Selective effects of arousal on population coding of natural sounds in auditory cortex

Heller, Charles R.; Schwartz, Zachary; Saderi, Daniela; David, Stephen V.

doi:10.1101/2020.08.31.276584

“…These adjustments likely arise from multiple neuromodulatory systems, principally the locus-coeruleus (LC) norepinephrine system and basal forebrain cholinergic system (Joshi & Gold, 2020; Joshi, Li, Kalwani, & Gold, 2016; Reimer et al, 2016), that can affect either bottom-up or top-down information processing in the brain under different conditions. For example, relatively slow fluctuations in baseline or “tonic” arousal levels have been linked to modulation of sensory neurons (Heller, Schwartz, Saderi, & David, 2020; Lin, Asinof, Edwards, & Isaacson, 2019; McGinley, David, & McCormick, 2015; McGinley, Vinck, et al, 2015; Reimer et al, 2014; Schwartz, Buran, & David, 2020; Vinck, Batista-Brito, Knoblich, & Cardin, 2015) and changes in perceptual sensitivity in animal models and human subjects (Gelbard-Sagiv, Magidov, Sharon, Hendler, & Nir, 2018; McGinley, David, et al, 2015; Waschke, Tune, & Obleser, 2019), suggesting bottom-up effects. In contrast, relatively fast, event-driven “phasic” changes in arousal have shown inconsistent relationships to perceptual sensitivity in human subjects (de Gee et al, 2017, 2020).…”

Section: Discussionmentioning

confidence: 99%

Rule-based and stimulus-based cues bias auditory decisions via different computational and physiological mechanisms

Tardiff

¹

,

Suriya-Arunroj

²

,

Cohen

³

et al. 2021

Preprint

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The varied effects of expectations on auditory perception are not well understood. For example, both top-down rules and bottom-up stimulus regularities generate expectations that can bias subsequent perceptual judgments. However, it is unknown whether these different sources of bias use the same or different computational and physiological mechanisms. We examined how rule-based and stimulus-based expectations influenced human subjects’ behavior and pupil-linked arousal, a marker of certain forms of expectation-based processing, during an auditory frequency-discrimination task. Rule-based cues biased choice and response times (RTs) toward the more-probable stimulus. In contrast, stimulus-based cues had a complex combination of effects, including choice and RT biases toward and away from the frequency of recently heard stimuli. These different behavioral patterns also had distinct computational signatures, including different modulations of key components of a novel form of a drift-diffusion model, and distinct physiological signatures, including substantial bias-dependent modulations of pupil size in response to rule-based but not stimulus-based cues. These results imply that different sources of expectations can modulate auditory perception via distinct mechanisms: one that uses arousal-linked, rule-based information and another that uses arousal-independent, stimulus-based information to bias the speed and accuracy of auditory perceptual decisions.

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“…Recording procedures followed those described previously [ 19 , 20 ]. Briefly, upon opening a craniotomy, 1–4 tungsten micro-electrodes (FHC, 1–5 MΩ) were inserted to characterize the tuning and response latency of the region of cortex.…”

Section: Methodsmentioning

confidence: 99%

Targeted dimensionality reduction enables reliable estimation of neural population coding accuracy from trial-limited data

Heller

¹

,

David

²

2022

Self Cite

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Rapidly developing technology for large scale neural recordings has allowed researchers to measure the activity of hundreds to thousands of neurons at single cell resolution in vivo. Neural decoding analyses are a widely used tool used for investigating what information is represented in this complex, high-dimensional neural population activity. Most population decoding methods assume that correlated activity between neurons has been estimated accurately. In practice, this requires large amounts of data, both across observations and across neurons. Unfortunately, most experiments are fundamentally constrained by practical variables that limit the number of times the neural population can be observed under a single stimulus and/or behavior condition. Therefore, new analytical tools are required to study neural population coding while taking into account these limitations. Here, we present a simple and interpretable method for dimensionality reduction that allows neural decoding metrics to be calculated reliably, even when experimental trial numbers are limited. We illustrate the method using simulations and compare its performance to standard approaches for dimensionality reduction and decoding by applying it to single-unit electrophysiological data collected from auditory cortex.

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“…N = 52 neurons were recorded simultaneously using a 64-channel laminar probe 27 as in. [28][29][30] Auditory stimuli consisting of narrowband (0.3 octave bandwidth) noise bursts were presented alone (-Inf dB) or with a pure tone embedded at varying SNRs (0 dB, −5 dB, −10 dB) in the hemifield contralateral to the recording site (see Experimental Methods). Each stimulus was repeated 50 times.…”

Section: Ddr Recovers More Decoding Information Than Standard Principmentioning

confidence: 99%

“…Recording procedures followed those described previously. 28,29 Briefly, upon opening a craniotomy, 1 -4 tungsten micro-electrodes (FHC, 1-5 MΩ) were inserted to characterize the tuning and response latency of the region of cortex. Sites were identified as A1 by characteristic short latency responses, frequency selectivity, and tonotopic gradients across multiple penetrations.…”

Section: Neurophysiologymentioning

confidence: 99%

Dimensionality reduction for neural population decoding

Heller

¹

,

Sv

²

2021

Preprint

Self Cite

0

View full text Add to dashboard Cite

Rapidly developing technology for large scale neural recordings has allowed researchers to measure the activity of hundreds to thousands of neurons at single cell resolution in vivo. Neural decoding analyses are a widely used tool used for investigating what information is represented in this complex, high-dimensional neural population activity. Most population decoding methods assume that correlated activity between neurons has been estimated accurately. In practice, this requires large amounts of data, both across observations and across neurons. Unfortunately, most experiments are fundamentally constrained by practical variables that limit the number of times the neural population can be observed under a single stimulus and/or behavior condition. Therefore, new analytical tools are required to study neural population coding while taking into account these limitations. Here, we present a simple and interpretable method for dimensionality reduction that allows neural decoding metrics to be calculated reliably, even when experimental trial numbers are limited. We illustrate the method using simulations and compare its performance to standard approaches for dimensionality reduction and decoding by applying it to single-unit electrophysiological data collected from auditory cortex.

show abstract

Selective effects of arousal on population coding of natural sounds in auditory cortex

Cited by 6 publications

References 80 publications

Rule-based and stimulus-based cues bias auditory decisions via different computational and physiological mechanisms

Rule-based and stimulus-based cues bias auditory decisions via different computational and physiological mechanisms

Targeted dimensionality reduction enables reliable estimation of neural population coding accuracy from trial-limited data

Dimensionality reduction for neural population decoding

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