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Dynamics on the manifold: Identifying computational dynamical activity from neural population recordings.
1Gatsby Computational Neuroscience Unit, University College London, London, UK; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA.
Current Opinion in Neurobiology
|November 27, 2021
Summary
Understanding neural population activity is key to brain function. This review explores methods to find dynamical structure in neural circuits, linking brain activity to behavior and computation.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Dynamical Systems Theory
Background:
- Understanding how neural populations generate complex behaviors is a fundamental neuroscience challenge.
- Neural computations for perception, decision-making, and motor control are thought to arise from dynamical activity in recurrent circuits.
- Identifying dynamical structure in neural activity is crucial for understanding neural computation.
Purpose of the Study:
- To review methods for quantifying structure in neural population recordings.
- To interpret this structure in the context of neural computation.
- To discuss the advantages and limitations of different modeling approaches.
Main Methods:
- Reviewing methods that quantify structure in neural population recordings.
- Utilizing dynamical systems defined in low-dimensional latent variable spaces.
- Analyzing time-varying activity patterns of neural populations.
Main Results:
- Methods exist to quantify dynamical structure in neural population activity.
- Interpreting this structure is essential for linking neural activity to computation.
- Various modeling approaches have distinct advantages and limitations.
Conclusions:
- Quantifying dynamical structure in neural population activity is a key challenge.
- Interpreting this structure is vital for understanding neural computation.
- Future research should address limitations and challenges in current modeling approaches.
