Neural correlates of sensory consonance and dissonance in primate primary auditory cortex.

2010 ◽  
Vol 127 (3) ◽  
pp. 1951-1951 ◽  
Author(s):  
Mitchell Steinschneider ◽  
Yonatan I. Fishman
Keyword(s):  
Auditory Cortex ◽  
Primary Auditory Cortex ◽  
Neural Correlates ◽  
Sensory Consonance ◽  
Consonance And Dissonance

Neural Correlates of Gap Detection in Three Auditory Cortical Fields in the Cat

1999 ◽  
Vol 81 (5) ◽  
pp. 2570-2581 ◽  
Author(s):  
Jos J. Eggermont
Keyword(s):  
Auditory Cortex ◽  
Lower Boundary ◽  
Primary Auditory Cortex ◽  
Noise Burst ◽  
Neural Correlates ◽  
Lower Envelope ◽  
Gap Detection ◽  
Modal Values ◽  
Onset Response ◽  
Gap Values

Neural correlates of gap detection in three auditory cortical fields in the cat. Mimimum detectable gaps in noise in humans are independent of the position of the gap, whereas in cat primary auditory cortex (AI) they are position dependent. The position dependence in other cortical areas is not known and may resolve this contrast. This study presents minimum detectable gap-in-noise values for which single-unit (SU), multiunit (MU) recordings and local field potentials (LFPs) show an onset response to the noise after the gap. The gap, which varied in duration between 5 and 70 ms, was preceded by a noise burst of either 5 ms (early gap) or 500 ms (late gap) duration. In 10 cats, simultaneous recordings were made with one electrode each in AI, anterior auditory field (AAF), and secondary auditory cortex (AII). In nine additional cats, two electrodes were inserted in AI and one in AAF. Minimum detectable gaps based on SU, MU, or LFP data in each cortical area were the same. In addition, very similar minimum early-gap values were found in all three areas (means, 36.1–41.7 ms). The minimum late-gap values were also similar in AI and AII (means, 11.1 and 11.7 ms), whereas AAF showed significantly larger minimum late-gap durations (mean 21.5 ms). For intensities >35 dB SPL, distributions of minimum early-gap durations in AAF and AII had modal values at ∼45 ms. In AI, the distribution was more uniform. Distributions for minimum late-gap duration were skewed toward low values (mode at 5 ms), but high values (≤60 ms) were found infrequently as well. A small fraction of units showed a response after the gap only for early-gap durations <20 ms. In AI and AII, the mean minimum early- and late-gap durations decreased significantly with increase in the neuron’s characteristic frequency (CF), whereas the lower boundary for the minimum early gap was CF independent. The findings suggest that human within-perceptual-channel gap detection, showing no dependence of the minimum detectable gap on the duration of the leading noise burst, likely is based on the lower envelope of the distribution of neural minimum gap values of units in AI and AAF. In contrast, across-perceptual-channel gap detection, which shows a decreasing minimum detectable gap with increasing duration of the leading noise burst, likely is based on the comparison ofon responses from populations of neurons that converge on units in AII.

scholarly journals Neural correlates of instrumental learning in primary auditory cortex

2002 ◽  
Vol 99 (15) ◽  
pp. 10114-10119 ◽  
Author(s):  
D. T. Blake ◽  
F. Strata ◽  
A. K. Churchland ◽  
M. M. Merzenich
Keyword(s):  
Auditory Cortex ◽  
Instrumental Learning ◽  
Primary Auditory Cortex ◽  
Neural Correlates

scholarly journals Feeling the beat: an investigation into the neural correlates of vibrotactile beat perception

2021 ◽  
Author(s):  
Sean A. Gilmore
Keyword(s):  
Auditory Cortex ◽  
Temporal Processing ◽  
Statistical Difference ◽  
Primary Auditory Cortex ◽  
Neural Correlates ◽  
Neural Signal ◽  
Neural Data ◽  
Neural Entrainment ◽  
Eeg Recordings

The current study investigates our ability to perceive and synchronize movements to the beat of rhythms presented through vibrations to the skin. I compared EEG recordings and tapping accuracy to rhythms that varied in modality: auditory-only, vibrotactile, multimodal (vibrotactile and auditory) and complexity: metronome and simple rhythms. The neural data showed that signals localized to the primary auditory cortex showed a larger spike in power at beat frequencies presentation of auditory compared to vibrotactile rhythms. Tapping ability was found to be lowest in vibrotactile compared to auditory and multimodal rhythms. Auditory only and multimodal rhythms did not show a statistical difference in the neural or tapping data. Tapping variability predicted neural entrainment, such that more variable tapping elicited a more entrained neural signal in primary auditory cortex, and less in pre-motor regions. In conclusion, these results show how the temporal processing of rhythm is superior in auditory modalities.

Neural correlates of auditory stream segregation in primary auditory cortex of the awake monkey

2001 ◽  
Vol 151 (1-2) ◽  
pp. 167-187 ◽  
Author(s):  
Yonatan I. Fishman ◽  
David H. Reser ◽  
Joseph C. Arezzo ◽  
Mitchell Steinschneider
Keyword(s):  
Auditory Cortex ◽  
Primary Auditory Cortex ◽  
Neural Correlates ◽  
Stream Segregation ◽  
Auditory Stream ◽  
Auditory Stream Segregation ◽  
Awake Monkey

scholarly journals Neural correlates of learning pure tones versus natural sounds in the auditory cortex

2018 ◽  
Author(s):  
Ido Maor ◽  
Ravid Shwartz-Ziv ◽  
Libi Feigin ◽  
Yishai Elyada ◽  
Haim Sompolinsky ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Perceptual Learning ◽  
Discrimination Performance ◽  
Primary Auditory Cortex ◽  
Neural Correlates ◽  
Natural Sounds ◽  
Complex Sounds ◽  
Tuning Curves ◽  
Pure Tones ◽  
Perceptual Limits

ABSTRACTAuditory perceptual learning of pure tones causes tonotopic map expansion in the primary auditory cortex (A1), but the function this plasticity sub-serves is unclear. We developed an automated training platform called the ‘Educage’, which was used to train mice on a go/no-go auditory discrimination task to their perceptual limits, for difficult discriminations among pure tones or natural sounds. Spiking responses of excitatory and inhibitory L2/3 neurons in mouse A1 revealed learning-induced overrepresentation of the learned frequencies, in accordance with previous literature. Using a novel computational model to study auditory tuning curves we show that overrepresentation does not necessarily improve discrimination performance of the network to the learned tones. In contrast, perceptual learning of natural sounds induced ‘sparsening’ and decorrelation of the neural response, and consequently improving discrimination of these complex sounds. The signature of plasticity in A1 highlights its central role in coding natural sounds as compared to pure tones.

Sign in / Sign up

Export Citation Format

Share Document