|Author:||Richard Durbin,Christopher Miall,Graeme Mitchison|
|Title:||Computing Neuron (Computation and Neural Systems Series)|
|Format:||lit rtf lrf lrf|
|ePUB size:||1853 kb|
|FB2 size:||1661 kb|
|DJVU size:||1344 kb|
|Publisher:||Addison-Wesley (December 1, 1989)|
Computation and neural systems series. Bibliography, etc. Note: Includes bibliographies and index. Personal Name: Durbin, Richard. Personal Name: Miall, Christopher. Personal Name: Mitchison, Graeme. The data of catalog based on open source database. All rights are reserved by their owners.
The Computing Neuron. This book provides an overview of recent developments in biological neuroscience and artificial neural networks. Based on a selection of work presented to a meeting on The Neuron as a Computational Unit held in Cambridge in 1988, it brings together the work of leading scientists in neurophysiology and computational neuroscience.
The Computing Neuron book. Computing Neuron (Computation and Neural Systems Series). 020118348X (ISBN13: 9780201183481).
The authors survey the most common neural-network architectures and show how neural networks can be used to solve actual scientific and engineering problems and describe methodologies for simulating neural-network architectures on traditional digital computing systems. Categories: Computers\Algorithms and Data Structures. ISBN 13: 9780201513769.
Volume 31. Issue 4 - April 2019. Issue 3 - March 2019. Issue 2 - February 2019. Issue 1 - January 2019.
Neural computing started in 1943 with the publication of a startling result by the American scientists Warren McCulloch and Walter Pitts. An important book on the subject with emphasis on information processing aspects and also on networks composed of logical (Boolean) neurons. 2. R. Beale and T. Jackson, Neural Computing (An Introduction), Adam Ililger (1990). Another highly recommended introduction to neural computing, with very few formulae, but a very clear description of basic principles. 3. S. Brunak and B. Lautrup, Neural Networks (Computers with Intuition), World Scientific (1990).
neural systems for time series processing and learning. plicative interactions in NDMA receptors at the level of a sin-. gle neuron, Durbin and Rumelhart proposed product. nodes in which different stimuli are raised to a power given. by the respective synaptic weights and multiplied together.
These readers will be most interested in how we bring together considerations of single neuron signal processing and population codes, how we characterize neural systems as (time-varying nonlinear) control structures, and how we apply our techniques for gener-ating large-scale, realistic simulations.
The authors survey the most common neural-network architectures and show how neural networks can be used to solve actual scientific and engineering problems and describe methodologies for simulating neural-network architectures on traditional digital computing systems.