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Neural Nets

Published online by Cambridge University Press:  17 March 2009

Jack D Cowan and
David H Sharp
Jack D Cowan
Affiliation:
Mathematics Department, The University of Chicago, Chicago, Illinois 60637
David H Sharp
Affiliation:
Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545
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The brain is one of the most highly organized structures in the known universe. It is a biological computer which has evolved over a billion years to program, monitor and control all bodily functions. It is also the organ of knowing, feeling and thinking. To understand how the brain works is perhaps the most difficult of all scientific problems. A scientific theory of the brain would provide a comprehensive understanding of a substantial body of facts on the basis of a few fundamental principles. In this sense, there is no theory of the brain.

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Research Article
Information
Quarterly Reviews of Biophysics , Volume 21 , Issue 3 , August 1988, pp. 365 - 427
Copyright
Copyright © Cambridge University Press 1988

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References

Abu-Mostafa, Y. S. & St Jacques, J. (1985). Information capacity of the Hopfield model. IEEE Trans Inf. Theory 7, 1–11.Google Scholar
Ackley, D. H., Hinton, G. E. & Sejnowski, T. J. (1985). A learning algorithm for Boltzmann machines. Cognitive Sci. 9, 147–160.Google Scholar
Adrian, E. D. (1914). The all-or-none principle in nerve. J. Physiol., Lond. XLVII, (6), 460–474.CrossRefGoogle Scholar
Albus, J. S. (1971). A theory of cerebellar function. Math. Biosci. 10, 25–61.CrossRefGoogle Scholar
Altman, J. (1987). A quiet revolution in thinking. Nature 328, 572.CrossRefGoogle ScholarPubMed
Amari, S.-I. (1972). Characteristics of random nets of analog neuron-like elements. IEEE Trans. Sys., Man, and Cybern, SMC-2 (5), 643–657.Google Scholar
Amit, D. J., Gutfreund, H. & Sompolinsky, H. (1985). Storing infinite numbers of patterns in a spin-glass model of neural networks. Phys. Rev. Lett. 55, 1530–1533.CrossRefGoogle Scholar
Andersen, R. & Zipser, D. (1987). A neural net model of posterior parietal cortex.Neurobiology of Neocortex, Dahlem Conferencz, 50,Berlin.Google Scholar
Anderson, J. A. (1968). A memory storage model utilizing spatial correlation functions. Kybernetik 5, 113–119.CrossRefGoogle ScholarPubMed
Aplevich, J. D. (1968). Models of certain nonlinear systems. In: Neural Networks (Ed. Caianiello, E. R.), pp. 115, Berlin: Springer-Verlag.Google Scholar
Arbib, M. A. & Amaris, S.-I. (1985). Sensori-motor transformations in the brain (with a critique of the tensor theory of the cerebellum). J. theor. Biol. 112, 121–155.CrossRefGoogle Scholar
Ashby, W. R. (1950). The stability of a randomly assembled nerve-network. EEG Clin. Neurophysiol. 2, 471–482.CrossRefGoogle ScholarPubMed
Ballard, D. H. (1986). Cortical connections and parallel processing. Behav. Brain Sci. 9, 67–120.CrossRefGoogle Scholar
Ballard, D. H. (1987). Modular learning in neural networks. (In preparation).Google Scholar
Ballard, D. H., Hinton, G. E. & Sejnowski, T. J. (1983). Parallel visual computation. Nature 306, 21–26.CrossRefGoogle ScholarPubMed
Barlow, H. B. (1972). Single units and sensation: a neuron doctrine for perceptual psychology? Perception 1, 371–394.CrossRefGoogle ScholarPubMed
Barlow, H. B. & Levick, W. R. (1965). The mechanism of directionally sensitive units in rabbit's retina. J. Physiol., Lond. 178, 477–504.CrossRefGoogle Scholar
Batchelor, G. K. (1967). An Introduction to Fluid Dynamics. Cambridge: Cambridge University Press.Google Scholar
Baum, E. B., Moody, J. & Wilczke, F. (1987). Internal representations for associative memory. Inst. for theor. Phys. University of California.Google Scholar
