Cortical Maps Data And Models

For more than 30 years, the visual cortex has been the source of new theories and ideas about how the brain processes information. The visual cortex is easily accessible through a variety of recording and imagining techniques and allows mapping of high level behavior relatively directly to neural mechanisms. Understanding the computations in the visual cortex is therefore an important step toward a general theory of computational brain theory.

There has been substantial progress in understanding the contributions of the auditory forebrain to hearing, sound localization, communication, emotive behavior, and cognition. The Auditory Cortex covers the latest knowledge about the auditory forebrain, including the auditory cortex as well as the medial geniculate body in the thalamus. This book will cover all important aspects of the auditory forebrain organization and function, integrating the auditory thalamus and cortex into a smooth, coherent whole. Volume One covers basic auditory neuroscience. It complements The Auditory Cortex, Volume 2: Integrative Neuroscience, which takes a more applied/clinical perspective.

How does your mind work? How does your brain give rise to your mind? These are questions that all of us have wondered about at some point in our lives, if only because everything that we know is experienced in our minds. They are also very hard questions to answer. After all, how can a mind understand itself? How can you understand something as complex as the tool that is being used to understand it? This book provides an introductory and self-contained description of some of the exciting answers to these questions that modern theories of mind and brain have recently proposed. Stephen Grossberg is broadly acknowledged to be the most important pioneer and current research leader who has, for the past 50 years, modelled how brains give rise to minds, notably how neural circuits in multiple brain regions interact together to generate psychological functions. This research has led to a unified understanding of how, where, and why our brains can consciously see, hear, feel, and know about the world, and effectively plan and act within it. The work embodies revolutionary Principia of Mind that clarify how autonomous adaptive intelligence is achieved. It provides mechanistic explanations of multiple mental disorders, including symptoms of Alzheimer's disease, autism, amnesia, and sleep disorders; biological bases of morality and religion, including why our brains are biased towards the good so that values are not purely relative; perplexing aspects of the human condition, including why many decisions are irrational and self-defeating despite evolution's selection of adaptive behaviors; and solutions to large-scale problems in machine learning, technology, and Artificial Intelligence that provide a blueprint for autonomously intelligent algorithms and robots. Because brains embody a universal developmental code, unifying insights also emerge about shared laws that are found in all living cellular tissues, from the most primitive to the most advanced, notably how the laws governing networks of interacting cells support developmental and learning processes in all species. The fundamental brain design principles of complementarity, uncertainty, and resonance that Grossberg has discovered also reflect laws of the physical world with which our brains ceaselessly interact, and which enable our brains to incrementally learn to understand those laws, thereby enabling humans to understand the world scientifically. Accessibly written, and lavishly illustrated, Conscious Mind/Resonant Brain is the magnum opus of one of the most influential scientists of the past 50 years, and will appeal to a broad readership across the sciences and humanities.

This book provides an overview of self-organizing map formation, including recent developments. Self-organizing maps form a branch of unsupervised learning, which is the study of what can be determined about the statistical properties of input data without explicit feedback from a teacher. The articles are drawn from the journal Neural Computation.The book consists of five sections. The first section looks at attempts to model the organization of cortical maps and at the theory and applications of the related artificial neural network algorithms. The second section analyzes topographic maps and their formation via objective functions. The third section discusses cortical maps of stimulus features. The fourth section discusses self-organizing maps for unsupervised data analysis. The fifth section discusses extensions of self-organizing maps, including two surprising applications of mapping algorithms to standard computer science problems: combinatorial optimization and sorting. Contributors J. J. Atick, H. G. Barrow, H. U. Bauer, C. M. Bishop, H. J. Bray, J. Bruske, J. M. L. Budd, M. Budinich, V. Cherkassky, J. Cowan, R. Durbin, E. Erwin, G. J. Goodhill, T. Graepel, D. Grier, S. Kaski, T. Kohonen, H. Lappalainen, Z. Li, J. Lin, R. Linsker, S. P. Luttrell, D. J. C. MacKay, K. D. Miller, G. Mitchison, F. Mulier, K. Obermayer, C. Piepenbrock, H. Ritter, K. Schulten, T. J. Sejnowski, S. Smirnakis, G. Sommer, M. Svensen, R. Szeliski, A. Utsugi, C. K. I. Williams, L. Wiskott, L. Xu, A. Yuille, J. Zhang

