Name: a step-by-step implementation of deepbehavior, deep learning toolbox for automated behavior analysis
Uploaded: 2020-02-06T15:00:00
Description: Watch this Scientific Journal Video about A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis at JoVE.com

Nos gustaría agradecer a Pingping Zhao y Peyman Golshani por proporcionar los datos brutos para las pruebas de interacción social de dos ratones utilizadas en el documento original2. Este estudio fue apoyado por NIH NS109315 y nvidial GPU (AA).

1. Configuración de GPU y Python
<ol><li>
GPU Software Cuando el equipo se configura por primera vez para aplicaciones de aprendizaje profundo, se debe instalar software y controladores apropiados para GPU que se pueden encontrar en el sitio web respectivo de la GPU. (véase la Tabla de Materiales para los utilizados en este estudio).</li>
	<li>
Instalación de Python 2.7 Abra un símbolo del sistema en el equipo. Línea de comandos: sudo apt-get ins...

<ol>
	<li>Krakauer, J. W., Ghazanfar, A. A., Gomez-Marin, A., MacIver, M. A., Poeppel, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neuroscience+Needs+Behavior:+Correcting+a+Reductionist+Bias.">Neuroscience Needs Behavior: Correcting a Reductionist Bias.</a> Neuron. 93 (3), 480-490 (2017).</li><li>Arac, A., Zhao, P., Dobkin, B. H., Carmichael, S. T., Golshani, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DeepBehavior:+A+Deep+Learning+Toolbox+for+Automated+Analysis+of+Animal+and+Human+Behavior+Imaging+Data.">DeepBehavior: A Deep Learning Toolbox for Automated Analysis of Animal and Human Behavior Imaging Data.</a> Front Syst Neurosci. 13, 20 (2019).</li><li>Pereira, T. D., Aldarondo, D. E., Willmore, L., Kislin, M., Wang, S. S., Murthy, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+animal+pose+estimation+using+deep+neural+networks.">Fast animal pose estimation using deep neural networks.</a> Nat Methods. 16 (1), 117-125 (2019).</li><li>Mathis, A., Mamidanna, P., Cury, K. M., Abe, T., Murthy, V. N., Mathis, M. W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DeepLabCut:+markerless+pose+estimation+of+user-defined+body+parts+with+deep+learning.">DeepLabCut: markerless pose estimation of user-defined body parts with deep learning.</a> Nat Neurosci. 21 (9), 1281-1289 (2018).</li><li>Stern, U., He, R., Yang, C. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analyzing+animal+behavior+via+classifying+each+video+frame+using+convolutional+neural+networks.">Analyzing animal behavior via classifying each video frame using convolutional neural networks.</a> Sci Rep. 5, 14351 (2015).</li><li>Tinbergen, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=On+aims+and+methods+of+ethology.">On aims and methods of ethology.</a> Zeitschrift fu&#776;r Tierpsychologie. 20, 410-433 (1963).</li><li>LeCun, Y., Bengio, Y., Hinton, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deep+Learning.">Deep Learning.</a> Nature. 521 (7553), 436-444 (2015).</li><li>Zhao, Z., Zheng, P., Xu, S., Wu, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Object+Detection+With+Deep+Learning:+A+Review.">Object Detection With Deep Learning: A Review.</a> IEEE Transactions on Neural Networks and Learning Systems. , 1-21 (2019).</li><li>He, K., Zhang, X., Ren, S., Deep Sun, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Residual+Learning+for+Image+Recognition.">Residual Learning for Image Recognition.</a> arXiv. , (2015).</li><li>Krizhevsky, A., Sutskever, I., Hinton, G. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ImageNet+classification+with+deep+convolutional+neural+networks.">ImageNet classification with deep convolutional neural networks.</a> Proceedings of the 25th International Conference on Neural Information Processing Systems. 1, 1097-1105 (2012).</li><li>Szegedy, C., Wei, L., Yangqing, J., Sermanet, P., Reed, S., Anguelov, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Going+deeper+with+convolutions.">Going deeper with convolutions.</a> 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). , 7-12 (2015).</li><li>Stewart, R., Andriluka, M., Ng, A. Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=End-to-End+People+Detection+in+Crowded+Scenes.">End-to-End People Detection in Crowded Scenes.</a> 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). , 27-30 (2016).</li><li>Redmon, J., Farhadi, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=YOLOv3:+An+Incremental+Improvement.">YOLOv3: An Incremental Improvement.</a> arXiv. , (2018).</li><li>Cao, Z., Simon, T., Wei, S. E., Sheikh, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Realtime+Multi-Person+2D+Pose+Estimation+using+Part+Affinity+Fields.">Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields.</a> arXiv. , (2017).</li><li>Simon, T., Joo, H., Matthews, I., Sheikh, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hand+Keypoint+Detection+in+Single+Images+using+Multiview+Bootstrapping.">Hand Keypoint Detection in Single Images using Multiview Bootstrapping.</a> arXiv. , (2017).</li><li>Wei, S. E., Ramakrishna, V., Kanade, T., Sheikh, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Convolutional+Pose+Machines.">Convolutional Pose Machines.</a> arXiv. , (2016).</li></ol>

