DOI: 10.5061/dryad.1c59zw488
title: Data from: NEURONpyxl: Fast, flexible, Python-integrated simulation of biophysical neural networks with complex plastic synapses

description: 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.
description: # 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 al.* (2025, unpublished).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
 folders labeled by figure number. The **benchmark** folder contains
 datasets used to compare the computational performance of NEURONpyxl and
 SNNAP under comparable conditions. * **./fig2**: data generated in SNNAP
 for different timesteps, indicated in the file name in milliseconds.
 Comparison between the convergence of SNNAP and NEURON simulations with
 the exact solution is shown in Figure 2. * **./fig3**: voltage (mV) and
 current (nA) data for the B4 *Aplysia* neuron regulated by calcium which
 is shown in Figure 3. * **./fig4**: voltage (mV) traces for a small
 network of two electrically coupled spiking neurons shown in Figure 5. *
 **./fig5**: voltage (mV) traces for a small network of three neurons
 coupled by a series of chemical synapses. * **./fig6**: voltage (mV)
 traces for a small network of two neurons coupled by a voltage-dependent
 chemical synapse. * **./fig7**: Simulation data from SNNAP and NEURON
 simulations of a B4 *Aplysia* neuron without any stimulation, injected
 with noise (see *Dickman et al.* (2025, unpublished)) * **./fig9**:
 Simulation data from SNNAP and NEURON simulations of a full network for
 *Aplysia* feeding, from *Momohara et al.* (2022). * **./fig10-11**:
 Simulation data from optimizing a full network for *Aplysia* feeding, from
 *Momohara et al.* (2022). * gillchiel_2020_data.csv: data from *Gill &
 Chiel* (2020) * data_test_*.csv: voltage traces from B64a and B31a neurons
 when plugging in conductance values for the loaded, unloaded, and control
 conditions (see *Dickman et al.* (2025, unpublished) for detailed
 description). * meandur_*.csv: mean duration and standard deviation of
 protraction and retraction phases for the data in data_test_*.csv for the
 loaded, unloaded, and control conditions. Also includes the number of BMP
 cycles are included in each measurement. * results.csv: results from the
 parameter optimization procedure. *vdg_g_B64s_kpp* and *cs_g_B30_B63_fast*
 are the values of the conductances. *protraction* and *retraction* are the
 mean duration of protraction and retraction phases. *std1* and *std2* are
 the standard deviations of protraction and retraction duration. *n1* and
 *n2* are the number of BMP cycles included in the calculation of the mean
 protraction and retraction duration. * **./benchmark**: durations of 10
 simulations of SNNAP and NEURON simulations of running the full *Momohara
 et al. (2022)* network. SNNAP simulation times computed manually, NEURON
 simulations timed with Python's time module. ## Code/software The
 scripts that produce the figures and results from the data in this dataset
 are located in a Github repository, which will be made public upon
 publication of the manuscript. The scripts uses the NEURONpyxl Python
 package which is described in the manuscript of *Dickman et al.* (2025,
 unpublished). The SNNAP simulation data was created using SNNAP version
 8.1 with files constructed in Matlab using makeSNNAP. The data can be
 viewed using any software that can read CSV, TSV and HDF5 files, such as
 Pandas and Microsoft Excel. ## Access information Other publicly
 accessible locations of the data: * None Data was derived from the
 following sources (NEURONpyxl and Dickman-2025-figures will be made public
 upon publication of *Dickman et al.* (2025, unpublished)): * Figure
 scripts:
 [https://github.com/CWRUChielLab/Dickman-2025-figures](https://github.com/CWRUChielLab/Dickman-2025-figures) * NEURONpyxl package: [https://github.com/CWRUChielLab/neuronpyxl](https://github.com/CWRUChielLab/neuronpyxl) * NEURON simulator: [https://github.com/neuronsimulator/nrn](https://github.com/neuronsimulator/nrn) * SNNAP: [https://med.uth.edu/nba/snnap/](https://med.uth.edu/nba/snnap/) * makeSNNAP: [https://github.com/Byrne-Lab/makeSNNAP](https://github.com/Byrne-Lab/makeSNNAP)