Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-25T06:15:14.614Z Has data issue: false hasContentIssue false
  • English
  • Français

The Hebbian paradigm reintegrated: Local reverberations as internal representations

Published online by Cambridge University Press:  04 February 2010

Daniel J. Amit
Daniel J. Amit
Affiliation:
Racah Institute of Physics, Hebrew University, Jerusalem; Instituto di Fisica, Universita di Roma, Rome, Italy. Electronic mail: ilios.fiz.huji.ac.il
Get access Rights & Permissions [Opens in a new window]

Abstract

The neurophysiological evidence from the Miyashita group's experiments on monkeys as well as cognitive experience common to us all suggests that local neuronal spike rate distributions might persist in the absence of their eliciting stimulus. In Hebb's cell-assembly theory, learning dynamics stabilize such self-maintaining reverberations. Quasi-quantitive modeling of the experimental data on internal representations in association-cortex modules identifies the reverberations (delay spike activity) as the internal code (representation). This leads to cognitive and neurophysiological predictions, many following directly from the language used to describe the activity in the experimental delay period, others from the details of how the model captures the properties of the internal representations.

Keywords

active memoryassociative cortexattractor dynamicscontent sensitivityinternal representationslearningmodeling
Type
Target Article
Information
Behavioral and Brain Sciences , Volume 18 , Issue 4 , December 1995 , pp. 617 - 626
Copyright
Copyright © Cambridge University Press 1995

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abeles, M. (1991) Corticonics: Neuronal circuits of the cerebral cortex. Cambridge University Press. [JP]Google Scholar
Amit, D. J. (1989) Modeling brain junction. Cambridge University Press. [arDJA, JP, FVDV]CrossRefGoogle Scholar
Amit, D. J. (1993) In defense of single electrode recordings. Network 3:385. [aDJA]CrossRefGoogle Scholar
Amit, D. J. (1994) Persistent delay activity in cortex: A Galilean phase in neurophysiology? Network: Computation in Neural Systems 5:429–36. [MWH]Google Scholar
Amit, D. J. & Brunel, N. (1994) Learning internal representations in an attractor neural network with analogue neurons. Network 6(3): 359 [rDJA]Google Scholar
Amit, D. J. (1995) Global spontaneous activity and local structured (learned) activity in cortex. Submitted. [rDJA]Google Scholar
Amit, D. J., Brunel, N. & Tsodyks, M. V. (1994) Correlations of Hebbian reverberations. Journal of Neuroscience. 14:6435. [arDJA]CrossRefGoogle ScholarPubMed
Amit, D. J. & Fusi, S. (1994) Learning in neural networks with material synapses. Neural Computation 6:957. [rDJA]CrossRefGoogle Scholar
Amit, D. J., Gutfreund, H. & Sompolinsky, H. (1985) Spin-glass models of neural networks. Physiological Reviews A32:1007. [rDJA]CrossRefGoogle ScholarPubMed
Amit, D. J. & Tsodyks, M. V. (1991a) Quantitative study of attractor neural network retrieving at low spike rates: 1. Substrate—spikes, rates and neuronal gain. Network 2:259. [arDJA, JJW]CrossRefGoogle Scholar
Amit, D. J. & Tsodyks, M. V. (1991b) Quantative study of attractor neural network retrieving at low spike rates: 2. Low-rate retrieval in symmetric networks. Network 2:275. [arDJA, JJW]CrossRefGoogle Scholar
Amit, D. J. & Tsodyks, M. V. (1992) Effective neurons and attractor neural networks in cortical environment. Network 3:121–37. [EA]Google Scholar
Amsel, A. & Rashotte, M. E. (1984) Mechanisms of adaptive behavior: Clark Hull's theoretical papers, with commentary. Columbia University Press. [FVDV]Google Scholar
Anderson, J. A., Silverstein, J. W., Ritz, S. A. & Jones, R. S. (1977) Distinctive features, categorical perception, and probability learning: Some applications of a neural model. Psychological Review 84:413–51. [MH]CrossRefGoogle Scholar
Anderson, M. (1994) Sexual selection. Monographs in behavior and ecology, ed. Krebs, J. R. & Clutton-Rrock, T.. Princeton University Press. [DCK]Google Scholar
Anisfeld, M. & Knapp, M. (1968) Association, synonymity, and directionality in false recognition. Journal of Experimental Psychology 77:171. [aDJA]CrossRefGoogle ScholarPubMed
Anson, J. G. & Bird, Y. N. (1993) Neuromotor programming: Bilateral and unilateral effects on simple reaction time. Human Movement Science 12:37–50. [FP]Google Scholar
Arak, A. (1988) Callers and satellites in the natterjack toad: Evolutionary stable decision rules. Anitnal Behavior 36:416–32. [DCK]CrossRefGoogle Scholar
Artola, A. & Singer, W. (1933) Long-term depression of excitatory synaptic transmission and its relationship to long-term potentiation. Trends in Neurosciences 16(11):480–87. [EC]Google Scholar
Atick, J. J. (1992) Could information theory provide an ecological theory of sensory processing? Network 3:213. [rDJA]CrossRefGoogle Scholar
Atwood, H. L. & Nguyen, P. V. (1990) Physiological properties of crustacean motor neurons and the alteration of these properties. In: Frontiers in crustacean neurobiology. Birkhauser Verlag. [EC]Google Scholar
