Weitzenfeld A

Language: Spanish
References: 28
Page: 46-53
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ABSTRACT

NSL (Neural Simulation Language) is a simulation system for the development of biological and artificial neural networks. NSL enables the simulation of models consisting of different levels of neural detail, from the simpler ones having only a few neurons to the more complex ones made of millions of neurons. NSL incorporates a rich environment for modeling and simulation integrating a compiled and interpreted language for efficiency and interactivity reasons, in addition to numerical libraries, graphic interfaces and visualization tools. The system is object-oriented, offering a simulation environment for inexperienced users as well as those with more programming experience. NSL was originally developed 10 years ago, having been used during this last decade for research and teaching purposes, giving rise to a number of neural models in different laboratories throughout the world. The current version, NSL 3.0, is the third generation of the system ported to different environments while based on C++ and Java programming.

REFERENCES

1. Alexander A, Arbib MA, Weitzenfeld A. 1999, Web Simulation of Brain Models. International Conference on Web-Based Modeling and Simulation, 1999; 31(3): 29-33, Enero 17-20, San Francisco, CA.

2. Amari S, Arbib MA. Competition and Cooperation in Neural Nets, Systems Neuroscience (J. Metzler, ed.), 1977: 119-165, Academic Press.

3. Arbib MA. The Metaphorical Brain 2: Neural Networks and Beyond, Wiley. 1989.

4. Arbib MA. Schema Theory, in the Encyclopedia of Artificial Intelligence, 2da Edition, Editor Stuart Shapiro, 1992; 2: 1427-1443, Wiley.

5. Arkin RC, Cervantes-Perez F, Weitzenfeld A. Ecological Robotics: A Schema-Theoretic Approach, en Intelligent Robots: Sensing, Modelling and Planning, Eds. RC Bolles, H Bunke y H Noltemeier World Scientific. 1997: 377-393.

6. De Schutter E. A Consumer Guide to Neuronal Modeling Software, en Trends in Neuroscience, 1992; 15(11): 462-464.

7. Didday RL. A Model of Visuomotor Mechanisms in the Frog Optic Tectum, Math. Biosci 1976; 30: 169-180.

8. Dominey P, Arbib MA. A Cortico-Subcortical Model for Generation of Spatially Accurate Sequential Saccades, Cerebral Cortex, Marzo/Abril 1992; 2: 153-175.

9. Hodgkin AL, Huxley AF. A Quantitative Description of Membrane Current and its Application to Conduction and Excitation in Nerve, J. Physiol. London, 1952; 117: 500-544.

10. Hopfield J. Neural Networks and Physical Systems with Emergent Collective Computational Abilities, Proc. Of the National Academy of Sciences 1982; 79: 2554-2558.

11. House D. Depth Perception in Frogs and Toads: A study in Neural Computing, Lecture Notes in Biomathematics 80, Springer-Verlag. 1985.

12. McCulloch WS, Pitts WH. A Logical Calculus of the Ideas Immanent in Nervous Activities, Bull. Math. Biophys 1943; 5: 15-133.

13. Murre J. Neurosimulators, e Handbook of Brain Theory and Neural Networks, Ed. Michael Arbib, MIT Press 1995.

14. Ousterhout J. Tcl and the Tk Toolkit, Addison-Wesley. 1994.

15. Rall W. Branching Dendritic Trees and Motoneuron Membrane Resistivity. Exp Neurol 1959; 2: 503-532.

16. Rumelhart DE, Hinton GE, Williams RJ. Learning internal representations by error propagation, in Parallel Distributed Processing: Explorations in the Microstructure of Cognition (D.E. Rumelhart, J.L. McClelland, and PDP Research Group, Eds.), vol. 1, Foundations, Cambridge, MA: MIT Press, 1986; 318-362.

17. Teeters JL, Arbib MA, Corbacho F, & Lee HB. Quantitative modeling of responses of anuran retina: Stimulus shape and size dependency. Vision Research 1993; 33: 2361-2379.

18. Wegner P. Concepts and Paradigms of Object-Oriented Programming, OOPS Messenger, 1990; 1(1): 7-87.

19. Weitzenfeld A. A Unified Computational Model for Schemas and Neural Networks in Concurrent Object-Oriented Programming, Tesis Doctoral, Computer Science Dept., University of Southern California, Los Angeles. 1992.

20. Weitzenfeld A, Arbib M. A Concurrent Object-Oriented Framework for the Simulation of Neural Networks, ECOOP/OOPSLA ’90 Workshop on Object-Based Concurrent Programming, OOPS Messenger, 1991; 2(2): 120-124.

21. Weitzenfeld A. ASL: Hierarchy, Composition, Heterogeneity, and Multi-Granularity in Concurrent Object-Oriented Programming, Workshop on Neural Architectures and Distributed AI: From Schema Assemblages to Neural Networks 1993: 19-20, Center for Neural Engineering, USC, Los Angeles, CA.

22. Weitzenfeld A, Arkin RC, Cervantes-Perez F, Olivares R, Corbacho F. A Neural Schema System Architecture for Autonomous Robots, Proc. of 1998 International Symposium on Robotics and Automation, Diciembre 1998: 12-14, Saltillo, Coahuila, México.

23. Weitzenfeld A, Arkin RC, Cervantes-Perez F, Peniche JF. Visualization of Multi-level Neural-based Robotic Systems, 2nd Conference on Visual Computing, Mexico, DF, Mexico. 1998: 20-24.

24. Weitzenfeld A, Arbib MA. NSL Neural Simulation Language, en Neural Network Simulation Environments, Ed. J. Skrzypek, Kluwer 1994.

25. Weitzenfeld A, Arbib M, Cervantes F, Rudomin P, Alexander A. Multi-level Simulation Methodology: A Computational and Experimental Approach to Neural Systems, NSF Design and Manufacturing Grantees Conference, Monterrey, Mexico, 1998: 651A-652A.

26. Weitzenfeld A, Alexander A, Arbib MA. NSL - Neural Simulation Language: System and Applications, MIT Press (en preparación) 2001.

27. Werbos P. Beyond Regression, New Tools for Prediction and Analysis in the Behavioral Sciences, Tesis Doctoral, Harvard University, Boston, MA. 1974.

28. Wiskott L, Fellous JM, Krüger N, von der Malsburg C. Face recognition by elastic bunch graph matching, IEEE Transactions on Pattern Analysis and Machine Intelligence 1997; 19(7): 775-779.