NEURONpyxl: Python Tool for Biophysical Neural Network Simulation with Plastic Synapses

A Python tool named NEURONpyxl reads parameters from a spreadsheet to construct and simulate biophysical neural networks. It was developed by Uri Dickman and last updated on 2026-05-19. The tool was validated against the SNNAP simulator and used to optimize a model of Aplysia feeding behavior.

Use Cases

• Building multi-compartment conductance-based neural networks based on the description of NEURONpyxl's purpose.
• Simulating networks with short-term synaptic plasticity such as depression or facilitation based on the tool's stated capabilities.
• Conducting parameter grid searches to optimize network conductances for matching biological behavior based on the described methodology.
• Creating ensembles of network models or integrating biomechanics with neural networks based on the stated future work.

Strengths

• Validated against the SNNAP simulator with numerically equivalent results.
• Enables simulation of networks with complex synaptic connections and multiple forms of plasticity.
• Released under the CC-BY-4.0 license, allowing for open use and modification.

Limitations

• The dataset is a 2.1 MB PDF document; the underlying simulation data or parameter tables are not directly provided.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

Uri Dickman via figshare.

Collection Method

Tool development and validation through comparative simulation and parameter grid search.

Freshness

Last updated 2026-05-19 05:38:45; freshness should be verified.