Badoni, D., Bertazzoni, S., Buglioni, S., Salina, C., Amit, D. J. & Fusi, S. (1995) Electronic implementation of an analog attractor neural network with stochastic learning. Network 6:125. [rDJA]CrossRefGoogle Scholar
Baudry, M. & Davis, J. L., eds. (1991) Long-term potentiation: A debate of current issues. MIT Press. [FVDV]Google Scholar
Baylis, G. C. & Rolls, E. T. (1987) Responses of neurons in the inferior temporal cortex in short term and serial recognition memory tasks. Experimental BrainResearch 65:614. [rDJA]Google ScholarPubMed
Bialek, W. & Rieke, F. (1992) Reliability and information transmission in spiking neurons. Trends in Nettroscience 15:428–33. [REH]CrossRefGoogle ScholarPubMed
Bienenstock, E. (1994) A model of neocortex. Technical report. Division of AppliedMathematics, Brown University [JP]Google Scholar
Birbaumer, N., Elbert, T., Canavan, A. G. M. & Rockstroh, B. (1990) Slow potentials of the cerebral cortex and behavior. Physiological Reviews 70:1–41. [FP]Google Scholar
Braitenberg, V. (1978) Cell assemblies in the cerebral cortex. In: Theoretical approaches to complex systems [Lecture notes in biomathematics, vol. 21], ed. Heim, R. & Palm, G.. Springer. [FP]Google Scholar
Braitenberg, V. (1984) Vehicles: Experiments in Synthetic Psychology. MIT Press. [MH]Google Scholar
Braitenberg, V. & Schutz, A. (1991) Anatomy of the cortex: Statistics and geometry. Springer-Verlag. [arDJA, WJF, FP]CrossRefGoogle Scholar
Bridgeman, B., Van der Heijden, A. H. C. & Velichkovsky, (1994) A theory of visual stability across saccadic eye movements. Behavioral and Brain Sciences 17:247–92. [DCB]CrossRefGoogle Scholar
Brunel, N. (in press) Stochastic learning of temporal correlations between stimuli in attractor neural networks. Neural Computation. [arDJA]Google Scholar
Buhmann, J., Divko, R. & Schulten, K. (1989) Associative memory with high information content. Physical Review A39:2689. [aDJA]Google Scholar
Burns, B. D. (1951) Some properties of isolated cerebral cortex in the unanesthesized cat. Journal of Physiology 112:156–75. [MH]CrossRefGoogle Scholar
Burr, D. C., Holt, J., Johnstone, J. R. & Ross, J. (1982) Selective depression of motion selectivity during saccades. Journal of Physiology (London) 333:1–15. [DCB]Google Scholar
Burr, D. C. & Ross, J. (1982) Contrast sensitivity at high velocities Vision Research 23:3567–69. [DCB]Google Scholar
Burr, D. C., Morrone, M. C. & Ross, J. (1994) Selective suppression of the magnocellular visual pathway during saccadic eye movements. Nature 371:511–13. [DCB]Google Scholar
Buss, A. H. (1956) Reversal and nonreversal shifts in concept formation with partial reinforcement eliminated. Journal of Experimental Psychology 52:162–66. [MEJR]Google Scholar
Campbell, F. W. & Wurtz, R. H. (1978) Saccadic omission: Why we donot see a greyout during a saccadic eye movement. Vision Research 18:1297–1303. [DCB]Google Scholar
Chekaluk, E. (1994) Is there a role for extraretinal factors in the maintenance of stability in a structured environment? Behavior and Brain Sciences 17:92. [DCB]CrossRefGoogle Scholar
Chown, E. (1994) Consolidation and learning: A connectionist model of human credit assignment. Doctoral dissertation, University of Michigan. [EC]Google Scholar
Cugliandolo, L. (1994) Correlated attractors from uncorrelated stimuli. Neural Computation 6:220. [aDJA]CrossRefGoogle Scholar
Cummins, R. (1989). Meaning and mental representation. MIT Press. [SE]Google Scholar
Daido, H. (1990) Intrinsic fluctuations and a phase transition in a class of large populations of interacting oscillators. Journal of Statistical Physics 60:753–800 [JP]Google Scholar
Dalenoort, G. J. (1982) In search of the conditions for the genesis of cell asemblies: A study—in self-organization. Social Biol. Struct. 5:161–87. [GJD]CrossRefGoogle Scholar
Dalenoort, G. J. (1990) Towards a general theory of representation Psychological Research 52:229–37. [GJD]Google Scholar
Damasio, A. R. & Damasio, H. (1991) Cortical systems underlying knowledge retrieval: Evidence from human lesion studies (background manuscript for the Dahlem Conference on Exploring Brain Function: Models in Neuroscience, Berlin). [aDJA]Google Scholar
Darwin, C. (1871) The descent of man, and selection in relation to sex. Murray. [DCK]Google Scholar
De Santillana, G. & von Dechend, H. (1969) Hamlet's mill. Gambit. [rDJA]Google Scholar
Dong, D. W. & Hopfield, J. J. (1992) Dynamic properties of neural networks with adapting synapses. Network 3:267. [rDJA]CrossRefGoogle Scholar
Doyon, B., Cessac, B., Quoy, M., Samuelides, M. (1993) Chaos in neural networks with random connectivity. International Journal of Bifurcation and Chaos [JP]Google Scholar
Edelman, G. M. (1987) Neural Darwinism: The theory of neuronal group selection. Basic Books. [MH]Google Scholar
Edelman, S. (1995) Similarity and the chorus of prototypes. Minds and machines 5:45–68. [SE]Google Scholar
Emery, J. D. & Freeman, W. J. (1969) Pattern analysis of cortical evoked potential parameters during attention changes. Physiology & Behavior 4:67–77. [WJF]Google Scholar
