Neuroscience: From Neural Networks to Artificial Intelligence (häftad)
Häftad (Paperback / softback)
Antal sidor
Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Arbib, Michael A. (ed.), Cervantes-Perez, Francisco (ed.), Romo, Ranulfo (ed.), Rudomin, Pablo (ed.)
3 Illustrations, black and white; X, 579 p. 3 illus.
244 x 170 x 30 mm
931 g
Antal komponenter
1 Paperback / softback

Neuroscience: From Neural Networks to Artificial Intelligence

Proceedings of a U.S.-Mexico Seminar held in the city of Xalapa in the state of Veracruz on December 911, 1991

Häftad,  Engelska, 1993-06-01
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The Central Nervous System can be considered as an aggregate of neurons specialized in both the transmission and transformation of information. Information can be used for many purposes, but probably the most important one is to generate a representation of the "external" world that allows the organism to react properly to changes in its external environment. These functions range from such basic ones as detection of changes that may lead to tissue damage and eventual destruction of the organism and the implementation of avoidance reactions, to more elaborate representations of the external world implying recognition of shapes, sounds and textures as the basis of planned action or even reflection. Some of these functions confer a clear survival advantage to the organism (prey or mate recognition, escape reactions, etc. ). Others can be considered as an essential part of cognitive processes that contribute, to varying degrees, to the development of individuality and self-consciousness. How can we hope to understand the complexity inherent in this range of functionalities? One of the distinguishing features of the last two decades has been the availability of computational power that has impacted many areas of science. In neurophysiology, computation is used for experiment control, data analysis and for the construction of models that simulate particular systems. Analysis of the behavior of neuronal networks has transcended the limits of neuroscience and is now a discipline in itself, with potential applications both in the neural sciences and in computing sciences.
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I. Scales of Analysis.- Neuronal networks of the mammalian brain have functionally different classes of neurons: Suggestions for a taxonomy of membrane ionic conductances.- Electrical coupling in networks containing oscillators.- Dynamical approach to collective brain.- Schema-theoretic models of arm, hand, and eye movements.- Cooperative distributed problem solving between (and within) intelligent agents.- II. Processing of Sensory Information.- Spinal processing of impulse trains from sensory receptors.- Central control of sensory information.- Parallel and serial processing in the somatosensory system.- Cortical representation of touch.- An introduction to human haptic exploration and recognition of objects for neuroscience and AI.- Common principles in auditory and visual processing.- III. Visual Processing.- Neuronal substrate of ligth-induced attraction and withdrawal in crayfish: A case of behavioral selection.- Neural and psychophysical models of chromatic and achromatic visual processes.- Computational vision: A probabilistic view of the multi-module paradigm.- State of the art in image processing.- Shape recognition in mind, brain, and machine.- IV. Learning And Knowledge Representation.- Contrasting properties of NMD A-dependent and NMDA-independent forms of LTP in hippocampal pyramidal cells.- Kindling.- Learning automata: An alternative to artificial neural networks.- Learning, from a logical point of view.- Knowledge representation for speech processing.- Data management and inference strategies in a human gait pathology expert system.- V. Neuronal Systems For Motor Integration.- Entrainment of the spinal neuronal network generating locomotion.- Cortical representation of intended movements.- Saccadic and fixation sytems of oculomotor control in monkey superior colliculus.- Modulatory effects on prey-recognition in amphibia: A theoretical- experimental study.- VI. Robotics And Control.- Outline for a theory of motor behavior: Involving cooperative actions of the cerebellum, basal ganglia, and cerebral cortex.- Neural networks and adaptive control.- Robustness issues in robot manipulators.- Symbolic planning versus neural control in robots.- Divine inheritance vs. experience in the world: Where does the knowledge base come from?.- VII. A Concluding Perspective.- Methodological considerations in Cognitive Science.- Viewpoints and controversies.