Evidence map›Paper›PMID 41948461›Full record

ReviewFrontiers in cellular neuroscience2026

Stochasticity in action potential backpropagation: consequences for neuronal computation.

Srdjan D Antic, Katarina D Milicevic, William W Lytton

Abstract readReview
In one paragraph

Review in Frontiers in cellular neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Srdjan D AnticDepartment of Neuroscience, UConn Health, School of Medicine, Institute for Systems Genomics, Farmington, CT, United States.
Katarina D MilicevicDepartment of Neuroscience, UConn Health, School of Medicine, Institute for Systems Genomics, Farmington, CT, United States.
William W LyttonDepartment of Physiology and Pharmacology, SUNY Downstate Health Sciences University, Brooklyn, NY, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In cortical and hippocampal pyramidal neurons, backpropagating action potentials (bAPs) play a central role in dendritic signaling, synaptic integration, and spike-timing-dependent plasticity (STDP). In most experimental and theoretical frameworks, bAPs are implicitly treated as reliable signals that faithfully inform dendritic synapses of somatic spiking. Here, we review experimental evidence demonstrating that this assumption is often violated. In large portions of the pyramidal neuron dendritic tree, particularly in distal apical branches and apical tuft dendrites, bAP amplitude exhibits pronounced spatial and temporal variability, including: (i) activity-dependent attenuation, (ii) frequency-dependent amplification, (iii) branch-specific propagation failures, and (iv) trial-to-trial stochastic AP flickering. We summarize five experimentally documented forms of bAP variability and discuss how stochastic backpropagation may shape synaptic plasticity in computational neuroscience, especially STDP, by introducing probabilistic gates that limit the coincidence of: (i) dendritic depolarization (bAP) and (ii) synaptic input (EPSP). Finally, we consider broader implications of the AP flickering in dendrites for cortical information processing, including redundancy, averaging, evidence accumulation, and error-correcting strategies in cortical circuits.

Indexed as

dendritic integrationnetwork computationpyramidal neuronsspike-timing-dependent plasticitystochastic neural signaling

Identifiers

PMID41948461
PMCPMC13050696

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.