I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

The organization of behavior: A neuropsychological theory

Many current neurophysiological, psychophysical, and psychological approaches to vision rest on the idea that when we see, the brain produces an internal representation of the world. The activation of this internal representation is assumed to give rise to the experience of seeing. The problem with this kind of approach is that it leaves unexplained how the existence of such a detailed internal representation might produce visual consciousness. An alternative proposal is made here. We propose that seeing is a way of acting. It is a particular way of exploring the environment. Activity in internal representations does not generate the experience of seeing. The outside world serves as its own, external, representation. The experience of seeing occurs when the organism masters what we call the governing laws of sensorimotor contingency. The advantage of this approach is that it provides a natural and principled way of accounting for visual consciousness, and for the differences in the perceived quality of sensory experience in the different sensory modalities. Several lines of empirical evidence are brought forward in support of the theory, in particular: evidence from experiments in sensorimotor adaptation, visual \"filling in,\" visual stability despite eye movements, change blindness, sensory substitution, and color perception.

A sensorimotor account of vision and visual consciousness-Authors' Response-Acting out our sensory experience

Let's read! We will often find out this sentence everywhere. When still being a kid, mom used to order us to always read, so did the teacher. Some books are fully read in a week and we need the obligation to support reading. What about now? Do you still love reading? Is reading only for you who have obligation? Absolutely not! We here offer you a new book enPDFd the perception of the visual world to read.

The Perception of the Visual World. By James J. Gibson. U.S.A.: Houghton Mifflin Company, 1950 (George Allen & Unwin, Ltd., London). Price 35s.

Essentials of the self-organizing map

Biological Background.- Computational Foundations.- LISSOM: A Computational Map Model of V1.- Development of Maps and Connections.- Understanding Plasticity.- Understanding Visual Performance: The Tilt Aftereffect.- HLISSOM: A Hierarchical Model.- Understanding Low-Level Development: Orientation Maps.- Understanding High-Level Development: Face Detection.- PGLISSOM: A Perceptual Grouping Model.- Temporal Coding.- Understanding Perceptual Grouping: Contour Integration.- Computations in Visual Maps.- Scaling LISSOM simulations.- Discussion: Biological Assumptions and Predictions.- Future Work: Computational Directions.- Conclusion.

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Computational Maps in the Visual Cortex

A self-organizing neural network model for the simultaneous development of topographic receptive fields and lateral interactions in cortical maps is presented. Both afferent and lateral connections adapt by the same Hebbian mechanism in a purely local and unsupervised learning process. Afferent input weights of each neuron self-organize into hill-shaped profiles, receptive fields organize topographically across the network, and unique lateral interaction profiles develop for each neuron. The model suggests that precise cortical maps develop only if the initial receptive fields are topographically ordered or if they cover the whole receptive surface. It demonstrates how patterned lateral connections develop based on correlated activity, and explains why lateral connection patterns closely follow receptive field properties such as ocular dominance. The model predicts a dual role for lateral connections: to support self-organization of receptive fields, and to represent low-level Gestalt knowledge acquired during development of the cortex.

/pdf/topographic-receptive-fields-and-patterned-lateral-1x3gd5k7as.pdf

Topographic receptive fields and patterned lateral interaction in a self-organizing model of the primary visual cortex

A self-organizing neural network model called LISSOM for the synergetic development of afferent and lateral connections in cortical feature maps is presented. The weight adaptation process is purely activity-dependent, unsupervised, and local. The afferent input weights self-organize into a topological map of the input space. At the same time, the lateral interconnection weights adapt, and a unique lateral interaction profile develops for each neuron. Weak lateral connections die off, leaving a pattern of connections that represents the significant long-term correlations of activity on the feature map. LISSOM demonstrates how self-organization can bootstrap based on input information only, without global supervision or predetermined lateral interaction. The model gives rise to a nontopographically organized lateral connectivity similar to that observed in the mammalian neocortex as illustrated by a LISSOM model of ocular dominance column formation in the primary visual cortex. In addition, LISSOM can potentially account for the development of multiple maps of different modalities on the same undifferentiated cortical architecture.

Cooperative self-organization of afferent and lateral connections in cortical maps

Based on a Hebbian adaptation process, the afferent and lateral connections in the RF-LISSOM model organize simultaneously and cooperatively, and form structures such as those observed in the primary visual cortex. The neurons in the model develop local receptive fields that are organized into orientation, ocular dominance, and size selectivity columns. At the same time, patterned lateral connections form between neurons that follow the receptive field organization. This structure is in a continuously-adapting dynamic equilibrium with the external and intrinsic input, and can account for reorganization of the adult cortex following retinal and cortical lesions. The same learning processes may be responsible for a number of low-level functional phenomena such as tilt aftereffects, and combined with the leaky integrator model of the spiking neuron, for segmentation and binding. The model can also be used to verify quantitatively the hypothesis that the visual cortex forms a sparse, redundancy-reduced encoding of the input, which allows it to process massive amounts of visual information efficiently.

http://nn.cs.utexas.edu/downloads/papers/miikkulainen.visual-cortex.pdf

Self-Organization, Plasticity, and Low-Level Visual Phenomena in a Laterally Connected Map Model of the Primary Visual Cortex

This work is aimed at modeling and analyzing the computational processes by which sensory information is learned and represented in the brain. First, a general self-organizing neural network architecture that forms efficient representations of visual inputs is presented. Two kinds of visual knowledge is stored in the cortical network: information about the principal feature dimensions of the visual world (such as line orientation and ocularity) is stored in the afferent connections, and correlations between these features in the lateral connections. During visual processing, the cortical network filters out these correlations, generating a redundancy-reduced sparse coding of the visual input. Through massively parallel computational simulations, this architecture is shown to give rise to structures similar to those in the primary visual cortex, such as (1) receptive fields, (2) topographic maps, (3) ocular dominance, orientation and size preference columns, and (4) patterned lateral connections between neurons. The same computational process is shown to account for many of the dynamic processes in the visual cortex, such as reorganization following retinal and cortical lesions, and perceptual shifts following dynamic receptive field changes. These results suggest that a single self-organizing process underlies development, plasticity and visual functions in the primary visual cortex.

Joseph Sirosh

Papers

Computational Maps in the Visual Cortex

Topographic receptive fields and patterned lateral interaction in a self-organizing model of the primary visual cortex

Cooperative self-organization of afferent and lateral connections in cortical maps

Self-Organization, Plasticity, and Low-Level Visual Phenomena in a Laterally Connected Map Model of the Primary Visual Cortex

A Self-Organizing Neural Network Model Of The Primary Visual Cortex