A dynamical pattern recognition model of gamma activity in auditory cortex

Zavaglia, Melissa; Canolty, Ryan T.; Schofield, Thomas M.; Leff, Alexander; Ursino, Mauro; Knight, Robert T.; Penny, Will D.

doi:10.1016/j.neunet.2011.12.007

Cited by 9 publications

(3 citation statements)

References 75 publications

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“…The results for the state-of-the-art speech recognition system using HMM (Hidden Markov Model) were reported in [18]. OT (Occurrence Time) features were used in [103].…”

Section: Resultsmentioning

confidence: 99%

“…This computational issue could be resolved by parallel ensemble-specific computations, which would be another step towards biological reality and probably improving recognition rates further. It would also be worthwhile extending the cochlear features in the present model with other biologically plausible preprocessing steps, such as occurrence times, which encode the onsets and offsets of specific features [50], [103].…”

Section: Discussionmentioning

confidence: 99%

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From Birdsong to Human Speech Recognition: Bayesian Inference on a Hierarchy of Nonlinear Dynamical Systems

2013

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Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. In addition, we show that recognition can be performed even when words are spoken by different speakers and with different accents—an everyday situation in which current state-of-the-art speech recognition models often fail. The model can also be used to qualitatively explain behavioral data on human speech learning and derive predictions for future experiments.

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“…The results for the state-of-the-art speech recognition system using HMM (Hidden Markov Model) were reported in [18]. OT (Occurrence Time) features were used in [103].…”

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

From Birdsong to Human Speech Recognition: Bayesian Inference on a Hierarchy of Nonlinear Dynamical Systems

2013

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“…Co-word analysis has been used to measure the association strength between keywords to show research trends and patterns in polymer chemistry [ 17 ], nervous systems [ 23 , 24 ], software engineering [ 25 , 26 ], info search [ 21 ], and bioengineering [ 27 , 28 ]. Moreover, few studies have used this approach in the field of health care and medicine.…”

Section: Introductionmentioning

confidence: 99%

Application of Social Network Analysis to Health Care Sectors

Jang

Lee

2012

Healthc Inform Res

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ObjectivesThis study aimed to examine the feasibility of social network analysis as a valuable research tool for indicating a change in research topics in health care and medicine.MethodsPapers used in the analysis were collected from the PubMed database at the National Library of Medicine. After limiting the search to papers affiliated with the National Institutes of Health, 27,125 papers were selected for the analysis. From these papers, the top 100 non-duplicate and most studied Medical Subject Heading terms were extracted. NetMiner V.3 was used for analysis. Weighted degree centrality was applied to the analysis to compare the trends in the change of research topics. Changes in the core keywords were observed for the entire group and in three-year intervals.ResultsThe core keyword with the highest centrality value was "Risk Factor," followed by "Molecular Sequence Data," "Neoplasms," "Signal Transduction," "Brain," and "Amino Acid Sequence." Core keywords varied between time intervals, changing from "Molecular Sequence Data" to "Risk Factors" over time. "Risk Factors" was added as a new keyword and its social network was expanded. The slope of the keywords also varied over time: "Molecular Sequence Data," with a high centrality value, had a decreasing slope at certain intervals, whereas "SNP," with a low centrality value, had an increasing slope at certain intervals.ConclusionsThe social network analysis method is useful for tracking changes in research topics over time. Further research should be conducted to confirm the usefulness of this method in health care and medicine.

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Neural Binding, Consciousness, and Mental Disorders: Complexity as a Common Element

Assis

2015

Act Nerv Super

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A dynamical pattern recognition model of gamma activity in auditory cortex

Cited by 9 publications

References 75 publications

From Birdsong to Human Speech Recognition: Bayesian Inference on a Hierarchy of Nonlinear Dynamical Systems

From Birdsong to Human Speech Recognition: Bayesian Inference on a Hierarchy of Nonlinear Dynamical Systems

Application of Social Network Analysis to Health Care Sectors

Neural Binding, Consciousness, and Mental Disorders: Complexity as a Common Element

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