A Biologically Plausible Computational Model for Auditory Object Recognition

Larson, Eric B.; Billimoria, Cyrus P.; Sen, Kamal

doi:10.1152/jn.90664.2008

Cited by 24 publications

(20 citation statements)

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“…Although these findings relate to aspects of speech and phonology, they do so in terms of multisensory processing and sensorimotor integration and are not the key paradigms indicated by computational theory for demonstrating the presence of pattern recognition networks (8)(9)(10)(11)(12)123). Those paradigms (CS and adaptation), systematically meta-analyzed here, find anterior localization.…”

Section: And Cohen and Colleagues' (2004) Hypothesis Of An Auditory Wmentioning

confidence: 56%

“…For example, whereas the phonemic structure of a word is fixed, there is considerable variation in physical, spectrotemporal form-attributable to accent, pronunciation, body size, and the like-among utterances of a given word. It has been proposed for visual cortical processing that a feed-forward, hierarchical architecture (7) may be capable of simultaneously solving the problems of complexity and variability (8)(9)(10)(11)(12). Here, we examine these ideas in the context of auditory cortex.…”

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confidence: 99%

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Phoneme and word recognition in the auditory ventral stream

DeWitt

Rauschecker²

2012

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

401

328

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Spoken word recognition requires complex, invariant representations. Using a meta-analytic approach incorporating more than 100 functional imaging experiments, we show that preference for complex sounds emerges in the human auditory ventral stream in a hierarchical fashion, consistent with nonhuman primate electrophysiology. Examining speech sounds, we show that activation associated with the processing of short-timescale patterns (i.e., phonemes) is consistently localized to left mid-superior temporal gyrus (STG), whereas activation associated with the integration of phonemes into temporally complex patterns (i.e., words) is consistently localized to left anterior STG. Further, we show left midto anterior STG is reliably implicated in the invariant representation of phonetic forms and that this area also responds preferentially to phonetic sounds, above artificial control sounds or environmental sounds. Together, this shows increasing encoding specificity and invariance along the auditory ventral stream for temporally complex speech sounds.functional MRI | meta-analysis | auditory cortex | object recognition | language S poken word recognition presents several challenges to the brain. Two key challenges are the assembly of complex auditory representations and the variability of natural speech (SI Appendix, Fig. S1) (1). Representation at the level of primary auditory cortex is precise: fine-grained in scale and local in spectrotemporal space (2, 3). The recognition of complex spectrotemporal forms, like words, in higher areas of auditory cortex requires the transformation of this granular representation into Gestalt-like, object-centered representations. In brief, local features must be bound together to form representations of complex spectrotemporal contours, which are themselves the constituents of auditory "objects" or complex sound patterns (4, 5). Next, representations must be generalized and abstracted. Coding in primary auditory cortex is sensitive even to minor physical transformations. Object-centered coding in higher areas, however, must be invariant (i.e., tolerant of natural stimulus variation) (6). For example, whereas the phonemic structure of a word is fixed, there is considerable variation in physical, spectrotemporal form-attributable to accent, pronunciation, body size, and the like-among utterances of a given word. It has been proposed for visual cortical processing that a feed-forward, hierarchical architecture (7) may be capable of simultaneously solving the problems of complexity and variability (8-12). Here, we examine these ideas in the context of auditory cortex.In a hierarchical pattern-recognition scheme (8), coding in the earliest cortical field would reflect the tuning and organization of primary auditory cortex (or core) (2, 3, 13). That is, single-neuron receptive fields (more precisely, frequency-response areas) would be tuned to particular center frequencies and would have minimal spectrotemporal complexity (i.e., a single excitatory zone and one-to-two inhibitory side bands). ...

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Section: And Cohen and Colleagues' (2004) Hypothesis Of An Auditory Wmentioning

confidence: 56%

mentioning

confidence: 99%

Phoneme and word recognition in the auditory ventral stream

DeWitt

Rauschecker²

2012

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

401

328

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“…Many neurons in field L, the avian analog of primary auditory cortex, are tightly tuned in time (Nagel and Doupe 2008), causing them to respond to syllable onsets with precisely time-locked spikes (Woolley et al 2006). This temporal pattern of spiking provides significant information about what song was heard (Narayan et al 2006), and several papers have therefore suggested that these temporal patterns of spiking are critical for birds' discrimination and recognition of songs (Larson et al 2009;Wang et al 2007). Because these patterns are primarily driven by syllable onsets, the absolute temporal pattern of spiking in a single neuron should change as a song is expanded or contracted in time.…”