Aquí, proporcionamos una guía paso a paso para la implementación de DeepBehavior, nuestra caja de herramientas recientemente desarrollada basada en el aprendizaje profundo para el análisis de datos de imágenes de comportamiento animal y humano2. Proporcionamos explicaciones detalladas para cada paso para la instalación de los marcos de trabajo para cada arquitectura de red y proporcionamos vínculos para la instalación de los requisitos de código abierto para poder ejecutar estos marcos. D...

Un análisis detallado del comportamiento es clave para entender el cerebro y las relaciones de comportamiento. Ha habido muchos avances emocionantes en metodologías para registrar y manipular poblaciones neuronales con alta resolución temporal, sin embargo, los métodos de análisis de comportamiento no se han desarrollado al mismo ritmo y se limitan a mediciones indirectas y un enfoque reduccionista1. Recientemente, se han desarrollado métodos basados en el aprendizaje profundo para realizar análisis de comportamiento automatizados y detallados2,3,4,5. Este protocolo proporciona una guía de implementación paso a paso para la caja de herramientas DeepBehavior.
Los métodos tradicionales de análisis de comportamiento a menudo incluyen la etiquetado manual de datos por varios evaluadores, lo que conduce a la varianza en la forma en que los experimentadores definen un comportamiento6. El etiquetado manual de los datos requiere tiempo y recursos que aumenten desproporcionadamente a la cantidad de datos recopilados. Además, los datos etiquetados manualmente reducen los resultados del comportamiento en mediciones categóricas que no capturan la riqueza del comportamiento y serán más subjetivos. Por lo tanto, los métodos tradicionales actuales pueden ser limitados en la captura de los detalles en los comportamientos naturales.
La caja de herramientas DeepBehavior presenta una solución precisa, detallada, altamente temporal y automatizada que utiliza el aprendizaje profundo para el análisis del comportamiento. El aprendizaje profundo se ha vuelto rápidamente accesible para todos con herramientas y paquetes de código abierto. Las redes neuronales convolucionales (CNN) han demostrado ser altamente eficaces en las tareas de reconocimiento y seguimiento de objetos7,8. Utilizando CNN modernas y unidades de procesamiento gráfico (GPU) de alto rendimiento, los grandes conjuntos de datos de imágenes y vídeo se pueden procesar rápidamente con alta precisión7,9,10,11. En DeepBehavior, hay tres arquitecturas de red neuronal convolucional diferentes, TensorBox, YOLOv3 y OpenPose2.
El primer framework, Tensorbox, es un marco versátil que incorpora muchas arquitecturas CNN diferentes para la detección de objetos12. TensorBox es el más adecuado para detectar solo una clase de objeto por imagen. Las salidas resultantes son cuadros delimitadores del objeto de interés(figura 1) y las coordenadas cartesianas del cuadro delimitador.
El segundo marco de CNN es YOLOv3, que significa "Sólo miraunas"13. YOLOv3 es ventajoso cuando hay varios objetos de interés que se deben realizar un seguimiento por separado. La salida de esta red incluye el cuadro delimitador con la clase de etiqueta de objeto asociada, así como las coordenadas cartesianas del cuadro delimitador del objeto en el fotograma de vídeo (Figura 2).
Los dos marcos anteriores son ventajosos para los datos de comportamiento generalizados recopilados de experimentos de laboratorio estándar en sujetos animales. El último marco de CNN es OpenPose14,15,16 que se utiliza para la estimación de la pose de articulación humana. OpenPose detecta los puntos clave del cuerpo, la mano, la cara y el pie en las imágenes. Los resultados del marco son imágenes etiquetadas del sujeto humano, así como las coordenadas de todos los 25 puntos clave en el cuerpo y 21 puntos clave de cada mano(Figura 3).
Esta guía detallada paso a paso para la implementación de nuestra caja de herramientas DeepBehavior de código abierto recientemente desarrollada emplea redes neuronales convolucionales de última generación para rastrear el comportamiento animal (por ejemplo, movimiento de una pata) o el comportamiento humano (por ejemplo, llegar a tareas). Mediante el seguimiento del comportamiento, la cinemática útil se puede derivar del comportamiento, como la posición, la velocidad y la aceleración. El protocolo explica la instalación de cada arquitectura CNN, demuestra cómo crear conjuntos de datos de entrenamiento, cómo entrenar las redes, cómo procesar nuevos videos en la red entrenada, cómo extraer los datos de la red en los nuevos videos, y cómo post-procesar los datos de salida para que sean útiles para su posterior análisis.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>CUDA v8.0.61</td>
			<td>NVIDIA</td>
			<td>n/a</td>
			<td>GPU Software</td>
		</tr>
		<tr>
			<td>MATLAB R2016b</td>
			<td>Mathworks</td>
			<td>n/a</td>
			<td>Matlab</td>
		</tr>
		<tr>
			<td>Python 2.7</td>
			<td>Python</td>
			<td>n/a</td>
			<td>Python Version</td>
		</tr>
		<tr>
			<td>Quadro P6000</td>
			<td>NVIDIA</td>
			<td>n/a</td>
			<td>GPU Processor</td>
		</tr>
		<tr>
			<td>Ubuntu v16.04</td>
			<td>Ubuntu</td>
			<td>n/a</td>
			<td>Operating System</td>
		</tr>
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a step-by-step implementation of deepbehavior, deep learning toolbox for automated behavior analysis