Beurle, R. L. (1956). Properties of a mass of cells capable of regenerating pulses. Phil. Trans. R. Soc. Lond. B240, 55–94.Google Scholar
Blakemore, C. & Cooper, G. F. (1970). Development of the brain depends on the visual environment. Nature 228, 477–478.CrossRefGoogle ScholarPubMed
Bliss, T. V. P. & Lømo, T. (1973). Long-lasting potentiation of synaptic transmission in the dentate area of the unaesthetized rabbit following stimulation of the perforant path. J. Physiol., Lond. 232, 331–356.CrossRefGoogle Scholar
Blomfield, S. & Marr, D. (1970). How the cerebellum may be used. Nature 227, 1224–1228.CrossRefGoogle ScholarPubMed
Bradley, G. W., Euler, C. V., Martilla, I. & Roos, B. (1975). A model of the central and reflex inhibition of inspiration in the cat. Biol. Cybern. 19, 105–116.CrossRefGoogle Scholar
Brindley, G. S. (1967). The classification of modifiable synapses and their use in models for conditioning. Proc. R. Soc. Lond. B168, 361–376.Google Scholar
Butz, E. & Cowan, J. D. (1974). Transient potentials in systems of arbitrary dendritic geometry. Biophys. J. 14, 661–689.CrossRefGoogle Scholar
Caianiello, E. R. (1961). Outline of a theory of thought processes and thinking machines. J. theor. Biol. 1, 204–235.CrossRefGoogle ScholarPubMed
Cajal, S. & Ramon, Y. (1908). Histology du système nerveux. Madrid: CSIC, (reprinted 1972).Google Scholar
Campbell, F. W. & Robson, J. G. (1968). Application of Fourier analysis to the visibility of gratings. J. Physiol., Lond. 197, 551–566.CrossRefGoogle Scholar
Cannon, S. C.Robinson, D. A. & Shamma, S. (1983). A proposed neural network for the integrator of the oculomotor system. Biol. Cybern. 49, 127–136.CrossRefGoogle ScholarPubMed
Carpenter, G. A. & Crossberg, S. (1986). Neural dynamics of catagory learning and recognition: attention, memory consolidation, and amnesia. In: Brain Structure (ed. Davis, J., Newburgh, R. & Wegman, E.). AAAS Symposium Series.Google Scholar
Cowan, J. D. (1965). The problem of organismic reliability. Prog. Brain Res. 17, 9–63.CrossRefGoogle ScholarPubMed
Cowan, J. D. (1968). Statistical mechanics of nervous nets. In: Neural Networks (ed. Caianiello, E. R.), pp. 181–188, Berlin: Springer-Verlag.CrossRefGoogle Scholar
Cragg, B. G. & Temperley, H. N. V. (1954). The organisation of neurones: a cooperative analogy. EEG Clin. Neurophysiol. 6, 85–92.CrossRefGoogle ScholarPubMed
Cragg, B. G. & Temperley, H. N. V. (1955). Memory: the analogy with ferromagnetic hysteresis. Brain 78, 304–316.CrossRefGoogle ScholarPubMed
Craik, K. J. W. (1943). The Nature of Explanation. Cambridge: Cambridge University Press.Google Scholar
Crick, F. H. C. (1984). Function of the thalamic reticular complex: the searchlight hypothesis. Proc. natn. Acad. Sci. U.S.A. 81, 4586–4590.CrossRefGoogle ScholarPubMed
Crossley, J. N., Ash, C. J., Brickhill, C. J., Stillwell, J. C. & Williams, N. H. (1972). What is Mathematical Logic? Oxford: Oxford University Press.Google Scholar
Denker, J. S. (ed.)(1986). Neural Networks for Computing, 151.Proc. AIP Conf., AIP.Google Scholar
Dev, P. (1975). Perception of depth surfaces in random-dot stereograms. Int. J. Man-Mach. Stud. 7, 511–528.CrossRefGoogle Scholar
Durbin, R. & Willshaw, D. J. (1987). An analogue approach to the travelling salesman problem using an elastic net method. Nature 326, 689–691.CrossRefGoogle Scholar
Easter, S. S. Jr., Purves, D., Rakic, P. & Spitzer, N. C. (1985). The changing view of neural specificity. Science 230, 507–510.CrossRefGoogle ScholarPubMed
Eccles, J. C. (1957). The Physiology of Nerve Cells, Baltimore: Johns Hopkins University Press.Google Scholar
Eccles, J. C. (1964). The Physiology of Synapses. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Eccles, J. C., Ito, M. & Szentagothai, J. (1967). The Cerebellum as a Neuronal Machine. New York: Springer-Verlag.CrossRefGoogle Scholar
