Perspectives in memory research

Perspectives in Memory Research integrates current knowledge about memory from both the brain and cognitive sciences. The existing literature on memory is vast, attesting to the longstanding fascination with commitment to ongoing research at all levels and from widely varying points of view. This exciting collection presents new empirical data and theories concerning the formation, the retrieval, and the integration of memory processes and, to some extent, tries to identify how studying memory processes might help augment learning and training procedures.The chapters on the neurobiologic approach include one on brain function at the molecular level, by Ira Black; one on structure function considerations in the study of memory in cortical networks, by Gary Lynch; one on basic circuits for cortical organization, by Gordon Shepherd; and one on connectionist models of learning and memory, by Terrence Sejnowski.The psychological dimensions are probed by Marta Kutas, who reports on tracking memory capacity in the human brain; William Hirst, who discusses the improvement of memory; and Stephen Kosslyn, who considers imagery in learning.Michael Gazzaniga and William Hirst conclude with an essay on present and future memory research and its applications. Michael Gazzaniga is director of the Division of Cognitive Neuroscience at Cornell University Medical College, president of the Cognitive Neuroscience Institute, and an adjunct professor at the Dartmouth Medical School. A Bradford Book.

[1]  D. L. Hintzman.  Repetition and Memory1 , 1976 .

[2]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory, Third Edition , 1989, Springer Series in Information Sciences.

[3]  Jerome A. Feldman,et al.  Neural Representation of Conceptual Knowledge. , 1986 .

[4]  David Zipser,et al.  Feature Discovery by Competive Learning , 1986, Cogn. Sci..

[5]  F. H. C. Crick,et al.  Certain aspects of the anatomy and physiology of the cerebral cortex , 1986 .

[6]  D B Bounds.  Numerical simulations of Boltzmann Machines , 1987 .

[7]  David B. Parker,et al.  A comparison of algorithms for neuron-like cells , 1987 .

[8]  Terrence J. Sejnowski,et al.  Parallel Networks that Learn to Pronounce English Text , 1987, Complex Syst..

[9]  Elie Bienenstock,et al.  A neural network for the retrieval of superimposed connection patterns , 1987 .

[10]  S. Petersen,et al.  Visual Topography and Function: Cortical Visual Areas in the Owl Monkey , 1981 .

[11]  John R. Anderson.  Acquisition of cognitive skill. , 1982 .

[12]  Stephen Grossberg,et al.  Absolute stability of global pattern formation and parallel memory storage by competitive neural networks , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[13]  E. Adrian,et al.  The physical background of perception , 1947 .

[14]  Bernard Widrow,et al.  Adaptive switching circuits , 1988 .

[15]  L. Squire.  Mechanisms of memory. , 1986, Lancet.

[16]  John H. R. Maunsell,et al.  Hierarchical organization and functional streams in the visual cortex , 1983, Trends in Neurosciences.

[17]  L. R. Peterson,et al.  Effect of spacing presentations on retention of a paired associate over short intervals. , 1963, Journal of experimental psychology.

[18]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[19]  James A. Reggia.  Virtual Lateral Inhibition in Parallel Activation Models of Associative Memory , 1985, IJCAI.

[20]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[21]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Geoffrey E. Hinton,et al.  Distributed Representations , 1986, The Philosophy of Artificial Intelligence.

[23]  D. Norman.  Learning and Memory , 1982 .

[24]  A. Glenberg.  Monotonic and nonmonotonic lag effects in paired-associate and recognition memory paradigms , 1976 .

[25]  Richard F. Thompson,et al.  Modeling the Neural Substrates of Associative Learning and Memory: A Computational Approach , 1987 .

[26]  Terrence J. Sejnowski,et al.  Open questions about computation in cerebral cortex , 1986 .

[27]  John Lyons,et al.  Introduction to Theoretical Linguistics , 1971 .

[28]  Paul Smolensky,et al.  Schema Selection and Stochastic Inference in Modular Environments , 1983, AAAI.

[29]  Geoffrey E. Hinton,et al.  OPTIMAL PERCEPTUAL INFERENCE , 1983 .

[30]  Douglas L. Hintzman,et al.  Theoretical implications of the spacing effect. , 1974 .

[31]  Klaus Schulten,et al.  Stochastic spin models for pattern recognition , 1987 .

[32]  Paul Smolensky,et al.  Information processing in dynamical systems: foundations of harmony theory , 1986 .

