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Spatiotemporal refinement of signal flow through association cortex during learning.
Ariel Gilad1,2, Fritjof Helmchen3,4
1Brain Research Institute, University of Zurich, CH-8057, Zurich, Switzerland.
Nature Communications
|April 10, 2020
Summary
Mice learning texture discrimination show two phases of brain activity: initial suppression in association cortex, followed by enhanced signal flow to specific areas during task learning. This refines neural pathways for efficient discrimination.
Area of Science:
- Neuroscience
- Cognitive Neuroscience
Background:
- Association areas in the neocortex are crucial for learning new stimulus-outcome relationships.
- The precise mechanisms of how these areas engage during task acquisition are not fully understood.
Purpose of the Study:
- To investigate the spatiotemporal dynamics of cortical activity in mice during a whisker-based texture discrimination task.
- To elucidate the role of association cortex in learning novel sensory-motor associations.
Main Methods:
- Chronic wide-field calcium imaging was employed in mice performing a tactile discrimination task in darkness.
- Activity in various cortical areas, including association cortices (rostro-lateral, posteromedial, retrosplenial dorsal) and barrel cortex, was monitored.
- Correlation analysis was used to examine learning-related refinements in neural activity.
Main Results:
- A two-phase refinement of layer 2/3 cortical activity was observed during learning.
- Initially, association cortex activity was suppressed early in trials before learning threshold.
- As learning progressed, a specific activation sequence emerged, with signal flow from auditory areas to rostro-lateral cortex before texture touch, while other association areas remained suppressed.
Conclusions:
- General suppression in association cortex during a pre-learning phase precedes task-specific signal flow enhancement during learning.
- This dynamic refinement within the association cortex is critical for efficient texture discrimination.
- The findings reveal distinct temporal patterns of neural engagement during associative learning.
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