Analysis and Modeling of Neural Systems

Author:   Frank H. Eeckman
Publisher:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of the original 1st ed. 1992
ISBN:  

9781461367932


Pages:   414
Publication Date:   23 October 2012
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Analysis and Modeling of Neural Systems


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Overview

The recentexplosionofactivity inneural modelingseemsto have beendriven more by advances inthe theories and applicationsoflearning paradigms for artificial neural networks than by advances in our knowledge of real nervous systems. In the past few years, major conferences on neural networks and neural modeling have emerged and, appropriately, have focussed on technological exploitation of these advances. Sensingthat the recentleaps in both computational powerand knowledge ofthe nervous system may have setthe stage for a revolution intheoretical neurobiology, neuroscientists have welcomed thenew neural modeling; butmanyofthem would like tosee itdirected as heavily toward understanding of the nervou$ system as it is presently directed toward computertechnology and control-system engineering. Furthermore, some neuroscientists believe thattechnologists shouldnotbe satisfiedonly with exploiting or extending the recent advances in learning paradigms, that emerging knowledge about real nervous systems will suggest other, comparably valuable, paradigms forsignal processingand control. Ourmotive as organizers was to have a conference that focussed on both of these areas -- emerging modeling tools and concepts for neurobiologists, and emerging neurobiological concepts and neurobiological knowledge ofpotential use to technologists. Ourprinciple ofdesign was simple. We attempted to organize aconference withagroup ofspeakers that would be most illuminating and exciting to us and to our students. We succeeded. EdwinR. Lewis INTRODUCTION This volume contains the collected papers of the 1990 Conference on Analysis and ModelingofNeural Systems, held July 25-27, in Berkeley, California. There were 21 invited talks at the meeting, covering aspects ofanalysis and modeling from the subcellularlevel to the networklevel. Inaddition, thirty six posters were accepted forpresentation.

Full Product Details

Author:   Frank H. Eeckman
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of the original 1st ed. 1992
Dimensions:   Width: 15.50cm , Height: 2.20cm , Length: 23.50cm
Weight:   0.658kg
ISBN:  

9781461367932


ISBN 10:   146136793
Pages:   414
Publication Date:   23 October 2012
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

I - Analysis and Modeling Tools and Techniques.- Section 1: Analysis.- Assembly Connectivity and Activity: Methods, Results, Interpretations.- Visualization of Cortical Connections With Voltage Sensitive Dyes.- Channels, Coupling, and Synchronized Rhythmic Bursting Activity.- Sparse-stimulation and Wiener Kernels.- Quantitative Search for Stimulus-Specific Patterns in the Human Electroencephalogram (EEG) During a Somatosensory Task.- Section 2: Modeling.- Functional Insights About Synaptic Inputs to Dendrites.- Dendritic Control of Hebbian Computations.- Low Threshold Spikes and Rhythmic Oscillations in Thalamic Neurons.- The Role of 3-D Reconstruction in the Analysis and Modeling of Neural Systems.- Efficient Modeling of Realistic Neural Networks with Application to the Olfactory Bulb.- CAJAL: A Biological Neural Network Simulator.- Neurobuilder: A User Interface and Network Simulator for Building Neurobiological Networks.- Design for Purkinje Cells.- A Computational Model of the Cerebellum.- II: Sensory Systems.- Section 3: Visual System.- Retinal Circuits for Daylight: Why Ballplayers Don’t Wear Shades.- Explaining Lateral Interactions in the Retina with the Help of Models.- Dynamic Aspects of Receptive Field Organization in Area VI of the Macaque Monkey.- Sequence Discriminating Neural Network in the Eye of the Fly.- Temporal Resolution in Vision: Psychophysical Experiments and Neural Structure.- Voltage- and Current-Clamp Simulation of Hermissenda Type B Cell Responses.- Light Sensitivity in Cones is affected by the Feedback from Horizontal Cells.- A Model for the Changes in Coupling and Kinetics of Cone Driven Retinal Horizontal Cells During Light/Dark Adaptation.- Optimal Filtering in the Salamander Retina.- Receptive Field Image Modeling Through CellularNeural Networks.- A Cellular Automaton Simulation of the Development of Ocular Dominance Columns.- Section 4: Auditory System.- Hearing as Seeing: Space and Time in Auditory Processing.- Delay Lines for Detection of Interaural Time Differences in the Barn Owl.- A Parallel Neural Model for Auditory Front End Processing.- A Modeling Study of Single Neurons and Neural Circuits of the Ventral and Dorsal Cochlear Nucleus.- Models of the Computation of Sound Elevation in the Barn Owl.- Self Organization in Auditory Neural Models.- Section 5: Other Sensory Systems.- GABAergic Modulation of Average Evoked Potentials in Rat Olfactory Bulb.- Learning with Synaptic Nonlinearities in a Coupled Oscillator Model of Olfactory Cortex.- Reading Between the Spikes in the Cereal Filiform Hair Receptors of the Cricket.- III: Motor Systems.- Section 6: Central Pattern Generators (CPG’s).- Dynamic Re-Wiring of CPG Circuits in a Simple Nervous System.- Modeling Neural Systems: Interactions between Mathematical Analysis, Simulation, and Experimentation in the Lamprey.- Section 7: Cortex, Cerebellum and Spinal Cord.- Positive Feedback in the Cerebro-Cerebellar Recurrent Network May Explain Rotation of Population Vectors.- Motor Organization in the Frog’s Spinal Cord.- Information Transmission in Parallel Neuronal Channels.- The Cerebellar Cortex as a Sequential Associative Memory: A Novel Structural/Functional Interpretation.- A Neuronal Model for Variable Latency Response.

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