Engel, A. K., König, P., Kreiter, A., Schillen, T. & Singer, W. (1992) Temporal coding in the visual cortex: New vistas on integration in the nervous system. Trends in Neuroscience 15(6):218–26. [WK, JP]CrossRefGoogle ScholarPubMed
Enquist, M. & Arak, A. (1993) Selection of exaggerated male traitsby female aesthetic senses. Nature 361:446–48. [DCK]Google Scholar
Field, D. J. (1987) Relations between the statistics of natural images and the response properties of cortical cells. Journal of the Optical Society of America A 4:2379–94. [DCB]Google Scholar
Fodor, J. A. (1975) The language of thought. Crowell. [JPR]Google Scholar
Fodor, J. A. & McLaughlin, B.P. (1990) Connectionism and the problem of systematicity: Why Smolensky's solution doesn't work. Cognition 35:183–204.CrossRefGoogle ScholarPubMed
Fodor, J. A. & Pylyshyn, Z. W. (1988) Connectionism and cognitive architecture: A critical analysis. In: Connections and symbols, ed. Pinker, S. & Mehler, J.. MIT Press. [JP, FVDV]Google Scholar
Fransén, E. & Lansner, A. (1994) Low spiking rates in a network with overlapping assemblies. In: The neurobiology of computation: Proceedings of the annual computational neuroscience meeting, ed. Bower, J. M.. Kluwer. [AL]Google Scholar
Fransén, E., Lansner, A. & Liljenström, H. (1992) A model of cortical associative memory based on Hebbian cell assemblies. In: Computation and neural systems, ed. Eeckman, F. & Bower, J. M.. Kluwer. [AL]Google Scholar
Freeman, W. J. (1967) Analysis of function of cerebral cortex by use of control systems theory. Logistics Review 3:5–40. [WJF]Google Scholar
Freeman, W. J. (1968) Analog simulation of prepyriform cortex in the cat.Mathematical BioScience 2:181–90. [WJF]Google Scholar
Freeman, W. J. (1975) Mass action in the nervous system. Academic Press. [WJF, MH]Google Scholar
Freeman, W. J. (1979) Nonlinear gain mediating cortical stimulus-response relations. Biological Cybernetics 33:237–47. [WJF]Google Scholar
Freeman, W. J. (1987) Simulation of chaotic EEC patterns with a dynamic model of the olfactory system. Biological Cybernetics 56:139–43. [WJF, MWH]CrossRefGoogle Scholar
Freeman, W. J. (1992) Tutorial in neurobiology. International Journalof Bifurcation & Chaos 2:451–82. [WJF]Google Scholar
Freeman, W. J. (1993) Valium, histamine, and neural networks. Biological Psychiatry 34:1–2. [WJF]Google Scholar
Freeman, W. J. (1995) Societies of brains: A study in the neuroscience of loveand hate. Erlbaum. [WJF]Google Scholar
Freeman, W. J. & Baird, B. (1987) Relation of olfactory EEC to behavior: Spatial analysis. Behavioral Neuroscience 101:393–408. [MWH]CrossRefGoogle Scholar
Freeman, W. J. & Barrie, J. M. (1994) Chaotic oscillations and thegenesis of meaning in cerebral cortex. In: Temporal Coding in the Brain, ed. Buzsaki, G., Linàs, R., Singer, W., Berthoz, A. & Christen, Y.. Springer-Verlag. [WJF]Google Scholar
Freeman, W. J. & Skarda, C. A. (1990) Chaotic dynamics versus representation. Behavioral and Brain Sciences 13:167–68. [REH]Google Scholar
Freeman, W. J. & Viana di, Prisco G. (1986) EEC spatial pattern differences with discriminated odors manifest chaotic and limit cycle attractors in olfactory bulb of rabbits. In: Proceedings of the First Trieste Meeting on Brain Theory, ed. Palm, G. & Aertsen, A.. Springer-Verlag. [MWH]Google Scholar
Fujita, I., Tanaka, K., Ito, M. & Cheng, K. (1992) Columns for visual features of objects in monkey inferotemporal cortex. Nature 360:343–46. [SE]Google Scholar
Funahashi, S., Bruce, C. J. & Goldman-Rakic, P. S. (1989) Mnemonic coding of visual space in the monkey's dorsolateral prefrontal cortex. Journal of Neurophysiology 61:331. [rDJA]Google Scholar
Fuster, J. M. (1973) Unit activity in prefrontal cortex during delayed-response performance: Neuronal correlates of transient memory. Journal of Neurophysiology 36:61. [aDJA]Google Scholar
Fuster, J. M. (1989) The prefrontal cortex: Anatomy, physiology, and neuropsychology of the frontal lobe, 2d ed.Haven. [JMF]Google Scholar
Fuster, J. M. (1990) Inferotemporal units in selective visual attention and short-term memory. Journal of Neurophysiology 64:681–97. [JMF]Google Scholar
Fuster, J. M. (1994) Memory in the cerebral cortex: An empirical approach to neural networks in the human ami nonhuman primate. MIT Press. [FP]Google Scholar
Fuster, J. M. (1995) Memory in the cerebral cortex: An empirical approach to neural networks in the human and nonhuman primate. MIT Press. [JMF]Google Scholar
Fuster, J. M., Bauer, R. H. & Jervey, J. P. (1982) Cellular discharge in the dorsolateral prefrontal cortex of the monkey in cognitive tasks. Experimental Neurology 77:679–94. [JMF]CrossRefGoogle ScholarPubMed
Fuster, J. M., Bauer, R. H. & Jervey, J. P. (1985) Functional interactions between inferotemporal and prefrontal cortex in a cognitive task. Brain Research 330:299–307. [JMF]Google Scholar
Fuster, J. M. & Jervey, J. (1981) Inferotemporal neurons distinguish and retain behaviorally relevant features of visual stimuli. Science 212:952–55. [JMF]Google Scholar
