Neural Resolution of Formant Frequencies in the Primary Auditory Cortex of Rats

Honey, Christian; Schnupp, Jan W. H.

doi:10.1371/journal.pone.0134078

Cited by 4 publications

(6 citation statements)

References 51 publications

(53 reference statements)

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“…In the natural world, harmonics and formants are usually considered to be specific structures of an animal's voice, which are related to the richness and spread of the spectral components. Studies on mammals have indicated that neurons in the primary auditory cortex encode the spectral structure of sounds in these brain regions (Fishman, Steinschneider, & Micheyl, ; Honey & Schnupp, ), which are analogous to the CMM and NCM in songbirds (Vates et al, ; Wang, Brzozowska‐Prechtl, & Karten, ). In our results, for time windows shorter than the length of one syllable, Ent and Mfr had strong TSC coefficients compared with the other acoustic factors measured (Figure ).…”

Section: Discussionmentioning

confidence: 99%

Neural properties of fundamental function encoding of sound selectivity in the female avian auditory cortex

Inda

Hotta

Oka

2019

Eur J of Neuroscience

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Zebra finches (Taeniopygia guttata) use their voices for communication. Song structures in the songs of individual males are important for sound recognition in females.The caudomedial mesopallium (CMM) and nidopallium (NCM) are known to be essential higher auditory regions for sound recognition. These two regions have also been discussed with respect to their fundamental functions and song selectivity. To clarify their functions and selectivity, we investigated latencies and spiking patterns and also developed a novel correlation analysis to evaluate the relationship between neural activity and the characteristics of acoustic factors. We found that the latencies and spiking patterns in response to song stimuli differed between the CMM and NCM. In addition, our correlation analysis revealed that amplitude and frequency structures were important temporal acoustic factors for both regions. Although the CMM and NCM have different fundamental functions, they share similar encoding systems for acoustic factors. K E Y W O R D S auditory cortex, electrophysiology, songbird, sound selectivity | 1771 INDA et Al.

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Section: Discussionmentioning

confidence: 99%

Neural properties of fundamental function encoding of sound selectivity in the female avian auditory cortex

Inda

Hotta

Oka

2019

Eur J of Neuroscience

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“…Synchrony-based coding of vowels might be less effective in central nuclei, given lower-frequency synchronization limits compared to the auditory nerve (Joris et al 2004). Central neurons may instead encode vowel formant structure through average discharge rate (Mesgarani et al 2008;Perez et al 2013;Carney et al 2015;Honey and Schnupp 2015), but this hypothesis is untested in background noise. Here, we focus on coding of synthetic vowel-like sounds in the auditory midbrain.…”

Section: Introductionmentioning

confidence: 99%

Midbrain Synchrony to Envelope Structure Supports Behavioral Sensitivity to Single-Formant Vowel-Like Sounds in Noise

Henry

Abrams

Forst

et al. 2016

JARO

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Vowels make a strong contribution to speech perception under natural conditions. Vowels are encoded in the auditory nerve primarily through neural synchrony to temporal fine structure and to envelope fluctuations rather than through average discharge rate. Neural synchrony is thought to contribute less to vowel coding in central auditory nuclei, consistent with more limited synchronization to fine structure and the emergence of average-rate coding of envelope fluctuations. However, this hypothesis is largely unexplored, especially in background noise. The present study examined coding mechanisms at the level of the midbrain that support behavioral sensitivity to simple vowel-like sounds using neurophysiological recordings and matched behavioral experiments in the budgerigar. Stimuli were harmonic tone complexes with energy concentrated at one spectral peak, or formant frequency, presented in quiet and in noise. Behavioral thresholds for formant-frequency discrimination decreased with increasing amplitude of stimulus envelope fluctuations, increased in noise, and were similar between budgerigars and humans. Multiunit recordings in awake birds showed that the midbrain encodes vowel-like sounds both through response synchrony to envelope structure and through average rate. Whereas neural discrimination thresholds based on either coding scheme were sufficient to support behavioral thresholds in quiet, only synchrony-based neural thresholds could account for behavioral thresholds in background noise. These results reveal an incomplete transformation to average-rate coding of vowel-like sounds in the midbrain. Model simulations suggest that this transformation emerges due to modulation tuning, which is shared between birds and mammals. Furthermore, the results underscore the behavioral relevance of envelope synchrony in the midbrain for detection of small differences in vowel formant frequency under real-world listening conditions.

