scholarly journals Neural Correlates of Task Switching in Prefrontal Cortex and Primary Auditory Cortex in a Novel Stimulus Selection Task for Rodents

Neuron ◽  
2014 ◽  
Vol 82 (5) ◽  
pp. 1157-1170 ◽  
Author(s):  
Chris C. Rodgers ◽  
Michael R. DeWeese
1999 ◽  
Vol 81 (5) ◽  
pp. 2570-2581 ◽  
Author(s):  
Jos J. Eggermont

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.


2002 ◽  
Vol 99 (15) ◽  
pp. 10114-10119 ◽  
Author(s):  
D. T. Blake ◽  
F. Strata ◽  
A. K. Churchland ◽  
M. M. Merzenich

2021 ◽  
Author(s):  
Sean A. Gilmore

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.


2019 ◽  
Vol 121 (3) ◽  
pp. 785-798 ◽  
Author(s):  
Zhenling Zhao ◽  
Lanlan Ma ◽  
Yifei Wang ◽  
Ling Qin

Discriminating biologically relevant sounds is crucial for survival. The neurophysiological mechanisms that mediate this process must register both the reward significance and the physical parameters of acoustic stimuli. Previous experiments have revealed that the primary function of the auditory cortex (AC) is to provide a neural representation of the acoustic parameters of sound stimuli. However, how the brain associates acoustic signals with reward remains unresolved. The amygdala (AMY) and medial prefrontal cortex (mPFC) play keys role in emotion and learning, but it is unknown whether AMY and mPFC neurons are involved in sound discrimination or how the roles of AMY and mPFC neurons differ from those of AC neurons. To examine this, we recorded neural activity in the primary auditory cortex (A1), AMY, and mPFC of cats while they performed a Go/No-go task to discriminate sounds with different temporal patterns. We found that the activity of A1 neurons faithfully coded the temporal patterns of sound stimuli; this activity was not affected by the cats’ behavioral choices. The neural representation of stimulus patterns decreased in the AMY, but the neural activity increased when the cats were preparing to discriminate the sound stimuli and waiting for reward. Neural activity in the mPFC did not represent sound patterns, but it showed a clear association with reward and was modulated by the cats’ behavioral choices. Our results indicate that the initial auditory representation in A1 is gradually transformed into a stimulus–reward association in the AMY and mPFC to ultimately generate a behavioral choice. NEW & NOTEWORTHY We compared the characteristics of neural activities of primary auditory cortex (A1), amygdala (AMY), and medial prefrontal cortex (mPFC) while cats were performing the same auditory discrimination task. Our results show that there is a gradual transformation of the neural code from a faithful temporal representation of the stimulus in A1, which is insensitive to behavioral choices, to an association with the predictive reward in AMY and mPFC, which, to some extent, is correlated with the animal’s behavioral choice.


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