From cell types to population dynamics: Making hippocampal manifolds physiologically interpretable.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• The hippocampal code is increasingly studied using population dynamics, projecting neuronal activity into low-dimensional manifolds.
• Interpreting these neural manifolds physiologically remains a challenge.

Purpose of the Study:

• To review recent literature on hippocampal population dynamics.
• To propose strategies for physiologically interpreting neural manifolds.

Main Methods:

• Review of recent literature on hippocampal neural coding.
• Integration of behavioral variables, local field potential (LFP) oscillations, and cell-type-specific information.
• Application to low-dimensional manifold analysis.

Main Results:

• Neural manifolds can reveal population representation structures.
• Physiological interpretation of these manifolds is challenging.
• Integrating behavioral, oscillatory, and cell-type data offers a path to interpretability.

Conclusions:

• Integrating behavioral traits, LFP oscillations, and cell-type specificity into neural manifolds enhances their physiological interpretability.
• This approach bridges population dynamics and physiological mechanisms in the hippocampus.