lFCI Pipeline Results: Intrinsic Dimension Estimates for Neural Activity

A 5.5 KB dataset containing results from applying a local full correlation integral (lFCI) estimator to assess the intrinsic dimension of neural manifolds. The data, authored by Jacopo Fadanni and last updated in April 2026, includes analyses of synthetic benchmarks, recurrent neural network tasks, and experimental visual response data. It supports investigations into the geometry of neural codes in biological and artificial networks.

Use Cases

• Benchmarking intrinsic dimension estimation methods based on the synthetic benchmark data from Altan et al., 2019
• Analyzing the relationship between task structure and neural manifold dimensionality based on the 'cog-Task' battery results
• Investigating the dimensionality of visual response manifolds based on the reference experimental dataset by Stringer et al., 2019

Strengths

• Dataset is small (5.5 KB), facilitating quick download and inspection.
• Applies a specific, described pipeline (lFCI) to overcome limitations of common estimation methods.
• Includes analyses of three distinct data sources: synthetic benchmarks, trained networks, and experimental data.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset's scope is limited to the specific results of the described lFCI pipeline application.

Provenance

Source

figshare

Collection Method

Results generated by applying the lFCI estimation pipeline to specified datasets.

Time Range

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Freshness

Last updated 2026-04-03 17:45:27; freshness should be verified.

Geography

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