Cortical reactivations of recent sensory experiences predict bidirectional network changes during learning.

Area of Science:

• Neuroscience
• Systems Neuroscience
• Cognitive Neuroscience

Background:

• Salient experiences are mentally replayed, involving reactivation of neural patterns in the visual association cortex during quiet wakefulness.
• The precise circuit-level effects of these reactivations remain incompletely understood.

Purpose of the Study:

• To investigate the circuit-level consequences of neural reactivation in the visual association cortex during a learned visual discrimination task.
• To determine how neural ensemble reactivation during quiet waking influences functional connectivity.

Main Methods:

• Multi-day in vivo calcium imaging of hundreds of neurons in the mouse visual association cortex.
• Monitoring neural activity during a visual discrimination task involving distinct cues and rewards.
• Analyzing neural reactivation patterns during subsequent quiet waking periods.

Main Results:

• Distinct neuronal ensembles in the visual association cortex were activated by specific visual cues.
• These cue-specific patterns were reactivated during quiet waking, with higher frequency during early learning and for reward-predicting cues.
• Reactivation involving ensembles encoding both cue and reward strengthened next-day functional connectivity; cue-only reactivation weakened it.

Conclusions:

• Neural reactivation during quiet waking dynamically modifies functional connectivity within the visual association cortex.
• Task-relevant neuronal ensembles strengthen their network interactions, while task-irrelevant ones weaken them, through distinct reactivation mechanisms.
• This provides a circuit-level explanation for how memory representations are consolidated and refined.