ObjSim

ObjSim

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

• "Using Spatiotemporal Correlations to Learn Topographic Maps for Invariant Object Recognition", Frank Michler, Reinhard Eckhorn, and Thomas Wachtler (2009)
• "Adaptive Feedback Inhibition Improves Pattern Discrimination Learning" Frank Michler, Thomas Wachtler, Reinhard Eckhorn (2006)

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

start_jupyter.sh starts jupyter notebook in subdirectory python. The notebook python/ipynb/run_objsim_with_gaussian_input.ipynb 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.

python/ipynb/calc_maps.ipynb demonstrates how to load simulation data and calculate topographic maps.