NEURONpyxl: Fast, flexible, Python-integrated simulation of biophysical neural networks with complex plastic synapses

This dataset contains the data that are required to reproduce the figures and results from Dickman et al. (2025, submitted). The manuscript describes the development of a Python package that reads parameters from a preformatted Excel spreadsheet to construct a conductance-based neuronal network using the NEURON simulator. The manuscript demonstrates the building of networks based on the formalism used to develop the Simulator for Neural Networks and Action Potentials (SNNAP). The dataset contains voltage and current traces from SNNAP and NEURON simulations, and data from a parameter search that tuned parameters in a complex network.
, , # Data from: NEURONpyxl: Fast, flexible, Python-integrated simulation of biophysical neural networks with complex plastic synapses

Dataset DOI: [10.5061/dryad.1c59zw488](https://doi.org/10.5061/dryad.1c59zw488)

## Description of the data and file structure

This archive contains the simulation data required to reproduce all figures in *Dickman et al.* (2025). The dataset is provided as a benchmark for **NEURONpyxl**, enabling direct comparison with legacy SNNAP simulations. Because SNNAP simulations are no longer practical to run, these data serve as a reference standard for validating NEURONpyxl as a functional replacement. Each of the simulations were done with the corresponding spreadsheet located in the Github repository hosting figure scripts.

### Files and variables

#### File: neuronpyxl_data.zip

**Description:** This file contains all data used to generate the figures and results in *Dickman et al.* (2025, unpublished). Data required for each figure are organized into folder...,