About

Source code for:

McDougal, R. A., Conte, C., Eggleston, L., Newton, A. J. H., & Galijasevic, H. (2022). Efficient simulation of 3D reaction-diffusion in models of neurons and networks. bioRxiv. doi:10.1101/2022.01.01.474683

Description of files

070314F_11.ASC

CA1 pyramidal cell morphology from Malik et al., 2016 via NeuroMorpho.Org (Ascoli et al., 2007)

conservation_of_mass.py

Tests fixed and variable step conservation of mass in a pure diffusion problem on a Y-shape geometry.

do_timings.py

Short control script for time_discretization.py that loops over choices of dx and cell morphologies. This generates data and stores it in a sqlite3 database; use get_timings.py to generate the plots.

Figure1A_3Dwave_time_contour.py

Propagating wave test near the soma on a realistic morphology (070314F_11.ASC), generates contour maps showing wave front at different time points.

Figure1A_3Dwave_time_contour70.py

Like Figure1A_3Dwave_time_contour.py but doesn't generate the contour maps and is instead focused on detecting soma crossing times.

Figure1A_3Dwave_time_contour100.py

Like Figure1A_3Dwave_time_contour70.py but does more of the problem in 3D (includes sections whose center lies within 100 µm path distance of the center of the soma instead of just 70 µm).

Figure1A_hybrid.py

Like Figure1A_3Dwave_time_contour.py but doesn't generate the contour maps and is instead focused on detecting soma crossing times.

get_timings.py

Generates plots from data produced by do_timings.py

morph_volume_analysis_truebound.py

Tool for comparing volume of bounding box to volume of cell

plot_simple_geometry_convergence.py

Plots data generated by simple_geometry_convergence.py

plot_thread_scaling.py

Plots data generated by thread_scaling.py

plot_wave_time_3d.py

Plots data generated by wave_time_3d.py

readme.html

This file, which provides an overview of all the files in this archive.

segment-alignment.py

Visually tests relationship between segment boundaries and 3D voxel segment assignment.

simple_geometry_convergence.py

Measures surface area, volume, relative errors, and runtimes for various cylinders with different discretization options. Visualize results by running plot_simple_geometry_convergence.py

thread_scaling.py

Measures run-time as the number of threads are varied for different choices of morphology, kinetics, and dx.

time_discretization.py

Times the discretization for a specified morphology and dx; also stores the computed volume, surface area, number of voxels, number of surface voxels, total section lengths, and number of sections. Invoked by time_discretization.py

volume_functions_truebound.py

???

wave_time_3d.py

???

All morphologies in the swc folder are via NeuroMorpho.Org:

            \texttt{9CL-IVxAnk2-IR\_ddaC} \citep{nanda2018morphological}, 
            \texttt{29-1-8} \citep{martinez2013chronic}, 
            \texttt{64-8-L-B-JB} \citep{ehlinger2017nicotine},
            \texttt{243-3-39-AW} \citep{nguyen2020comparative},
            \texttt{2017-25-04-slice-2-cell-2-rotated} \citep{scala2019layer},
            \texttt{070601-exp1-zB} \citep{groh2010cell},
            \texttt{160524\_7\_4} \citep{kunst2019cellular},
            \texttt{15892037} \citep{takagi2017divergent},
            \texttt{AE5\_EEA\_Outer-thirds\_DG-Mol\_sec1-cel4-aev5me} \citep{de2020long},
            \texttt{AM61-2-1} and \texttt{AM81-2-3} \citep{trevelyan2006modular},
            \texttt{B4-CA1-L-D63x1zACR3\_1} \citep{canchi2017simulated},
            \texttt{Dnmt3bKO-cell-8 and WT-iPS-derived-cell-12MR} \citep{tarusawa2016establishment},
            \texttt{Fig5C} \citep{herget2017single},
            \texttt{glia\_4090} \citep{helmstaedter2013connectomic},
            \texttt{KC-s-4505762} \citep{takemura2017connectome},
            \texttt{Mouse\_CA2\_Ma\_Cell\_5} \citep{helton2019diversity},
            \texttt{RatS1-6-107} \citep{nogueira2012distribution},
            \texttt{RP4\_scaled} \citep{weiss2020multi}, and
            \texttt{WT-mPFC-A-20X-3-2} \citep{juan2014phenotypic
        

References

Ascoli, G. A., Donohue, D. E., & Halavi, M. (2007). NeuroMorpho. Org: a central resource for neuronal morphologies. Journal of Neuroscience, 27(35), 9247-9251.

Malik, R., Dougherty, K. A., Parikh, K., Byrne, C., & Johnston, D. (2016). Mapping the electrophysiological and morphological properties of CA 1 pyramidal neurons along the longitudinal hippocampal axis. Hippocampus, 26(3), 341-361.