Fuster, J. M. (1982) Neuronal firing in the inferotemporal cortex of the monkey in a visual memory task. Journal of Neurosciencc 2:361–75. [JMF]Google Scholar
Gardner, E. (1987) Maximum storage capacity in neural networks. Europhysics Letters 4:481. [rDJA]Google Scholar
Gerstein, G. L., Bedenbaugh, P. & Aertsen, A. M. H. J. (1989) Neuronal assemblies. IEEE Transactions on Biomedical Engineering 36:4–14. [FP]CrossRefGoogle ScholarPubMed
Goldman-Rakic, P. S. (1990) Cellular and circuit basis of working memory in prefrontal cortex of nonhuman primates. In: Progress in Brain Research, vol. 85, ed. Uylings, H. B. M., Van Eden, C. G., Bruin, J. P. C. De, Corner, M. A. & Feenstra, M. G. P.. [MM]Google Scholar
Goldman-Rakic, P. S. (1992) Working memory and the mind. Scientific American 267:110–17. [JPR]Google Scholar
Grant, B. R. (1985) Selection on bill characters in a population of Darwin's finches, Geospiza fortis, on Isla Genovesa, Galápagos. Evolution 39:523–32. [DCK]Google Scholar
Gray, C. & Singer, W. (1987) Stimulus-dependent neuronal oscillations in the cat visual cortex area 17. Neuroscience (Suppl.) 22:434. [WK]Google Scholar
Griniasty, M., Tsodyks, M. V. & Amit, D. J. (1993) Conversion of temporal correlations between stimuli to spatial correlations between attractors. Neural Computation 5:1. [arDJA]Google Scholar
Grossberg, S. (1987) Competitive learning: From interactive activation to adaptive resonance. Cognitive Science 11:23–63. [MH]CrossRefGoogle Scholar
Haidarliu, S., Shulz, D. & Ahissar, E. (1995) A multielectrode array for combined microiontophoresis and multiple single-unit recordings. Journal of Neurosciencc Mcthoils 56:125–31. [EA]Google Scholar
Harnad, S. (1990) The symbol grounding problem. Physica D 42:335–46. [SE]Google Scholar
Harrow, M. & Friedman, G. B. (1958) Comparing reversal and nonreversal shifts in concept formation with partial reinforcement control. Journal of ExperimentalPsychology 55:592–98. [MEJR]Google Scholar
Hebb, D. O. (1949) The organisation of behaviour. Wiley. [aDJA, GJD, PMM, FP, JPR, JJW]Google Scholar
Hebb, D. O. & Donderi, D. C. (1987) Textbook of psychology, 4th ed.Erlbauin. [aDJA, JPR]Google Scholar
Hilgard, E. R. & Marquis, D. G. (1940) Conditioning and learning. Appleton Century. [PMM]Google Scholar
Hinton, G. & Sejnowski, T. (1986) Learning and releaming in Boltzmann machines. In: Parallel Distrilmted Processing, vol. 1, ed. Rumelhart, D. E. & McClelland, J. L.. MIT Press. [MH]Google Scholar
Hintzman, D. L. (1993) Twenty-five years of learning and memory: Was the cognitive revolution a mistake? In: Attention and performance 14, ed. Meyer, D. E. & Kornblum, S.. MIT Press. [FVDV]Google Scholar
Hirsch, M. W. (1995) Realism in mathematics. Bulletin of the America Mathematical Society 32:137–47. [MWH]Google Scholar
Hoffman, R. E. (1987) Computer simulations of neural information processing and the schizophrenia-mania dichotomy. Archives of Ceneral Psychiatry 44:178. [aDJA]Google Scholar
Holcomb, P. J. & Neville, H. J. (1990) Auditory and visual semantic priming in lexical decision: A comparison using event-related brain potentials. Language and Cognitive Processes 5:281–312. [FP]Google Scholar
Hopfield, J. J. (1982) Neural networks and physical systems with emergent selective computational abilities. Proceedings of the National Academy of Sciences USA 79:2554–58. [aDJA. GJD, REH, JPR]Google Scholar
Hopfield, J. J. (1984) Neurons with graded response have collective computational properties like those of two-state neurons. Proceedings of the National Academy of Sciences USA 81:3088–92. [REH]Google Scholar
Ilg, U. J. & Hoffmann, K.-P. (1993) Motion perception during saccades. Vision Research 33:211–20. [DCB]Google Scholar
Ito, M. (1992) Posttetanic depression. In: Enclyclopedia of learning and memory, ed. Squire, L. R.. Macmillan. [EC]Google Scholar
James, (1902/1987) Pragmatism: A new name for an old way of thinking. The library of America. [DCK]Google Scholar
Kanerva, P. (1988) Sparse distributed memory. MIT Press. [MH]Google Scholar
Kaplan, S. & Kaplan, R. (1982) Cognition and Environment. Praeger. Republished by Ulrichs, Ann Arbor, Michigan, 1989. [MH]Google Scholar
Kaplan, S., Sonntag, M. & Chown, E. (1991) Tracing recurrent activity in cognitive elements (TRACE): A model of temporal dynamics in a cell assembly. Connection Science 3:179–206. [EC]Google Scholar
Kaplan, S., Weaver, M. & French, R. (1990) Active symbols and internal models: Towards a cognitive connectionism. Al & Society 4:51–71. [MM]Google Scholar
Kelleher, R. T. (1956) Discrimination learning as function of reversal and nonreversal shifts. Journal of Experimental Psychology 51(6):379–84. [MEJR]Google Scholar
Kendler, M. H. & Kendler, T. S. (1962) Vertical and horizontal processes in problem solving. Psychological Review 69(1):1–6. [MEJR]Google Scholar
Kendler, M. H. (1975) From discrimination learning to cognitive development: A neobehavioristic odyssey. In: llandlwok of learning and cognitive processes, ed. Estes, W. K.. Erlbaum. [MEJR]Google Scholar
