NEURONpyxl: Simulation Parameters for Biophysical Neural Networks with Plastic Synapses

NEURONpyxl is a Python tool for constructing biophysical neural network simulations from spreadsheet parameters. The dataset, a 169.6 KB PDF authored by Uri Dickman and last updated in May 2026, describes the tool's development and validation against the SNNAP simulator. It was used to optimize conductances in a model of Aplysia californica feeding behavior.

Use Cases

• Constructing multi-compartment conductance-based neural network models based on parameters read from a spreadsheet.
• Simulating networks with short-term synaptic plasticity such as depression or facilitation.
• Conducting parameter grid searches to optimize model conductances for matching biological behavior.
• Validating neural simulation results by comparing outputs with other simulators like SNNAP.

Strengths

• The tool and its validation are described in a detailed, peer-reviewed-style PDF document.
• The method was validated by producing numerically equivalent results to the established SNNAP simulator.
• Successfully located physiologically relevant parameter values for a model of Aplysia feeding behavior.

Limitations

• The dataset is a 169.6 KB PDF document; the underlying simulation parameters or results are not provided as a separate, machine-readable data file.
• Column-level documentation for any potential parameter tables is absent; data semantics must be inferred from the text.
• The row count and specific data structure are unknown, limiting suitability assessment for direct analysis.

Provenance

Source

Uri Dickman

Collection Method

Parameters were read from a spreadsheet to construct neural network simulations in the NEURON environment.

Freshness

Last updated 2026-05-19 05:38:46; freshness should be verified.