214
Views
43
CrossRef citations to date
0
Altmetric
Original Article

A working memory model based on fast Hebbian learning

, &
Pages 789-802 | Received 18 Feb 2003, Accepted 12 Aug 2003, Published online: 09 Jul 2009

References

  • Amari S-I. Dynamics of pattern formation in lateral-inhibition type neural fields. Biol. Cybern. 1977; 27: 77–87
  • Amit D, Brunel N. Dynamics of a recurrent network of spiking neurons before and following learning. Network: Comput. Neural Syst. 1997; 8: 373–404
  • Amit D J. The Hebbian paradigm reintegrated: local reverberations as internal representations. Behav. Brain Sci. 1994; 18: 617–26
  • Bao J-X, Kandel E R, Hawkins R D. Involvement of pre- and postsynaptic mechanisms in posttetaic potentiation at aplysia synapses. Science 1997; 275: 969–70
  • Ben-Yishai R, Bar-Or R L, Sompolinsky H. Theory of orientation tuning in visual cortex. Proc. Natl Acad. Sci. USA 1995; 92: 3844–8
  • Bichot N P, Schall J D. Effects of similarity and history on neural mechanisms of visual selection. Nat. Neurosci. 1999; 2: 549–54
  • Cartling B. Control of computational dynamics of coupled integrate-and-fire neurons. Biol. Cybern. 1997; 76: 383–95
  • Cohen J, Braver T, Brown J. Computational perspectives on dopamine function in prefrontal cortex. Curr. Opin. Neurobiol. 2002; 12: 223–9
  • Compte A, Brunel N, Goldman-Rakic P S, Wang X-J. Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. Cereb. Cortex 2000; 10: 910–23
  • Doyere V, Burette F, Negro C R-D, Laroche S. Long-term potentiation of hippocampal afferents and efferents to prefrontal cortex: implications for associative learning. Neuropsychologia 1993; 31: 1031–53
  • Durstewitz D, Kelc M, Güntürkün O. A neurocomputational theory of the dopaminergic modulation of working memory functions. J. Neurosci. 1999; 19: 2807–22
  • Durstewitz D, Seamans J K, Sejnowski T J. Neurocomputational models of working memory. Nat. Neurosci. 2000; 3: 1184–91
  • Fransén E, Lansner A. Low spiking rates in a population of mutually exciting pyramidal cells. Network: Comput. Neural Syst. 1995; 6: 271–88
  • Fransén E, Lansner A. A model of cortical associative memory based on a horizontal network of connected columns. Network: Comput. Neural Syst. 1998; 9: 235–64
  • Funahashi S, Bruce C J, Goldman-Rakic P. Mnemonic coding of visual space in the monkey's dorsolateral prefrontal cortex. J. Neurophysiol. 1989; 61: 331–49
  • Fuster J. The Prefrontal Cortex2nd edn. Raven, New York 1989
  • Gaiarsa J-L, Caillard O, Ben-Ari Y. Long-term plasticity at GABAergic and glycinergic synapses: mechanisms and functional significance. Trends Neurosci. 2002; 25: 564–70
  • Goldman-Rakic P. Cellular basis of working memory. Neuron 1995; 14: 477–85
  • Gustafsson B, Asztely F, Hanse E, Wigström H. Onset characteristics of long-term potentiation in the guinea-pig hippocampal ca1 regionin vitro. Eur. J. Neurosci. 1989; 1: 382–94
  • Gutkin B S, Laing C R, Colby C L, Chow C C, Ermentrout G B. Turning on and off with excitation: the role of spike-timing asynchrony and synchrony in sustained neural activity. J. Comput. Neurosci. 2001; 11: 121–34
  • Hansel D, Sompolinsky H. Chaos and synchrony in a model of a hypercolumn in visual cortex. J. Comput. Neurosci. 1996; 3: 7–34
  • Hempel C M, Hartman K H, Wang X-J, Turrigiano G G, Nelson S B. Multiple forms of short-term plasticity at excitatory synapses in rat medial prefrontal cortex. J. Neurophysiol. 2000; 83: 3031–41
  • Hirsch J, Crepel F. Use-dependent changes in synaptic efficacy in rat prefrontal neurons in vitro. J. Physiol. 1990; 427: 31–49
  • Holst A. The use of a Bayesian neural network model for classification tasks. Department of Numerical Analysis and Computing Science, Royal Institute of Technology, StockholmSweden 1997, PhD Thesis, Sept. TRITA-NA-P9708
  • Hopfield J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl Acad. Sci. USA 1982; 79: 2554–8
  • Laing C R, Chow C C. Stationary bumps in networks of spiking neurons. Neural Comput. 2001; 13: 1473–94
  • Laing C R, Chow C C. A spiking neuron model for binocular rivalry. J. Comput. Neurosci. 2002; 12: 39–53
  • Laing C R, Longtin A. Noise-induced stabilization of bumps in systems with long-range spatial coupling. Physica D 2001; 160: 149–72