Kendler, T. S. & D'Amato, M. F. (1955) A comparison of reversal shifts and nonreversal shifts in human concept formation behavior. Journal of Experimental Psychology 49:165–74. [MEJR]Google Scholar
Kennedy, M. B. (1989) Regulation of neuronal function by calcium. Trends in Neurosciences 12:417–20. [PMM]Google Scholar
Klimesch, W. (in press) Memory processes described as brain oscillations in the theta andalpha band. Psycoloquy 95.6.55.memory-brain.l.klimesch. [WK]Google Scholar
Kohonen, T. (1984) Self-organization and associative memory. Springer. [JPR]Google Scholar
Kopell, N. & Ermentrout, G. B. (1990) Phase transitions and other phenomena in chains of coupled oscillators. SIAM Journal of Applied Mathematics 50:1014–52. [JP]Google Scholar
Krakauer, D. C. & Johnstone, R. U. (in press) The evolution and honesty in animal communication: A model using artificial neural networks. Phil. Trans. Roy. Soc. B. [DCK]Google Scholar
Kruger, J., ed. (1991) Neuronal cooperativity. Springer-Verlag. [EA]Google Scholar
Kruschke, J. K. (1992) ALCOVE: An examplar-based connectionist model of category learning. Psychological Review 99(1):22–44. [MEJR]Google Scholar
Kuhl, P. K., Williams, K. A. & Meltzoff, A. N. (1991) Cross-modal speech perception in adults and infants using nonspeech auditory stimuli. Journal of Experimental Psychology: Human Perception and Performance 17:829–40. [JPR]Google Scholar
Kuramoto, Y. & Nishikawa, I. (1987) Statistical macrodynamics of large dynamical systems: Case of a phase transition in oscillator communities. Journal of Statistical Physics 49:569–605 [JP]Google Scholar
Lachter, J. & Bever, T. G. (1988) The relationship between linguistic structure and associative theories of language learning: A constructive critique of some connectionist learning models. Cognition 28(1–2):195–247. [MH]Google Scholar
Langton, C. G. (1990) Computation to the edge of chaos: Phase transitions and emergent computation. Physica 42D:12.Google Scholar
Lansner, A. (1982) Information processing in a network of model neurons: A computer simulation study (Technical Report No. TRITA-NA-8211). Stockholm, Sweden: NADA, Royal Institute of Technology. [AL]Google Scholar
Lansner, A. & Fransén, E. (1992) Modeling Hebbian cell assemblies comprised of cortical neurons. Network 3:105–119. [AL]Google Scholar
Lansner, A. (1994) Improving the realism of attractor models by using corticalcolumns as functional units. In: The neurobiology of computation: Proceedings of the annual computational neuroscience meeting, ed. Bower, J. M.. Kluwer. [AL]Google Scholar
Lansner, A. & Liljenström, H. (1994) Computer models of the brain—How far can they take us. Journal of Theoretical Biology 171:61–73. [AL]Google Scholar
Lashley, K. S. (1951) In search of the engram. Symposia of the Society of Experimental Biology 4:454–82. [GJD]Google Scholar
Laurent, G. & Davidowitz, H. (1994) Encoding of olfactory information with oscillating neural assemblies. Science 265:1872–75. [MH]Google Scholar
Lauro, Grotto R., Reich, S. & Virasoro, A. M. (1994) The computational role of conscious processing in a model of semantic memory, ed. Ito, M.. Proceedings of the HAS Symposium on Cognition, Computation and Consciousness, Kyoto, 08 31, 1994. [rDJA]Google Scholar
Lem, S. (1985) Star diaries. Harcourt Brace Jovanovich. [SE]Google Scholar
Liley, D. T. J. & Wright, J. J. (1994) Intracortical connectivity of pyramidal and stellate cells: Estimates of synaptic densities and coupling symmetry. Network 5:175–79. [JJW]Google Scholar
Llinás, R. & Ribary, U. (1993) Coherent 40-Hz oscillation characterizes dream state in humans. Proceedings of the National Academy of Sciences USA 90:2078–81. [PMM]Google Scholar
Locke, J. (1690) An essay concerning human understanding. Available electronically on the Internet at URL gopher://gopher.vt.edu:10010/02/116/3. [SE]Google Scholar
Nó, Lorente de (1949) In: Physiology of the nervous system, ed. Fulton, J.F.. Oxford University Press. [aDJA, JPR]Google Scholar
Lutzenberger, W., Pulvermüller, F. & Birbaumer, N. (1994) Words and pseudowords elicit distinct patterns of 30-Hz activity in humans. Neuroscience Letters 176:115–118. [FP]Google Scholar
Lutzenberger, W., Pulvermüller, F., Elbert, T. & Birbaumer, N. (1995) Local 40 Hz activity in human cortex induced by visual stimulation. Neuroscience Letters 183:139–42. [FP]Google Scholar
MacGregor, R. J. & McMullen, T. (1978) Computer simulation ofdiffusely connected neuronal populations. Biological Cybernetics 28:12–127. [AL]Google Scholar
Mackay, D. M. (1970) Elevation of visual threshold by displacement of visual images. Nature 225:90–92. [DCB]Google Scholar
Macknik, S. L., Bridgeman, B. & Switkes, E. (1991) Saccadic suppression of displacement at isoluminanee. Investigative Ophthalmology and Visual Science [Suppl.] 32:899. [DCB]Google Scholar
Maddy, P. (1993) Realism in mathematics. Oxford University Press. [MWH]Google Scholar
Magleby, K. L. (1987) Short-term changes in synaptic efficiency. In: Synoptic function, ed. Edelman, G. M., Gall, W. E. & Cowan, W. M.. Wiley. [EC]Google Scholar
