Intrinsic timing, not temporal prediction, underlies ramping dynamics in visual and parietal cortex during passive behavior.

Overview

1. Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
2. School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA

Abstract

Ramping neural activity is widely interpreted as a signature of predictive processing, but whether these signals truly reflect predictions or instead emerge from sensory mechanisms remains unclear. To address this question, we used two-photon calcium imaging across multiple cell types in visual and parietal cortex, while awake mice passively received repeated audiovisual stimuli presented under distinct temporal structures. Neurons segregated into two broad response classes: stimulus-activated (ramp-down) and stimulus-inhibited (ramp-up) populations with diverse temporal kinetics. Multiple findings argued against a predictive interpretation: Ramping activity was present in naïve animals, neural responses changed immediately after short/long interval transitions, and unexpected stimulus timings elicited nearly identical responses in predictable and irregular contexts. Population analyses further showed that ramping reflected relaxation from stimulus-evoked activity rather than anticipatory buildup. Heterogeneous kinetics generated a robust population code for elapsed time. Together, these findings show that neural ramps during passive stimulation arise from stimulus-evoked dynamics that intrinsically generate temporal signals, rather than from temporal predictive processing.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

• 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
• 63 scripts, each with its path and the digest of its content;
• 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
• neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

• zenodo:20350578 — at Zenodo; found in “Data, code, and materials availability:”

Data, code, and materials availability

All data and code needed to evaluate and reproduce the results in this paper are present in the paper and/or the Supplementary Materials. This study did not generate new materials. Data used in this paper are available at Zenodo: https://doi.org/10.5281/zenodo.20350578. Code for data processing and analysis is publicly available on the Najafi Lab GitHub page: https://github.com/najafi-laboratory/2p_imaging/tree/main/passive_interval_oddball_202412.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 7 MeSH terms, 4 funders, 133 references.

Cite

This paper

Huang, Y., Shamsnia, A., Chen, M., Wu, S., Stamm, T., Medico, S., & Najafi, F. (2026). Intrinsic timing, not temporal prediction, underlies ramping dynamics in visual and parietal cortex during passive behavior. Science advances, 12(34), eaed6417. https://doi.org/10.1126/sciadv.aed6417

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s41467-026-73999-w [code]

Independence and coherence in temporal sequence computation across the fronto-parietal network.

Journal: Nature communications

In common: scikit-learn, SciPy, Matplotlib, 1 other tool, systems, mouse, 10 references

[2] doi:10.1038/s41593-026-02255-7 [code]

Neural circuits encode prior knowledge of temporal statistics.

Journal: Nature neuroscience

In common: Plotly, scikit-learn, pandas, 3 other tools, systems, mouse, 5 references

[3] doi:10.1038/s41586-026-10528-1 [code]

A critical initialization for biological neural networks.

Journal: Nature

In common: scikit-learn, SciPy, Matplotlib, 1 other tool, mouse, 8 references

[4] doi:10.1016/j.neuron.2026.07.016 [code]

Inferring brain-wide interactions using data-constrained recurrent neural network models.

Journal: Neuron

In common: Matplotlib, NumPy, systems, mouse, 8 references

[5] doi:10.1038/s41593-026-02350-9 [code]

Probing inter-areal computations with a two-photon holographic mesoscope.

Journal: Nature neuroscience

In common: h5py, SciPy, NumPy, systems, mouse, 6 references

[6] doi:10.1371/journal.pone.0356243 [code]

Functional organization and natural scene responses across mouse visual cortical areas revealed with encoding manifolds.

Journal: PloS one

In common: Plotly, scikit-learn, pandas, 3 other tools, mouse, 4 references

[7] doi:10.1371/journal.pbio.3003915 [code]

Noise-invariant representations of sound emerge along the canonical cortical hierarchy.

Journal: PLoS biology

In common: scikit-image, h5py, seaborn, 5 other tools, mouse, 3 references

[8] doi:10.1371/journal.pbio.3003831 [code]

Disinhibitory signaling enables flexible coding of top-down information in cortical networks.

Journal: PLoS biology

In common: h5py, scikit-learn, pandas, 3 other tools, systems, mouse, 4 references

[9] doi:10.1038/s41467-026-71725-0 [code]

Interactions across hemispheres in prefrontal cortex reflect global cognitive processing.

Journal: Nature communications

In common: scikit-learn, pandas, SciPy, 2 other tools, 6 references

[10] doi:10.1523/jneurosci.0987-25.2026 [code]

Cell-Type-Specific Synaptic Scaling Mechanisms Differentially Contribute to Associative Learning.

Journal: The Journal of neuroscience : the official journal of the Society for Neuroscience

In common: seaborn, pandas, SciPy, 2 other tools, 5 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.