Entender el comportamiento es el primer paso para entender realmente los mecanismos neuronales en el cerebro que lo impulsan. Los métodos tradicionales de análisis conductual a menudo no capturan la riqueza inherente al comportamiento natural. Aquí, proporcionamos instrucciones detalladas paso a paso con visualizaciones de nuestra metodología reciente, DeepBehavior. La caja de herramientas DeepBehavior utiliza marcos de aprendizaje profundo creados con redes neuronales convolucionales para procesar y analizar rápidamente videos de comportamiento. Este protocolo muestra tres marcos diferentes para la detección de objetos únicos, la detección de objetos múltiples y el seguimiento de poses de unión humana tridimensionales (3D). Estos marcos devuelven coordenadas cartesianas del objeto de interés para cada fotograma del vídeo de comportamiento. Los datos recopilados de la caja de herramientas DeepBehavior contienen mucho más detalle que los métodos de análisis de comportamiento tradicionales y proporcionan información detallada sobre la dinámica de comportamiento. DeepBehavior cuantifica las tareas de comportamiento de una manera robusta, automatizada y precisa. Después de la identificación del comportamiento, se proporciona código de postprocesamiento para extraer información y visualizaciones de los videos de comportamiento.

Cuando se sigue el protocolo, los datos de cada arquitectura de red deben ser similares a los siguientes. Para TensorBox, genera un cuadro delimitador alrededor del objeto de interés. En nuestro ejemplo, usamos videos de una tarea de llegar a pellets de alimentos, y etiquetamos las patas derechas para rastrear su movimiento. Como se ve en la Figura 1,la pata derecha se puede detectar en diferentes posiciones tanto en las cámaras de vista frontal como latera...

Watch this Scientific Journal Video about A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis at JoVE.com

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

El propósito de este protocolo es utilizar redes neuronales convolucionales preconstruidas para automatizar el seguimiento del comportamiento y realizar un análisis detallado del comportamiento. El seguimiento del comportamiento se puede aplicar a cualquier dato de vídeo o secuencias de imágenes y se puede generalizable realizar un seguimiento de cualquier objeto definido por el usuario.

