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Brains and pseudorandom generators

by Mark Goldsmith, Nan Yang
"... ar ..."
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Numerical Recipes in C: The Art of Scientific Computing. Second Edition

by William H. Press, Saul A. Teukolsky, William T. Vetterling, Brian P. Flannery , 1992
"... This reprinting is corrected to software version 2.10 ..."
Abstract - Cited by 177 (0 self) - Add to MetaCart
This reprinting is corrected to software version 2.10

Controlling Activity Fluctuations in Large, Sparsely Connected Random Networks

by A C Smith, X B Wu, W B Levy - Network , 2000
"... . Controlling activity in recurrent neural network models of brain regions is essential both to enable effective learning and to reproduce the low activities that exist in some cortical regions such as hippocampal region CA3. Previous studies of sparse, random, recurrent networks constructed with Mc ..."
Abstract - Cited by 5 (3 self) - Add to MetaCart
with McCulloch--Pitts neurons used probabilistic arguments to set the parameters that control activity. Here, we extend this work by adding an additional, biologically appropriate, parameter to control the magnitude and stability of activity oscillations. The new constant can be considered to be the rest

Perceptual learning in speech

by Dennis Norris, James M. McQueen, Anne Cutler - COGNITIVE PSYCHOLOGY , 2002
"... This study demonstrates that listeners use lexical knowledge in perceptual learning of speech sounds. Dutch listeners first made lexical decisions on Dutch words and nonwords. The final fricative of 20 critical words had been replaced by an ambiguous sound, between [f] and [s]. One group of listener ..."
Abstract - Cited by 129 (20 self) - Add to MetaCart
This study demonstrates that listeners use lexical knowledge in perceptual learning of speech sounds. Dutch listeners first made lexical decisions on Dutch words and nonwords. The final fricative of 20 critical words had been replaced by an ambiguous sound, between [f] and [s]. One group of listeners heard ambiguous [f]-final words (e.g., [WI WItlo?], from witlof, chicory) and unambiguous [s]-final words (e.g., naaldbos, pine forest). Another group heard the reverse (e.g., ambiguous [na:ldbo?], unambiguous witlof). Listeners who had heard [?] in [f]-final words were subsequently more likely to categorize ambiguous sounds on an [f]–[s] continuum as [f] than those who heard [?] in [s]-final words. Control conditions ruled out alternative explanations based on selective adaptation and contrast. Lexical information can thus be used to train categorization of speech. This use of lexical information differs from the on-line lexical feedback embodied in interactive models of speech perception. In contrast to online feedback, lexical feedback for learning is of benefit to spoken word recognition (e.g., in

On the role of computational complexity theory in the study of brain function

by Brendan Juba
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Can brains generate random numbers?

by unknown authors
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Encoding of Visual Information by LGN Bursts

by Pamela Reinagel , Dwayne Godwin, S. Murray Sherman, Christof Koch - J NEUROPHYSIOL , 1999
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Shortlist B: A Bayesian model of continuous speech recognition

by Dennis Norris, James M. Mcqueen, D. Norris, J. M. Mcqueen, A. Cutler, S. Butterfield - Psychological Review , 2008
"... A Bayesian model of continuous speech recognition is presented. It is based on Shortlist (D. Norris, 1994; ..."
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A Bayesian model of continuous speech recognition is presented. It is based on Shortlist (D. Norris, 1994;

Whole Brain Emulation

by Anders Sandberg, Nick Bostrom
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and Brain Sciences Unit

by James M. Mcqueen, Max Planck, Dennis Norris, Anne Cutler
"... In 5 experiments, listeners heard words and nonwords, some cross-spliced so that they contained acoustic-phonetic mismatches. Performance was worse on mismatching than on matching items. Words cross-spliced with words and words cross-spliced with nonwords produced parallel results. However, in lexic ..."
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In 5 experiments, listeners heard words and nonwords, some cross-spliced so that they contained acoustic-phonetic mismatches. Performance was worse on mismatching than on matching items. Words cross-spliced with words and words cross-spliced with nonwords produced parallel results. However, in lexical decision and 1 of 3 phonetic decision experiments, performance on nonwords cross-spliced with words was poorer than on nonwords cross-spliced with nonwords. A gating study confirmed that there were misleading coarticulatory cues in the cross-spliced items; a sixth experiment showed that the earlier results were not due to interitem differences in the strength of these cues. Three models of phonetic decision making (the Race model, the TRACE model, and a postlexical model) did not explain the data. A new bottom-up model is outlined that accounts for the findings in terms of lexical involvement at a dedicated decision-making stage. How do listeners use lexical knowledge when they make phonetic decisions about spoken language, such as when they categorize phonemes or detect phonetic targets? Al-though there is no dispute that lexical information can, at
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