Recent experimental work in neurobiology has defined asymmetries and lateralization in the topographic maps found in mirror-image regions of the sensorimotor cerebral cortex. However, the mechanisms underlying these asymmetries are currently not established, and in some cases are quite controversial. In order to explore some possible causes of map asymmetry and lateralization, several neural network models of cortical map lateralization and asymmetries based on self-organizing maps are created and studied both computationally and theoretically. Activation levels of the elements in the models are governed by large systems of highly nonlinear ordinary differential equations (ODEs), where coefficients change with time and their changes depend on the activation levels. Special metrics for objective evaluation of simulation results (represented as paired receptive field maps) are introduced and analysed. The behavior of the models is studied when their parameters are varied systematically and also when simulated lesions are introduced into one of the hemispheric regions. Some very sharp transitions and other interesting phenomena have been found computationally. Many of these computationally observed phenomena are explained by theoretical analysis of total hemispheric activation in a simplified model. The connection between a bifurcation point of the system of ODEs and the sharp transition in the model's computational behavior is established. More general understanding of topographic map formation and changes under various conditions is achieved by analysis of activation patterns (i.e., omega-limit sets of the above system of ODEs). This is the first mathematical model to demonstrate spontaneous map lateralization and asymmetries, and it suggests that such models may be generally useful in better understanding the mechanisms of cerebral lateralization. The mathematical analysis of the models leads to a better understanding of the mechanisms of self-organization in the topographic maps based on competitive distribution of activation and competitive learning.

The study of neural networks is enjoying a great renaissance, both in computational neuroscience, the development of information processing models of living brains, and in neural computing, the use of neurally inspired concepts in the construction of "intelligent" machines. Thus the title of this volume has two interpretations: It presents models and data on the dynamic interactions occurring in the brain, and it exhibits the dynamic interactions between research in computational neuroscience and in neural computing, as scientists seek to find common principles to guide the understanding of the living brain and the design of artificial neural networks. This collection of contributions presents the current state of research, future trends and open problems in an exciting field of today's science.

In this thesis two topography preserving Gaussian Mixture Models are used to model cortical maps. Two major novel contributions are presented; the application of the Generative Topographic Mapping (GTM) to the formation of ocular dominance stripes and using the Elastic Net (EN) as a visual category representation. The applications are motivated by Marr's computational framework in that the contributions correspond to different levels of abstraction. Firstly, motivated by theoretical and experimental comparisons of the GTM and EN, the GTM is applied for the formation of ocular dominance stripes. Simulations are presented that demonstrate that the GTM can indeed produce realistic looking patterns in some instances, though careful parameter selection is required. In addition the GTM exhibits properties that the EN does not, such as twisting and 'hyper sensitivity'. Consequently it is proposed that further mathematical investigation may relate the neuronal interoprability of the GTM to that of the EN. In the second application the EN is used to model population of neurons in higher level processing that are postulated to code visual categories. Using the EN visualisations are presented that allow some novel insights. Motivated by the ability of the EN to capture 'high level' concepts a novel visual categorisation scheme is suggested. The proposed system is evaluated on the Caltech101 data set and shows good performance. A number of interesting future directions that pertain to both artificial and biological vision are suggested.

Exploration of Cortical Function summarizes recent research efforts aiming at the revelation of cortical population coding and signal processing strategies. Topics include optical detection techniques of population activity in the sub-millimeter range, advanced methods for the statistical analysis of these data, and biologically inspired neuronal modeling techniques for population activities in the frameworks of optimal coding, statistical learning theory, and mean-field recurrent networks. Exploration of Cortical Function is unique in that it covers one complete branch of population-based brain research ranging from techniques for data acquisition over data analysis up to modeling techniques for the quantification of functional principles. The volume covers an area which is of great current interest to researchers working on cerebral cortex. The combination of models and image analysis techniques to examine the activity of large cohorts of neurons is especially intriguing and prone to considerable error and debate.