Implementación paso a paso de DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Understanding behavior is the first step to truly understanding neural mechanisms in the brain that drive it. Traditional behavioral analysis methods ...

Realizar un análisis detallado del comportamiento es crucial para entender la relación de comportamiento cerebral. Una de las mejores maneras de evaluar el comportamiento es a través de observaciones cuidadosas. Sin embargo, cuantificar el comportamiento observado es lento y desafiante.Los métodos clásicos de análisis de comportamiento no son fácilmente cuantificables y son inherentemente subjetivos. Los recientes desarrollos en el aprendizaje profundo, una rama de los campos de aprendizaje automático e inteligencia artificial, proporcionan oportunidades para la cuantificación automatizada y objetiva de imágenes y videos. Aquí presentamos nuestros métodos desarrollados recientemente utilizando redes neuronales profundas para realizar análisis detallados del comportamiento en roedores y humanos.La principal ventaja de esta técnica es su flexibilidad y aplicabilidad a cualquier dato de imagen para el análisis del comportamiento. DeepBehavior Toolbox admite la identificación de un solo objeto, la detección de varios objetos y el seguimiento de posturas humanas. También proporcionamos el código de postprocesamiento en MATLAB para métodos de análisis cinemático más detallados.Comience configurando Tensor Box. Active el entorno y, a continuación, utilice GitHub para clonar Tensor Box e instalarlo en el equipo y en dependencias adicionales. A continuación, inicie la interfaz gráfica de usuario de etiquetado y etiquete al menos 600 imágenes de una amplia distribución de marcos de comportamiento.Para etiquetar una imagen, haga clic en la esquina superior izquierda del objeto de interés y, a continuación, en la esquina inferior derecha. A continuación, asegúrese de que el cuadro delimitador captura todo el objeto. Haga clic al lado para pasar al siguiente fotograma.Para vincular las imágenes de entrenamiento a un archivo de hipertráculos de red, abra overfeat_rezoom. json en un editor de texto y reemplace la ruta de acceso del archivo en train_idl a labels.json. A continuación, agregue la misma ruta de archivo en test-idl y guarde los cambios.Inicie el script de entrenamiento que comenzará a entrenar para 600.000 iteraciones y genere los pesos entrenados resultantes de la red neuronal convolucional en la carpeta de salida. A continuación, realice la predicción en nuevas imágenes y vea las salidas de la red como imágenes etiquetadas y como coordenadas de cuadro delimitador. Instale YOLOv3.A continuación, etiquete los datos de entrenamiento con Yolo_mark colocando las imágenes en la carpeta Yolo_mark-data-obga y etiquete una por una en la interfaz gráfica de usuario. Etiquete aproximadamente 200 imágenes. A continuación, configure el archivo de configuración.Para modificar el archivo de configuración, abra el YOLO-obj. cfg. Modifique las líneas de lote, subdivisión y clases.A continuación, cambie el filtro para cada capa de convolución antes de una capa YOLO. Descargue las ponderaciones de red y colóquelas en darknet-build. x64.Ejecute el algoritmo de entrenamiento y, una vez que se haya completado, vea las iteraciones. Para realizar un seguimiento de varias partes del cuerpo en un sujeto humano, instale OpenPose y utilícelo para procesar el vídeo deseado. Las capacidades de DeepBehavior Toolbox se demostraron en videos de ratones realizando una tarea de llegar a un pellet de comida.Sus patas derechas fueron etiquetadas y el movimiento fue rastreado con cámaras de visión frontal y lateral. Después del postprocesamiento con calibración de la cámara, se obtuvieron trayectorias 3D del alcance. Las salidas de YOLOv3 son varios cuadros delimitadores porque se puede realizar un seguimiento de varios objetos.Los cuadros delimitadores están alrededor de los objetos de interés que pueden ser partes del cuerpo. En OpenPose, la red detectó las posiciones de las articulaciones y después del procesamiento posterior con la calibración de la cámara, se creó un modelo 3D del sujeto. Un paso crítico que no se trata en este protocolo es asegurarse de que el dispositivo tiene las versiones y dependencias de Python adecuadas, así como un dispositivo configurado por GPU antes de comenzar.Después de obtener con éxito el comportamiento de la pista de la red se puede hacer un postprocesamiento adicional para analizar más a fondo la cinemática y los patrones del comportamiento. Por qué DeepBehavior Toolbox es aplicable para los enfoques de diagnóstico en modelos de enfermedades de roedores y seres humanos no es un beneficio terapéutico directo. El uso de estas técnicas como herramienta de diagnóstico o pronóstico está bajo investigación activa dentro de nuestro laboratorio.Esta técnica se utiliza para investigar los mecanismos neuronales del comportamiento motor calificado en roedores, así como para ser utilizado en estudios clínicos para evaluar la recuperación motora en pacientes con enfermedades neurológicas.

