Local Field Potential Proxies from Integrate-and-Fire Network Simulations

Aggregating simulation outputs comparing candidate proxies for the local field potential (LFP) derived from leaky integrate-and-fire (LIF) network models against a ground-truth LFP from a 3D multi-compartmental neuron model. It was created by Alberto Mazzoni and published in 2020 to facilitate quantitative comparison between computational models and experimental LFP recordings.

Use Cases

• Compare LFP proxy predictions based on LIF network synaptic currents against ground-truth LFP from a 3D network model.
• Analyze the performance of a fixed linear combination of LIF synaptic currents as an LFP proxy across different neuronal morphologies.
• Validate LFP approximations derived from point-neuron LIF network output, such as firing rates and membrane potentials, for a single pyramidal population.

Strengths

• Data is derived from a methodologically rigorous comparison between point-neuron LIF models and a detailed 3D multi-compartmental neuron model.
• The associated proxy formula was validated over a broad set of conditions, including variations in neuronal morphologies.
• Published under a permissive CC0 1.0 license, allowing for unrestricted reuse.

Limitations

• The dataset scope is specific to cases where a single pyramidal population dominates LFP generation, limiting generalizability.
• The exact size, format, and structure of the data files (e.g., row count, columns) are unknown from the provided input.
• The data is from 2020 and may not reflect the latest computational neuroscience models or validation techniques.

Provenance

Source

Dryad digital repository.

Collection Method

Computational simulations comparing LIF network model outputs to a ground-truth 3D multi-compartmental neuron model.

Freshness

Last updated on 2020-06-30.