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A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics
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In: Computer Science: Faculty Publications and Other Works (2020)
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Towards High-End Scalability on Bio-Inspired Computational Models
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In: Computer Science: Faculty Publications and Other Works (2020)
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A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics
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Grammar emergence in cortical dynamics, a computational approach ...
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A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics ...
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A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics ...
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A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics ...
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neurophon/neurophon: Minor update to ensure future updates include Zenodo author/keyword metadata ...
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neurophon/neurophon: A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics ...
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neurophon/neurophon: A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics ...
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Datasets used to train and test the Cortical Spectro-Temporal Model (CSTM)
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In: Computer Science: Faculty Publications and Other Works (2019)
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Neurocomputational cortical memory for spectro-temporal phonetic abstraction.
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In: Computer Science: Faculty Publications and Other Works (2019)
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Phonetic acquisition in cortical dynamics, a computational approach
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In: Computer Science: Faculty Publications and Other Works (2019)
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Phonetic acquisition in cortical dynamics, a computational approach
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In: Software Systems Laboratory (2019)
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Datasets used to train and test the Cortical Spectro-Temporal Model (CSTM). ...
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Datasets used to train and test the Cortical Spectro-Temporal Model (CSTM). ...
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Phonetic acquisition in cortical dynamics, a computational approach
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Abstract:
Many computational theories have been developed to improve artificial phonetic classification performance from linguistic auditory streams. However, less attention has been given to psycholinguistic data and neurophysiological features recently found in cortical tissue. We focus on a context in which basic linguistic units–such as phonemes–are extracted and robustly classified by humans and other animals from complex acoustic streams in speech data. We are especially motivated by the fact that 8-month-old human infants can accomplish segmentation of words from fluent audio streams based exclusively on the statistical relationships between neighboring speech sounds without any kind of supervision. In this paper, we introduce a biologically inspired and fully unsupervised neurocomputational approach that incorporates key neurophysiological and anatomical cortical properties, including columnar organization, spontaneous micro-columnar formation, adaptation to contextual activations and Sparse Distributed Representations (SDRs) produced by means of partial N-Methyl-D-aspartic acid (NMDA) depolarization. Its feature abstraction capabilities show promising phonetic invariance and generalization attributes. Our model improves the performance of a Support Vector Machine (SVM) classifier for monosyllabic, disyllabic and trisyllabic word classification tasks in the presence of environmental disturbances such as white noise, reverberation, and pitch and voice variations. Furthermore, our approach emphasizes potential self-organizing cortical principles achieving improvement without any kind of optimization guidance which could minimize hypothetical loss functions by means of–for example–backpropagation. Thus, our computational model outperforms multiresolution spectro-temporal auditory feature representations using only the statistical sequential structure immerse in the phonotactic rules of the input stream.
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Keyword:
Research Article
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URL: http://www.ncbi.nlm.nih.gov/pubmed/31173613 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6555517/ https://doi.org/10.1371/journal.pone.0217966
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