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Context codes and the effect of noisy learning on a simplified hippocampal CA3 model

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Abstract

This paper investigates how noise affects a minimal computational model of the hippocampus and, in particular, region CA3. The architecture and physiology employed are consistent with the known anatomy and physiology of this region. Here, we use computer simulations to demonstrate and quantify the ability of this model to create context codes in sequential learning problems. These context codes are mediated by local context neurons which are analogous to hippocampal place-coding cells. These local context neurons endow the network with many of its problem-solving abilities. Our results show that the network encodes context on its own and then uses context to solve sequence prediction under ambiguous conditions. Noise during learning affects performance, and it also affects the development of context codes. The relationship between noise and performance in a sequence prediction is simple and corresponds to a disruption of local context neuron firing. As noise exceeds the signal, sequence completion and local context neuron firing are both lost. For the parameters investigated, extra learning trials and slower learning rates do not overcome either of the effects of noise. The results are consistent with the important role played, in this hippocampal model, by local context neurons in sequence prediction and for disambiguation across time.

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References

  • Abbott LF, Blum KI (1995) Functional significance of long-term potentiation for sequence learning and prediction. Cerebral Cortex, in press

  • Amaral DG, Witter MP (1989) The three-dimensional organization of the hippocampal formation: a review of anatomical data. Neuro science 31:571–591

    CAS  Google Scholar 

  • Amari S (1971) Characteristics of randomly connected threshold-element networks and network systems. Proc IEEE 59:35–47

    Google Scholar 

  • Amari S (1972) Learning patterns and pattern sequences by selforganizing nets of threshold elements. IEEE Trans Comput 21:1197–1206

    Google Scholar 

  • Bartholomeus M, Coolen ACC (1992) Sequences of smoothly correlated patterns in neural networks with random transmission delays. Biol Cybern 67:285–290

    Article  CAS  PubMed  Google Scholar 

  • Bauer K, Krey U (1990) On learning and recognition of temporal sequences of correlated patterns. Physica B: Condensed Matter 79:461–475

    Google Scholar 

  • Breese CR, Hampson RE, Deadwyler SA (1989) Hippocampal place cells: stereotypy and plasticity. J Neurosci 9:1097–1111

    CAS  PubMed  Google Scholar 

  • Buhmann J, Schulten K (1987) Noise-driven temporal association in neural networks. Europhys Lett 4:1205–1209

    Google Scholar 

  • Cohen MA, Grossberg S (1983) Absolute stability of global pattern formation and parallel memory storage by competitive neural networks. IEEE Trans Systems Man Cybern 815–826

  • Coolen ACC, Gielen CCAM (1988) Delays in neural networks. Europhys Lett 7:281–285

    Google Scholar 

  • Eichenbaum H, Buckingham J (1991) Studies on hippocampal processing: experiment, theory, and model. In: Gabriel M, Moore J (eds) Neurocomputation and learning: foundations of adaptive networks. MIT Press, Cambridge, Mass., pp 171–231

    Google Scholar 

  • Eichenbaum H, Wiener SI, Shapiro ML, Cohen NJ (1989) The organization of spatial coding in the hippocampus: a study of neural ensemble activity. J Neurosci 9:2764–2775

    CAS  PubMed  Google Scholar 

  • Elman JL (1990) Finding structure in time. Cognit Sci 14:179–211

    Google Scholar 

  • Fukushima K (1973) A model of associative memory in the brain. Kybernetik 12:58–63

    Article  CAS  PubMed  Google Scholar 

  • Gluck MA, Myers CE (1993a) Adaptive stimulus representations: a computational theory of hippocampal-region function. In: Hanson SJ, Cowan JD, Giles CL (eds) Advances in neural information processing systems 5. Morgan Kaufmann, San Mateo, Calif, pp 937–944

    Google Scholar 

  • Gluck MA, Myers CE (1993b) Hippocampal mediation of stimulus representation: a computational theory. Hippocampus 3:491–516

    Article  CAS  PubMed  Google Scholar 

  • Griniasty M, Tsodyks MV, Amit DJ (1993) Conversion of temporal correlations between stimuli to spatial correlations between attractors. Neural Comput 5:1–17

    Google Scholar 

  • Hasselmo ME, Schnell E (1994) Laminar selectivity of the cholinergic suppression of synaptic transmission in rat hippocampal region CA1: computational modeling and brain slice physiology. J Neurosci 14:3898–3914

    CAS  PubMed  Google Scholar 

  • Heskes TM, Gielen S (1992) Retrieval of pattern sequences at variable speeds in a neural network with delays. Neural Networks 5:145–152

    Article  Google Scholar 

  • Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 79:2554–2558

    CAS  PubMed  Google Scholar 

  • Jordan MI (1986) Attractor dynamics and parallelism in a connectionist sequential machine. In: Proceedings of the eighth conference of the Cognitive Science Society. Erlbaum, Hillsdale, NJ, pp 531–546

    Google Scholar 

  • Kesner RP (1990) Learning and memory in rats with an emphasis on the role of the hippocampal formation. In: Kesner RP, Olton DS (eds) Neurobiology of comparative cognition. Erlbaum, Hillsdale, NJ, pp 179–204

    Google Scholar 

  • Kesner RP (1991) The role of the hippocampus within an attribute model of memory. Hippocampus 1:279–282

    Article  CAS  PubMed  Google Scholar 

  • Kesner RP, Hardy JD (1983) Long-term memory for contextual attributes: dissociation of amygdala and hippocampus. Behav Brain Res 8:139–149

