MétaCan
Menu
← Back to cohort
Record W4224117423 · doi:10.1101/2022.04.01.486760

Impact on backpropagation of the spatial heterogeneity of sodium channel kinetics in the axon initial segment

2022· preprint· en· W4224117423 on OpenAlexaff
Benjamin Barlow, André Longtin, Béla Joós

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAxonBackpropagationSodium channelGatingStimulationBiophysicsChemistryNeuroscienceSodiumComputer scienceBiologyArtificial neural networkArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract In a variety of neurons, action potentials (APs) initiate at the proximal axon, within a region called the axon initial segment (AIS), which has a high density of voltage-gated sodium channels (Na V s) on its membrane. In pyramidal neurons, the proximal AIS has been reported to exhibit a higher proportion of Na V s with gating properties that are “ right-shift ed” to more depolarized voltages, compared to the distal AIS. Further, recent experiments have revealed that as neurons develop, the spatial distribution of Na V subtypes along the AIS can change substantially, suggesting that neurons tune their excitability by modifying said distribution. When neurons are stimulated axonally, computational modelling has shown that this spatial separation of gating properties in the AIS enhances the backpropagation of APs into the dendrites. In contrast, in the more natural scenario of somatic stimulation, our simulations show that the same distribution can impede backpropagation. We implemented a range of hypothetical Na V distributions in the AIS of three multicompartmental pyramidal cell models and investigated the precise kinetic mechanisms underlying such effects, as the spatial distribution of Na V subtypes is varied. With axonal stimulation, proximal Na V availability dominates, such that concentrating right-shift ed Na V s in the proximal AIS promotes backpropagation. However, with somatic stimulation, the models are insensitive to availability . Instead, the higher activation threshold of right-shift ed Na V s in the AIS impedes backpropagation. Therefore, recently observed developmental changes to the spatial separation and relative proportions of Na V 1.2 and Na V 1.6 in the AIS differentially impact activation and availability . The effects on backpropagation, and potentially learning, are opposite for orthodromic versus antidromic stimulation. Author Summary Neurons use sodium ion currents, controlled by a neuron’s voltage, to trigger signals called action potentials (APs). These APs typically result from synaptic input from other neurons onto the dendrites and soma. An AP is generated at the axon initial segment (AIS) just beyond the soma. From there, it travels down the axon to other cells, but can also propagate “backwards” towards the soma and dendrites. This “backpropagation” allows a comparison at synapses of the timing of outgoing and incoming signals, a feedback process that modifies synaptic connection strengths linked to learning. It is puzzling that in many neurons, sodium ion channels come in two types: high-voltage threshold channels clustered near the soma where the AIS begins, and low-voltage ones further away towards the axon. This separation changes in the early development of the animal, which raises the question of its role in backpropagation. We constructed a detailed mathematical model to explore how separation affects backpropagation. Separation either impedes or enhances learning, depending on whether the AP results from synaptic inputs or, less typically, currents moving backwards from the axon. This is explained by the different effects the separation has on two key kinetic processes that govern sodium currents.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.260
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicNeural dynamics and brain function→French-language works237,207→