Abstract
A novel approach for learning of temporally extended, continuous signals is developed within the framework of rate coded neurons. A new temporal Hebb like learning rule is devised which utilizes the predictive capabilities of bandpass filtered signals by using the derivative of the output to modify the weights. The initial development of the weights is calculated analytically applying signal theory and simulation results are shown to demonstrate the performance of this approach. In addition we show that only few units suffice to process multiple inputs with long temporal delays.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Donald O. Hebb. The organization of behavior. Science Ed., New York, 1967.
A. H. Klopf. A neuronal model of classical conditioning. Psychobiology, 16(2):85–123, 1988.
Richard Kempter, Wulfram Gerstner, and J. Leo van Hemmen. Hebbian learning and spiking neurons. Physical Review E, 59:4498–4514, 1999.
S. Song, K. D. Miller, and L. F. Abbott. Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neuroscience, 3:919–926, 2000.
Gerstner, Kempter, van Hemmen, and Wagner. A neuronal learning rule for sub-millisecond temporal coding. Nature, 383:76–78, 1996.
Ken D. Miller. Receptive fields and maps in the visual cortex: Models of ocular dominance and orientation columns. In E Donnay, J.L. van Hemmen, and K. Schulten, editors, Models of Neural Networks III, pages 55–78. Springer-Verlag, 1996.
Wolfram Schultz, Peter Dayan, and P. Read Montague. A neural substrate of prediction and reward. Science, 275:1593–1599, 1997.
R.S. Sutton and A.G. Barto. Toward a modern theory of adaptive networks: expectation and prediction. Psychol Rev, 88(2):135–170, 1981.
E. Oja. A simplified neuron model as a principal component analyzer. J Math Biol, 15(3):267–273, 1982.
P Dayan and L. F. Abbott. Theoretical Neuroscience. MIT Press, Cambridge MA, 2001.
S. S. Haykin. Adaptive filter theory. Prentice Hall, Englewood Cliffs, 1991.
S. M. Bozic. Digital and Kalman filtering: an introduction to discrete-time filtering and optimum linear estimation. E. Arnold, London, 1994.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Porr, B., Wörgötter, F. (2001). Temporal Hebbian Learning in Rate-Coded Neural Networks: A Theoretical Approach towards Classical Conditioning. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_155
Download citation
DOI: https://doi.org/10.1007/3-540-44668-0_155
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42486-4
Online ISBN: 978-3-540-44668-2
eBook Packages: Springer Book Archive