a-step-step-implementation-deepbehavior-deep-learning-toolbox-for

Research

JoVE Journal

Behavior

Contextual and Cued Fear Conditioning Test Using a Video Analyzing System in Mice

The contextual and cued fear conditioning test is one of the behavioral tests that assesses the ability of mice to learn and remember an association between environmental cues and aversive experiences. In this test, mice are placed into a conditioning chamber and are given parings of a conditioned stimulus (an auditory cue) and an aversive unconditioned stimulus (an electric footshock). After a delay time, the mice are exposed to the same conditioning chamber and a differently shaped chamber with presentation of the auditory cue. Freezing behavior during the test is measured as an index of fear memory. To analyze the behavior automatically, we have developed a video analyzing system using the ImageFZ application software program, which is available as a free download at http://www.mouse-phenotype.org/. Here, to show the details of our protocol, we demonstrate our procedure for the contextual and cued fear conditioning test in C57BL/6J mice using the ImageFZ system. In addition, we validated our protocol and the video analyzing system performance by comparing freezing time measured by the ImageFZ system or a photobeam-based computer measurement system with that scored by a human observer. As shown in our representative results, the data obtained by ImageFZ were similar to those analyzed by a human observer, indicating that the behavioral analysis using the ImageFZ system is highly reliable. The present movie article provides detailed information regarding the test procedures and will promote understanding of the experimental situation.

This article presents a protocol for a contextual and cued fear conditioning test using a video analyzing system to assess fear learning and memory in mice.

Contextuales y complementada el condicionamiento del miedo de prueba utilizando un sistema de análisis de vídeo en ratones

The contextual and cued fear conditioning test is one of the behavioral tests that assesses the ability of mice to learn and remember an association ...

Investigación

Comportamiento

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device (ALDM) Test Systems

Fagot &#38; Paleressompoulle1 and Fagot &#38; Bonte2 have published an automated learning device (ALDM) for the study of cognitive abilities of monkeys maintained in semi-free ranging conditions. Data accumulated during the last five years have consistently demonstrated the efficiency of this protocol to investigate individual/physical cognition in monkeys, and have further shown that this procedure reduces stress level during animal testing3. This paper demonstrates that networks of ALDM can also be used to investigate different facets of social cognition and in-group expressed behaviors in monkeys, and describes three illustrative protocols developed for that purpose. The first study demonstrates how ethological assessments of social behavior and computerized assessments of cognitive performance could be integrated to investigate the effects of socially exhibited moods on the cognitive performance of individuals. The second study shows that batteries of ALDM running in parallel can provide unique information on the influence of the presence of others on task performance. Finally, the last study shows that networks of ALDM test units can also be used to study issues related to social transmission and cultural evolution. Combined together, these three studies demonstrate clearly that ALDM testing is a highly promising experimental tool for bridging the gap in the animal literature between research on individual cognition and research on social cognition.

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems

Fagot &#38; Paleressompoulle1 have published an automated learning device (ALDM) aimed at testing individual cognitive abilities in semi-free ranging monkeys. The main goal of our protocol is to use a network of ALDM test units to study social cognition in non-human primates.

Evaluación de la cognición social en primates no humanos mediante una red de Computarizado Dispositivo Automatizado Learning Systems (ALDM) prueba

Fagot &#38; Paleressompoulle1 and Fagot &#38; Bonte2 have published an automated learning device (ALDM) for the study of cognitive abilities of monkeys ...