Edelman, G. M. (1978). Group selection and phasic reentrant signalling: a theory of higher brain function. In: The Mindful Brain: Cortical Organization and the Group-selective Theory of Higher Brain Function (ed. Edelman, G. M. & Mountcastle, V. B.), Cambridge, Mass.: MIT Press.Google Scholar
Edelman, G. M. (1984). A. Rev. Neurosci. 7, 339.CrossRefGoogle Scholar
Edwards, S. F. & Anderson, P. W. (1975). Theory of spin-glasses. I. J. Phys. F: Metal Phys. 5, 965.CrossRefGoogle Scholar
Edwards, S. F. & Anderson, P. W. (1976). Theory of spin-glasses. II. J. Phys. F: Metal Phys. 6, 1927.CrossRefGoogle Scholar
Ermentrout, G. B. & Cowan, J. D. (1979 a). Temporal oscillations in neural networks. J. Math. Biol. 7, 265–280.CrossRefGoogle Scholar
Ermentrout, G. B. & Cowan, J. D. (1979 b). A mathematical theory of visual hallucination patterns. Biol. Cybernet. 34, 137–150.CrossRefGoogle ScholarPubMed
Farley, B. G. & Clark, W. A. (1961). Activity in networks of neuron-like elements. In: Information Theory, vol. 4, (ed. Cherry, E. C.) 242–251. London: Butterworths.Google Scholar
Feldman, J. L. & Cowan, J. D. (1975 a). Large-scale activity in neural nets. I. Theory with application to motoneuron pool responses. Biol. Cybern. 17, 29–38.CrossRefGoogle ScholarPubMed
Feldman, J. L. & Cowan, J. D. (1975 b). Large-scale activity in neural nets. II. A model for the brainstem respiratory oscillator. Biol. Cybern. 17, 39–51.CrossRefGoogle Scholar
Fraser, S. E. & Hunt, R. K. (1980). Retinotectal specificity: models and experiments in search of a mapping function. A. Rev. Neurosci. 3, 319–352.CrossRefGoogle ScholarPubMed
Frégnac, Y. & Imbert, M. (1978). Early development of visual cortical cells in normal and dark-reared kittens: relationship between orientational selectivity and ocular dominance. J. Physiol., Lond. 278, 27–44.CrossRefGoogle ScholarPubMed
Friesen, W. O. & Stent, G. S. (1978). Neural circuits for generating rhythmic movements. A. Rev. Biophys. Bioengng 7, 37–61.CrossRefGoogle ScholarPubMed
Gabor, D. (1954). Communication theory and cybernetics. IRE Trans. CT-1, (4), 19–31.Google Scholar
Gabor, D. (1969). Associative holographic memories. IBM J. Res. Dev. 13, 156–159.CrossRefGoogle Scholar
Gaze, R. M. (1970). The Formation of Nerve Connections. New York: Academic Press.Google Scholar
Georgopoulos, A. P., Schwartz, A. B. & Kettner, R. E. (1986). Neuronal population coding of movement direction. Science 233, 1416–1419.CrossRefGoogle ScholarPubMed
Griffiths, J. S. (1965). A field theory of neural nets II. Bull. Math. Biophys. 27, 187–195.CrossRefGoogle Scholar
Griffiths, J. S. (1965). 27, 187–195.CrossRefGoogle Scholar
Griffiths, J. S. (1967). A View of the Brain. Oxford: Clarendon Press.Google Scholar
Grossberg, S. (1973). Contour enhancement, short term memory, and constancies in reverberating neural networks. Studies in Appl. Math. LII (3), 213–257.CrossRefGoogle Scholar
Harth, E. M., Csermely, T. J., Beek, B. & Lindsay, R. D. (1970). Brain functions and neural dynamics. J. theor. Biol. 26, 93–120.CrossRefGoogle ScholarPubMed
Hassenstein, B. & Reichardt, W. (1956). Systemtheorische analyse der zeitereihenfolgen- und vordeichenauswerung bei der bewegunsperzeption des Rüsselkäfers Chlorophanus. Z. naturforsch. 11 b, 513–524.CrossRefGoogle Scholar
Hayek, F. A. (1952). The Sensory Order. Chicago: University of Chicago Press.Google Scholar
Hebb, D. O. (1964). The Organization of Behavior. New York: Wiley.Google Scholar
Hinton, G. E. (1984). Distributed representations. Tech. Rep. CMU-CS, 84–157.Google Scholar
Hinton, G. E. & Senjnowski, T. J. (1983). Optimal perceptual inference.Proc. IEEE Computer Soc. Conf. Computer Vision and Pattern Recognition.Washington, DC pp. 448–453.Google Scholar
Hirose, G. & Jacobson, M. (1979). Clonal organization of the central nervous system of the frog. I. Clones stemming from individual blastomeres of the 16-cell and earlier stages. Dev. Biol. 71, 191–202.CrossRefGoogle ScholarPubMed