    Article  CAS  PubMed  Google Scholar 

  • Kleinfeld D (1986) Sequential state generation by model neural networks. Proc Natl Acad Sci USA 83:9469–9473

    CAS  PubMed  Google Scholar 

  • Levy WB (1982) Associative encoding at synapses. In: Proceedings of the fourth annual conference of the Cognitive Science Society, pp 135–136

  • Levy WB (1985) An information/computation theory of hippocampal function. Soc Neuroci Abstr 11:493

    Google Scholar 

  • Levy WB (1989) A computational approach to hippocampal function. In: Hawkins RD, Bower GH (eds) Computational models of learning in simple neural systems. Academic Press, New York, pp 243–305

    Google Scholar 

  • Levy WB (1994) Unification of hippocampal function via computational considerations. World Congress on Neural Networks IV:661–666

    Google Scholar 

  • Levy WB, Steward O (1983) Temporal contiguity requirements for long-term associative potentiation/depression in the hippocampus. Neuroscience 8:791–797

    CAS  PubMed  Google Scholar 

  • Levy WB, Wu XB, Baxter RA (1995) Unification of hippocampal functions via computational/encoding considerations. Int J Neural Syst, Supplement: 71–80

  • Liljenström H, Wu XB (1995) Noise-enhanced performance in a cortical associative memory model. Int J Neural Syst 6:19–29

    Article  PubMed  Google Scholar 

  • McNaughton BL, Nadel L (1989) Hebb-Marr networks and the neurobiological representation of action in space. In: Gluck MA, Rumelhart D (eds) Neuroscience and connectionist theory. Erlbaum, Hillsdale, NJ, pp 1–63

    Google Scholar 

  • Minai AA, Levy WB (1993a) The dynamics of sparse random networks. Biol Cybern 70:177–187

    Article  CAS  PubMed  Google Scholar 

  • Minai AA, Levy WB (1993b) Predicting complex behavior in sparse asymmetric networks. In: Giles CL, Hanson SJ, Cowen JD (eds) Advances in neural information processing systems. Morgan Kaufmann, San Mateo, Calif, pp 556–563

    Google Scholar 

  • Minai AA, Levy WB (1993c) Sequence learning in a single trial. World Congress on Neural Networks II:505–508

    Google Scholar 

  • Minai AA, Levy WB (1994) Setting the activity level in sparse random networks. Neural Comput 6:85–99

    Google Scholar 

  • Minai AA, Barrows G, Levy WB (1994) Disambiguation of pattern sequences with recurrent networks. World Congress on Neural Networks IV: 176–181

    Google Scholar 

  • Mozer MC (1989) A focused backpropagation algorithm for temporal pattern recognition. Complex Syst 3:349–381

    Google Scholar 

  • Muller RU, Kubie JL (1989) The firing of hippocampal place cells predicts the future position of freely moving rats. J Neurosci 9:4101–4110

    CAS  PubMed  Google Scholar 

  • O'Keefe J, Nadel L (1978) The hippocampus as a cognitive map. Oxford University Press, Oxford

    Google Scholar 

  • O'Reilly RC, McClelland JL (1994) Hippocampal conjunctive encoding, storage, and recall: avoiding a tradeoff. Hippocampus 4:661–682

    Article  PubMed  Google Scholar 

  • Prepscius C, Levy WB (1994) Sequence prediction and cognitive mapping by a biologically plausible neural network. World Congress on Neural Networks IV:164–169

    Google Scholar 

  • Reiss M, Taylor JG (1991) Storing temporal sequences. Neural Networks 4:773–787

    Article  Google Scholar 

  • Saypoff R, Muller RU, Kubie JL (1992) How place cells connected by Hebbian synapses can solve spatial problems. Soc Neurosci Abstr 2:1211

    Google Scholar 

  • Schmajuk N, Thieme A (1992) Purposive behavior and cognitive mapping: a neural network model. Biol Cybern 67:165–174

    Article  CAS  PubMed  Google Scholar 

  • Sharp PE (1991) Computer simulation of hippocampal place cells. Psychobiology 19:103–115

    Google Scholar 

  • Sompolinsky H, Kanter I (1986) Temporal association in asymmetric neural networks. Phys Rev Lett 57:2861–2864

    Article  PubMed  Google Scholar 

  • Squire LR (1992) Memory and the hippocampus: a synthesis from findings with rats, monkeys, and humans. Psychol Rev 99:195–231

    CAS  PubMed  Google Scholar 

  • Treves A, Rolls ET (1992) Computational constraints suggest the need for two distinct input systems to the hippocampal CA3 network. Hippocampus 2:189–200

    Article  CAS  PubMed  Google Scholar 

  • Wu XB, Liljenström H (1994) Regulating the nonlinear dynamics of olfactory cortex. Network: Comput Neural Syst 5:47–60

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Neurosurgery, University of Virginia Health Sciences Center, Box 420, 22908, Charlottesville, VA, USA

    Xiangbao Wu, Robert A. Baxter & William B. Levy

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  1. Xiangbao Wu
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  2. Robert A. Baxter
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  3. William B. Levy
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Wu, X., Baxter, R.A. & Levy, W.B. Context codes and the effect of noisy learning on a simplified hippocampal CA3 model. Biol. Cybern. 74, 159–165 (1996). https://doi.org/10.1007/BF00204204

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  • DOI: https://doi.org/10.1007/BF00204204

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