Evidence map›Paper›PMID 39261006›Full record

ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2024

Distinct Neuron Types Contribute to Hybrid Auditory Spatial Coding.

Chenggang Chen, Sen Song

Abstract read
In one paragraph

Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Chenggang ChenTsinghua Laboratory of Brain and Intelligence and School of Biomedical Engineering, McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China ccg1988@yeah.net.ORCID 0000-0003-4143-2427
Sen SongTsinghua Laboratory of Brain and Intelligence and School of Biomedical Engineering, McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China.ORCID 0000-0001-5587-0730

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neural decoding is a tool for understanding how activities from a population of neurons inside the brain relate to the outside world and for engineering applications such as brain-machine interfaces. However, neural decoding studies mainly focused on different decoding algorithms rather than different neuron types which could use different coding strategies. In this study, we used two-photon calcium imaging to assess three auditory spatial decoders (space map, opponent channel, and population pattern) in excitatory and inhibitory neurons in the dorsal inferior colliculus of male and female mice. Our findings revealed a clustering of excitatory neurons that prefer similar interaural level difference (ILD), the primary spatial cues in mice, while inhibitory neurons showed random local ILD organization. We found that inhibitory neurons displayed lower decoding variability under the opponent channel decoder, while excitatory neurons achieved higher decoding accuracy under the space map and population pattern decoders. Further analysis revealed that the inhibitory neurons' preference for ILD off the midline and the excitatory neurons' heterogeneous ILD tuning account for their decoding differences. Additionally, we discovered a sharper ILD tuning in the inhibitory neurons. Our computational model, linking this to increased presynaptic inhibitory inputs, was corroborated using monaural and binaural stimuli. Overall, this study provides experimental and computational insight into how excitatory and inhibitory neurons uniquely contribute to the coding of sound locations.

Indexed as

Inferior ColliculiNeuronsSound LocalizationAcoustic StimulationAnimalsAuditory PerceptionFemaleMaleMiceMice, Inbred C57BLModels, Neurologicalauditory midbraininferior colliculusneural decodingneuron typessound localizationtwo-photon calcium imaging

Identifiers

PMID39261006
PMCPMC11502229

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.