Hodgkin, A. L. & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol., Lond. 117, 500–544.CrossRefGoogle ScholarPubMed
Hoffman, K. P. (1973). Conduction velocity in pathways from retina to superior colliculus in the cat: a correlation with receptive field properties. J. Neurophysiol. 36, 409–424.CrossRefGoogle Scholar
Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press.Google Scholar
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proc. natn. Acad. Sci. U.S.A. 79, 2554–2558.CrossRefGoogle ScholarPubMed
Hopfield, J. J. (1984). Neurons with graded response have collective computational properties like those of two-state neurons. Proc. natn. Acad. Sci. U.S.A. 81, 3088–3092.CrossRefGoogle ScholarPubMed
Hopfield, J. J. & Tank, D. W. (1985). ‘Neural’ computation and constraint satisfaction problems and the traveling salesman. Biol. Cybern. 55, 141–152.CrossRefGoogle Scholar
Horn, B. K. P. (1975). Obtaining shape from shading information. In: The Psychology of Computer Vision (ed. Winston, P. H.), pp. 115–155. New York: McGraw-Hill.Google Scholar
Hubel, D. H. & Wiesel, T. N. (1962). Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J. Physiol., Lon. 160, 106–154.CrossRefGoogle ScholarPubMed
Hubel, D. H. & Wiesel, T. N. (1977). Functional architecture of macaque monkey visual cortex. Proc. R. Soc. Lond. B198, 1–59.Google Scholar
Ito, M. (1984). The Cerebellum and Neural Control. New York: Raven.Google Scholar
Jacobson, M. (1978). Developmental Neurobiology. New York: Plenum Press.CrossRefGoogle Scholar
Jacobson, M. & Hirose, G. (1981). Clonal organization of the central nervous system of the frog. II. Clones stemming from individual blastomeres of the 32- and 64-cell stages. J. Neurosci. 1, 271–284.CrossRefGoogle ScholarPubMed
Jardine, N. & Sibson, R. (1971). Mathematical Taxonomy. New York: Wiley.Google Scholar
Julesz, B. (1960). Binocular depth perception of computer generated patterns. Bell Sys. Tech. J. 39, 1125–1162.CrossRefGoogle Scholar
Kanerva, P. (1988). Self-propagating Search: A Unified Theory of Memory. Bradford Books/MIT Press (in Press).Google Scholar
Kirkpatrick, S., Gelatt, C. D. Jr. & Vecchi, M. P. (1983). Optimization by simulated annealing. Science 229, 671–679.CrossRefGoogle Scholar
Koch, C. & Poggio, T. (1983). A theoretical analysis of electrical properties of spines. Proc. R. Soc. Lond. B218, 455–471.Google Scholar
Kohonen, T. (1977). Associative Memory – a System–Theoretical Approach. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biol. Cybern. 43, 59–70.CrossRefGoogle Scholar
Kohonen, T. (1984). Self-organization and Associative Memory. Berlin: Springer-Verlag.Google Scholar
Kristan, W. B. Jr., Stent, G. S. & Ort, C. A. (1974). Neuronal control of swimming in the medicinal leach, I: Dynamkf of the swimming rhythm, J. comp. Physiol. 94, 97–119.CrossRefGoogle Scholar
Land, E. H. (1977). The Retinex theory of color vision. Scient. Am 237, 108–128.CrossRefGoogle ScholarPubMed
Land, E. H. & McCann, J. J. (1971). Lightness and Retinex theory, J. opt. Soc. Am. 61, 1–11.CrossRefGoogle ScholarPubMed
Landahl, H. D., McCulloch, W. S. & Pitts, W. (1943). A statistical consequence of the logical calculus of nervous nets. Bull. Math. Biophys. 5, 135–137.CrossRefGoogle Scholar
Lapedes, A. & Farber, R. (1986). A self-optimizing, nonsymmetrical neural net for content addressable memory and pattern recognition. Physica D22, 247–259.Google Scholar
Lapedes, A. & Farber, R. (1987). Nonlinear signal processing using neural networks: prediction and system modelling. LA-UR-87–2662, Los Alamos National Laboratory Preprint.Google Scholar
Lashley, K. S. (1942). Persistent problems in the evolution of mind. Q. Rev. Biol. 24, 28–42.CrossRefGoogle Scholar
