# 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"](https://doi.org/10.1152/jn.90651.2008), 
  Frank Michler, Reinhard Eckhorn, and Thomas Wachtler (2009)
- ["Adaptive Feedback Inhibition Improves Pattern Discrimination Learning"](https://doi.org/10.1007/11829898_3)
  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)](https://doi.org/10.1152/jn.90651.2008)
      - `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.