Online Learning and Memory of Neural Trajectory Replays for Prefrontal Persistent and Dynamic Representations in the...

Area of Science:

• Computational neuroscience
• Systems neuroscience
• Cognitive neuroscience

Background:

• Higher-order cognitive functions in the prefrontal cortex (PFC) depend on dynamic neural trajectories.
• Existing models often use unrealistic rules or separate learning from replay.
• The role of biological plasticity in online learning and replay of trajectories under natural brain dynamics is unclear.

Purpose of the Study:

• To investigate how neural trajectories are learned, memorized, and replayed online in the PFC.
• To explore the impact of realistic synaptic plasticity rules and asynchronous irregular dynamics on trajectory processing.
• To provide a theoretical framework for understanding trajectory-based representations in the PFC.

Main Methods:

• Developed a recurrent neural network model of local PFC circuitry.
• Incorporated realistic synaptic spike-timing-dependent plasticity and scaling.
• Simulated learning, memorization, and online replay of neural trajectories under asynchronous irregular dynamics.

Main Results:

• The model learned and memorized large neural trajectories as synaptic engrams within seconds.
• Engrams demonstrated long-term stability (hours) and enabled chunking of overlapping trajectories.
• Replays could be triggered over an hour and preserved the network's asynchronous irregular dynamics.
• Replay activity mimicked observed PFC dynamics, including temporal tuning and population-level persistence.

Conclusions:

• Realistic synaptic plasticity enables online learning, memorization, and replay of neural trajectories in PFC networks.
• The model provides a framework for understanding how PFC circuits support flexible representations and adaptive behaviors.
• Neural trajectories can be learned and replayed while maintaining the brain's natural irregular dynamics.