Lashley, K. S. (1950). In search of the engram. Symp. Soc. exp. Biol. 4, 454–482.Google Scholar
Lawler, E. L., Lenstra, J. K., Rinooy Kahn, A. H. G. & Shmoys, D. B. (eds) (1985). The Traveling Salesman Problem. New York: Wiley.Google Scholar
Le Cun, Y. (1985). A learning scheme for asymmetric threshold networks. Proc. Cognitiva 85, 599–607.Google Scholar
Lee, Y. C., Doolen, G., Chen, H. H., Sun, G. Z., Maxwell, T., Lee, H. Y. & Giles, C. L. (1986). Machine learning using a higher order correlation network. Physica D22, 276–289.Google Scholar
Levick, W. R. & Thibos, L. N. (1980). Orientation bias of cat retinal ganglion cells. Nature 268, 389–390.CrossRefGoogle Scholar
Lin, S. & Kernighan, B. W. (1973). An algorithm for the TSP problem. Oper. Res. 21, 498–522.CrossRefGoogle Scholar
Linsker, R. (1986 a). From basic network principles to neural architecture: emergence of spatial-opponent cells. Proc. natn. Acad. Sci. U.S.A. 83, 7508–7512.CrossRefGoogle ScholarPubMed
Linsker, R. (1986 b). From basic network principles to neural architecture: emergence of orientation-selective cells. Proc. natn. Acad. Sci. U.S.A. 83, 8390–8394.CrossRefGoogle Scholar
Linsker, R. (1986 c). From basic network principles to neural architecture: emergence of orientation columns. Proc. natn. Acad. Sci. U.S.A. 83, 8779–8783.CrossRefGoogle ScholarPubMed
Lippmann, R. P. (1987). An introduction to computing with neural nets. IEEE ASSP Mag. 4, 4–22.CrossRefGoogle Scholar
Lippold, O. (1973). The Origin of the Alpha Rhythm. Edinburgh, London: Churchill Livingstone.Google Scholar
Little, W. A. (1974). The existence of persistent states in the brain. Math. Biosci. 19, 101–120.CrossRefGoogle Scholar
Little, W. A. & Shaw, G. L. (1975). A statistical theory of short and long-term memory. Beh. Biol. 14, 115–133.CrossRefGoogle ScholarPubMed
Llinás, R. (ed.) (1969). Neurobiology of Cerebellar Evolution and Development. Chicago: American Medical Association.Google Scholar
Mackay, D. M. (1953). Some experiments on the perception of patterns modulated at the alpha frequency. EEG Clin. Neurophysiol. 5, 559–562.CrossRefGoogle ScholarPubMed
Mackay, D. M. (1954). On comparing the brain with machines. Am. Scient. 42, 261–268.Google Scholar
Malsburg, Ch. v. d. (1973). Self-organization of orientation sensitive cells in the striate cortex. Kybernetik 14, 85–100.CrossRefGoogle ScholarPubMed
Malsburg, Ch. v. d. (1985). Nervous structures with dynamical links. Ber. Bunsenges. phys. Chem. 89, 703–710.CrossRefGoogle Scholar
Malsburg, Ch. v. d. & Cowan, J. D. (1982). Outline of a theory for the ontogenesis of iso-orientation columns in visual cortex. Biol. Cybern. 45, 49–56.CrossRefGoogle Scholar
Marr, D. (1969). A theory of cerebellar cortex. J. Physiol., Lond. 202, 437–470.CrossRefGoogle ScholarPubMed
Marr, D. (1970). A theory for cerebral neocortex. Proc. R. Soc. Lond. B176, 161–234.Google Scholar
Marr, D. (1971). Simple memory: a theory for archicortex. Phil. Trans. R. Soc. Lond. B262, 23–81.Google Scholar
Marr, D. (1974). The computation of lightness by the primate retina. Vision Res. 14, 1377–1388.CrossRefGoogle ScholarPubMed
Marr, D. & Hildreth, E. (1980). Theory of edge detection. Proc. R. Soc. Lond. B207, 187–217.Google Scholar
Marr, D. & Poggio, T. (1976). Cooperative computation of stereo disparity. Science 194, 283–287.CrossRefGoogle ScholarPubMed
McCulloch, W. S. (1964). A historical introduction to the postulational foundations of experimental epistemology. In: Cross-cultural Understanding: Epistemology in Anthropology (ed. Northrop, F. S. C. & Livingstone, H. H.), pp. 180–193. New York: Harper & Row.Google Scholar
McCulloch, W. S. & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133.CrossRefGoogle Scholar
McEliece, R. J., Posner, E. C, Rodemich, E. R. & Venkatesh, S. S. (1987). The capacity of the Hopfield associative memory. IEEE Trans. Inf. Theory 1, 33–45.Google Scholar
