ObjSim

This is a c++ based object oriented library for simulating spiking neural networks. It was used for simulations in the following publications:

"Michler F, Eckhorn R, Wachtler T (2009)" Using Spatiotemporal Correlations to Learn Topographic Maps for Invariant Object Recognition. J Neurophysiol 102:953-64.
"Michler F, Wachtler T, Eckhorn R (2006)" Adaptive Feedback Inhibition Improves Pattern Discrimination Learning. Lecture Notes in Artificial Intelligence 4087:21-32.

Compiling

Running build_all.sh will compile:

the simulation library csim (source code is in src)
simulation main programs:
- simulations/fm/som02/som02.cpp which was used in Frank Michler et al. (2009)
- minimal/minimal.cpp is a minimal simulation program demonstrating how to use the library.
some unit tests in mycxxtests using the cxxtest library

Binaries will be installed in install.

Running via command line

You can run the simulations via:

cd install/bin/som02
./som02

settings_som02.cfg contains default simulation parameters that can be overwritten via command line arguments.

Running via Jupyter noteooks

After compiling, simulations can also be started and evaluated from jupyter notebooks.

Before you must provide a shell script in env/activate_python_env.sh to setup your python environment. You can use env/activate_python_env_EXAMPLE.sh as an example.
start_jupyter.sh starts jupyter notebook in subdirectory python.
The notebook python/ipynb/run_objsim_with_gaussian_input.ipynb can be used to run som02.cpp
python/ipynb/calc_maps.ipynb demonstrates how to load simulation data and calculate topographic maps.