Abstract
A recurrently connected attractor neural network with a Hebbian learning rule is currently our best ANN analogy for a piece cortex. Functionally biological memory operates on a spectrum of time scales with regard to induction and retention, and it is modulated in complex ways by sub-cortical neuro-modulatory systems. Moreover, biological memory networks are commonly believed to be highly distributed and engage many co-operating cortical areas.
Here we focus on the temporal aspects of induction and retention of memory in a connectionist type attractor memory model of a piece of cortex. A continuous time, forgetful Bayesian-Hebbian learning rule is described and compared to the characteristics of LTP and LTD seen experimentally. More generally, an attractor network implementing this learning rule can operate as a long-term, intermediate-term, or short-term memory. Modulation of the print-now signal of the learning rule replicates some experimental memory phenomena, like e.g. the von Restorff effect.
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
Willshaw D, Buneman O, and Longuet-Higgins H. Holographic associative memory. Nature 222 (1969), 960, 1969.
Hopfield J J. Neural Networks and physical systems with emergent collective computational properties, Proc. Natl. Acad. Sci. USA, 81(3088–3092), 1982.
Hebb D O. The Organization of Behavior. John Wiley Inc., New York, 1949.
Freeman W. J. The physiology of perception. Scientific American 264, 78–85, 1991.
Quinlan P. Connectionism and Psychology. A Psychological Perspective on Connectionist Research. Harvester, Wheatsheaf, New York, 1991.
Haberly L B and Bower J M. Olfactory cortex: model circuit for study of associative memory? Trends Neurosci 12, 258–264, 1989.
Fransén E and Lansner A. A model of cortical associative memory based on a horizontal network of connceted columns. Network 9, 235–264, 1998.
Le Vay S, and Gilbert C D. Laminar patterns of geniculocortical projections in the cat, Brain Res, 113, 1–19, 1976.
Artola A, Bröcher S, and Singer W. Different voltage-dependent thresholds for the induction of long-term depression and long-term potentiation in slices of the rat visual cortex, Nature (London), 347, 69–72, 1990.
MeGaugh J M. Memory — a century of consolidation. Science, 287, 248–251, 2000.
Fletcher P C, Frith C D, and Rugg M D. The functional neuroanatomy of episodic memory. TINS 20, 213–218, 1997.
Lansner A and Ekeberg Ö. A one-layer feedback artificial neural network with a Bayesian learning rule”, Int. J Neural Systems, 1, 77–87, 1989.
Lansner A and Holst A. A higher order Bayesian neural network with spiking units. Int J Neural Systems 7, 115–128, 1996.
Holst A. The Use of a Bayesian Neural Network Model for Classification Tasks. PhD thesis, Dept of Numerical Analysis and Computing Science, Royal Institute of Technology, Stockholm, Sweden, TRITA-NA-P9708, 1997.
Sandberg A, Lansner A, Petterson K-M, and Ekeberg Ö. An incremental Bayesian learning rule. Tech rep TRITA-NA-P9908, Royal Institute of Technology, NADA, 1999.
Bi G-Q and Poo M-M. Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type. J Neurosci 18, 10464–10472, 1998.
Stanton P K. LTD, LTP, and the sliding threshold for long-term synaptic plasticity. Hippocampus 6, 35–42, 1996.
Christianson S-A, editor. Handbook of Emotion and Memory: Current Research and Theory. Erlbaum, Hillsdale, NJ, 1992.
Martinez J L, Schulteis G, and Weinberger S B. Learning and Memory: A Biological View, chapter 4, pages 149-198. Academic Press, second edition, 1991.
von Restorff H:. Analyse von Vorgängen in Spurenfeld. Psychologiche Forschung 18, 299–342, 1933.
Parker A, Wilding E, and Akerman C. The von Restorff effect in visual object recognition memory in humans and monkeys: The role of frontal/perirhinal interaction. J Cognitive Neurosci, 10, 691–703, 1998.
Wickens J and Kötter R. Cellular models of reinforcement. In Houk J C, Davis J L, and Beiser D Q, editors, Models of Information Processing in the Basal Ganglia, 187–214. MIT Press, 1995.
Woolf N J. The critical role of cholinergic basal forebrain neurons in morphological change and memory encoding: a hypothesis. Neurobiol Learn Mem, 66, 258–266, 1996.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag London
About this paper
Cite this paper
Lansner, A., Sandberg, A., Petersson, K.M., Ingvar, M. (2000). On Forgetful Attractor Network Memories. In: Malmgren, H., Borga, M., Niklasson, L. (eds) Artificial Neural Networks in Medicine and Biology. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0513-8_7
Download citation
DOI: https://doi.org/10.1007/978-1-4471-0513-8_7
Publisher Name: Springer, London
Print ISBN: 978-1-85233-289-1
Online ISBN: 978-1-4471-0513-8
eBook Packages: Springer Book Archive