Learning Pattern Recognition Through Quasi-Synchronization of Phase Oscillators

Vassilieva, Ekaterina A.; Pinto, Geraldo Ronivan; Barros, J. Acácio de; Suppes, Patrick

doi:10.1109/tnn.2010.2086476

Cited by 55 publications

(51 citation statements)

References 41 publications

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“…This result supports use of phase patterns of brain waves to recognize phonemes. Although the model does not provide a physical mechanism, such as phase locking of oscillators, the recognition criterion of minimum mean phase differences is close to the useful criterion of quasisynchronization for oscillators (23).…”

Section: −46mentioning

confidence: 91%

“…Evidence from many studies suggests that phase modulation or phase synchronization can be the mechanism underlying the neural activities relative to the cognitive processes of language and memory retrieval (21,22). Given what we know about the phase locking or synchronization of oscillators (23,24), the synchronization of phases is conjectured to be the physical mechanism of retrieval. An unknown sound reaching the cortex is recognized as the specific spoken syllable /pi/ by the synchronization of its phase pattern with the stored phase pattern of /pi/ in verbal memory.…”

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confidence: 99%

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Using phase to recognize English phonemes and their distinctive features in the brain

Wang

Perreau-Guimaraes

Carvalhaes

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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The neural mechanisms used by the human brain to identify phonemes remain unclear. We recorded the EEG signals evoked by repeated presentation of 12 American English phonemes. A support vector machine model correctly recognized a high percentage of the EEG brain wave recordings represented by their phases, which were expressed in discrete Fourier transform coefficients. We show that phases of the oscillations restricted to the frequency range of 2-9 Hz can be used to successfully recognize brain processing of these phonemes. The recognition rates can be further improved using the scalp tangential electric field and the surface Laplacian around the auditory cortical area, which were derived from the original potential signal. The best rate for the eight initial consonants was 66.7%. Moreover, we found a distinctive phase pattern in the brain for each of these consonants. We then used these phase patterns to recognize the consonants, with a correct rate of 48.7%. In addition, in the analysis of the confusion matrices, we found significant similarity-differences were invariant between brain and perceptual representations of phonemes. These latter results supported the importance of phonological distinctive features in the neural representation of phonemes. phase synchronization | EEG classification H ow the human brain processes phonemes has been a subject of interest for linguists and neuroscientists for a long time. From the beginning of the 20th century, behavioral experiments were carried out to explore the perceptual discrimination of phonemes under various conditions (1-5). Since 1978, Mismatch Negativity in EEG (6) has been used extensively to measure the neural discrimination of phonemes (7). These results suggest the existence of a language-specific phoneme representation (8). More recent findings (9-11) using magnetoencephalograph recordings support the brain's encoding of phonological features before the lexical information is retrieved. Invasive recordings of animal neural responses to human speech also exhibited temporal and spatial characteristics, reflecting the distinctive features of phonemes (12,13). Related research has shown that animal recordings from the auditory cortex match animals' behavioral discrimination of phonemes (14) as well as the pattern of human psychological confusions (15). Functional MRI (fMRI) provides a noninvasive method to locate cortical activities of phoneme perception in healthy human brains (16). Success in recognizing fMRI evoked by isolated vowels has also been reported (17), but this work is difficult to extend because of the limited temporal resolution of fMRI.In most EEG studies of phoneme perception, the interest has been in the temporal information rather than its spectral structure. However, it has been shown that EEG rhythms may be related to a specific cognitive state of the brain (18,19). In the present study, we show that we can recognize, with some success, brain representations of specific phonemes using only the phase properties of brain waves in a narrow...

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Section: −46mentioning

confidence: 91%

mentioning

confidence: 99%

Using phase to recognize English phonemes and their distinctive features in the brain

Wang

Perreau-Guimaraes

Carvalhaes

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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“…Algorithmic applications of the coupled oscillator model (1) include limit-cycle estimation through particle filters (Tilton et al, 2012), clock synchronization in decentralized computing networks (Simeone et al, 2008;Baldoni et al, 2010;, central pattern generators for robotic locomotion (Aoi and Tsuchiya, 2005;Righetti and Ijspeert, 2006;Ijspeert, 2008), decentralized maximum likelihood estimation (Barbarossa and Scutari, 2007), and human-robot interaction (Mizumoto et al, 2010). Further envisioned applications of oscillator networks obeying equations similar to (1) include generating music (Huepe et al, 2012), signal processing (Shim et al, 2007), pattern recognition (Vassilieva et al, 2011), and neuro-computing through micromechanical (Hoppensteadt and Izhikevich, 2001) or laser (Hoppensteadt and Izhikevich, 2000;Wang and Ghosh, 2007) oscillators.…”

Section: Applications In Engineeringmentioning

confidence: 99%

Synchronization in complex networks of phase oscillators: A survey

2014

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The emergence of synchronization in a network of coupled oscillators is a fascinating subject of multidisciplinary research. This survey reviews the vast literature on the theory and the applications of complex oscillator networks. We focus on phase oscillator models that are widespread in real-world synchronization phenomena, that generalize the celebrated Kuramoto model, and that feature a rich phenomenology. We review the history and the countless applications of this model throughout science and engineering. We justify the importance of the widespread coupled oscillator model as a locally canonical model and describe some selected applications relevant to control scientists, including vehicle coordination, electric power networks, and clock synchronization. We introduce the reader to several synchronization notions and performance estimates. We propose analysis approaches to phase and frequency synchronization, phase balancing, pattern formation, and partial synchronization. We present the sharpest known results about synchronization in networks of homogeneous and heterogeneous oscillators, with complete or sparse interconnection topologies, and in finite-dimensional and infinite-dimensional settings. We conclude by summarizing the limitations of existing analysis methods and by highlighting some directions for future research.

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“…There are studies devoted to adaptation of the Kuramoto model for solving various problems, for example, several algorithms and approaches of cluster analysis [3,17,20], image compression method [9], learning patterns [21], also there are papers devoted to graph coloring problem that is solved by oscillatory network based on the considered model with negative connections [25,26]. In the following section, the oscillatory network based on modified Kuramoto model has been presented for image segmentation problem.…”

Section: Preliminariesmentioning

confidence: 99%

Oscillatory Network Based on Kuramoto Model for Image Segmentation

Новиков

Benderskaya

2015

Lecture Notes in Computer Science

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Abstract. Oscillatory networks represent biologically inspired models that implement cognitive functions such as vision, motion and memory. Vision functions are most attractive brain ability. Despite recognition problems have been successfully solved by traditional neural networks, segmentation problems still require close attention. In this paper, we propose oscillatory network based on Kuramoto phase oscillator for image segmentation where each allocated feature is encoded by ensemble of synchronous oscillators like in biologically plausible systems. The proposed model is designed to perform color segmentation and object segmentation using synchronization phenomena. Processes of synchronization between oscillators during image processing and multi-core implementation of the network for simulation are discussed. Experimental results of segmentation by the network using formal and real images have been presented.

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Learning Pattern Recognition Through Quasi-Synchronization of Phase Oscillators

Abstract: Abstract-

Cited by 55 publications

References 41 publications

Using phase to recognize English phonemes and their distinctive features in the brain

Using phase to recognize English phonemes and their distinctive features in the brain

Synchronization in complex networks of phase oscillators: A survey

Oscillatory Network Based on Kuramoto Model for Image Segmentation

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