Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

View all journals
Search
Log in

nature
search

Search

Advanced search

Filter By:

Journal Check one or more journals to show results from those journals only.

Nature (2)
Nature Methods (2)

Choose more journals

Article type Check one or more article types to show results from those article types only.

Research (3)
Amendments and Corrections (1)

Subject Check one or more subjects to show results from those subjects only.

Biological techniques
Computational biology and bioinformatics
Neuroscience

Date Choose a date option to show results from those dates only.

Today
Last 7 days
Last 30 days
Last 12 months
Last 2 years
Last 5 years

Custom date range

Clear all filters

Sort by:

Relevance

Date published (new to old)

Date published (old to new)

Showing 1–4 of 4 results

Advanced filters: Author: Diego Aldarondo Clear advanced filters

Author Correction: A virtual rodent predicts the structure of neural activity across behaviours

Diego Aldarondo
Josh Merel
Bence P. Ölveczky
Amendments and CorrectionsOpen Access11 Aug 2025
Nature

Volume: 645, P: E1
Fast animal pose estimation using deep neural networks

LEAP is a deep-learning-based approach for the analysis of animal pose. LEAP’s graphical user interface facilitates training of the deep network. The authors illustrate the method by analyzing Drosophila and mouse behavior.

Talmo D. Pereira
Diego E. Aldarondo
Joshua W. Shaevitz
Research20 Dec 2018
Nature Methods

Volume: 16, P: 117-125
A virtual rodent predicts the structure of neural activity across behaviours

We built an artificial neural network to control a biomechanically realistic virtual rodent, which, when trained to imitate real rats, predicts neural activity and variability across natural behaviours.

Diego Aldarondo
Josh Merel
Bence P. Ölveczky
Research11 Jun 2024
Nature

Volume: 632, P: 594-602
Geometric deep learning enables 3D kinematic profiling across species and environments

DANNCE enables robust 3D tracking of animals’ limbs and other features in naturalistic environments by making use of a deep learning approach that incorporates geometric reasoning. DANNCE is demonstrated on behavioral sequences from rodents, marmosets, and chickadees.

Timothy W. Dunn
Jesse D. Marshall
Bence P. Ölveczky
Research19 Apr 2021
Nature Methods

Volume: 18, P: 564-573