README.md

A set of tools for fitting single- or multi-compartment neuron models parameters, using NSGA2 or Krayzman's dynamically weighted multi-objective optimization

Scripts, templates, and helpers to fit neuron model

├── README.md	This file
├── pyneuronautofit	module with extension of inspyred
│   ├── autofit.py	helper functions
│   ├── evaluator.py	evaluates a model
│   ├── fitter.py	main script for fitting
│   ├── __init__.py
│   ├── __main__.py -> fitter.py	: just a link for python -m pyneuronautofit
│   └── runandtest.py	: runs and tests a model can be call independently
├── scripts	: directory with useful scripts
│   ├── recovery-archive.sh	: recover archive if run was aborted
│   └── unique-in-ArXive.py	: collects only unique models
└── templates
└── project.py	templay of a project file with all settings

Components

Evaluator

The Evaluator can perform analysis of the data and copare two data set against each other What kind of analysis it will perform is defined by mod variable. The mod variable is a string with one or more upper-case letters, each for specific analysis.

key	description
A	average spike shape during stimulus
C	distance between voltages during stimulus
D	distance between voltages during after stimulus tails
S	spike shapes during stimulus
T	spike times
R	resting potential
L	post-stimulus tail statistics
M	voltage stimulus statistics
N	number of spikes
O	Just total number of spikes
P	difference in probability dencity on v,dv/dt plane weighted by 1 - target_dencity/sum(target_dencity)
Q	the same as P but only during stimulus.
U	squared error of subthreshold voltage
V	distance between voltages
W	spike width during stimulus
Z	distance between voltages with zooming weight on spikes