False Discovery Rate Regression: Neural Synchrony Detection in Primary Visual Cortex

A dataset of neural recordings from primary visual cortex used to demonstrate the false discovery rate regression method. The method detected roughly 50% more synchronous neuron pairs than a standard false discovery rate-controlling analysis. The data and methods are associated with an article and R package by James G. Scott of The University of Texas at Austin.

Use Cases

• Benchmarking false discovery rate regression methods based on neural spike train data.
• Detecting fine-time-scale interactions between neuron pairs based on firing patterns.
• Incorporating covariate information into large-scale multiple-testing problems as described in the article.

Strengths

• Demonstrates a method that detected roughly 50% more synchronous neuron pairs than a standard analysis.
• Associated with a peer-reviewed article and a companion R package (FDRreg) implementing the described methods.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

James G. Scott, The University of Texas at Austin

Collection Method

Neural recordings from primary visual cortex, as described in the associated article.