McIlwain, J. T. (1976). Large receptive fields and spatial transformations in the visual system. Int. Rev. Physiol., Neurophysiol. II 10, 223–248.Google Scholar
Meir, R. & Domany, E. (1987). Storing information in a layered feed forward Hopfield net. Phys. Rev. Lett. 59, 359–363.CrossRefGoogle Scholar
Merzenich, M. M. & Kaas, J. H. (1982). Reorganization of somatosensory cortex in mammals following peripheral nerve injury. Trends in Neuroscience 5, 434–436.CrossRefGoogle Scholar
Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, M. & Teller, E. (1953). Equations of state calculation by fast computing machines. J. chem. Phys. 21, 1087–1092.CrossRefGoogle Scholar
Meyer, R. L. (1982). Ordering of retinotectal connections: a multivariate operational analysis. Curr. Top. Developmental Biol. 17, 101–145.CrossRefGoogle ScholarPubMed
Minsky, M. & Papert, S. (1969). Perceptrons: An Introduction to Computational Geometry. Cambridge, Mass: MIT Press.Google Scholar
Mjolsness, E. & Sharp, D. H. (1986). A preliminary analysis of recursively generated networks. In: Neural Networks for Computing (ed. Denker, J. S.Mjolsness, E.Sharp, D. H. & Alpert, B. K.). Proc. AIP Conf., 151.Google Scholar
Mjolsness, E. & Sharp, D. H. (1987). Recursively Generated Networks. Yale University.Google Scholar
Mjolsness, E. (1987). Control of Attention of Neural Networks. Yale University.Google Scholar
Nass, M. M. & Cooper, L. N. (1975). A thoery for the development of feature detecting cells in visual cortex. Biol. Cybern. 19, 1–18.CrossRefGoogle Scholar
Neumann, J. von (1951). The general and logical theory of automata. In: Cerebral Mechanisms in Behavior (ed. Jeffress, L. A.), the Hixon Symposium. New York: Wiley.Google Scholar
Neumann, J. von (1956). Probabilistic logics and the snythesis of reliable organisms from unreliable components. Automata Studies (ed. Shannon, C. E. & McCarthy, J.), pp. 43–98. Princeton University Press.Google Scholar
North, G. (1987). A celebration of connectionism. Nature 328, 107.CrossRefGoogle ScholarPubMed
Novikoff, A. (1963). On convergence proofs for Perceptrons. Symp. on Mathematical Theory of Automata (ed. Fox, J.), pp. 615–622. New York: Polytechnic Press.Google Scholar
O'Keefe, J. & Nadel, L. (1978). The Hippocampus as a Cognitive Map. Oxford: Clarendon Press.Google Scholar
Parker, D. B. (1986). A comparison of algorithms for neuron-like cells. In: Neural networks for computing (ed. Denker, J. S.). Proc. AIP Conf. 151.Google Scholar
Pellionisz, A. & Llinás, R. (1980). Tensorial approach to the geometry of brain function: cerebellar coordination via a metric tensor. Neurosci. 5, 1125–1136.CrossRefGoogle Scholar
Pérez, R., Glass, L. & Schlaer, R. (1975). Development of specificity in the cat visual cortex, J. Math. Biol. 1, 275–288.CrossRefGoogle ScholarPubMed
Pierce, W. H. (1965). Failure Tolerant Computer Design. New York: Academic Press.Google Scholar
Pitts, W. & McCulloch, W. S. (1947). How we know universals: the perception of auditory and visual forms. Bull. Math. Biophys. 9, 127–147.CrossRefGoogle ScholarPubMed
Plaut, D. C., Nowlan, S. J. & Hinton, G. E. (1986). Experiments on learning by back-propagation. Tech. Rep., Carnegie Mellon University.Google Scholar
Polyak, S. A. (1975). The Vertebrate Visual Pathway. University of Chicago Press.Google Scholar
Prestige, M. & Willshaw, D. J. (1975). On a role for competition in the formation of patterned neural connections. Proc. R. Soc. Lond. B190, 77–98.Google Scholar
Rall, W. (1955). A statistical theory of monosynaptic input–output relations. J. cell. comp. Physiol. 46, 373–411.CrossRefGoogle ScholarPubMed
Rall, W. (1977). Core conductor theory and cable properties of neurons. In: Handbook of Physiology – The Nervous System. I. Cellular Biology of Neurons. (ed. Kandel, E. R. & Geiger, S.), vol. 3, pp. 39–97.Google Scholar