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“…In general, STRF prediction accuracy in auditory cortex tends to be relatively poor compared to lower auditory structures such as inferior colliculus, where nonlinearities and contextual influences are less dominant, and responses to a repeated stimulus are more stereotyped [ 68 – 70 ]. As discussed elsewhere [ 56 , 71 – 73 ], intertrial response variability imposes an upper limit on prediction accuracy that can be obtained with a linear STRF model. This is because the activity of units with distinctive responses across trials is principally governed by factors other than the stimulus (e.g., contextual effects, top-down influences, measurement noise).…”

Section: Resultsmentioning

confidence: 99%

“…As illustrated here, however, prediction correlations were heavily influenced by statistical correction choices, as well as the temporal resolution of the binned responses and predictions. Many additional methodological choices can influence prediction accuracy results, including the size (or fraction used) of the estimation and validation datasets [ 32 , 56 , 76 ], compensating for spike time jitter [ 77 ], smoothing response/prediction functions or receptive field kernels [ 16 , 72 , 76 ], excluding onset responses [ 72 ], and various technical details regarding stimulus representation approaches [ 78 ]. These factors raise important caveats for evaluating system response linearity [ 70 ], and especially for comparing results across studies obtained with different methodologies.…”

Section: Discussionmentioning

confidence: 99%

“…We approached these objectives by applying a range of liberal to conservative gain and cluster-based thresholds to raw STAs obtained from awake primate auditory cortex. The validity of each threshold setting was then evaluated by comparing observed neuronal responses elicited by a novel validation stimulus to the predicted response obtained using each version of the corrected STRF [ 29 , 32 – 34 , 56 – 57 ]. We then extended these correction approaches by implementing a simple cross-validation heuristic for selecting the best threshold settings for individual STRFs.…”

Section: Introductionmentioning

confidence: 99%

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Cluster-based analysis improves predictive validity of spike-triggered receptive field estimates

Bigelow

Malone

2017

PLoS ONE

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Spectrotemporal receptive field (STRF) characterization is a central goal of auditory physiology. STRFs are often approximated by the spike-triggered average (STA), which reflects the average stimulus preceding a spike. In many cases, the raw STA is subjected to a threshold defined by gain values expected by chance. However, such correction methods have not been universally adopted, and the consequences of specific gain-thresholding approaches have not been investigated systematically. Here, we evaluate two classes of statistical correction techniques, using the resulting STRF estimates to predict responses to a novel validation stimulus. The first, more traditional technique eliminated STRF pixels (time-frequency bins) with gain values expected by chance. This correction method yielded significant increases in prediction accuracy, including when the threshold setting was optimized for each unit. The second technique was a two-step thresholding procedure wherein clusters of contiguous pixels surviving an initial gain threshold were then subjected to a cluster mass threshold based on summed pixel values. This approach significantly improved upon even the best gain-thresholding techniques. Additional analyses suggested that allowing threshold settings to vary independently for excitatory and inhibitory subfields of the STRF resulted in only marginal additional gains, at best. In summary, augmenting reverse correlation techniques with principled statistical correction choices increased prediction accuracy by over 80% for multi-unit STRFs and by over 40% for single-unit STRFs, furthering the interpretational relevance of the recovered spectrotemporal filters for auditory systems analysis.

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Neural Resolution of Formant Frequencies in the Primary Auditory Cortex of Rats

Cited by 4 publications

References 51 publications

Neural properties of fundamental function encoding of sound selectivity in the female avian auditory cortex

Neural properties of fundamental function encoding of sound selectivity in the female avian auditory cortex

Midbrain Synchrony to Envelope Structure Supports Behavioral Sensitivity to Single-Formant Vowel-Like Sounds in Noise

Cluster-based analysis improves predictive validity of spike-triggered receptive field estimates

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