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Task-specific actions emerge from spontaneous movement during infancy. It has been proposed that task-specific actions emerge through a discovery-learning process. Here a method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process. This discovery-learning task uses an infant activated mobile that rotates and plays music based on specified leg action of infants. Supine infants activate the mobile by moving their feet vertically across a virtual threshold. This paradigm is unique in that as infants independently discover that their leg actions activate the mobile, the infants&#8217; leg movements are tracked using a motion capture system allowing for the quantification of the learning process. Specifically, learning is quantified in terms of the duration of mobile activation, the position variance of the end effectors (feet) that activate the mobile, changes in hip-knee coordination patterns, and changes in hip and knee muscle torque. This information describes infant exploration and exploitation at the interplay of person and environmental constraints that support task-specific action. Subsequent research using this method can investigate how specific impairments of different populations of infants at risk for movement disorders influence the discovery-learning process for task-specific action.

A method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process.

La cuantificación de aprendizaje en jóvenes Bebés: Seguimiento de la pierna acciones durante una tarea de descubrimiento-learning

Task-specific actions emerge from spontaneous movement during infancy. It has been proposed that task-specific actions emerge through a discovery-learning ...

A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

This protocol describes how to temporarily and remotely silence neuronal activity in discrete brain regions while animals are engaged in learning and memory tasks. The approach combines pharmacogenetics (Designer-Receptors-Exclusively-Activated-by-Designer-Drugs) with a behavioral paradigm (sensory preconditioning) that is designed to distinguish between different forms of learning. Specifically, viral-mediated delivery is used to express a genetically modified inhibitory G-protein coupled receptor (the Designer Receptor) into a discrete brain region in the rodent. Three weeks later, when designer receptor expression levels are high, a pharmacological agent (the Designer Drug) is administered systemically 30 min prior to a specific behavioral session. The drug has affinity for the designer receptor and thus results in inhibition of neurons that express the designer receptor, but is otherwise biologically inert. The brain region remains silenced for 2-5 hr (depending on the dose and route of administration). Upon completion of the behavioral paradigm, brain tissue is assessed for correct placement and receptor expression. This approach is particularly useful for determining the contribution of individual brain regions to specific components of behavior and can be used across any number of behavioral paradigms.

This protocol describes how to temporarily and remotely silence neuronal activity in discrete brain regions while rats are engaged in learning and memory tasks. The approach combines pharmacogenetics (Designer-Receptors-Exclusively-Activated-by-Designer-Drugs) with a behavioral paradigm (sensory preconditioning) that is designed to distinguish between different components of learning.

Un método para Remotamente silenciamiento actividad neural en roedores Durante fases discretas de aprendizaje

This protocol describes how to temporarily and remotely silence neuronal activity in discrete brain regions while animals are engaged in learning and ...

A General Method for Evaluating Deep Brain Stimulation Effects on Intravenous Methamphetamine Self-Administration

Substance use disorders, particularly to methamphetamine, are devastating, relapsing diseases that disproportionally affect young people. There is a need for novel, effective and practical treatment strategies that are validated in animal models. Neuromodulation, including deep brain stimulation (DBS) therapy, refers to the use of electricity to influence pathological neuronal activity and has shown promise for psychiatric disorders, including drug dependence. DBS in clinical practice involves the continuous delivery of stimulation into brain structures using an implantable pacemaker-like system that is programmed externally by a physician to alleviate symptoms. This treatment will be limited in methamphetamine users due to challenging psychosocial situations. Electrical treatments that can be delivered intermittently, non-invasively and remotely from the drug-use setting will be more realistic. This article describes the delivery of intracranial electrical stimulation that is temporally and spatially separate from the drug-use environment for the treatment of IV methamphetamine dependence. Methamphetamine dependence is rapidly developed in rodents using an operant paradigm of intravenous (IV) self-administration that incorporates a period of extended access to drug and demonstrates both escalation of use and high motivation to obtain drug.

This article describes the delivery of intracranial electrical stimulation that is temporally and spatially separate from the drug-use environment for the treatment of IV methamphetamine dependence.