Rall, W. & Hunt, C. C. (1956). Analysis of reflex variability in terms of partially correlated excitability fluctuation in a population of motoneurons. J. gen. Physiol. 39, 397–422.CrossRefGoogle Scholar
Rashevsky, N. (1938). Mathematical Biophysics. University of Chicago Press.Google Scholar
Reeke, G. N. Jr. & Edelman, G. M. (1988). Real brains and artificial intelligence. Daedalus, Boston, Mass.Google Scholar
Reichl, L. E. (1980). A Model Course in Statistical Physics. University of Texas Press.Google Scholar
Reiss, R. F. (1964). A theory of resonant networks. In: Neural theory and modeling (ed. Reiss, R. F.), pp. 105–137. Stanford University Press.Google Scholar
Rescorla, R. A. & Wagner, A. R. (1972). A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement and nonreinforcement. In: Classical conditioning II (eds. Black, A. H. & Prokasy, W. F.), pp. 64–99. New York: Appleton-Century-Crofts.Google Scholar
Rinzel, J. & Rall, W. (1974). Transient response in a dendritic neuron model for current injected at one branch. Biophys. J. 14, 759–790.CrossRefGoogle Scholar
Robinson, D. A. (1981). The use of control systems analysis in the neurophysiology of eye movements. Ann. Rev. Neurosci. 4, 463–503.CrossRefGoogle ScholarPubMed
Robinson, D. A. (1982). The use of matrices in analyzing the three-dimensional behavior of the vestibulo-ocular reflex. Biol. Cybern. 46, 53–66.CrossRefGoogle ScholarPubMed
Rosenblatt, F. (1958). The Perceptron, a probabilistic model for information storage and organization in the brain. Psychol. Rev. 62, 386–408.CrossRefGoogle Scholar
Rosenblatt, F. (1961). Principles of Neurodynamics Washington DC., Spartan Books.Google Scholar
Rosenbleuth, A., Wiener, N., Pitts, W. & Garcia Ramos, J. (1949). A statistical analysis of synaptic excitation. J. cell. comp. Physiol. 34, 173–205.CrossRefGoogle Scholar
Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986 a). Learning internal representations by error propagation. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition. 1, Foundations. (ed. Rumelhart, D. E. & McClelland, J. L.). Cambridge, Mass: MIT Press.CrossRefGoogle Scholar
Rumelhart, D. E. & McClelland, J. L. (1986 b). Learning representations by back–propagating errors. Nature 323, 533–536.CrossRefGoogle Scholar
Santini, M. (ed.) (1975). Golgi Centennial Symposium. New York: Raven Press.Google Scholar
Schmidt, J. T. (ed.) (1985). Activity-dependent synaptic changes. Cellular and Molec. Biol. 5, 1–2.Google Scholar
Schmitt, F. O., Worden, F. G., Adelman, G. & Dennis, S. G. (1981). The Organization of the Cerebral Cortex. Cambridge, Mass: MIT Press.Google Scholar
Sejnowski, T. & Rosenberg, C. R. (1986). NETtalk, a parallel network that learns to read aloud. Tech. Rep., John Hopkins University.Google Scholar
Sherrington, C. S. (1906). The Integrative Action of the Nervous System. Yale University Press.Google Scholar
Sherrington, D. & Kirkpatrick, S. (1975). Spin Glasses. Phys. Rev. Lett. 35, 1972.CrossRefGoogle Scholar
Sholl, D. A. (1956). The Organization of the Cerebral Cortex. London: Methuen.Google ScholarPubMed
Southwell, R. V. (1946). Relaxation Methods in Theoretical Phsyics. Oxford University Press.Google Scholar
Spain, B. (1963). Tensor Calculus. New York: Interscience.Google Scholar
Sperry, R. W. (1941). The effect of crossing nerves to antagonistic muscles in the hind limbs of the rat. J. comp. Neurol. 75, 1–19.CrossRefGoogle Scholar
Spitzer, N. C. (ed) (1982). Neuronal Development. New York: Plenum.CrossRefGoogle Scholar
Steinbuch, K. (1961). Die lernmatrix. Kybernetik 1, 36–45.CrossRefGoogle Scholar
Stornetta, W. S. & Huberman, B. A. (1987). An improved three-layer back-propagation algorithm. Xerox Corp., Palo Alto, Preprint.Google Scholar
Strick, P. L. (1985) Science 229, 547.CrossRefGoogle Scholar
