Sparse ensemble neural code for a complete vocal repertoire

Robotka, H.; Thomas, L.; Yu, Kevin; Wood, William E.; Elie, Julie E.; Gahr, Manfred; Theunissen, Frédéric E.

doi:10.1016/j.celrep.2023.112034

Cited by 9 publications

(5 citation statements)

References 66 publications

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“…Individual shell IC neurons were generally broadly tuned to sAM sounds, leading to perhaps unreliable representation of sAM features at the single neuron level. Nevertheless, instead of relying on single neuron activity, information was conveyed more accurately by population responses, in agreement with a recent study showing that ensemble neural discrimination of vocal signals correlates poorly with single unit selectivity in the avian auditory cortex (Robotka et al, 2023). Indeed, sAM rate representational fidelity remained significantly above chance even after excluding individual highly tuned neurons from the neural population, suggesting that the discriminative capacity of in individual neurons is not necessarily a robust index of population-level representations in the shell IC layers.…”

Section: Discussionsupporting

confidence: 89%

“…Our data suggest that single neuron discrimination of sAM rate is variable, particularly at low sAM depths. However, in circuits where the feature selectivity of individual neurons is noisy or ambiguous, neural population codes (i.e., collective activity in large sets of neurons) nevertheless provide an accurate representation of sensory signals for downstream circuits (Partridge et al, 1981;Lee et al, 1988;Vogels, 1990;Heiligenberg, 1991;Stringer et al, 2021;Robotka et al, 2023).…”

Section: Neural Population Coding Of Sam Ratementioning

confidence: 99%

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Population coding of time-varying sounds in the non-lemniscal Inferior Colliculus

Shi,

Quass,

Rogalla

et al. 2023

Preprint

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The inferior colliculus (IC) of the midbrain is important for complex sound processing, such as discriminating conspecific vocalizations and human speech. The non-lemniscal, dorsal shell region of IC is likely important for this process, as neurons in these layers project to higher-order thalamic nuclei that subsequently funnel acoustic signals to the amygdala and non-primary auditory cortices; forebrain circuits important for vocalization coding in a variety of mammals, including humans. However, the extent to which shell IC neurons transmit acoustic features necessary to discern vocalizations is less clear, owing to the technical difficulty of recording from neurons in the IC superficial layers via traditional approaches. Here we use 2-photon Ca2+ imaging in mice of either sex to test how shell IC neuron populations encode the rate and depth of amplitude modulation, important sound cues for speech perception. Most shell IC neurons were broadly tuned, with a low neurometric discrimination of amplitude modulation rate; only a subset were highly selective to specific modulation rates. Nevertheless, neural network classifier trained on fluorescence data from shell IC neuron populations accurately classified amplitude modulation rate, and decoding accuracy was only marginally reduced when highly tuned neurons were omitted from training data. Rather, classifier accuracy increased monotonically with the modulation depth of the training data, such that classifiers trained on full-depth modulated sounds had median decoding errors of ~0.2 octaves. Thus, shell IC neurons may transmit time-varying signals via a population code, with perhaps limited reliance on the discriminative capacity of any individual neuron.

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Section: Discussionsupporting

confidence: 89%

Section: Neural Population Coding Of Sam Ratementioning

confidence: 99%

Population coding of time-varying sounds in the non-lemniscal Inferior Colliculus

Shi,

Quass,

Rogalla

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…These single neuron responses gave rise to prolonged, time-varying ensembles whose activity systematically varied with mice’s instrumental choice ( Figure 5 ). Despite low individual selectivity, task-relevant information might thus still be transmitted in population activity (Robotka et al, 2023). We tested this idea by training SVM classifiers to decode specific task-relevant variables – sAM depth, trial category (Go or No-Go), and lick responses – using integrated fluorescence activity from discrete 100 ms time bins along the trial (Figure 6A).…”

Section: Resultsmentioning

confidence: 99%

“…It is important to note that population trajectory differences could stem from differences in neuronal co-activity or decorrelation, which have been observed in mouse prefrontal cortex and hippocampus (El-Gaby et al, 2021; Klee et al, 2021) and are undetectable without the use of population analyses. Indeed, the general lack of specific trial outcome or sAM sound encoding in single shell IC neurons does not necessarily prohibit a neuronal population from accurately encoding complex variables (Robotka et al, 2023). These data suggest that the individually broad sound feature tuning of shell IC neurons may be advantageous for multiplexing acoustic and task-related information, such that a categorical representation of acoustic features which predict sound-driven decisions may already arise in the midbrain (Caruso et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Mixed representations of sound and action in the auditory midbrain

Quass,

Rogalla,

Ford

et al. 2023

Preprint

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Linking sensory input and its consequences is a fundamental brain operation. Accordingly, neural activity of neo-cortical and limbic systems often reflects dynamic combinations of sensory and behaviorally relevant variables, and these mixed representations are suggested to be important for perception, learning, and plasticity. However, the extent to which such integrative computations might occur in brain regions upstream of the forebrain is less clear. Here, we conduct cellular-resolution 2-photon Ca2+ imaging in the superficial shell layers of the inferior colliculus (IC), as head-fixed mice of either sex perform a reward-based psychometric auditory task. We find that the activity of individual shell IC neurons jointly reflects auditory cues and actions, such that trajectories of neural population activity diverge depending on behavioral choice. Consequently, simple classifier models trained on shell IC neuron activity can predict trial-by-trial outcomes, even when training data are restricted to neural activity occurring prior to instrumental actions. Thus in behaving animals, auditory midbrain neurons transmit a population code that reflects a joint representation of sound and action.

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“…Dimensionality reduction algorithms exhibit notable advantages, first of all their relatively rapid run times. These approaches are particularly effective in detecting recurrent and stereotypical neuronal activation patterns, as evidenced in instances like bird songs [24, 76], however, they also share some limitations. One common challenge is the necessity to create a binned spike matrix, introducing edge effects where millisecond-scale changes in firing times may misattribute an action potential to a preceding or succeeding bin.…”

Section: Detecting Cell Assemblies With Dimensionality Reductionmentioning

confidence: 99%

Detection of Cell Assemblies in High-Density Extracellular Electrophysiological Recordings

Makdah,

Wiener,

Pompili

2024

Preprint

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Cell assemblies, i.e., concurrently active groups of neurons, likely underlie neural processing for higher brain functions. Recent technological progress has enabled large-scale recording of neuronal activity, permitting the exploration and analysis of cell assembly dynamics. This review aims to provide both conceptual insights and practical knowledge pertaining to principal methodologies used for detecting cell assemblies in the last fifteen years. The goal is to assist readers in selecting and comparing various protocols to optimize their data processing and analysis pipeline. Each algorithm is explained with its fundamental principles, their application in neuroscience for cell assembly detection, and illustrated with published studies. Recognizing the similarities, advantages, and drawbacks of diverse methodologies may pave the way for developing new procedures for cell assembly identification to facilitate future endeavors in the understanding of brain activity.

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Sparse ensemble neural code for a complete vocal repertoire

Cited by 9 publications

References 66 publications

Population coding of time-varying sounds in the non-lemniscal Inferior Colliculus

Population coding of time-varying sounds in the non-lemniscal Inferior Colliculus

Mixed representations of sound and action in the auditory midbrain

Detection of Cell Assemblies in High-Density Extracellular Electrophysiological Recordings

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