[33]  Robert L. Mercer,et al.  An information theoretic approach to the automatic determination of phonemic baseforms , 1984, ICASSP.

[34]  Geoffrey E. Hinton,et al.  Parallel visual computation , 1983, Nature.

[35]  A. Tversky,et al.  Subjective Probability: A Judgment of Representativeness , 1972 .

[36]  E. Bienenstock,et al.  Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[37]  Geoffrey E. Hinton,et al.  Massively Parallel Architectures for AI: NETL, Thistle, and Boltzmann Machines , 1983, AAAI.

[38]  T. Sejnowski.  Statistical constraints on synaptic plasticity. , 1977, Journal of theoretical biology.

[39]  T. D. Harrison,et al.  Boltzmann machines for speech recognition , 1986 .

[40]  Terrence J. Sejnowski,et al.  NETtalk: a parallel network that learns to read aloud , 1988 .

[41]  David B. Pisoni,et al.  Text-to-speech: the mitalk system , 1987 .

[42]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[43]  H. C. LONGUET-HIGGINS,et al.  Holographic Model of Temporal Recall , 1968, Nature.

[44]  A. Harry Klopf,et al.  A drive-reinforcement model of single neuron function , 1987 .

[45]  B. McNaughton,et al.  Hippocampal synaptic enhancement and information storage within a distributed memory system , 1987, Trends in Neurosciences.

[46]  A. Gamba,et al.  Further experiments with PAPA , 1961 .

[47]  Geoffrey E. Hinton,et al.  Learning symmetry groups with hidden units: beyond the perceptron , 1986 .

[48]  G. Bower,et al.  From conditioning to category learning: an adaptive network model. , 1988 .

[49]  G. Lynch,et al.  Synapses, circuits, and the beginnings of memory , 1986 .

[50]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[51]  J. Hopfield,et al.  Computing with neural circuits: a model. , 1986, Science.

[52]  Barak A. Pearlmutter,et al.  G-maximization: An unsupervised learning procedure for discovering regularities , 1987 .

[53]  A. A. Mullin,et al.  Principles of neurodynamics , 1962 .

[54]  R. Sperber.  Developmental changes in effects of spacing of trials in retardate discrimination learning and memory. , 1974 .

[55]  L. Jacoby.  On interpreting the effects of repetition: Solving a problem versus remembering a solution , 1978 .

[56]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[57]  Kenneth Ward Church.  Stress assignment in letter‐to‐sound rules for speech synthesis , 1985 .

[58]  S Dehaene,et al.  Spin glass model of learning by selection. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[59]  R. Rescorla,et al.  A theory of Pavlovian conditioning : Variations in the effectiveness of reinforcement and nonreinforcement , 1972 .

[60]  Geoffrey E. Hinton,et al.  Separating Figure from Ground with a Parallel Network , 1986, Perception.

[61]  L. Finkel,et al.  Interaction of synaptic modification rules within populations of neurons. , 1985, Proceedings of the National Academy of Sciences of the United States of America.

[62]  S. Kaplan.  The Physiology of Thought , 1950 .

[63]  Jerome A. Feldman,et al.  Connectionist Models and Their Properties , 1982, Cogn. Sci..

[64]  James L. McClelland,et al.  On learning the past-tenses of English verbs: implicit rules or parallel distributed processing , 1986 .

[65]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[66]  T. Sejnowski,et al.  Storing covariance with nonlinearly interacting neurons , 1977, Journal of mathematical biology.

[67]  S. Kelso,et al.  Hebbian synapses in hippocampus. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[68]  Geoffrey E. Hinton,et al.  Learning and relearning in Boltzmann machines , 1986 .

[69]  Teuvo Kohonen,et al.  Correlation Matrix Memories , 1972, IEEE Transactions on Computers.

[70]  Brian Everitt,et al.  Cluster analysis , 1974 .

[71]  Geoffrey E. Hinton,et al.  Learning distributed representations of concepts. , 1989 .

[72]  Eric Saund.  Abstraction and Representation of Continuous Variables in Connectionist Networks , 1986, AAAI.

[73]  R. Venezky,et al.  Development of Two Letter-Sound Patterns in Grades One Through Three. , 1973 .

[74]  D Marr,et al.  Cooperative computation of stereo disparity. , 1976, Science.

[75]  J.A. Anderson.  Two models for memory organization using interacting traces , 1970 .

[76]  E. Tulving.  How many memory systems are there , 1985 .