A Computational Model of Auditory Selective Attention

Wrigley, Stuart N.; Brown, Guy J.

doi:10.1109/tnn.2004.832710

Cited by 48 publications

(37 citation statements)

References 42 publications

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“…In addition, Bregman suggested a set of top-down grouping processes which he termed "schema-driven" mechanisms, based on acquired expectations from prior experience or knowledge. Recent results (Carlyon, 2004;Cusack et al, 2004;Wrigley and Brown, 2004;Molholm et al, 2005, Snyder et al 2006) also suggest the presence of two cortical mechanisms of streaming -an automatic "pre-attentive" segregation of sounds and an attention-dependent streaming mechanism. The process of auditory scene analysis sets the stage and seamlessly interacts with the auditory attention system (Naatanen et al, 2001;Opitz et al, 2005;Sussman, 2005).…”

Section: I2 Brief Review Of Auditory Attentionmentioning

confidence: 95%

Does attention play a role in dynamic receptive field adaptation to changing acoustic salience in A1?

et al. 2007

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Acoustic filter properties of A1 neurons can dynamically adapt to stimulus statistics, classical conditioning, instrumental learning and the changing auditory attentional focus. We have recently developed an experimental paradigm that allows us to view cortical receptive field plasticity on-line as the animal meets different behavioral challenges by attending to salient acoustic cues and changing its cortical filters to enhance performance. We propose that attention is the key trigger that initiates a cascade of events leading to the dynamic receptive field changes that we observe. In our paradigm, ferrets were initially trained, using conditioned avoidance training techniques, to discriminate between background noise stimuli (temporally orthogonal ripple combinations) and foreground tonal target stimuli. They learned to generalize the task for a wide variety of distinct background and foreground target stimuli. We recorded cortical activity in the awake behaving animal and computed on-line spectrotemporal receptive fields (STRFs) of single neurons in A1. We observed clear, predictable task-related changes in STRF shape while the animal performed spectral tasks (including single tone and multi-tone detection, and two-tone discrimination) with different tonal targets. A different set of task-related changes occurred when the animal performed temporal tasks (including gap detection and click-rate discrimination). Distinctive cortical STRF changes may constitute a "task-specific signature". These spectral and temporal changes in cortical filters occur quite rapidly, within 2 minutes of task onset, and fade just as quickly after task completion, or in some cases, persisted for hours. The same cell could multiplex by differentially changing its receptive field in different task conditions. On-line dynamic task-related changes, as well as persistent plastic changes, were observed at a single-unit, multi-unit and population level. Auditory attention is likely to be pivotal in mediating these task-related changes since the magnitude of STRF changes correlated with behavioral performance on tasks with novel targets. Overall, these results suggest the presence of an attention-triggered plasticity algorithm in A1 that can swiftly change STRF shape by transforming receptive fields to enhance figure/ground separation, by using a contrast matched filter to filter out the background, while simultaneously enhancing the salient acoustic target in the foreground. These results favor the view of a nimble, dynamic, attentive and adaptive brain that can quickly reshape its sensory filter properties and sensori-motor links on a moment-to-moment basis, depending upon the current challenges the animal faces. In this review, we summarize our results in the context of a broader survey of the field of auditory attention, and then consider neuronal networks that could give rise to this phenomenon of attention-driven receptive field plasticity in A1.

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Section: I2 Brief Review Of Auditory Attentionmentioning

confidence: 95%

Does attention play a role in dynamic receptive field adaptation to changing acoustic salience in A1?

et al. 2007

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“…Fig. 3 shows the evolution, during 15 seconds of the one-minute testing fragment, of the different terms in (11), each behaving according to the model described in section II. Most of the time, the external excitation equals the total excitation, as local excitation only exceeds zero in certain timesteps where the iterative clustering mechanism rises this term to noticeable values.…”

Section: Resultsmentioning

confidence: 99%

“…Consequently, the model is aimed to be embedded in low-cost hardware that has to run continuously for weeks to months. This requirement makes it not feasible to use similar but much more detailed models for auditory attention, such as the one presented in [11]. Nevertheless, while some of the submodels presented in the present work behave according to greatly simplified rules, the proposed model's architecture, and the way in which its different submodels interact are strongly based on available knowledge of the human auditory system.…”

Section: Introductionmentioning

confidence: 99%

Attention-driven auditory stream segregation using a SOM coupled with an excitatory-inhibitory ANN

Boes

Oldoni

Coensel

et al. 2012

The 2012 International Joint Conference on Neural Networks (IJCNN)

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-Auditory attention is an essential property of human hearing. It is responsible for the selection of information to be sent to working memory and as such to be perceived consciously, from the abundance of auditory information that is continuously entering the ears. Thus, auditory attention heavily influences human auditory perception and systems simulating human auditory scene analysis would benefit from an attention model. In this paper, a human-mimicking model of auditory attention is presented, aimed to be used in environmental sound monitoring. It relies on a Self-Organizing Map (SOM) for learning and classifying sounds. Coupled to this SOM, an excitatory-inhibitory artificial neural network (ANN), simulating the auditory cortex, is defined. The activation of these neurons is calculated based on an interplay of various excitatory and inhibitory inputs. The latter simulate auditory attention mechanisms in a humaninspired but simplified way, in order to keep the computational cost within bounds. The behavior of the model incorporating all of these mechanisms is investigated, and plausible results are obtained.

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“…McCabe and Denham (1997) proposed a different neural network that simulates psychophysical results on auditory streaming. Recently, Wrigley and Brown (2004) put forward a neural oscillator model of auditory attention and used it to quantitatively simulate a set of psychological data.…”

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confidence: 99%