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What's in a cell assembly?

Published online by Cambridge University Press:  04 February 2010

G. J. Dalenoort  and
P. H. de Vries
G. J. Dalenoort
Affiliation:
Department of Psychology, University of Groningen, 9700 AB Groningen, The Netherlands, [email protected]
P. H. de Vries
Affiliation:
Department of Psychology, University of Groningen, 9700 AB Groningen, The Netherlands, [email protected]
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Abstract

The cell assembly as a simple attractor cannot explain many cognitive phenomena. It must be a highly structured network that can sustain highly structured excitation patterns. Moreover, a cell assembly must be more widely distributed in space than on a square millimeter.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 18 , Issue 4 , December 1995 , pp. 629 - 630
Copyright
Copyright © Cambridge University Press 1995

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References

Abeles, M. (1991) Corticonics: Neuronal circuits of the cerebral cortex. Cambridge University Press. [JP]CrossRefGoogle 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]Google 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]CrossRefGoogle 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]CrossRefGoogle 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]Google 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]Google Scholar
Anson, J. G. & Bird, Y. N. (1993) Neuromotor programming: Bilateral and unilateral effects on simple reaction time. Human Movement Science 12:37–50. [FP]CrossRefGoogle 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]CrossRefGoogle Scholar
Atick, J. J. (1992) Could information theory provide an ecological theory of sensory processing? Network 3:213. [rDJA]Google 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]Google 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 Scholar
Bialek, W. & Rieke, F. (1992) Reliability and information transmission in spiking neurons. Trends in Nettroscience 15:428–33. [REH]Google Scholar
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]Google 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]CrossRefGoogle ScholarPubMed
Burns, B. D. (1951) Some properties of isolated cerebral cortex in the unanesthesized cat. Journal of Physiology 112:156–75. [MH]Google 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]CrossRefGoogle ScholarPubMed
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]Google 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]Google 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]Google 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]Google 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]CrossRefGoogle 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]Google Scholar
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]CrossRefGoogle ScholarPubMed
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]CrossRefGoogle ScholarPubMed
Freeman, W. J. (1987) Simulation of chaotic EEC patterns with a dynamic model of the olfactory system. Biological Cybernetics 56:139–43. [WJF, MWH]Google Scholar
Freeman, W. J. (1992) Tutorial in neurobiology. International Journalof Bifurcation & Chaos 2:451–82. [WJF]CrossRefGoogle 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]CrossRefGoogle ScholarPubMed
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]CrossRefGoogle ScholarPubMed
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]Google Scholar
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]CrossRefGoogle 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]Google Scholar
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]CrossRefGoogle ScholarPubMed
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]Google 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 ScholarPubMed
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]CrossRefGoogle 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]CrossRefGoogle ScholarPubMed
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.Google 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]Google 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]Google Scholar
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]CrossRefGoogle 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