Differential Encoding of Time by Prefrontal and Striatal Network Dynamics

Area of Science:

• Neuroscience
• Computational Neuroscience
• Behavioral Neuroscience

Background:

• Telling time is crucial for learning and anticipating rewards.
• Neural mechanisms of time perception are not fully understood.
• Brain timing may rely on neural network dynamics, with activity patterns encoding elapsed time in areas like the striatum and cortex.

Purpose of the Study:

• To quantitatively compare how well the striatum and orbitofrontal cortex encode elapsed time.
• To investigate the neural basis of time perception and reward anticipation.

Main Methods:

• Large-scale extracellular recordings from the striatum and orbitofrontal cortex in mice.
• Mice were trained on a stimulus-reward association task with a delay period.
• A machine-learning algorithm quantified neural population encoding of elapsed time.

Main Results:

• Both striatal and orbitofrontal cortical networks encoded elapsed time.
• The striatum consistently outperformed the orbitofrontal cortex in time encoding accuracy.
• Striatal networks were more reliable in predicting licking behavior up to ~1 second in advance.

Conclusions:

• Temporal information is distributed across brain areas, but the striatum plays a privileged role in timing.
• The striatum may refine temporal codes by integrating information from multiple cortical inputs.
• The striatum acts as a more accurate neural clock for anticipating events.