Functional coupling networks inferred from prefrontal cortex activity show experience-related effective plasticity

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Functional coupling networks are crucial for understanding neural population activity.
• Learning involves subtle physiological changes, but their reflection in neural networks is not fully understood.

Purpose of the Study:

• To investigate if functional couplings in the prefrontal cortex (PFC) change during learning.
• To determine if these changes are linked to sleep-dependent plasticity and experience-related cell assemblies.

Main Methods:

• Inferred functional network models from simultaneous neural recordings in rat PFC during a rule-shift task and intervening sleep epochs.
• Analyzed 96 sessions to identify effective plasticity between sleep periods.
• Utilized an automated procedure to identify specific neuron groups (potentiated groups) involved in plasticity.

Main Results:

• Detected effective plasticity in ~20% of sessions between sleep epochs, correlating with task-epoch couplings.
• Identified potentiated neuron groups that increased coactivation frequency during sleep, forming putative experience-related cell assemblies.
• Observed that reactivation dynamics of potentiated groups are driven by hippocampal ripples before learning and become more autonomous after learning consolidation.

Conclusions:

• Functional couplings in the PFC exhibit plasticity during sleep, linked to learning.
• Specific neuron groups are critical for experience-dependent network modifications.
• The findings suggest a mechanism for memory consolidation involving sleep-dependent neural plasticity and reactivation patterns.