Hostname: page-component-84c44f86f4-hlrw8 Total loading time: 0 Render date: 2025-10-14T19:56:17.082Z Has data issue: false hasContentIssue false

Local versus distributed: A poor taxonomy of neural coding strategies

Published online by Cambridge University Press:  17 March 2005

Michael W. Spratling*
Affiliation:
Centre for Brain and Cognitive Development, Birkbeck College, London, WC1E 7JL, United Kingdom

Abstract:

Page is to be congratulated for challenging some misconceptions about neural representation. However, his target article, and the commentaries to it, highlight that the terms “local” and “distributed” are open to misinterpretation. These terms provide a poor description of neural coding strategies and a better taxonomy might resolve some of the issues.

Information

Type
Continuing Commentary
Copyright
Copyright © Cambridge University Press 2004

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.)

Article purchase

Temporarily unavailable

Footnotes

Commentary on Mike Page (2000). Connectionist modelling in psychology: A localist manifesto. BBS 23(4):443–512.

References

Barlow, H. B. (1972) Single units and sensation: A neuron doctrine for perceptual psychology? Perception 1:371–94. [MWS]CrossRefGoogle ScholarPubMed
Barlow, H. B. (1994) What is the computational goal of the neocortex? In: Large-scale neuronal theories of the brain, ed. Koch, C. & Davis, J. L., Ch. 1. MIT Press. [MWS]Google Scholar
Barlow, H. B. & Gardner-Medwin, A. (2000) Localist representation can improve efficiency for detection and counting. Behavioral and Brain Sciences 23:467–68. [MWS]10.1017/S0140525X00223352CrossRefGoogle Scholar
Bartlett, F. E. (1932) Remembering. Cambridge University Press. [PAK]Google Scholar
Bell, A. J. & Sejnowski, T. J. (1997) The independent components of natural scenes are edge filters. Vision Research 37:3327–38. [MWS]CrossRefGoogle ScholarPubMed
Clark, A. & Thornton, C. (1997) Trading spaces: Computation, representation and the limits of uninformed learning. Behavioral and Brain Sciences 20:5766. [MWS]CrossRefGoogle ScholarPubMed
Eccles, J. C. (1986) Mechanism of learning in complex neural systems. In: Handbook of physiology: The nervous system V, ed. Plum, F., pp. 137–67. Williams & Wilkins. [PAK]Google Scholar
Edelman, S. & Duvdevani-Bar, S. (1995) Similarity, connectionism, and the problem of representation in vision. Neural Computation 9:701–20. [MWS]CrossRefGoogle Scholar
Feldman, J. A. (1990) Computational constraints on higher neural representations. In: Computational neuroscience, ed. Schwartz, E. L.. MIT Press. [MWS]Google Scholar
Földiák, P. (1990) Forming sparse representations by local anti-Hebbian learning. Biological Cybernetics 64:165–70. [MWS]CrossRefGoogle ScholarPubMed
Fried, I., MacDonald, K. A. & Wilson, C. L. (1997) Singe neuron activity in human hippocampus and amygdala during recognition of faces and objects. Neuron 18:735–65. [PAK]10.1016/S0896-6273(00)80315-3CrossRefGoogle Scholar
Georgopoulos, A. P., Schwartz, A. B. & Kettner, R. E. (1986) Neuronal population coding of movement direction. Science 233:1416–19. [MWS]CrossRefGoogle ScholarPubMed
Hebb, D. O. (1949) The organization of behavior. Wiley. [PAK]Google Scholar
Hebb, D. O. (1972) Textbook of psychology. W. B. Saunders. [PAK]Google Scholar
Hummel, J. E. (2000) Localism as a first step toward symbolic representation. Behavioral and Brain Sciences 23:480–81. [MWS]10.1017/S0140525X0036335XCrossRefGoogle Scholar
Jelasity, M. (2000) Instance-based manifesto? Behavioral and Brain Sciences 23:482–83. [MWS]CrossRefGoogle Scholar
Koch, P. & Leisman, G. (1990) A continuum model of activity waves in layered neuronal networks: A neuropsychology of brain-stem seizures. International Journal of Neuroscience 54:4162. [PAK]10.3109/00207459008986621CrossRefGoogle Scholar
Koch, P. & Leisman, G. (1996) Wave theory of large-scale organization of cortical activity. International Journal of Neuroscience 86(3–4):179–96. [PAK]CrossRefGoogle ScholarPubMed
Koch, P. & Leisman, G. (2001) Effect of local synaptic strengthening on global activity-wave growth in the hippocampus. International Journal of Neuroscience 108(1–2):127–46. [PAK]CrossRefGoogle ScholarPubMed
