Retrosplenial Cortex Neuron Electrophysiology and Connectivity Data

A neuroscience dataset characterizing the intrinsic physiology and local connectivity of neurons in the superficial layers of the retrosplenial granular cortex. The data was produced by Ellen K.W. Brennan at the University of Michigan and is available via paperswithcode. It includes paired whole-cell recordings to identify excitatory and inhibitory connections.

Use Cases

• Training models of cortical microcircuits based on described cell types like hyperexcitable LR pyramidal cells and FS inhibitory neurons.
• Simulating feedforward inhibitory networks based on the described connectivity statistics, such as FS cells inhibiting over 52% of LR cells.
• Investigating alternatives to PING rhythm generation in neural circuits based on the described sparse reciprocal connections.
• Analyzing the relationship between intrinsic physiological properties like rheobase, input resistance, and spike width across different neuron classes.

Strengths

• Includes specific, quantified connectivity statistics, such as LR cells exciting only 17% of FS cells and FS cells inhibiting over 52% of LR cells.
• Provides a detailed physiological characterization of a prominent cell type, including low rheobase, high input resistance, and lack of spike-frequency adaptation.
• Identifies a novel circuit motif with hyperexcitable excitatory neurons under dominant feedforward inhibition.

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

University of Michigan research by Ellen K.W. Brennan, shared via paperswithcode.

Collection Method

Systematic characterization via paired whole-cell recordings from neurons in the superficial retrosplenial granular cortex.