Strumwasser, F. (1967). Types of information stored in single neurons. In: Invertebrate Neurvous Systems. (ed. Wiersma, C. A. G.), pp. 291–320. University of Chicago Press.Google Scholar
Stryker, M. P. & Sherk, H. (1975). Modification of cortical orientation selectivity in the cat by restricting visual experience: a re-examination. Science 190, 904–905.CrossRefGoogle Scholar
Swindale, N. V. (1980). A model for the formation of ocular dominance stripes. Proc. R. Soc. Lond. B208, 243–264.Google Scholar
Swindale, N. V. (1982). A model for the formation of orientation columns. Proc. R. Soc. Lond. B215, 211–230.Google Scholar
Szekely, G. (1965). Logical network for controlling limb movements in urodela. Acta physiol. hung. 27, 285–302.Google Scholar
Szentágothai, J. & Arbib, M. A. (1974). Conceptual models of neural organization. Neurosciences Research Program Bulletin 12, 3.Google ScholarPubMed
Taylor, J. (1932). Selected Writings of John Hughlings Jackson. London: Hodder & Stoughton. Reprinted 1958 New York.Google Scholar
Taylor, W. K. (1956). Electrical simulation of some nervous system functional activities. Information Theory, vol. 3 (ed. Cherry, E. C.), pp. 314–328. London: Butterworths.Google Scholar
Taylor, W. K. (1964). Cortico-thalamic organization and memory. Proc. R. Soc. Lond. B159, 466–478.Google Scholar
Torre, V. & Poggio, T. (1978). A synaptic mechanism possibly underlying directional selectivity to motion. Proc. R. Soc. Lond. B202, 409–416.Google Scholar
Turing, A. M. (1937). On computable numbers with an application to the Entscheidüngsproblem, I. Proc. Lond. math. Soc. XLII, 230–265.CrossRefGoogle Scholar
Turing, A. M. (1938). On computable numbers with an application to the Entscheidüngsproblem, II. Proc. Lond. math. Soc. XLIII, 544–546.CrossRefGoogle Scholar
Uhr, L. (ed.) (1966). Pattern Recognition. New York: Wiley.Google Scholar
Uttley, A. M. (1954). The classification of signals in the nervous system. EEG Clin. Neurophysiol. 6, 479–494.CrossRefGoogle ScholarPubMed
Waltz, D. L. (1988). The prospects for building truly intelligent machines. Daedalus, Boston, Mass.Google Scholar
Whitelaw, V. A. & Cowan, J. D. (1981). Specificity and plasticity of retinotectal connections: a computational model. J. Neurosci. 1, 1369–1387.CrossRefGoogle ScholarPubMed
Widrow, B. & Hoff, M. E. (1960). Adaptive switching circuits. WESCON convention record IV, 96–104.Google Scholar
Wiener, N. (1948). Cybernetics, or Control and Communication in the Animal and the Machine. New York: Wiley.Google ScholarPubMed
Willshaw, D. J., Buneman, O. P. & Longuet-Higgins, H. C. (1969). Non-holographic associative memory. Nature 222, 960–962.CrossRefGoogle ScholarPubMed
Willshaw, D. J. & Malsburg, Ch. v. d. (1976). How patterned neural connections can be set up by self-organization. Proc. R. Soc. Lond. B194, 431–445.Google Scholar
Willshaw, D. J. & Malsburg, Ch. v. d. (1979). A marker induction mechanism for the establishment of ordered neural mappings: Its application to the retinotectal problem. Phil. Trans. R. Soc. Lond. B287, 203–243.Google Scholar
Wilson, H. R. & Bergen, J. R. (1979). A four mechanism model for threshold spatial vision. Vision Res. 19, 19–32.CrossRefGoogle ScholarPubMed
Wilson, H. R. & Cowan, J. D. (1972). Excitatory and inhibitory interactions in localized populations of model neurons. Biophys. J. 12, 1–24.CrossRefGoogle ScholarPubMed
Wilson, H. R. & Cowan, J. D. (1973). A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue. Kybernetik 13, 55–80.CrossRefGoogle ScholarPubMed
Winograd, S. & Cowan, J. D. (1963). Reliable Computation in the Presence of Noise. Cambridge, Mass: MIT Press.Google Scholar
Winston, P. H. (ed.) (1975). The Psychology of Computer Vision. New York: McGraw-Hill.Google Scholar
Wolpert, L. (1969). Positional information and the spatial pattern of cellular differentiation. J. theor. Biol. 25, 1–47.CrossRefGoogle ScholarPubMed