Section: Discussionmentioning

confidence: 99%

Differential Influence of Frequency, Timing, and Intensity Cues in a Complex Acoustic Categorization Task

Nagel

McLendon

Doupe

2010

Journal of Neurophysiology

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Nagel KI, McLendon HM, Doupe AJ. Differential influence of frequency, timing, and intensity cues in a complex acoustic categorization task. J Neurophysiol 104: 1426 -1437, 2010. First published July 7, 2010 doi:10.1152/jn.00028.2010. Songbirds, which, like humans, learn complex vocalizations, provide an excellent model for the study of acoustic pattern recognition. Here we examined the role of three basic acoustic parameters in an ethologically relevant categorization task. Female zebra finches were first trained to classify songs as belonging to one of two males and then asked whether they could generalize this knowledge to songs systematically altered with respect to frequency, timing, or intensity. Birds' performance on song categorization fell off rapidly when songs were altered in frequency or intensity, but they generalized well to songs that were changed in duration by Ͼ25%. Birds were not deaf to timing changes, however; they detected these tempo alterations when asked to discriminate between the same song played back at two different speeds. In addition, when birds were retrained with songs at many intensities, they could correctly categorize songs over a wide range of volumes. Thus although they can detect all these cues, birds attend less to tempo than to frequency or intensity cues during song categorization. These results are unexpected for several reasons: zebra finches normally encounter a wide range of song volumes but most failed to generalize across volumes in this task; males produce only slight variations in tempo, but females generalized widely over changes in song duration; and all three acoustic parameters are critical for auditory neurons. Thus behavioral data place surprising constraints on the relationship between previous experience, behavioral task, neural responses, and perception. We discuss implications for models of auditory pattern recognition.

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“…Many modeling studies have theorized potential mechanisms (Tank and Hopfield 1987;Hopfield and Brody 2000;Hopfield and Brody 2001;Barak and Tsodyks 2006;Gollisch 2008;Gutig and Sompolinsky 2009). Previously, we have proposed biologically plausible computational model for song recognition (Larson et al 2009(Larson et al , 2010. One of these models (Larson et al 2009) uses a van Rossum-like scheme to compare an incoming spike train to a large set of memorized ones and chooses the most likely song among them.…”

Section: Discussionmentioning

confidence: 99%

“…Previously, we have proposed biologically plausible computational model for song recognition (Larson et al 2009(Larson et al , 2010. One of these models (Larson et al 2009) uses a van Rossum-like scheme to compare an incoming spike train to a large set of memorized ones and chooses the most likely song among them. We found that a simple extension to the model by incorporating multiple memory banks at different speeds enables time-warp-invariant recognition on the experimental data reported here (data not shown), suggesting that neural responses in field L to time-warped stimuli could form the basis for time-warp-invariant recognition of natural sounds.…”

Section: Discussionmentioning

confidence: 99%

Auditory Forebrain Neurons Track Temporal Features of Time-Warped Natural Stimuli

Maddox

Sen

Billimoria

2013

JARO

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A fundamental challenge for sensory systems is to recognize natural stimuli despite stimulus variations. A compelling example occurs in speech, where the auditory system can recognize words spoken at a wide range of speeds. To date, there have been more computational models for time-warp invariance than experimental studies that investigate responses to timewarped stimuli at the neural level. Here, we address this problem in the model system of zebra finches anesthetized with urethane. In behavioral experiments, we found high discrimination accuracy well beyond the observed natural range of song variations. We artificially sped up or slowed down songs (preserving pitch) and recorded auditory responses from neurons in field L, the avian primary auditory cortex homolog. We found that field L neurons responded robustly to time-warped songs, tracking the temporal features of the stimuli over a broad range of warp factors. Time-warp invariance was not observed per se, but there was sufficient information in the neural responses to reliably classify which of two songs was presented. Furthermore, the average spike rate was close to constant over the range of time warps, contrary to recent modeling predictions. We discuss how this response pattern is surprising given current computational models of time-warp invariance and how such a response could be decoded downstream to achieve time-warp-invariant recognition of sounds.

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A Biologically Plausible Computational Model for Auditory Object Recognition

Cited by 24 publications

References 25 publications

Phoneme and word recognition in the auditory ventral stream

Phoneme and word recognition in the auditory ventral stream

Differential Influence of Frequency, Timing, and Intensity Cues in a Complex Acoustic Categorization Task

Auditory Forebrain Neurons Track Temporal Features of Time-Warped Natural Stimuli

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