De novo motor learning creates structure in neural activity that shapes adaptation.
Nat Commun
; 15(1): 4084, 2024 May 14.
Article
in En
| MEDLINE
| ID: mdl-38744847
ABSTRACT
Animals can quickly adapt learned movements to external perturbations, and their existing motor repertoire likely influences their ease of adaptation. Long-term learning causes lasting changes in neural connectivity, which shapes the activity patterns that can be produced during adaptation. Here, we examined how a neural population's existing activity patterns, acquired through de novo learning, affect subsequent adaptation by modeling motor cortical neural population dynamics with recurrent neural networks. We trained networks on different motor repertoires comprising varying numbers of movements, which they acquired following various learning experiences. Networks with multiple movements had more constrained and robust dynamics, which were associated with more defined neural 'structure'-organization in the available population activity patterns. This structure facilitated adaptation, but only when the changes imposed by the perturbation were congruent with the organization of the inputs and the structure in neural activity acquired during de novo learning. These results highlight trade-offs in skill acquisition and demonstrate how different learning experiences can shape the geometrical properties of neural population activity and subsequent adaptation.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Adaptation, Physiological
/
Learning
/
Models, Neurological
/
Motor Cortex
Limits:
Animals
Language:
En
Journal:
Nat Commun
Journal subject:
BIOLOGIA
/
CIENCIA
Year:
2024
Document type:
Article
Affiliation country: