Discrete Neocortical Dynamics Predict Behavioral Categorization of Sounds

Bathellier, Brice; Ushakova, Lyubov; Rumpel, Simon

doi:10.1016/j.neuron.2012.07.008

Cited by 191 publications

(273 citation statements)

References 47 publications

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“…Their activation is accompanied by elevated firing in the corresponding neural ensemble and population oscillations in the gamma range. Our stimulus induced change of state is thus congruent with discrete firing rate changes (Bathellier et al, 2012) and stimulus induced alpha to gamma transitions observed in vivo. We interpret the activation of a stimulated coding attractor as successful stimulus detection and study how the phase, power, and synchronization of ongoing alpha oscillations modulate performance.…”

Section: Introductionsupporting

confidence: 83%

“…1A). These minicolumns do not necessarily correspond to anatomical columns but rather to local subgroups of cells that tend to be connected (Yoshimura et al, 2005;Kampa et al, 2006) and coactive (Bathellier et al, 2012). Each layer 2/3 portion of a minicolumn contained 30 pyramidal cells (Peters and Yilmaz, 1993).…”

Section: Methodsmentioning

confidence: 99%

“…Second, these layers appear as the main drivers of stimulus-evoked activity (Binzegger et al, 2009;Weiler et al, 2008). Thirdly, superficial recordings have revealed discrete, stimulus-evoked dynamics already in the early stages of cortical processing (Kenet et al, 2003;Bathellier et al, 2012), which also motivated us to use an attractor network. Finally, stimulus driven transitions from alpha to gamma oscillations, observed in our network and believed to reflect active cortical processing, are specific to layers 2/3 Buffalo et al, 2011).…”

Section: Model Simplificationsmentioning

confidence: 99%

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Effect of Prestimulus Alpha Power, Phase, and Synchronization on Stimulus Detection Rates in a Biophysical Attractor Network Model

Lundqvist

Herman

Lansner

2013

Journal of Neuroscience

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Spontaneous oscillations measured by local field potentials, electroencephalograms and magnetoencephalograms exhibit a pronounced peak in the alpha band (8 -12 Hz) in humans and primates. Both instantaneous power and phase of these ongoing oscillations have commonly been observed to correlate with psychophysical performance in stimulus detection tasks. We use a novel model-based approach to study the effect of prestimulus oscillations on detection rate. A previously developed biophysically detailed attractor network exhibits spontaneous oscillations in the alpha range before a stimulus is presented and transiently switches to gamma-like oscillations on successful detection. We demonstrate that both phase and power of the ongoing alpha oscillations modulate the probability of such state transitions. The power can either positively or negatively correlate with the detection rate, in agreement with experimental findings, depending on the underlying neural mechanism modulating the oscillatory power. Furthermore, the spatially distributed alpha oscillators of the network can be synchronized by global nonspecific weak excitatory signals. These synchronization events lead to transient increases in alpha-band power and render the network sensitive to the exact timing of target stimuli, making the alpha cycle function as a temporal mask in line with recent experimental observations. Our results are relevant to several studies that attribute a modulatory role to prestimulus alpha dynamics.

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Section: Introductionsupporting

confidence: 83%

Section: Methodsmentioning

confidence: 99%

Section: Model Simplificationsmentioning

confidence: 99%

See 1 more Smart Citation

Effect of Prestimulus Alpha Power, Phase, and Synchronization on Stimulus Detection Rates in a Biophysical Attractor Network Model

Lundqvist

Herman

Lansner

2013

Journal of Neuroscience

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“…So far, various Ca 2+ indicators including rhod-2 [6], fura-2 [7,8], OGB-1 [9][10][11][12][13], fluo-2 [14], fluo-4 [9,15,16], Alexa Fluor 594 together with fluo-5F [17,18], and genetically encoded Ca 2+ indicators (GCaMP3 [1] and GCaMP6s [3]) [14,19,20] have been widely used in studies of the Au1 with two-photon Ca 2+ imaging in both in vitro and in vivo conditions. The in vivo experiments, particularly the functional mapping experiments, have provided important insights into the topographic organization of the Au1, but the degree of precision in tonotopic mapping has been controversial.…”

Section: Introductionmentioning

confidence: 99%

Functional imaging of neuronal activity of auditory cortex by using Cal-520 in anesthetized and awake mice

Zhang

Wang

et al. 2017

Biomed. Opt. Express

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Abstract:The organization in the primary auditory cortex (Au1) is critical to the basic function of auditory information processing and integration. However, recent mapping experiments using in vivo two-photon imaging with different Ca 2+ indicators have reached controversial conclusions on this topic, possibly because of the different sensitivities and properties of the indicators used. Therefore, it is essential to identify a reliable Ca 2+ indicator for use in in vivo functional imaging of the Au1, to understand its functional organization. Here, we demonstrate that a previously reported indicator, Cal-520, performs well in both anesthetized and awake conditions. Cal-520 shows a sufficient sensitivity for the detection of single action potentials, and a high signal-to-noise ratio. Cal-520 reliably reported on both spontaneous and sound-evoked neuronal activity in anesthetized and awake mice. After testing with pure tones at a range of frequencies, we confirmed the local heterogeneity of the functional organization of the mouse Au1. Therefore, Cal-520 is a reliable and useful Ca 2+ indicator for in vivo functional imaging of the Au1.

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“…We tested whether coupling was due to global population changes 28,29 or to coordinated activity in small neuronal groups 30 by comparing the performance of variants of the coupled model. We compared coupled models that included as coupling predictors only the mean population activity or the activity of factorized subsets of cells.…”

mentioning

confidence: 99%

Distinct timescales of population coding across cortex

Runyan

Piasini

Panzeri

et al. 2017

Nature

345

429

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The cortex represents information across widely varying timescales1–5. For instance, sensory cortex encodes stimuli that fluctuate over few tens of milliseconds6,7, whereas in association cortex behavioral choices can require the maintenance of information over seconds8,9. However, it remains poorly understood if diverse timescales result mostly from features intrinsic to individual neurons or from neuronal population activity. This question is unanswered because the timescales of coding in populations of neurons have not been studied extensively, and population codes have not been compared systematically across cortical regions. Here we discovered that population codes can be essential to achieve long coding timescales. Furthermore, we found that the properties of population codes differ between sensory and association cortices. We compared coding for sensory stimuli and behavioral choices in auditory cortex (AC) and posterior parietal cortex (PPC) as mice performed a sound localization task. Auditory stimulus information was stronger in AC than in PPC, and both regions contained choice information. Although AC and PPC coded information by tiling in time neurons that were transiently informative for ~200 milliseconds, the areas had major differences in functional coupling between neurons, measured as activity correlations that could not be explained by task events. Coupling among PPC neurons was strong and extended over long time lags, whereas coupling among AC neurons was weak and short-lived. Stronger coupling in PPC led to a population code with long timescales and a representation of choice that remained consistent for approximately one second. In contrast, AC had a code with rapid fluctuations in stimulus and choice information over hundreds of milliseconds. Our results reveal that population codes differ across cortex and that coupling is a variable property of cortical populations that affects the timescale of information coding and the accuracy of behavior.

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Discrete Neocortical Dynamics Predict Behavioral Categorization of Sounds

Cited by 191 publications

References 47 publications

Effect of Prestimulus Alpha Power, Phase, and Synchronization on Stimulus Detection Rates in a Biophysical Attractor Network Model

Effect of Prestimulus Alpha Power, Phase, and Synchronization on Stimulus Detection Rates in a Biophysical Attractor Network Model

Functional imaging of neuronal activity of auditory cortex by using Cal-520 in anesthetized and awake mice

Distinct timescales of population coding across cortex

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