Literature DB >> 12662541

Varieties of Helmholtz Machine.

Geoffrey E. Hinton1, Peter Dayan.   

Abstract

The Helmholtz machine is a new unsupervised learning architecture that uses top-down connections to build probability density models of input and bottom-up connections to build inverses to those models. The wake-sleep learning algorithm for the machine involves just the purely local delta rule. This paper suggests a number of different varieties of Helmholtz machines, each with its own strengths and weaknesses, and relates them to cortical information processing. Copyright 1996 Elsevier Science Ltd.

Year:  1996        PMID: 12662541     DOI: 10.1016/s0893-6080(96)00009-3

Source DB:  PubMed          Journal:  Neural Netw        ISSN: 0893-6080


  23 in total

1.  The multifaceted nature of unsupervised category learning.

Authors:  Bradley C Love
Journal:  Psychon Bull Rev       Date:  2003-03

Review 2.  Knowing how much you don't know: a neural organization of uncertainty estimates.

Authors:  Dominik R Bach; Raymond J Dolan
Journal:  Nat Rev Neurosci       Date:  2012-07-11       Impact factor: 34.870

Review 3.  Glutamatergic model psychoses: prediction error, learning, and inference.

Authors:  Philip R Corlett; Garry D Honey; John H Krystal; Paul C Fletcher
Journal:  Neuropsychopharmacology       Date:  2010-09-22       Impact factor: 7.853

Review 4.  Bayesian quantitative electrophysiology and its multiple applications in bioengineering.

Authors:  Roger C Barr; Loren W Nolte; Andrew E Pollard
Journal:  IEEE Rev Biomed Eng       Date:  2010

5.  Processing of object motion and self-motion in the lateral subdivision of the medial superior temporal area in macaques.

Authors:  Ryo Sasaki; Dora E Angelaki; Gregory C DeAngelis
Journal:  J Neurophysiol       Date:  2019-01-30       Impact factor: 2.714

Review 6.  Hallucinations and Strong Priors.

Authors:  Philip R Corlett; Guillermo Horga; Paul C Fletcher; Ben Alderson-Day; Katharina Schmack; Albert R Powers
Journal:  Trends Cogn Sci       Date:  2018-12-21       Impact factor: 20.229

7.  Patterns across multiple memories are identified over time.

Authors:  Blake A Richards; Frances Xia; Adam Santoro; Jana Husse; Melanie A Woodin; Sheena A Josselyn; Paul W Frankland
Journal:  Nat Neurosci       Date:  2014-06-01       Impact factor: 24.884

8.  A simple approach to ignoring irrelevant variables by population decoding based on multisensory neurons.

Authors:  HyungGoo R Kim; Xaq Pitkow; Dora E Angelaki; Gregory C DeAngelis
Journal:  J Neurophysiol       Date:  2016-06-22       Impact factor: 2.714

9.  Dissociation of Self-Motion and Object Motion by Linear Population Decoding That Approximates Marginalization.

Authors:  Ryo Sasaki; Dora E Angelaki; Gregory C DeAngelis
Journal:  J Neurosci       Date:  2017-10-13       Impact factor: 6.167

10.  What do we mean by prediction in language comprehension?

Authors:  Gina R Kuperberg; T Florian Jaeger
Journal:  Lang Cogn Neurosci       Date:  2015-11-13       Impact factor: 2.331

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.