The xfit class¶
This document describes the
xfit is a toolbox that helps you find neuron or network models satisfying arbitrary constraints. It is a bridge
between the Global Optimization Toolbox in MATLAB and the xolotl neuron and network simulator
The first step in using
xfit is to create a
xfit object using:
xf = xfit('particleswarm');
Every xfit object has the following properties. To access a property, use dot notation, e.g.:
You can view all the properties of an xfit object
using the built-in
The best cost holds the lowest value computed by the simulation function during an optimization procedure. This is a read-only property.
data property can hold any user-defined data. You may want to use this if your cost function required additional data to measure the cost. For example, if you want to fit a neuron to a specifiy voltage trace, you would store it here.
This option determines the optimization algorithm used.
|Engine Name||Engine Keyword|
ub are x 1 vectors of numerical lower bound and upper bound values. During optimization, parameters are bounded between their upper and lower bounds.
Nonlinear inequality and equaity constraints, only supported for engines that support them. To understand how to use these constraints, look at MATLAB's documentation here
This property is a struct that holds options for the selected optimization engine. It is automatically generated from MATLAB's built-in optimoptions function.
This cell array of character vectors contains the names of xolotl parameters to optimize over.
find method of xolotl is the best way to populate this list.
ub share one-to-one correspondence with
parameter_names, so all should be the same dimensions.
The seed is an x 1 vector of numerical parameter values for starting an optimization protocol, where is the number of parameters to optimize over.
A function handle to the simulation function used to evaluate the model cost. The simulation function can be any MATLAB function, provided that the following are true:
- The first output must be the cost, which is a positive, real-valued scalar.
- The function accepts two arguments, the first of which is a xolotl object.
The function thus has the (minimal) signature:
function [cost, ...] = functionName(xolotl_object, data)
When xfit performs a parameter optimization routine,
it calls the
SimFcn using the xolotl object stored in the
x property, which has been set up with trial parameters.
This property keeps track of the duration of a simulation. This is a read-only property.
This property contains a xolotl object. Since xfit uses xolotl to run actual simulations, this is necessary for all projects.
c = evaluate(self,params);
Updates parameters in the xolotl object using
params (a vector), evaluate the cost function,
and return a cost (a double).
It is assumed that you have the following things configured in the xfit object:
x(the xolotl object)
best_fit_params = xf.fit;
xf is a
xfit object, runs the optimization
algorithm in an effort to minimze the cost function using
specified conditions. Returns a vector of the best-fit
parameters. Only the last (best-fit) value is returned.
The best-fit value is also used to update the seed.
xf is a
xfit object, save results of
this optimization run in a database called
xf is a
xfit object, and you have run the xfit
algorithm many times and run
xf.save, and you have been
saving results to a
.xfit file, then this method runs
ShowFcn and goes over all the results
ShowFcn is not defined, then it simply plots the result