Mangler, G. (1975) Consciousness: Respectable, useful, and probably necessary. In: Information processing and cognitive psychology, ed. Solso, R. L.. Erlbaum. [EC]Google Scholar
Mason, A., Nicoll, A. & Stratford, K. (1991) Synaptic transmission between individual pyramidal neurons of the rat visual cortex in vitro. Journal of Neuroscience 11:72. [aDJA]Google Scholar
Matin, E. (1974) Saccadic suppression: A review and an analysis. Psychological Bulletin 81:899–917. [DCB]Google Scholar
McGaugh, J. L. & Herz, M. J. (1972) Memory consolidation. Albion. [JPR]Google Scholar
McKenna, T. M., Ashe, J. H. & Weinberger, N. M. (1989) Cholinergic modulation of frequency receptive fields in auditory cortex: 1. Frequencyspecific effects of muscarinic agonists. Synapse 4:30–43. [EA]Google Scholar
McNaughton, B. L., Barnes, C. A. & Andersen, P. (1981) Synaptic efficacy and EPSP summation in granule cells of rat fascia dentata in vitro. Journal of Neurophysiology 46:952. [aDJA]Google Scholar
Metherate, R. & Weinberger, N. M. (1989) Acetylcholine produces stimulusspecific receptive field alterations in cat auditory cortex. Brain Research 480:372–77. [EA]Google Scholar
Miller, E. K. & Desimone, R. (1994) Dual mechanism for short-term memory in inferior temporal cortex. NIMH reprint. [rDJA]Google Scholar
Miller, E. K., Li, L. & Desimone, R. (1991) A neural mechanism for working and recognition memory in inferior temporal cortex. Science 254:1377–79. [JPR]Google Scholar
Miller, E. K., Li, L. & Desimone, R. (1993) Activity of neurons in anterior inferior temporal cortex during a shortterm memory task. Journal of Neuroscience 13(4):1460–78. [MH, rDJA]Google Scholar
Miller, R. R. & Martin, N. A. (1984) The physiology and semantics of consolidation. In: Memory consolidation: Psychobiology of cognition, ed. Weingartner, H. & Parker, E. S.. Erlbaum. [EC]Google Scholar
Milner, P. M. (1957) The cell assembly: Mk. II. Psychological Review 64:242–52. [PMM]Google Scholar
Milner, P. M. (1989) A cell assembly theory of hippocampal amnesia. Neuropsychologia 27:23–30. [PMM]Google Scholar
Miyashita, Y. (1988) Neuronal correlate of visual associative long-term memory in the primate temporal cortex. Nature 335:817–20. [aDJA, EA, GJD, MH, REH, PMM, MEJR]Google Scholar
Miyashita, , (1988) Nature 335:819.CrossRefGoogle Scholar
Miyashita, Y. & Chang, H. S. (1988) Neuronal correlate of pictorial short-term memory in the primate temporal cortex. Nature 331:68–70. [aDJA, EA, GJD, MH, DCK, PMM, MEJR]Google Scholar
Mohr, B., Pulvermuller, F., Rayman, J. & Zaidel, E. (1994) Interhemispheric cooperation during lexical processing is mediated by the corpus callosum: Evidence from the split-brain. Neuroscience Letters 181:17–21. [FP]Google Scholar
Mohr, B., Pulvermüller, F. & Zaidel, E. (1994) Lexical decision after left, right and bilateral presentation of content words, function words and non-words: Evidence for interhemispheric interaction. Neuropsychologia 32:105–124. [FP]Google Scholar
Morita, M. (1992) A neural network model of the dynamics of a short-term memory system in the temporal cortex. Systems and Computers in Japan 23(4):14–24. [MM]Google Scholar
Morita, M. (1993) Associative memory with nonmonotone dynamics. Neural Networks 6:115–26. [MM]Google Scholar
Morita, M. (1994) Smooth recollection of a pattern sequence by nonmonotone analog neural networks. Proceedings of the 1994 IEEE International Conference on Neural Networks 2:1032–37. [MM]Google Scholar
Muller, G. E. & Pilzecker, A. (1900) Experimentelle Beitrage zur Lehre vom Gedächtniss. Zeitschrift fur Psychologie und Physiologie der Sinnesorgane, Erganzungsband 1:1–288. [PMM]Google Scholar
Niki, H. (1974) Prefrontal unit activity during delay alternation in the monkey. Brain Research 68:185. [aDJA]Google Scholar
O'Keefe, J. & Speakman, A. (1987) Single unit activity in therat hippocampus during a spatial memory task. Experimental Brain Research 68:1. [aDJA]CrossRefGoogle Scholar
Packard, N. H., Crutchfield, J. P., Fanner, J. D. & Shaw, R. S. (1980) Geometry from a time series. Physical Review Letters 45:712. [MWH]Google Scholar
Palm, G. (1982) Neural Assemblies. Springer-Verlag. [MH, FP, JPR]Google Scholar
Petitot, J. (1989) Morphodynamics and the categorical perception of phonological units. Theoretical Linguistics 15(1/2):25–71. [JP]Google Scholar
Petitot, J. (1991) Why connectionism is such a good thing: A criticism of Fodor's and Pylyshyn's criticism of Smolensky. Philosophica 47:1: 49–79. [JP]Google Scholar
Petitot, J. (1995) Morphodynamics and attractor syntax: Dynamical and morphological models for constituency in visual perception and cognitive grammar. In: Mind as Motion, ed. van Gelder, T. & Port, R.. MIT Press. [JP]Google Scholar
Pinker, S. & Prince, A. (1988a) On language and connectionism: Analysis of a parallel distributed processing model of language acquisition. Cognition 28(1–2):73–193. [MH]Google Scholar
