Neural Spike Covariance Data from Periodically-Driven Recurrent Networks

A 2020 dataset from Tobias Kühn investigates the locking of correlated neural spiking to beta-oscillations in cortical networks. It contains analytical results from mean-field and linear response theory applied to binary recurrent random networks. The data models how pairwise zero-time-lag covariances and mean activities are modulated by a periodic driving stimulus.

Use Cases

• Analyze the time-dependent modulation of pairwise zero-time-lag covariances relative to the oscillatory cycle.
• Validate mean-field theory predictions for cyclostationary mean activities in periodically-driven networks.
• Investigate the resonant behavior of covariances under effectively inhibitory recurrent feedback conditions.
• Study the distinct mechanisms of susceptibility modulation and time-varying single-unit variances on correlated spiking.

Strengths

• Data is derived from a formal analytical framework combining mean-field and linear response theory.
• Dataset is openly available under a CC0 1.0 public domain license.
• Focuses on a specific, salient question in neuroscience regarding the interaction of oscillations and spike synchrony.

Limitations

• Dataset size, row count, and specific file formats are not provided in the input.
• Data is theoretical/model-based rather than empirical experimental recordings, limiting direct biological validation.
• The last update was in June 2020, which may not reflect the most recent research developments.

Provenance

Source

Tobias Kühn via Dryad digital repository.

Collection Method

Theoretical modeling and analysis using mean-field theory and linear response theory on binary recurrent random networks.

Freshness

Last updated 2020-06-24.