Abstract:
:In "Isotropic Sequence Order Learning" (pp. 831-864 in this issue), we introduced a novel algorithm for temporal sequence learning (ISO learning). Here, we embed this algorithm into a formal nonevaluating (teacher free) environment, which establishes a sensor-motor feedback. The system is initially guided by a fixed reflex reaction, which has the objective disadvantage that it can react only after a disturbance has occurred. ISO learning eliminates this disadvantage by replacing the reflex-loop reactions with earlier anticipatory actions. In this article, we analytically demonstrate that this process can be understood in terms of control theory, showing that the system learns the inverse controller of its own reflex. Thereby, this system is able to learn a simple form of feedforward motor control.
journal_name
Neural Computjournal_title
Neural computationauthors
Porr B,von Ferber C,Wörgötter Fdoi
10.1162/08997660360581930subject
Has Abstractpub_date
2003-04-01 00:00:00pages
865-84issue
4eissn
0899-7667issn
1530-888Xjournal_volume
15pub_type
杂志文章abstract::A key problem in computational neuroscience is to find simple, tractable models that are nevertheless flexible enough to capture the response properties of real neurons. Here we examine the capabilities of recurrent point process models known as Poisson generalized linear models (GLMs). These models are defined by a s...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01021
更新日期:2017-12-01 00:00:00
abstract::Primary visual cortical complex cells are thought to serve as invariant feature detectors and to provide input to higher cortical areas. We propose a single model for learning the connectivity required by complex cells that integrates two factors that have been hypothesized to play a role in the development of invaria...
journal_title:Neural computation
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976699300016160
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.1997.9.5.971
更新日期:1997-07-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00257
更新日期:2012-04-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
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更新日期:1991-04-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976600300015079
更新日期:2000-09-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976602320263971
更新日期:2002-09-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2009.03-08-721
更新日期:2009-07-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.1996.8.6.1135
更新日期:1996-08-15 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00238
更新日期:2012-03-01 00:00:00
abstract::To date, Hebbian learning combined with some form of constraint on synaptic inputs has been demonstrated to describe well the development of neural networks. The previous models revealed mathematically the importance of synaptic constraints to reproduce orientation selectivity in the visual cortical neurons, but biolo...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2009.04-08-752
更新日期:2009-09-01 00:00:00
abstract::Modeling stereo transparency with physiologically plausible mechanisms is challenging because in such frameworks, large receptive fields mix up overlapping disparities, whereas small receptive fields can reliably compute only small disparities. It seems necessary to combine information across scales. A coarse-to-fine ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00722
更新日期:2015-05-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00461
更新日期:2013-07-01 00:00:00
abstract::In a previous article, we considered game trees as graphical models. Adopting an evaluation function that returned a probability distribution over values likely to be taken at a given position, we described how to build a model of uncertainty and use it for utility-directed growth of the search tree and for deciding o...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976699300016881
更新日期:1999-01-01 00:00:00
abstract::The brain is known to be active even when not performing any overt cognitive tasks, and often it engages in involuntary mind wandering. This resting state has been extensively characterized in terms of fMRI-derived brain networks. However, an alternate method has recently gained popularity: EEG microstate analysis. Pr...
journal_title:Neural computation
pub_type: 信件
doi:10.1162/neco_a_01229
更新日期:2019-11-01 00:00:00
abstract::In this letter, we propose a noisy nonlinear version of independent component analysis (ICA). Assuming that the probability density function (p. d. f.) of sources is known, a learning rule is derived based on maximum likelihood estimation (MLE). Our model involves some algorithms of noisy linear ICA (e. g., Bermond & ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/0899766052530866
更新日期:2005-01-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976603321891846
更新日期:2003-07-01 00:00:00
abstract::We propose a new principle for replicating receptive field properties of neurons in the primary visual cortex. We derive a learning rule for a feedforward network, which maintains a low firing rate for the output neurons (resulting in temporal sparseness) and allows only a small subset of the neurons in the network to...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00341
更新日期:2012-10-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2008.07-07-571
更新日期:2009-04-01 00:00:00
abstract::Synaptic runaway denotes the formation of erroneous synapses and premature functional decline accompanying activity-dependent learning in neural networks. This work studies synaptic runaway both analytically and numerically in binary-firing associative memory networks. It turns out that synaptic runaway is of fairly m...
journal_title:Neural computation
pub_type: 杂志文章,评审
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00077
更新日期:2011-02-01 00:00:00
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journal_title:Neural computation
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journal_title:Neural computation
pub_type: 杂志文章
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更新日期:2007-07-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.1991.3.2.213
更新日期:1991-07-01 00:00:00
abstract::The free-energy principle is a candidate unified theory for learning and memory in the brain that predicts that neurons, synapses, and neuromodulators work in a manner that minimizes free energy. However, electrophysiological data elucidating the neural and synaptic bases for this theory are lacking. Here, we propose ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00862
更新日期:2016-09-01 00:00:00
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journal_title:Neural computation
pub_type: 信件
doi:10.1162/neco.2008.09-06-342
更新日期:2008-07-01 00:00:00
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journal_title:Neural computation
pub_type: 信件
doi:10.1162/neco.2008.04-07-506
更新日期:2008-07-01 00:00:00
abstract::We present a first-order nonhomogeneous Markov model for the interspike-interval density of a continuously stimulated spiking neuron. The model allows the conditional interspike-interval density and the stationary interspike-interval density to be expressed as products of two separate functions, one of which describes...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2009.06-07-548
更新日期:2009-06-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00961
更新日期:2017-06-01 00:00:00