LISSOM
Released 2001

The LISSOM package contains the C++, Python, and Scheme source code and examples for training and testing firing-rate LISSOM models, specifically RF-LISSOM, CRF-LISSOM, and HLISSOM. These self-organizing models support detailed simulations of the development and function of the mammalian visual system.

The simulator includes a graphical user interface (GUI), a command language for scripts, and a command-line interface. Sample command files are provided for running a variety of orientation, ocular dominance, motion direction, and face perception simulations. Extensive documentation is also included on disk and via online help at the command line. For more details about LISSOM-based models, see this paper on RF-LISSOM (and others under Visual Cortex and Self-Organization), and the Visual Cortex and Self-Organization research descriptions.

In addition to the supplied sample simulations, the simulator allows you to define arbitrary networks of maps that you can arrange into a hierarchy representing the visual system. Currently-supported map types include input regions (e.g. a Retina), convolving regions (e.g. ON/OFF cell layers), and RF-LISSOM regions (with modifiable afferent and lateral connections.) Environmental input is controlled by a simple but flexible language that allows arbitrary patterns and natural images to be rendered, scaled, rotated, combined, etc. This language makes it possible to use LISSOM for many of your own projects without having to write any new simulator code. However, we strongly recommend that you use Topographica for new projects, because it supports many more types of models (including LISSOM) in a much more flexible way.

The installation instructions, GUI documentation, command language documentation, and code documentation for the current version are available online.

New! (1/2003): A short LISSOM tutorial is now available.

New! (6/2005): More recent changes are now available via CVS; you might want to try the CVS version if you are having problems with the official release. See README.CVS for more information. Note however that these changes will not make the code compile on GCC 4.x.

Note: As of GCC 3.x and 4.x (e.g. in Fedora Core 3, 4, and 5 releases), the GNU compiler has gotten more strict about certain formerly accepted template code, and LISSOM will no longer compile with these compilers. If you are interested in contributing patches, please contact the author. In the meantime, binaries compiled under earlier compiler versions (GCC 3.4.x and below) can be used on any system, or you can install GCC 3.4.x and use that to compile LISSOM.

Comments to jbednar at cs.utexas dot edu.

Versions:


v1.0 07/08/1994 sirosh@cs.utexas.edu  

- Initial public K&R C release, without RFs.  

v2.0 10/28/1998 jbednar@cs.utexas.edu, sirosh@cs.utexas.edu  

- Reimplemented in ANSI C supporting RF-LISSOM, interactive prompt, 

  picture generation, and online help.  

v2.1  11/09/1998 jbednar@cs.utexas.edu  

- ANSI C release with enhanced command language, orientation handling, etc.  

v3.0a1 08/21/2000 jbednar@cs.utexas.edu  

- Now C++;  added input command language; last version with full Cray T3E support.  

v3.0b1 04/08/2001 jbednar@cs.utexas.edu  

- Added support for multiple maps, arbitrary map sizes, and map scaling.  

v3.0 11/25/2001 jbednar@cs.utexas.edu  

- Fully released version of 3.0a1 (alpha) and 3.0b1 (beta).  

v4.0 01/19/2003 jbednar@cs.utexas.edu  

- Added GUI interface.  

- Additional sample orientation, ocular dominance, direction, and face 

  simulations.  

- Added support for back-projection, transparent input images, and Matlab 

  plot output. 

v5.0 09/29/2004 jbednar@cs.utexas.edu

- Added python version of GUI.

- Allowed support for color opponent cells and others

  with incoming weights from multiple areas.

- Added sample red/green color map simulation.

- Updated to work with GCC 3.3.

