ARTSTREAM: a neural network model of auditory scene analysis and source segregation

Grossberg, Stephen; Govindarajan, Krishna K.; Wyse, Lonce; Cohen, Michael A.

doi:10.1016/j.neunet.2003.10.002

Cited by 64 publications

(44 citation statements)

References 73 publications

(99 reference statements)

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“…Subsequent modelling studies provide additional evidence for such a circuit (e.g. Gove et al 1995;Grossberg & Myers 2000;Grossberg et al 2004;Dranias et al 2008), and laminar cortical models predict identified cell types (Grossberg 1999;Raizada & Grossberg 2003;Grossberg & Pearson 2008;Grossberg & Versace 2008). Many anatomical and neurophysiological experiments support this prediction (e.g.…”

Section: Expectations and Biased Competition: Modulatory On-centre Omentioning

confidence: 84%

Cortical and subcortical predictive dynamics and learning during perception, cognition, emotion and action

Grossberg

2009

Phil. Trans. R. Soc. B

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An intimate link exists between the predictive and learning processes in the brain. Perceptual / cognitive and spatial/motor processes use complementary predictive mechanisms to learn, recognize, attend and plan about objects in the world, determine their current value, and act upon them. Recent neural models clarify these mechanisms and how they interact in cortical and subcortical brain regions. The present paper reviews and synthesizes data and models of these processes, and outlines a unified theory of predictive brain processing.

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Section: Expectations and Biased Competition: Modulatory On-centre Omentioning

confidence: 84%

Cortical and subcortical predictive dynamics and learning during perception, cognition, emotion and action

Grossberg

2009

Phil. Trans. R. Soc. B

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“…This condition appears to be a crucial test for any continuity illusion mechanism; to our knowledge, none of the previously-proposed models for the continuity illusion (Grossberg et al, 2004;Husain et al, 2005;Husain and Horwitz, 2006) were tested for compatibility with this aspect of human perception. Transient responses time-locked to sound onsets are known to depend on the gaps between successive sounds (Eggermont, 1995;Steinschneider et al, 1999;Ross and Pantev, 2004); the relationship between onset responses and illusory continuity is an important future direction for research.…”

Section: Discussionmentioning

confidence: 99%

“…However, since silent gaps are also known to disrupt continuity illusions (Warren, 1972), models need to formally address the conditions under which these illusions break down. The lack of attention paid to this latter detail in previous models (Grossberg et al, 2004;Husain et al, 2005;Husain and Horwitz, 2006) motivated us to test the hypothesis that basic, temporally dynamic properties of neuronal responses may account for both the emergence and the disintegration of the continuity illusion.…”

Section: Introductionmentioning

confidence: 99%

A proposed neural mechanism underlying auditory continuity illusions

Vinnik

Itskov

Balaban

2010

The Journal of the Acoustical Society of America

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A numerical thought experiment was conducted to assess whether stimulus-specific, short-term changes in auditory neural responsiveness could explain the formation of auditory objects underlying the auditory continuity illusion. A tonotopic, two-layer feedforward network model with one time constant for synaptic weight augmentation based on firing rate, and an independent time constant for synaptic weight decay was presented with classical continuity illusion stimuli. The results suggest that the continuity illusion could, in principle, be explained by basic, duration-dependent auditory circuit behavior, which could emerge at either early or later stages of processing.

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“…Following our discussion of context-specific adaptation, the size of such integration window may be scaled flexibly based on the spectrotemporal contrast of the ongoing signal. This proposed input-driven adaptation can be incorporated in existing computational models for the continuity illusion (Cooke and Brown, 1993;Masuda-Katsuse and Kawahara, 1999;Grossberg et al, 2004;Husain et al, 2005;Vinnik et al, 2010) or acoustic edge detection (Fishbach et al, 2001). These augmented models may then be applied, for example, in hearing aids to adapt optimally the time constants of front-end dynamic-range compression.…”

Section: Adaptive Encoding Of Spectrotemporal Boundariesmentioning

confidence: 99%

Hearing an Illusory Vowel in Noise: Suppression of Auditory Cortical Activity

Riecke¹,

Vanbussel

Hausfeld

et al. 2012

Journal of Neuroscience

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Human hearing is constructive. For example, when a voice is partially replaced by an extraneous sound (e.g., on the telephone due to a transmission problem), the auditory system may restore the missing portion so that the voice can be perceived as continuous (Miller and Licklider, 1950; for review, see Bregman, 1990; Warren, 1999). The neural mechanisms underlying this continuity illusion have been studied mostly with schematic stimuli (e.g., simple tones) and are still a matter of debate (for review, see Petkov and Sutter, 2011). The goal of the present study was to elucidate how these mechanisms operate under more natural conditions. Using psychophysics and electroencephalography (EEG), we assessed simultaneously the perceived continuity of a human vowel sound through interrupting noise and the concurrent neural activity. We found that vowel continuity illusions were accompanied by a suppression of the 4 Hz EEG power in auditory cortex (AC) that was evoked by the vowel interruption. This suppression was stronger than the suppression accompanying continuity illusions of a simple tone. Finally, continuity perception and 4 Hz power depended on the intactness of the sound that preceded the vowel (i.e., the auditory context). These findings show that a natural sound may be restored during noise due to the suppression of 4 Hz AC activity evoked early during the noise. This mechanism may attenuate sudden pitch changes, adapt the resistance of the auditory system to extraneous sounds across auditory scenes, and provide a useful model for assisted hearing devices.

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ARTSTREAM: a neural network model of auditory scene analysis and source segregation

Cited by 64 publications

References 73 publications

Cortical and subcortical predictive dynamics and learning during perception, cognition, emotion and action

Cortical and subcortical predictive dynamics and learning during perception, cognition, emotion and action

A proposed neural mechanism underlying auditory continuity illusions

Hearing an Illusory Vowel in Noise: Suppression of Auditory Cortical Activity

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