Dataset DOI: 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 (2025, unpublished).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 (2025, unpublished))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 (2025, unpublished) for detailed description).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
• NEURONpyxl package: https://github.com/CWRUChielLab/neuronpyxl
• NEURON simulator: https://github.com/neuronsimulator/nrn
• SNNAP: https://med.uth.edu/nba/snnap/
• makeSNNAP: https://github.com/Byrne-Lab/makeSNNAP