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

Gostaríamos de agradecer a Pingping Zhao e Peyman Golshani por fornecer os dados brutos para testes de interação social de dois camundongos usados no artigo original2. Este estudo foi apoiado por concessões de GPU DA NIH NS109315 e NVIDIA GPU (AA).

1. Configuração GPU e Python
<ol><li>
GPU Software Quando o computador é configurado pela primeira vez para aplicativos de aprendizagem profunda, o software e os drivers apropriados para GPU devem ser instalados que podem ser encontrados no respectivo site da GPU. (ver a Tabela de Materiais para os utilizados neste estudo).</li>
	<li>
Instalação Python 2.7 Abra um prompt de linha de comando em sua máquina. Linha de comando: sudo apt-get instalar...

<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>

Aqui, fornecemos um guia passo a passo para a implementação do DeepBehavior, nossa caixa de ferramentas baseada em aprendizagem profunda recentemente desenvolvida para análise de dados de imagem de comportamento animal e humano2. Fornecemos explicações detalhadas para cada etapa para a instalação das estruturas para cada arquitetura de rede e fornecemos links para a instalação dos requisitos de código aberto para poder executar essas estruturas. Demonstramos como instalá-los, como criar...

Uma análise detalhada do comportamento é fundamental para entender as relações cerebrais e comportamentais. Houve muitos avanços emocionantes nas metodologias para registrar e manipular populações neuronais com alta resolução temporal, no entanto, os métodos de análise de comportamento não se desenvolveram no mesmo ritmo e estão limitados a medições indiretas e a uma abordagem reducionista1. Recentemente, métodos baseados em deep learning foram desenvolvidos para realizar análises de comportamento automatizadas e detalhadas2,3,4,5. Este protocolo fornece um guia de implementação passo a passo para a caixa de ferramentas DeepBehavior.
Os métodos tradicionais de análise comportamental geralmente incluem rotular manualmente dados por vários avaliadores, levando à variância na forma como os experimentadores definem um comportamento6. A rotulagem manual dos dados requer tempo e recursos que aumentam desproporcionalmente para a quantidade de dados coletados. Além disso, dados rotulados manualmente reduzem os desfechos de comportamento em medições categóricas que não capturam a riqueza do comportamento, e serão mais subjetivos. Assim, os métodos tradicionais atuais podem ser limitados na captura dos detalhes nos comportamentos naturais.
A caixa de ferramentas DeepBehavior apresenta uma solução precisa, detalhada, altamente temporal e automatizada usando aprendizado profundo para análise comportamental. O aprendizado profundo tornou-se rapidamente acessível a todos com ferramentas e pacotes de código aberto. As redes neurais convolucionais (CNNs) são comprovadamente altamente eficazes no reconhecimento de objetos e nas tarefas de rastreamento7,8. Usando CNNs modernos e unidades de processamento gráfico de alto desempenho (GPUs), grandes conjuntos de dados de imagem e vídeo podem ser processados rapidamente com alta precisão7,9,10,11. No DeepBehavior, existem três arquiteturas de rede neural convolucional diferentes, TensorBox, YOLOv3 e OpenPose2.
A primeira estrutura, Tensorbox, é uma estrutura versátil que incorpora muitas arquiteturas diferentes da CNN para detecção de objetos12. O TensorBox é mais adequado para detectar apenas uma classe de objeto por imagem. As saídas resultantes são caixas delimitadoras do objeto de interesse (Figura 1) e as coordenadas cartesianas da caixa de limitação.
O segundo quadro da CNN é YOLOv3, que significa "You Only Look Once"13. YOLOv3 é vantajoso quando há múltiplos objetos de interesse que devem ser rastreados separadamente. A saída desta rede inclui a caixa de limitadora com a classe de etiqueta de objeto associada, bem como as coordenadas cartesianas da caixa delimitadora do objeto no quadro de vídeo(Figura 2).
As duas estruturas anteriores são vantajosas para dados comportamentais generalizados coletados de experimentos laboratoriais padrão em indivíduos animais. O último quadro da CNN é OpenPose14,15,16 que é usado para estimativa de pose articular humana. OpenPose detecta pontos de chave do corpo, mão, facial e pé humano nas imagens. As saídas da estrutura são imagens rotuladas do sujeito humano, bem como as coordenadas de todos os 25 pontos-chave do corpo e 21 pontos-chave de cada mão (Figura 3).
Este guia detalhado passo a passo para a implementação de nossa caixa de ferramentas DeepBehavior de código aberto recém-desenvolvida emprega redes neurais convolucionais de última geração para rastrear o comportamento animal (por exemplo, movimento de uma pata) ou comportamento humano (por exemplo, chegar às tarefas). Ao rastrear o comportamento, os cinemáticas úteis podem ser derivados do comportamento como posição, velocidade e aceleração. O protocolo explica a instalação de cada arquitetura da CNN, demonstra como criar conjuntos de dados de treinamento, como treinar as redes, como processar novos vídeos na rede treinada, como extrair os dados da rede nos novos vídeos e como pós-processo os dados de saída para torná-los úteis para análise si só.