Pinker, S. (1988b) On language and connectionism: Analysis of a parallel distributed processing model of language acquisition. In: Connections and symbols, ed. Pinker, S. & Mehler, J.. MIT Press. [FVDV]Google Scholar
Plato, (360 BC) Theaetetus [transl. Jowett, B.]. Available electronically on the Internet at URL gopher://gopher.vt.edu:10010/02/131/23. [SE]Google Scholar
Pulvermüller, F., Lutzenberger, W. & Birbaumer, N. (in press) Electrocortical distinction of vocabulary types. Electroencephalography and Clintbal Neurophysiology. 94:357–70. [FP]Google Scholar
Pulvermüller, F., Preissl, H., Eulitz, C., Pantev, C., Lutzenberger, W., Elbert, T. & Birbaumer, N. (1994) Brain rhythms, cell assemblies, and cognition: Evidence from the processing of words and pseudowords. Psycoloquy 5(48). [FP]Google Scholar
Putnam, H. (1988) Representation and reality. MIT Press. [SE]Google Scholar
Pylyshyn, Z. W. (1980) Computation and cognition: Issues in the foundations of cognitive science. Behavioral and Brain Sciences 3:111. [JPR]Google Scholar
Quine, W. V. O. (1960) Word and object. MIT Press. [SE]Google Scholar
Quinlan, P. (1991) Connectionism and psychology. A psychological perspective on new connectionist research. Wheatsheaf, New York: Harvester. [AL]Google Scholar
Quintana, J., Fuster, J. M. & Yajeya, J. (1989) Effects ofcooling parietal cortex on prefrontal units in delay tasks. Brain Research 503:100–10. [JMF]Google Scholar
Quintana, J., Yajeya, J. & Fuster, J. M. (1988) Prefronta representation of stimulus attributes during delay tasks: 1. Unit activity in cross-temporal integration of sensory and sensory-motor information. Brain Research 474:211–21. [JMF]Google Scholar
Raijmakers, M. E. J., Koten, S. & Molenaar, P. C. M. (in press) On the validity of simulating stagewise development by means of PDP-networks: Application of catastrophe analysis and an experimental test of rule-like network performance. Cognitive Science.[MEJR]Google Scholar
Rauschecker, J. P. (1991) Mechanisms of visual plasticity: Hebb synapses, NMDA receptors, and beyond. Physiological Reviews 71:587–615. [JPR]Google Scholar
Rauschecker, J. P. (1995) Compensatory plasticity and sensory substitution in the cerebral cortex. Trends in Ncumsciences 18:36–43. [JPR]CrossRefGoogle ScholarPubMed
Rauschecker, J. P. & Hahn, S. (1987) Ketamine-xylazine anaesthesiablocks consolidation of ocular dominance columns in kitten visual cortex. Nature 326:183–85. [JPR]Google Scholar
Rauschecker, J. P. & Korte, M. (1993) Auditory compensation for early blindness in cat cerebral cortex. Journal of Neuroscience 13:4538–48. [JPR]Google Scholar
Rauschecker, J. P. & Sejnowski, T. (1994) Processing of visual andauditory space and its modification by experience. In: Advances in Neural Information Processing Systems, vol. 6, ed. Cowan, J. D., Tesauro, G. & Alspector, J.. [JPR]Google Scholar
Rauschecker, J. P., Tian, B., Korte, M. & Egert, U. (1992) Crossmodal changes in the somatosensory vibrissa/barrel system of visually deprived animals, Proceedings of the National Academy of Sciences USA 89:5063–67. [JPR]Google Scholar
Reese, H. W. (1989) Rules and rule-governance: Cognitive and behavioristic views. In: Rule-governed behavior: Cognition, contingencies, and instructional control, ed. Hayes, S. C.. Plenum. [MEJR]Google Scholar
Reichenbach, H. (1956) The direction of time. University of California Press. [DCK]Google Scholar
Reilly, R. G. & Sharkey, N. E. (1992) Representational adequacy. In: Connectionist approaches to natural language processing ed. Reilly, G. & Sharkey, N. E.. Erlbaum. [FVDV]Google Scholar
Renals, S. & Rohwer, R. (1990) A study of network dynamics. Journal of Statistical Physics 58:825–48,Google Scholar
Rochester, N., Holland, J. H., Haibt, L. H. & Duda, W. L. (1956) Tests on a cell assembly theory of the action of the brain, using a large digital computer. IRE Transactions on Information Theory IT–2:80–93. [AL, PMM]Google Scholar
Ruppin, E. (1995) Neural modeling for psychiatric disorders. Network 6(3). [rDJA]Google Scholar
Sakai, K. & Miyashita, Y. (1991) Neural organization for long-termmemory of paired associates. Nature 354:152–55. [aDJA, EA, GJD, MH, PMM, MEJR]Google Scholar
Sakai, K., Naya, Y. & Miyashita, Y. (1994) Neuronal tuningand associative mechanisms in form representation. Learning and Memory 1:83–105. [SE]Google Scholar
Sayer, R. J., Redman, S. J. & Andersen, P. (1989) Amplitude fluctuations in small EPSPs recorded from CA1 pyramidal cells in the guinea pig hippocampal slice. Journal of Neuroscience 9:840. [aDJA]Google Scholar
Schade, A. F. & Bitterman, M. (1966) Improvement in habit reversalas related to the dimensional set. Journal of Comparative and Physiological Psychology 62:43–48. [MEJR]Google Scholar
Sejnowski, T. J. (1977) Storing covariance with nonlinearly interacting neurons. Journal of Mathematical Biology 4:303–21. [JPR]Google Scholar
Shiori, S. & Cavanagh, P. (1989) Saccadic suppression of low-leve motion. Vision Research 29:915–28. [DCB]Google Scholar
Sholl, D. A. (1956) The organization of the cerebral cortex. Wiley. [WJF]Google Scholar
