These Jupyter notebooks associated with the paper:

Polynomial, piecewise-Linear, Step (PLS): a simple, scalable and efficient framework for modeling neurons**

by Ruben A. Tikidji-Hamburyan and Matthew T. Colonnese

Requirements

To use this scripts you need Python 3.8 and python’s libraries:

Files in this directory

File Description
WangBuzsaki-model-reduction.ipynb Wang&Buzsaki model reduction (section 2)
WangBuzsaki-model-reduction-euler.ipynb the same reduction with Euler method
WangBuzsaki-model-reduction-c-code/wbcl.c c-code for fully computed benchmark
WangBuzsaki-model-reduction-c-code/wbpol.c c-code for polynomial benchmark
WangBuzsaki-model-reduction-c-code/wbpwl.c c-code for piecewise-Linear benchmark
WangBuzsaki-model-reduction-c-code/wbmix.c c-code for PL2D benchmark
WangBuzsaki-model-reduction-c-code/*.cu the same benchmarks for CUDA GPU test
WangBuzsaki-model-reduction-c-code/makefile make file for building CPU (make all) and GPU (make gpu) tests
WangBuzsaki-model-reduction-c-code/test-cpu.sh compile and run test of CPU
WangBuzsaki-model-reduction-c-code/test-gpu.sh compile and run test of GPU
PLS-Integrator.ipynb Phenomenological model: Type 1 (section 4)
PLS-Resonator.ipynb Phenomenological model: Type 2 (section 4)
Plateau-Potentials-4models.ipynb Plateau Potentials test (section 5)
pls.py Helper: PLS Families in Python
pls.xpp Helper: PLS Families in XPP
pls.h Helper: PLS Families in C/C++/NEURON(mod)/etc
mrth.py General Helper
UPDATE
brian2Integrator_and_Resonator.py example of Type 1 and 2 in Brian2 simulator (uses brian2pls library)
brian2pls.py Helper: PLS Families for Brian2 (numpy,cython,cpp devices)