Unsupervised learning in networks of spiking neurons using temporal coding

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1327, Issue , 1997, Pages 362-366

  Ruf, Berthold ^a   Schmitt, Michael ^a  

^a GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

CODES (SYMBOLS); CONFORMAL MAPPING; NETWORK CODING; NEURONS; SELF ORGANIZING MAPS; UNSUPERVISED LEARNING;

BIOLOGICAL NEURAL SYSTEMS; IN NETWORKS; KOHONEN; SPIKING NEURON; TEMPORAL CODING; TOPOLOGY PRESERVING; USE RATE;

MACHINE LEARNING;

EID: 84956613215     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/bfb0020181     Document Type: Conference Paper

References (14)

1. Arbib, M. A.: The Handbook of Brain Theory and Neural Networks. MIT Press, Cambridge (1995).
2. Choe, Y., Miikkulainen, R: Serf-organization and segmentation with laterally connected spiking neurons. Technical Report AI TR 96-251, Department of Computer Science, University of Texas a Austin, September 1996.
3. Gerstner, W., van Hemmen, L. H.: How to describe neuronal activity: spikes, rates, or assemblies? In Advances in Neural Information Processing Systems fi, Morgan Kaufmann, San Mateo (1994) 463-470.
4. Goodhi U. G. J., Sejnowski, T. J.: Quantifying neighbourhood preservation in topographic mappings. Proceedings of the 3rd Joint Symposium on Neural Computation, San Diego, CA (1996) 61-82.
5. Kohonen, T.: Physiological interpretation of the serf-organizing map algorithm. Neural Networks 6 (1993) 895-905.
6. Kohonen, T.: Self-organizing maps. Springer, Berlin (1995).
7. Maass, W.: Lower bounds for the computational power of networks of spiking neurons. Neural Computation 8 (1996) 1-40.
8. Maass, W.: Fast sigmoidai networks via spiking neurons. Neural Computation 9 (1997) 279-304.
9. Maass, W.: Networks of spiking neurons: the third generation of neural network models. Neural Networks, to appeax; extended abstract in Proceedings of the Seventh Australian Conference on Neural Networks, Canberra (1996) 1-10.
10. Murray, A., Taxassenko, L.: Analogue Neural VLSI: A Pulse Stream Approach. Chapman and Hall, London (1994).
11. Pdeke, F., Waxland, D., de Ruyter van Steveninck, R., and Bialek, W.: SPIKES: Exploring the Neural Code. MIT Press, Cambridge (1996).
12. Ruf, B.: Computing functions with spiking neurons in temporal coding. In J. Mira, R. Moreno-Diaz and J. Cabestany (eds.). Biological and Artificial Computation: From Neuroscience to Technology. Proceedings of the International Work- Conference on Artificia2 and Natural Neural Networks IWANN'97, Lecture Notes in Computer Science, col. 1240, Springer, Berlin (1997) 265-272.
13. Sejnowski, T.: Time for a new neural code? Nature 376 (1995) 21-22.
14. Sirosh, J., Miikkulainen, R.: Topographic receptive fields and patterned lateral interaction in a self-organizing model of the primary visual cortex. Neural Computation 9 (1997) 577-594.