Retinal Ganglion Cell Spike Trains Under Natural Stimuli

A collection of single-trial population spike trains from salamander retinal ganglion cells recorded during natural image stimulation. It was created by Arno Onken and published in 2020 to investigate tensor factorization methods for analyzing spatial and temporal neural population codes. The data was used to demonstrate that spike timing, particularly first-spike latencies, carries significant information about image features.

Use Cases

• Apply tensor factorization to decompose spike trains into spatial firing patterns and temporal firing patterns for dimensionality reduction.
• Analyze first-spike latencies to quantify their information content about fine-scale and coarse natural image features.
• Compare the sensory information captured by spike timing versus spike counts from retinal ganglion cells.
• Validate non-negative tensor decompositions on non-independent and overlapping spike patterns from neural populations.

Strengths

• Data is validated on populations of ganglion cells simultaneously recorded in the salamander retina.
• Dataset is associated with a published scientific study investigating advanced analytical methods like tensor factorizations.
• Released under the permissive CC0 1.0 public domain license.

Limitations

• The dataset size, row count, column structure, and file formats are unknown from the provided input.
• Data is specific to the salamander retina (Ambystoma mexicanum), limiting direct applicability to other neural systems without transfer learning.
• The temporal coverage and specific number of trials or recorded neurons are not specified.

Provenance

Source

Dryad digital repository.

Collection Method

Simultaneous recordings of populations of retinal ganglion cells in the salamander retina under natural image stimuli.

Freshness

Last updated on 2020-06-24.