Download:
ZIP, TAR
James A. Bednar Postdoctoral Alumni jbednar [at] inf ed ac uk
Judah De Paula Ph.D. Alumni
Wilson S. Geisler Formerly affiliated Collaborator geisler [at] psy utexas edu
Jefferson Provost Ph.D. Alumni jefferson provost [at] gmail com
Joseph Sirosh Ph.D. Alumni joseph sirosh [at] gmail com
     [Expand to show all 16][Minimize]
Contour Integration and Segmentation with Self-Organized Lateral Connections 2004
Yoonsuck Choe and Risto Miikkulainen, Biological Cybernetics 90:75-88
Prenatal and Postnatal Development of Laterally Connected Orientation Maps 2004
James A. Bednar and Risto Miikkulainen, Neurocomputing, Vol. 58-60 (2004), pp. 985-992.
Learning Innate Face Preferences 2003
James A. Bednar and Risto Miikkulainen, Neural Computation, Vol. 15, 7 (2003), pp. 1525-1557.
Modeling Large Cortical Networks With Growing Self-Organizing Maps 2002
James A. Bednar, Amol Kelkar, and Risto Miikkulainen, Neurocomputing, Vol. 44--46 (2002), pp. 315-321.
Scaling Self-Organizing Maps To Model Large Cortical Networks 2001
James A. Bednar, Amol Kelkar, and Risto Miikkulainen, Neuroinformatics (2001), pp. 275-302.
Tilt Aftereffects In A Self-Organizing Model Of The Primary Visual Cortex 2000
James A. Bednar and Risto Miikkulainen, Neural Computation, Vol. 12 (2000), pp. 1721-1740.
Self-Organization And Segmentation In A Laterally Connected Orientation Map Of Spiking Neurons 1998
Yoonsuck Choe and Risto Miikkulainen, Neurocomputing (1998), pp. 139-157.
Self-Organization, Plasticity, and Low-Level Visual Phenomena in a Laterally Connected Map Model of the Primary Visual Cortex 1997
Risto Miikkulainen, James A. Bednar, Yoonsuck Choe, and Joseph Sirosh, In Perceptual Learning, R. L. Goldstone and P. G. Schyns and D. L. Medin (Eds.), pp. 257-308 1997.
Tilt Aftereffects in a Self-Organizing Model of the Primary Visual Cortex 1997
James A. Bednar, Masters Thesis, Department of Computer Sciences, The University of Texas at Austin. Technical Report AI97-259.
Laterally Interconnected Self-Organizing Maps In Hand-Written Digit Recognition 1996
Yoonsuck Choe, Joseph Sirosh, and Risto Miikkulainen, In Advances in Neural Information Processing Systems 8, David S. Touretzky and Michael C. Mozer and Michael E. Hasselmo (Eds.), pp. 736-742 1996. Cambridge, MA: MIT Press.
Self-Organization of Orientation Maps, Lateral Connections, and Dynamic Receptive Fields in the Primary Visual Cortex 1996
Joseph Sirosh, Risto Miikkulainen and James A. Bednar, In {P}roceedings of the {I}nternational {C}onference {on} {A}rtificial {N}eural {N}etworks, Joseph Sirosh and Risto Miikkulainen and Yoonsuck Choe (Eds.), pp. 1147-1152, Berlin 1996. Springer...
A Self-Organizing Neural Network Model Of The Primary Visual Cortex 1995
Joseph Sirosh, PhD Thesis, Department of Computer Sciences, The University of Texas at Austin. Technical Report AI95-237.
Laterally Interconnected Self-Organizing Feature Map In Handwritten Digit Recognition 1995
Yoonsuck Choe, Masters Thesis, Department of Computer Sciences, The University of Texas at Austin. 65. Technical Report AI95-236.
Modeling Cortical Plasticity Based On Adapting Lateral Interaction 1995
Joseph Sirosh and Risto Miikkulainen, In The Neurobiology of Computation: {T}he Proceedings of the Third Annual Computation and Neural Systems Conference, James M. Bower (Eds.), pp. 305-310 1995.
Cooperative Self-Organization Of Afferent And Lateral Connections In Cortical Maps 1994
Joseph Sirosh and Risto Miikkulainen, Biological Cybernetics (1994), pp. 66-78.
Self-Organizing Feature Maps With Lateral Connections: Modeling Ocular Dominance 1994
Joseph Sirosh and Risto Miikkulainen, In Proceedings of the 1993 Connectionist Models Summer School, M. C. Mozer and P. Smolensky and D. S. Touretzky and J. L. Elman and A. S. Weigend (Eds.), pp. 31-38 1994.