Un método general para evaluar los efectos de estimulación cerebral profunda en Intravenosa metanfetamina Auto-Administración

Substance use disorders, particularly to methamphetamine, are devastating, relapsing diseases that disproportionally affect young people.  There is a need ...

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

We demonstrate a method for capturing emotional response to beverages and liquefied foods in a sensory evaluation laboratory using automated facial expression analysis (AFEA) software. Additionally, we demonstrate a method for extracting relevant emotional data output and plotting the emotional response of a population over a specified time frame. By time pairing each participant's treatment response to a control stimulus (baseline), the overall emotional response over time and across multiple participants can be quantified. AFEA is a prospective analytical tool for assessing unbiased response to food and beverages. At present, most research has mainly focused on beverages. Methodologies and analyses have not yet been standardized for the application of AFEA to beverages and foods; however, a consistent standard methodology is needed. Optimizing video capture procedures and resulting video quality aids in a successful collection of emotional response to foods. Furthermore, the methodology of data analysis is novel for extracting the pertinent data relevant to the emotional response. The combinations of video capture optimization and data analysis will aid in standardizing the protocol for automated facial expression analysis and interpretation of emotional response data.

A protocol for capturing and statistically analyzing emotional response of a population to beverages and liquefied foods in a sensory evaluation laboratory using automated facial expression analysis software is described.

Protocolo para la Recolección de Datos y Análisis Aplicado a Automatizado facial Tecnología de Análisis de Expresión y Análisis temporal para la Evaluación Sensorial

We demonstrate a method for capturing emotional response to beverages and liquefied foods in a sensory evaluation laboratory using automated facial ...

Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation

Understanding the mechanisms underlying locomotor learning helps researchers and clinicians optimize gait retraining as part of motor rehabilitation. However, studying human locomotor learning can be challenging. During infancy and childhood, the neuromuscular system is quite immature, and it is unlikely that locomotor learning during early stages of development is governed by the same mechanisms as in adulthood. By the time humans reach maturity, they are so proficient at walking that it is difficult to come up with a sufficiently novel task to study de novo locomotor learning. The split-belt treadmill, which has two belts that can drive each leg at a different speed, enables the study of both short- (i.e., immediate) and long-term (i.e., over minutes-days; a form of motor learning) gait modifications in response to a novel change in the walking environment. Individuals can easily be screened for previous exposure to the split-belt treadmill, thus ensuring that all experimental participants have no (or equivalent) prior experience. This paper describes a typical split-belt treadmill adaptation protocol that incorporates testing methods to quantify locomotor learning and generalization of this learning to other walking contexts. A discussion of important considerations for designing split-belt treadmill experiments follows, including factors like treadmill belt speeds, rest breaks, and distractors. Additionally, potential but understudied confounding variables (e.g., arm movements, prior experience) are considered in the discussion.

We describe a protocol for investigating human locomotor adaptation using the split-belt treadmill, which has two belts that can drive each leg at a different speed. We specifically focus on a paradigm designed to test the generalization of adapted locomotor patterns to different walking contexts (e.g., gait speeds, walking environments).

Con una cinta de cinturón de Split para evaluar la generalización de la adaptación humana Aparato Locomotor

Understanding the mechanisms underlying locomotor learning helps researchers and clinicians optimize gait retraining as part of motor rehabilitation. ...

Automated Analysis of a Nematode Population-based Chemosensory Preference Assay

The nematode, Caenorhabditis elegans' compact nervous system of only 302 neurons underlies a diverse repertoire of behaviors. To facilitate the dissection of the neural circuits underlying these behaviors, the development of robust and reproducible behavioral assays is necessary. Previous C. elegans behavioral studies have used variations of a "drop test", a "chemotaxis assay", and a "retention assay" to investigate the response of C. elegans to soluble compounds. The method described in this article seeks to combine the complementary strengths of the three aforementioned assays. Briefly, a small circle in the middle of each assay plate is divided into four quadrants with the control and experimental solutions alternately placed. After the addition of the worms, the assay plates are loaded into a behavior chamber where microscope cameras record the worms' encounters with the treated regions. Automated video analysis is then performed and a preference index (PI) value for each video is generated. The video acquisition and automated analysis features of this method minimizes the experimenter's involvement and any associated errors. Furthermore, minute amounts of the experimental compound are used per assay and the behavior chamber's multi-camera setup increases experimental throughput. This method is particularly useful for conducting behavioral screens of genetic mutants and novel chemical compounds. However, this method is not appropriate for studying stimulus gradient navigation due to the close proximity of the control and experimental solution regions. It should also not be used when only a small population of worms is available. While suitable for assaying responses only to soluble compounds in its current form, this method can be easily modified to accommodate multimodal sensory interaction and optogenetic studies. This method can also be adapted to assay the chemosensory responses of other nematode species.

