Details

Autor(en) / Beteiligte

• Huber, D.
• Gutnisky, D. A.
• Peron, S.
• O’Connor, D. H.
• Wiegert, J. S.
• Tian, L.
• Oertner, T. G.
• Looger, L. L.
• Svoboda, K.

Titel

Multiple dynamic representations in the motor cortex during sensorimotor learning

Ist Teil von

• Nature (London), 2012-04, Vol.484 (7395), p.473-478

Ort / Verlag

London: Nature Publishing Group UK

Erscheinungsjahr

2012

Quelle

EBSCOhost Psychology and Behavioral Sciences Collection

Beschreibungen/Notizen

• The mechanisms linking sensation and action during learning are poorly understood. Layer 2/3 neurons in the motor cortex might participate in sensorimotor integration and learning; they receive input from sensory cortex and excite deep layer neurons, which control movement. Here we imaged activity in the same set of layer 2/3 neurons in the motor cortex over weeks, while mice learned to detect objects with their whiskers and report detection with licking. Spatially intermingled neurons represented sensory (touch) and motor behaviours (whisker movements and licking). With learning, the population-level representation of task-related licking strengthened. In trained mice, population-level representations were redundant and stable, despite dynamism of single-neuron representations. The activity of a subpopulation of neurons was consistent with touch driving licking behaviour. Our results suggest that ensembles of motor cortex neurons couple sensory input to multiple, related motor programs during learning. Genetically encoded neural activity markers were used in mice to simultaneously follow large populations of motor cortex neurons during sensorimotor learning, revealing that spatially intermingled neurons represent either sensory or motor behaviour, with population-level representations of subsets of motor programs strengthening as training progressed. The motor cortex observed during learning Although it is known that many neurons in a circuit experience plasticity changes during long-term learning, the limitations of electrophysiological methodology mean that these changes are usually examined in only a few neurons at a time. This study uses genetically encoded neural-activity markers to follow large populations of motor-cortex neurons simultaneously during sensorimotor learning in mice. The resulting images show that spatially intermingled neurons represent most of the features relevant to the learned task, including touch forces, whisker movements, licking and other behavioural variables. Population-level representations of motor programs strengthened as training progressed.