Phase-oscillator computations as neural models of stimulus–response conditioning and response selection

Suppes, Patrick; Barros, J. Acácio de; Oas, Gary

doi:10.1016/j.jmp.2012.01.001

Cited by 19 publications

(56 citation statements)

References 55 publications

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“…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%

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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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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“…Because transparallel processing by hyperstrings provides classical computers with quantum power, it strengthens ideas that quantum-like cognitive processing does not have to rely on actual quantum mechanical phenomena at the subneuron level (de Barros and Suppes 2009;Suppes et al 2012;Townsend and Nozawa 1995;Vassilieva et al 2011). Instead, as I argued, it might rely on interactions at the level of neurons.…”

Section: Discussionmentioning

confidence: 52%

Transparallel mind: classical computing with quantum power

Helm

2015

Artif Intell Rev

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Inspired by the extraordinary computing power promised by quantum computers, the quantum mind hypothesis postulated that quantum mechanical phenomena are the source of neuronal synchronization, which, in turn, might underlie consciousness. Here, I present an alternative inspired by a classical computing method with quantum power. This method relies on special distributed representations called hyperstrings. Hyperstrings are superpositions of up to an exponential number of strings, which-by a single-processor classical computercan be evaluated in a transparallel fashion, that is, simultaneously as if only one string were concerned. Building on a neurally plausible model of human visual perceptual organization, in which hyperstrings are formal counterparts of transient neural assemblies, I postulate that synchronization in such assemblies is a manifestation of transparallel information processing. This accounts for the high combinatorial capacity and speed of human visual perceptual organization and strengthens ideas that self-organizing cognitive architecture bridges the gap between neurons and consciousness.

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“…In searching for an appropriate neural synchronization mechanism several theoretical models have been developed (Malsburg, 1995;Jalili, 2009;Suppes et al, 2012;Vassilieva et al, 2011;Zavaglia et al, 2012;Burwick, 2014;Raffone & Van Leeuwen, 2001b). These models have shown that employing synchronization it is possible to perform various types of computing.…”

Section: Synchronization and Binding Problemmentioning

confidence: 99%

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

Assis

2015

Act Nerv Super

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Phase-oscillator computations as neural models of stimulus–response conditioning and response selection

Cited by 19 publications

References 55 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

Transparallel mind: classical computing with quantum power

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

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