We present a behavior recording setup and protocol that enables automated analysis of the nematode, Caenorhabditis elegans' preference for soluble compounds in a population-based assay. This article describes the construction of a behavior chamber, the behavioral assay protocol, and video analysis software usage.

Análisis Automatizado de un Ensayo de Preferencia Chemosensorial Basada en Población de Nematodo

The nematode, Caenorhabditis elegans' compact nervous system of only 302 neurons underlies a diverse repertoire of behaviors. To facilitate the dissection ...

Paw-Print Analysis of Contrast-Enhanced Recordings (PrAnCER): A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

Gait analysis is used to quantify changes in motor function in many rodent models of disease. Despite the importance of assessing gait and motor function in many areas of research, the available commercial options have several limitations such as high cost and lack of accessible, open code. To address these issues, we developed PrAnCER, Paw-Print Analysis of Contrast-Enhanced Recordings, for automated quantification of gait. The contrast-enhanced recordings are produced by using a translucent floor that obscures objects not in contact with the surface, effectively isolating the rat&#8217;s paw prints as it walks. Using these videos, our simple software program reliably measures a variety of spatiotemporal gait parameters. To demonstrate that PrAnCER can accurately detect changes in motor function, we employed a haloperidol model of Parkinson&#8217;s disease (PD). We tested rats at two doses of haloperidol: high dose (0.30 mg/kg) and low dose (0.15 mg/kg). Haloperidol significantly increased stance duration and hind paw contact area in the low dose condition, as might be expected in a PD model. In the high dose condition, we found a similar increase in contact area but also an unexpected increase in stride length. With further research, we found that this increased stride length is consistent with the bracing-escape phenomenon commonly observed at higher doses of haloperidol. Thus, PrAnCER was able to detect both expected and unexpected changes in rodent gait patterns. Additionally, we confirmed that PrAnCER is consistent and accurate when compared with manual scoring of gait parameters.

Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

We describe a novel gait analysis system, Paw-Print Analysis of Contrast-Enhanced Recordings (PrAnCER), an open-access automated system for the quantification of gait characteristics in rats that utilizes a novel semitransparent floor to automatically quantify gait. This system was validated using the haloperidol model of Parkinson&#8217;s Disease.

Análisis de impresión de patas de grabaciones mejoradas por contraste (PrAnCER): Un sistema de análisis automatizado de gait de bajo costo y acceso abierto para evaluar los déficits de los motores

Gait analysis is used to quantify changes in motor function in many rodent models of disease. Despite the importance of assessing gait and motor function ...

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

With the advent of new technological advances, it is possible to study social interactions at a microlevel with unprecedented accuracy. High frequency sensors, such as eye-trackers, electrodermal activity wristbands, EEG bands, and motion sensors provide observations at the millisecond level. This level of precision allows researchers to collect large datasets on social interactions. In this paper, I discuss how multiple eye-trackers can capture a fundamental construct in social interactions, joint visual attention (JVA). JVA has been studied by developmental psychologists to understand how children acquire language, learning scientists to understand how small groups of learners work together, and social scientists to understand interactions in small teams. This paper describes a methodology for capturing JVA in colocated settings using mobile eye-trackers. It presents some empirical results and discusses implications of capturing microobservations to understand social interactions.

Using multimodal sensors is a promising way to understand the role of social interactions in educational settings. This paper describes a methodology for capturing joint visual attention from colocated dyads using mobile eye-trackers.

Una metodología para capturar la atención visual conjunta mediante rastreadores oculares móviles

With the advent of new technological advances, it is possible to study social interactions at a microlevel with unprecedented accuracy. High frequency ...