Automated optimization of a reduced layer 5 pyramidal cell model based on experimental data

Evolutionary Multi-Objective Optimization (EMOO)

Evolutionary optimization is an established tool to explore complex parameter spaces using strategies from biological evolution to select, modify and breed new models. Classically the quality of a model is determined based on a single distance function. But if multiple properties of a model are to be optimized a rather arbitrary weighting of these properties is required to create a single distance function again.

Multi-objective optimization strategies instead optimize multiple and possibly confliciting distance functions at the same time which allows to target noisy data. This powerful optimization method is therefore well suited for optimizing multi-compartmental neurons models to experimental data and has already been successfully used in recent modelling studies in neuroscience (Druckmann et al. 2007; Druckmann et al. 2008; Bahl 2009; Hay et al. 2011; Bahl et al. 2012).

Here I provide this optimization framework which I have used to optimize a 17 parameter model of a layer 5 pyramidal neuron to experimental data. It is entirely written in Python and uses the Message Passing Interface (MPI) for communication between different processors and nodes.

Please contact me if you have comments, improvements or questions.

Enjoy your optimization!
Armin Bahl

Requirements

I strongly recommend to read the book by Kalyanmoy Deb
Python

numpy

optional for multi-core optimiziation: mpi4py + openmpi or mpich2

To get the full power of running MPI on a cluster, you should install a job scheduling engine like Torque.

Setup

Download and extract emoo_1_0.zip

Copy the folder emoo to your Python-library path (e.g. site-packages), or add the path to the folder containing emoo to your python path.
This should be all; emoo can be imported as a Python-module now.

General Usage

I have tested emoo on my MacBook Pro (OSX Lion) and under Ubuntu Linux, but it should also work under windows.
emoo can be imported as a standard python module, it should check itself whether mpi4py is properly installed.

import emoo

running your script on a single core without using mpi:

<pythonbinary> example1.py

running directly on multiple processors using MPI:

mpirun -np 2 <pythonbinary> example1.py
put in the number of processors (-np x) you would like to use

using the job scheduling engine

qsub jobrun.pbs

Examples

Example 1: Minimize a single distance function with three parameters
Example 2: Minimize two conflicting distance functions with two parameters
Example 3: An application to neuroscience: Find ion channel densities to tune a neuron model.

contact:

bahl "an at here" neuro "a dot here" mpg "another dot here" de

References

Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. New York, NY, USA: John Wiley & Sons, Inc.

Druckmann, S., Banitt, Y., Gidon, A., Schürmann, F., Markram, H., & Segev, I. (2007). A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data. Frontiers in Neuroscience, 1(1), 7–18.

Druckmann, S., Berger, T. K., Hill, S., Schürmann, F., Markram, H., & Segev, I. (2008). Evaluating automated parameter constraining procedures of neuron models by experimental and surrogate data. Biological Cybernetics, 99(4-5), 371–379.

Bahl A (2009). Automated optimization of a reduced layer 5 pyramidal cell model based on experimental data. Diplomarbeit (HU-Berlin)

Bahl A, Stemmler MB, Herz AVM, Roth A. (2012). Automated optimization of a reduced layer 5 pyramidal cell model based on experimental data. J Neurosci Methods.

Automated optimization of a reduced layer 5 pyramidal cell model based on experimental data