Simon, H. A. & Kaplan, C. A. (1989) Foundations of cognitive science. In: Foundations of cognitive science, ed. Posner, M. I., MIT Press. [FVDV]Google Scholar
Singer, W. (1990) The formation of cooperative cell assemblies in the visual cortex. journal of Exficrimcntal Biology 153:177–97. ]PR]Google Scholar
Singer, W. (1994) Putative functions of temporal correlations in neocortical processing. In: Large scale ncuronal theories of the brain, ed. Koch, C. & Davis, J.. MIT Press. [FP]Google Scholar
Singer, W. & Gray, C. M. (1995) Visual feature integration and thetemporal correlation hypothesis. Annual Review of Neuroscience 18:555–86.Google Scholar
Skarda, C. A. & Freeman, W. J. (1987) How brains make chaos in order to make sense of the world. Behavioral and Brain Sciences 10:161–95. [WJF, MWH]Google Scholar
Smolensky, P. (1988) On the proper treatment of connectionism. Behavioral and Brain Sciences 11:1–23. [JP]Google Scholar
Sompolinsky, H. (1986a) The theory of neural networks: The Hebb rule and beyond. In: Heidelberg Colloquium on glassy dynamics, ed. Hemmen, L. van & Morgenstem, I.. Springer-Verlag. [aDJA]Google Scholar
Sompolinsky, H. (1986b) Neural networks with nonlinear synapses and static noise. Physics Review A34:2571. [aDJA]Google Scholar
Sompolinsky, H., Crisanti, A., & Sominers, H.-J. (1988) Chaos in random neural networks. Physics Review Lett 61:259–62 [JP]Google Scholar
Spence, K. W. (1936) The nature of discrimination learning in animals. Psychological Review 43:427–49. [MEJRJGoogle Scholar
Squire, L. (1987) Memory ami brain. Oxford University Press. [JPR]Google Scholar
Stevens, C. F., Tonegawa, S. & Wang, Y. (1994) The role of calcium-calmodulin kinase II in three forms of synaptic plasticity. Current Biology 4:687–93. [JPR]Google Scholar
Takens, F. (1981) Detecting strange attractors in turbulence. In: Lecture notes in tnathematics 898, ed. Rand, D. & Young, L. S.. Springer-Verlag. [MWH]Google Scholar
Tanaka, K. (1992) Inferotemporal cortex and higher visual functions. Current Opinion in Ncurobiology 2:502–5. [arDJA, SE]Google Scholar
Tanaka, K. (1993) Neuronal mechanisms of object recognition. Science 262:685–88. [FVDV]Google Scholar
Tanzi, E. (1893) I fatti e le induzioni nell'odiema istologia del sistem nervosa. Rivista Speriinentale di Frcniatria 19:419–72. [GJD]Google Scholar
Thom, R. (1980) Modèle mathématiques de la Morphogenèse. Paris, Christian Bourgois. [JP]Google Scholar
Tighe, T. J. (1964) Reversal and nonreversal shifts in monkeys. Journal of Comparative and Physiological Psychology 58(2):324–26. [MEJR]Google Scholar
Tsodyks, M. V. & Feigel'man, M. V. (1988) The enhanced storage capacity in neural networks with low activity level. Europhysics Letters 46:101. [aDJA]Google Scholar
Tulving, E. (1984) Précis of Elements of episodic memory. Behavioral and Brain Sciences 7:223–68. [WK]Google Scholar
Uchikawa, K. & Sato, M. (in press) Saccadic suppression to achromatic and chromatic responses measured by increment-threshold spectral sensitivity. Journal of the Optical Society of America A. [DCB]Google Scholar
Van der Velde, F. (1994) Integrating connectionism and symbol manipulation: The importance of implementation in psychology. Technical report, Leiden University. [FVDV]Google Scholar
Van der Velde, F. (in press) Symbol manipulation with neural networks: Production of a contextfree language using a modifiable working memory. Connection Science. [FVDV]Google Scholar
Virasoro, A. M. (1988) Categorization in neural networks and prosopagnosia. Physics Reports 184:99. [aDJA]Google Scholar
Von der Malsburg, C. (1973) Self-organization of orientation sensitive cells in the striate cortex. Kybemetik 14:85–100. [JPR]Google Scholar
Von Neumann, J. (1954) The computer and the brain. Yale University Press. [aDJA]Google Scholar
Wickens, J., Hyland, B. & Anson, C. (1994) Cortical cell assemblies: A possible mechanism for motor programs. Journal of Motor Behavior 26:66–82. [FP]Google Scholar
Willshaw, D., Buneman, O. P. & Longuet-Higgins, H. (1969) Nonholographic associative memory. Nature 222:960. [rDJA]Google Scholar
Wilson, M. A. & McNaughton, B. L. (1993) Dynamics of the hippocampal ensemble code for space [see comments]. Science 261:1055–58. [EA]Google Scholar
Wright, J. J. & Liley, D. T. J. (1995) Simulation of electrocortical waves. Biological Cybernetics 72(4):347–56. [JJW]Google Scholar
Zeeman, E. C. (1962) The topology of the brain and visual perception. In: Topology of 3-Manifolds and Related Topics, ed. Fort, M. K. Jr, Hall, Prentice. [MWH]Google Scholar
Zeeman, E. C. (1965) Topology of the brain. Mathematics and computer science in biology and medicine. Medical Research Council. [JP]Google Scholar
Zeeman, E. C. (1976) Brain modelling. In Structural stability, tlie theory of catastrophes and applications in the sciences, Lecture notes in mathematics 525:367–372. Berlin, Springer. [JP]Google Scholar
Zipser, D., Kehoe, B., Littlewort, G. & Fuster, J. (1993) A spiking network model of short-term active memory. Journal of Neuroscience 13:3406–3420. [JMF, rDJA]Google Scholar