Tutorial 2 Outline

Tutorial 2 : Fitting a model to data

Note : this tutorial assumes that you have already worked through the first tutorial on the Multiple Run Fitter.

The problem

Given :

input resistance RN and a charging transient recorded with a patch electrode attached to the soma of a neuron (appropriate channel blockers were employed, and tests verified a linear I-V relationship)
cetailed morphometric data obtained from the same cell

The challenge : use these data to create an anatomically detailed, passive model with uniform biophysical properties that matches the experimentally observed charging transients.

This example employs current clamp data, with no loss of generality. Using voltage clamp data would, for the most part, involve similar steps and raise analogous considerations; the few exceptions will be noted as they arise.

The cell and the electrical measurements

This is a side view of the cell from which the anatomical and biophysical data were obtained. You may recognize it as the pyramidal cell that is included with NEURON's demonstration program.

The input resistance RN of this cell was ~153 megohms. The charging transient was elicited by applying a 1 nA current pulse that started at 1 ms and lasted 0.5 ms (below left); the entire recording was 200 ms long (below right). The (t,v) data are contained in iclamp.dat

Note that the recording of somatic Vm shows absolutely no electrode artifact--compensation for electrode capacitance and series resistance was perfect.

Using a brief current pulse emphasizes high frequency terms in the voltage response, which are necessary in order to estimate cytoplasmic resistivity. High frequency terms are also present in the current response to a step command under voltage clamp.

I cannot tell a lie--RN and the charging transient were generated with a model. I saved the charging transient from a simulation manually by Pick Vector followed by Vector / Save to File. You can do this yourself by using NEURON to execute init_makedata.hoc, which (along with several files that it needs) is in code.zip .

An important note about methods : The following simulations were performed with fixed time step integration (dt = 0.025 ms), and the spatial grid of the model was specified using d_lambda = 0.1. Using adaptive integration with an absolute tolerance of 0.0001 and reducing d_lambda to 0.01 caused no significant change in the voltage trajectory under current clamp.

Outline

Step 1. Create an "unoptimized" model.

Step 2. Set up a current clamp experiment on this model.

Step 3. Configure a MultipleRunFitter to do a "run fitness" optimization.

Step 4. Load the Experimental Data into the iclamp Run Fitness Generator

Step 5. Specify the parameters that will be adjusted

Step 6. Perform the optimization

Go back to the main page ("Using NEURON's Optimization Tools") of the optimization tutorial.