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Record W2148043484 · doi:10.1038/srep03110

Optical control of neuronal excitation and inhibition using a single opsin protein, ChR2

2013· article· en· W2148043484 on OpenAlexfundno aff
Holly Liske, Xiang Qian, Polina Anikeeva, Karl Deisseroth, Scott L. Delp

Bibliographic record

VenueScientific Reports · 2013
Typearticle
Languageen
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and StrokeStanford Bio-XNational Institutes of HealthNational Institute of General Medical SciencesMcGill University
KeywordsStimulationExcitatory postsynaptic potentialOpsinChannelrhodopsinNeuroscienceIn vivoOptogeneticsInhibitory postsynaptic potentialElectrophysiologyBiophysicsNeurotransmissionChemistryMembrane potentialBiologyRhodopsinBiochemistry

Abstract

fetched live from OpenAlex

The effect of electrical stimulation on neuronal membrane potential is frequency dependent. Low frequency electrical stimulation can evoke action potentials, whereas high frequency stimulation can inhibit action potential transmission. Optical stimulation of channelrhodopsin-2 (ChR2) expressed in neuronal membranes can also excite action potentials. However, it is unknown whether optical stimulation of ChR2-expressing neurons produces a transition from excitation to inhibition with increasing light pulse frequencies. Here we report optical inhibition of motor neuron and muscle activity in vivo in the cooled sciatic nerves of Thy1-ChR2-EYFP mice. We also demonstrate all-optical single-wavelength control of neuronal excitation and inhibition without co-expression of inhibitory and excitatory opsins. This all-optical system is free from stimulation-induced electrical artifacts and thus provides a new approach to investigate mechanisms of high frequency inhibition in neuronal circuits in vivo and in vitro.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.048
GPT teacher head0.288
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations43
Published2013
Admission routes1
Has abstractyes

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