Krone, G., Mallot, H., Palm, G. & Schuz, A. (1986) Spatiotemporal receptive fields: A dynamical model derived from cortical architectonics. Proceedings of the Royal Society of London, B: Biological Sciences 226:421–44. [PAK]Google Scholar
Leisman, G. & Koch, P. (2000) Continuum model of mnemonic and amnesiac phenomena. Journal of the International Neuropsychological Society 6:589603. [PAK]CrossRefGoogle Scholar
Logothetis, N. (1998) Object vision and visual awareness. Current Opinion in Neurobiology 8:536–44. [MWS]10.1016/S0959-4388(98)80043-3CrossRefGoogle ScholarPubMed
Logothetis, N. & Sheinberg, D. L. (1996) Visual object recognition. Annual Review of Neuroscience 19:577621. [MWS]CrossRefGoogle ScholarPubMed
Muller, R. U., Ranck, J. B. Jr. & Taub, J. S. (1996) Head direction cells: Properties and functional significance. Current Opinion in Neurobiology 6:196206. [PAK]10.1016/S0959-4388(96)80073-0CrossRefGoogle ScholarPubMed
Newsome, W. T., Britten, K. H. & Movshon, J. A. (1989) Neuronal correlates of a perceptual decision. Nature 341:5254. [MWS]10.1038/341052a0CrossRefGoogle ScholarPubMed
Oja, E. (1982) A simplified neuron model as a principal component analyser. Journal of Mathematical Biology 15:267–73. [MWS]10.1007/BF00275687CrossRefGoogle Scholar
Page, M. (2000a) Connectionist modelling in psychology: A localist manifesto. Behavioral and Brain Sciences 23:443–67. [MWS]10.1017/S0140525X00003356CrossRefGoogle Scholar
Page, M. (2000b) Sticking to the manifesto. (Author's Response to commentary.) Behavioral and Brain Sciences 23:496505. [MWS]CrossRefGoogle Scholar
Perrett, D. I., Hietanen, J. K., Oram, M. W. & Benson, P. J. (1992) Organisation and functions of cells responsive to faces in the temporal cortex. Philosophical Transactions of the Royal Society of London 335:2330. [MWS]Google ScholarPubMed
Rolls, E. T., Treves, A., Robertson, R. G., Georges-Francois, P. & Panzeri, S. (1998) Information about spatial view in an ensemble of primate hippocampal cells. Journal of Neurophysiology 79(4):1797–813. [PAK]10.1152/jn.1998.79.4.1797CrossRefGoogle Scholar
Sanger, T. D. (1989) Optimal unsupervised learning in a single-layer linear feedforward neural network. Neural Networks 2:459–73. [MWS]10.1016/0893-6080(89)90044-0CrossRefGoogle Scholar
Tanaka, K. (1996) A continuous map of higher-level visual features of objects in monkey inferotemporal cortex. In: Coincidence detection in the nervous system, ed. Konnerth, A., Tsien, R., Mikoshiba, K. & Altman, J., pp. 143–51. Human Frontier Science Program. [MWS]Google Scholar
Traub, R. D., Miles, R. & Wong, R. K. S. (1987) Models of synchronized hippocampal bursts in the presence of inhibition. I. Single population events. Journal of Neurophysiology 58:739–51.10.1152/jn.1987.58.4.739CrossRefGoogle ScholarPubMed
Traub, R. D., Miles, R. & Wong, R. K. S. (1988) Large scale simulations of the hippocampus. IEEE Engineering in Medicine and Biology Magazine 7:3138.10.1109/51.20378CrossRefGoogle ScholarPubMed
Traub, R. D., Miles, R. & Wong, R. K. S. (1989) Model of the origin of rhythmic population oscillations in the hippocampal slice. Science 243:1319–25.CrossRefGoogle ScholarPubMed
Traub, R. D., Whittington, M. A., Colling, S. B., Buzaki, G. & Jefferys, J. G. R. (1990) Analysis of gamma rhythms in the rat hippocampus in vitro and in vivo. Journal of Physiology (London) 493:471–84.10.1113/jphysiol.1996.sp021397CrossRefGoogle Scholar
Tsotsos, J. K. (1995) Behaviourist intelligence and the scaling problem. Artificial Intelligence 75:135–60. [MWS]CrossRefGoogle Scholar
van Hateren, J. H. & Ruderman, D. L. (1998) Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex. Proceedings of the Royal Society of London, Series B 265:2315–20. [MWS]10.1098/rspb.1998.0577CrossRefGoogle ScholarPubMed
Wallis, G. & Rolls, E. T. (1997) A model of invariant object recognition in the visual system. Progress in Neurobiology 51:167–94. [MWS]CrossRefGoogle Scholar
Wilson, H. R. & Cowan, J. D. (1973) A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue. Kybernetic 13:5580.CrossRefGoogle ScholarPubMed
Wilson, S. W. (1991) The animat path to AI. In: From animals to animats: Proceedings of the First International Conference on the Simulation of Adaptive Behaviour (SAB91), ed. Meyer, J.-A. & Wilson, S. W., pp. 1521. MIT Press. [MWS]10.7551/mitpress/3115.003.0004CrossRefGoogle Scholar