  • Lansner A, Ekeberg Ö. A one-layer feedback artificial neural network with a Bayesian learning rule. Int. J. Neural Syst. 1989; 1: 77–87
  • Lansner A, Fransén E. Modeling Hebbian cell assemblies comprised of cortical neurons. Network: Comput. Neural Syst. 1992; 3: 105–19
  • Lansner A, Holst A. A higher order Bayesian neural network with spiking units. Int. J. Neural Syst. 1996; 7: 115–28
  • Lashley K. The problem of serial order in behavior. Cerebral Mechanisms in Behavior, L Jeffress. Wiley, New York 1951
  • McCormick D, Connors B, Lighthall J, Prince D. Comparative electrophysiology of pyramidal and sparsely spiny stellate neurons of the neocortex. J. Neurophysiol. 1985; 54: 782–805
  • Orlov T, Yakovlev V, Amit D, Hochstein S, Zohary E. Serial memory strategies in macaque monkeys: behavioral and theoretical aspects. Cereb. Cortex 2002; 12: 306–17
  • Otani S, Blond O, Desche J-M, Crépel F. Dopamine facilitates long-term depression of glutamatergic transmission in rat prefrontal cortex. Neuroscience 1998; 85: 669–76
  • Pantic L, Torres J, Kappen H, Gielen S C. Associative memory with dynamic synapses. Neural Comput. 2002; 14: 2903–23
  • Perez Y, Morin F, Lacaille J-C. A Hebbian form of long-term potentiation dependent on mGluR1a in hippocampal inhibitory interneurons. Proc. Natl Acad. Sci. USA 2001; 96: 9401–6
  • Ploner C J, Gaymard B, Rivaud S, Agid Y, Pierrot-Deseilligny C. Temporal limits of spatial working memory in humans. Eur. J. Neurosci. 1998; 10: 794–7
  • Romo R, Brody C D, Hernandez A, Lemus L. Neuronal correlates of parametric working memory in the prefrontal cortex. Nature 1999; 399: 470–3
  • Sandberg A. Bayesian attractor neural network models of memory. Department of Numerical Analysis and Computing Science, Royal Institute of Technology, StockholmSweden 2003, PhD Thesis, TRITA-NA-0310, ISBN 91-7265-684-0
  • Sandberg A, Lansner A. Synaptic depression as an intrinsic driver of reinstatement dynamics in an attractor network. Neurocomputing 2002; 44–46: 615–22
  • Sandberg A, Lansner A, Petersson K. Selective enhancement of recall through plasticity modulation in an autoassociative memory. Neurocomputing 2001; 38–40: 867–73
  • Sandberg A, Lansner A, Petersson K-M, Ekeberg Ö. A Bayesian attractor network with incremental learning. Network: Comput. Neural Syst. 2002; 13: 179–94
  • Seung H. How the brain keeps the eyes still. Proc. Natl Acad. Sci. USA 1996; 93: 13339–44
  • Seung H. Learning continuous attractors in recurrent networks. Adv. Neural Inf. Process. Syst. 1998; 10: 654–60
  • Seung H, Lee D, Reis B, Tank D. Stability of the memory of eye position in a recurrent network of conductance-based model neurons. Neuron 2000; 26: 259–71
  • Tegnér J, Compte A, Wang X. The dynamical stability of reverberatory dynamics. Biol. Cybern. 2002; 87: 471–81
  • Torres J J, Pantic L, Kappen H J. Storage capacity of attractor neural networks with depressing synapses. Phys. Rev. E 2002; 66, 061910
  • Undergleider L G, Courtney S M, Haxby J V. A neural system for human visual working memory. Proc. Natl Acad. Sci. USA 1998; 95: 883–90
  • von der Malsburg C. The correlation theory of brain function. Technical Report. Department of Neurobiology, Max-Planck-Institute for Biophysical Chemistry, Göttingen 1981, Reprinted in: Domany E, van Hemmen J L and Schulten K (ed) 1994 Models of Neural Networks vol 2 (Berlin: Springer) chapter 2 pp 95–119
  • Wang X-J. Synaptic reverberation underlying mnemonic persistent memory. Trends Neurosci. 2001; 24: 455–63
  • White J M, Sparks D L, Stanford T R. Saccades to remembered target locations: an analysis of systematic and variable errors. Vis. Res. 1994; 34: 79–92
  • Wickens J, Kötter R. Cellular models of reinforcement. Models of Information Processing in the Basal Ganglia, J C Houk, J L Davis, D G Beiser. MIT Press, Cambridge, MA 1995; 187-214
  • Willshaw D, Buneman O, Longuet-Higgins H. Non-holographic associative memory. Nature 1969; 222: 960–2
  • Wu X, Liljenström H. Regulating the nonlinear dynamics of olfactory cortex. Network: Comput. Neural Syst. 1994; 5: 47–60

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.