An Oscillatory Correlation Theory of Temporal Pattern Segmentation

Wang, De Liang

doi:10.1007/978-1-4615-1919-5_3

Cited by 3 publications

(3 citation statements)

References 45 publications

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“…Perceptual tasks involving neural oscillations have also been observed in audition [26,43, see Wang [68] for a model] and olfaction [25]. Oscillations have also been explored in associative recall networks [11,69,1].…”

Section: Discussionmentioning

confidence: 99%

Synchronization and desynchronization in a network of locally coupled Wilson-Cowan oscillators

Campbell

Wang²

1996

IEEE Trans. Neural Netw.

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A network of Wilson-Cowan (WC) oscillators is constructed, and its emergent properties of synchronization and desynchronization are investigated by both computer simulation and formal analysis. The network is a 2D matrix, where each oscillator is coupled only to its neighbors. We show analytically that a chain of locally coupled oscillators (the piecewise linear approximation to the WC oscillator) synchronizes, and we present a technique to rapidly entrain finite numbers of oscillators. The coupling strengths change on a fast time scale based on a Hebbian rule. A global separator is introduced which receives input from and sends feedback to each oscillator in the matrix. The global separator is used to desynchronize different oscillator groups. Unlike many other models, the properties of this network emerge from local connections that preserve spatial relationships among components and are critical for encoding Gestalt principles of feature grouping. The ability to synchronize and desynchronize oscillator groups within this network offers a promising approach for pattern segmentation and figure/ground segregation based on oscillatory correlation.

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Section: Discussionmentioning

confidence: 99%

Synchronization and desynchronization in a network of locally coupled Wilson-Cowan oscillators

Campbell

Wang²

1996

IEEE Trans. Neural Netw.

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“…Most existing strategies for separating sounds on the basis of fundamental frequency attempt to group frequency channels together by finding F0-related features in each channel, e. g., [21], [22], [95], [107]. The RTN model demonstrates an alternate, time-domain strategy for auditory object separation that 1) exploits fine timing information and 2) uses no explicit feature detection (i.e., F0-detectors).…”

Section: Recurrent Neural Timing Nets (Rtns)mentioning

confidence: 99%

“…The lines remain labeled to signify the features they encode, while the time domain is used to signify which channels are grouped or separated [23], [24]. The temporal label can involve either timing relative to a reference wave [92]- [94] or spike synchronization between channels [19]- [22], [95].…”

Section: (A)-(c)]mentioning

confidence: 99%

Temporal Codes and Computations for Sensory Representation and Scene Analysis

Cariani

2004

IEEE Trans. Neural Netw.

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This paper considers a space of possible temporal codes, surveys neurophysiological and psychological evidence for their use in nervous systems, and presents examples of neural timing networks that operate in the time-domain. Sensory qualities can be encoded temporally by means of two broad strategies: stimulus-driven temporal correlations (phase-locking) and stimulus-triggering of endogenous temporal response patterns. Evidence for stimulus-related spike timing patterns exists in nearly every sensory modality, and such information can be potentially utilized for representation of stimulus qualities, localization of sources, and perceptual grouping. Multiple strategies for temporal (time, frequency, and code-division) multiplexing of information for transmission and grouping are outlined. Using delays and multiplications (coincidences), neural timing networks perform time-domain signal processing operations to compare, extract and separate temporal patterns. Separation of synthetic double vowels by a recurrent neural timing network is used to illustrate how coherences in temporal fine structure can be exploited to build up and separate periodic signals with different fundamentals. Timing nets constitute a time-domain scene analysis strategy based on temporal pattern invariance rather than feature-based labeling, segregation and binding of channels. Further potential implications of temporal codes and computations for new kinds of neural networks are explored.

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Primitive auditory segregation based on oscillatory correlation

Wang

1996

Cognitive Science

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An Oscillatory Correlation Theory of Temporal Pattern Segmentation

Cited by 3 publications

References 45 publications

Synchronization and desynchronization in a network of locally coupled Wilson-Cowan oscillators

Synchronization and desynchronization in a network of locally coupled Wilson-Cowan oscillators

Temporal Codes and Computations for Sensory Representation and Scene Analysis

Primitive auditory segregation based on oscillatory correlation

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