Publication

Real-time, low-latency closed-loop feedback using markerless posture tracking

Alexander Mathis, Mackenzie Mathis
2020
Journal Articles

Résumé

The ability to control a behavioral task or stimulate neural activity based on animal behavior in real-time is an important tool for experimental neuroscientists. Ideally, such tools are noninvasive, low-latency, and provide interfaces to trigger external hardware based on posture. Recent advances in pose estimation with deep learning allows researchers to train deep neural networks to accurately quantify a wide variety of animal behaviors. Here, we provide a new DeepLabCut Live ! package that achieves low-latency real-time pose estimation (within 15 ms, >100 FPS), with an additional forward -prediction module that achieves zero-latency feedback, and a dynamic-cropping mode that allows for higher inference speeds. We also provide three options for using this tool with ease: (1) a stand-alone GUI (called DLC Live! GUI), and integration into (2) Bonsai, and (3) Autopilot. Lastly, we benchmarked performance on a wide range of systems so that experimentalists can easily decide what hardware is required for their needs.

Source officielle

https://infoscience.epfl.ch/entities/publication/e3028b5f-74d7-4647-98ea-f5afd5e28c1a

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Alexander Mathis, Mackenzie Mathis
2020
Journal Articles

Résumé

Source officielle

https://infoscience.epfl.ch/entities/publication/e3028b5f-74d7-4647-98ea-f5afd5e28c1a

À propos de ce résultat

Concepts associés (23)

Apprentissage profond

L'apprentissage profond ou apprentissage en profondeur (en anglais : deep learning, deep structured learning, hierarchical learning) est un sous-domaine de l’intelligence artificielle qui utilise des réseaux neuronaux pour résoudre des tâches complexes grâce à des architectures articulées de différentes transformations non linéaires. Ces techniques ont permis des progrès importants et rapides dans les domaines de l'analyse du signal sonore ou visuel et notamment de la reconnaissance faciale, de la reconnaissance vocale, de la vision par ordinateur, du traitement automatisé du langage.

Types of artificial neural networks

There are many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate functions that are generally unknown. Particularly, they are inspired by the behaviour of neurons and the electrical signals they convey between input (such as from the eyes or nerve endings in the hand), processing, and output from the brain (such as reacting to light, touch, or heat). The way neurons semantically communicate is an area of ongoing research.

Réseau de neurones artificiels

Un réseau de neurones artificiels, ou réseau neuronal artificiel, est un système dont la conception est à l'origine schématiquement inspirée du fonctionnement des neurones biologiques, et qui par la suite s'est rapproché des méthodes statistiques. Les réseaux de neurones sont généralement optimisés par des méthodes d'apprentissage de type probabiliste, en particulier bayésien.

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The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

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Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

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