Here, to facilitate this, we built a 'virtual rodent', in which an artificial neural network actuates a biomechanically realistic model of the rat1 in a physics simulator2. We used deep reinforcement learning3-5 to train the virtual agent to imitate the behaviour of freely moving rats, thus allowing us to compare neural activity recorded in real rats to the network activity of a virtual rodent mimicking their behaviour. We found that neural activity in the sensorimotor striatum and motor cortex was better predicted by the virtual rodent's network activity than by any features of the real rat's movements, consistent with both regions implementing inverse dynamics6. Furthermore, the network's latent variability predicted the structure of neural variability across behaviours and afforded robustness in a way consistent with the minimal intervention principle of optimal feedback control7. These results demonstrate how physical simulation of biomechanically realistic virtual animals can help interpret the structure of neural activity across behaviour and relate it to theoretical principles of motor control.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Comportamento Animal
/
Redes Neurais de Computação
/
Realidade Virtual
/
Modelos Neurológicos
Limite:
Animals
Idioma:
En
Revista:
Nature
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos