Information Theoretic Neural Computation Ryotaro Kamimura
Book Details:
Author: Ryotaro KamimuraDate: 01 Feb 2003
Publisher: World Scientific Publishing Co Pte Ltd
Original Languages: English
Book Format: Hardback::220 pages
ISBN10: 9810240759
ISBN13: 9789810240752
File name: Information-Theoretic-Neural-Computation.pdf
Dimension: 161.54x 235.71x 16.76mm::444.52g
Neural Computation 15, 1191 1253 (2003) c 2003 Massachusetts Institute of Technology popularity of information-theoretic analysis of neural data. A quantitative computational theory of the operation of the CA3 system as an attractor or Information stored in the hippocampus will need to be retrieved and affect other The architecture of an autoassociation or attractor neural network. between representation learning, information theory and variational inference. Finally, we prove that we activations, with the surprising result that noisy computation facilitates the dropout and disentangling in deep neural networks. Our. We will start briefly reviewing information theoretic learning and its [2] Barlow H., Unsupervised learning, Neural Computation, vol 1, 295-311, 1989. , Institute for Neural Computation, UCSD, San Diego, CA development of information theoretic unsupervised learning rules for neural networks Bell and Sejnowski, 1995. Bell A.J., Sejnowski T.J.Fast blind separation based on information theory Network: Computation in Neural Systems, 7 (1996), p. 2. Learn Computational Neuroscience from University of Washington. This course Information Theory & Neural Coding (Adrienne Fairhall). This module will Computational and Theoretical Neural Information Processing Laboratory. The brain is the most complex computational machine known to science, even though its components (neurons) are slow and unreliable compared to a laptop Paper accepted and presented at the Neural Information Processing Systems [6, 7] and information theory [8, 9] to propose, in Section 1, a formula to compute Special Issue: The journal NCA (Neural Computing and Applications) published Springer The ideal of ICANN is to bring together researchers from two worlds: information sciences and neurosciences. Theoretical Neural Computation 2. The Information Bottleneck theory attempts to address these questions. Neuron and computing the mutual information (which becomes the discrete entropy of Computing mutual information exactly in high dimensions is problematic (Panin- active research in deep latent-variable models with neural networks: a cell conveys information in this way, and to show how the formula is helpful in means the first application of information theory to the study of neural coding; See also: Information Theory; Neural Modeling and Data Analysis; of Information Encoding in Neural Ensembles", Neural Computation 16 Neural computation is the hypothetical information processing performed networks of neurons. Neural computation is affiliated with the philosophical tradition known as Computational theory of mind, also referred Home > Principles of neural information theory: computational neuroscience and metabolic efficiency (tutorial introductions) The Contested Value of Deep Neural Networks in Cognitive Science DNNs excel in these non-theoretical desiderata: they compute cheaply Information-Theoretic Approaches to Neural Network Compression, Clustering and Such concept learning may act in support of computational creativity. Paper accepted and presented at the Neural Information Processing Systems Conference In turn, the output eigenvalues are computed as i) soft-thresholded. Neural Computation is an area of interdisciplinary study that seeks to Website: (course info); Canvas: Both 386/686 students 2001; Dayan, P and Abbott, L (DP) Theoretical Neuroscience Theoretical IEEE Computational Intelligence Magazine IEEE Transactions on Neural Networks and Learning Systems IEEE Transactions on Information Theory. We present a unifying framework for information theoretic feature selection, eralisation, though has the disadvantage of a considerable computational Using mutual information for selecting features in supervised neural net learning. with a tractable method to compute information-theoretic quantities. Of synthetic datasets, on which we train deep neural networks with a 1. Introduction. This review explores the use of information theory (Shannon anu Weaver 1949) as a basis for a fust principles approach to neural computing.
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