<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 o comportamento é o primeiro passo para realmente entender mecanismos neurais no cérebro que o impulsionam. Os métodos tradicionais de análise comportamental muitas vezes não capturam a riqueza inerente ao comportamento natural. Aqui, fornecemos instruções detalhadas passo a passo com visualizações de nossa metodologia recente, deepbehavior. A caixa de ferramentas DeepBehavior usa estruturas de deep learning construídas com redes neurais convolucionais para processar rapidamente e analisar vídeos comportamentais. Este protocolo demonstra três estruturas diferentes para detecção de objetoúnico, detecção múltipla de objetos e rastreamento de pose articular humana tridimensional (3D). Esses quadros devolvem coordenadas cartesianas do objeto de interesse para cada quadro do vídeo de comportamento. Os dados coletados na caixa de ferramentas DeepBehavior contêm muito mais detalhes do que os métodos tradicionais de análise de comportamento e fornecem insights detalhados sobre a dinâmica do comportamento. DeepBehavior quantifica tarefas de comportamento de forma robusta, automatizada e precisa. Após a identificação do comportamento, o código pós-processamento é fornecido para extrair informações e visualizações dos vídeos comportamentais.

Quando o protocolo é seguido, os dados de cada arquitetura de rede devem ser semelhantes aos seguintes. Para o TensorBox, ele produz uma caixa de limitador em torno do objeto de interesse. Em nosso exemplo, usamos vídeos de uma tarefa de alcance de pelotas de comida, e rotulamos as patas certas para rastrear seu movimento. Como visto na Figura 1,a pata direita pode ser detectada em diferentes posições tanto na visão frontal quanto nas câmeras de visão ...

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

O objetivo deste protocolo é utilizar redes neurais convolucionais pré-construídas para automatizar o rastreamento de comportamentos e realizar análises detalhadas de comportamento. O rastreamento de comportamento pode ser aplicado a quaisquer dados de vídeo ou sequências de imagens e é generalizável para rastrear qualquer objeto definido pelo usuário.

Uma implementação passo a passo do DeepBehavior, caixa de ferramentas de deep learning para análise automatizada de comportamento

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

Realizar uma análise detalhada do comportamento é crucial para entender a relação de comportamento cerebral. Uma das melhores maneiras de avaliar o comportamento é através de observações cuidadosas. No entanto, quantificar o comportamento observado é demorado e desafiador.Métodos clássicos de análise comportamental não são facilmente quantificáveis e são inerentemente subjetivos. Desenvolvimentos recentes em deep learning, um ramo de aprendizado de máquina e inteligência artificial, oferecem oportunidades para quantificação automatizada e objetiva de imagens e vídeos. Aqui apresentamos nossos métodos recentemente desenvolvidos utilizando redes neurais profundas para realizar análises detalhadas de comportamento em roedores e humanos.A principal vantagem dessa técnica são sua flexibilidade e aplicabilidade a quaisquer dados de imagem para análise de comportamento. A caixa de ferramentas DeepBehavior suporta identificação de objeto único, detecção de vários objetos e rastreamento de pose humana. Também fornecemos o código de pós-processamento no MATLAB para métodos de análise cinemática mais aprofundados.Comece configurando a Caixa tensor. Ative o ambiente e use o GitHub para clonar a Tensor Box e instalá-lo na máquina e em dependências adicionais. Em seguida, inicie a interface gráfica de rotulagem e rotule pelo menos 600 imagens de uma ampla distribuição de quadros de comportamento.Para rotular uma imagem clique no canto superior esquerdo do objeto de interesse e, em seguida, no canto inferior direito. Em seguida, certifique-se de que a caixa delimitação capture todo o objeto. Clique ao lado para mover-se para o próximo quadro.Para vincular as imagens de treinamento a um arquivo de hiper parâmetros de rede, abra overfeat_rezoom. json em um editor de texto e substituir o caminho de arquivo sob train_idl para labels.json. Em seguida, adicione o mesmo caminho de arquivo em test-idl e salve as alterações.Inicie o roteiro de treinamento que começará a treinar para 600.000 iterações e gere os pesos treinados resultantes da rede neural convolucional na pasta de saída. Em seguida, execute a previsão em novas imagens e visualize as saídas da rede como imagens rotuladas e como coordenadas de caixa delimitadoras. Instale o YOLOv3.Em seguida, rotule os dados de treinamento com Yolo_mark colocando as imagens na pasta Yolo_mark-data-obga e rotulando-as uma a uma na interface gráfica do usuário. Rotule aproximadamente 200 imagens. Em seguida, configure o arquivo de configuração.Para modificar o arquivo de configuração abra o YOLO-obj. pasta cfg. Modifique as linhas de lote, subdivisão e classes.Em seguida, altere o filtro para cada camada de convolução antes de uma camada YOLO. Baixe os pesos da rede e coloque-os na construção da darknet. pasta x64.Execute o algoritmo de treinamento, e uma vez que ele é visualização completa das iterações. Para rastrear várias partes do corpo em um sujeito humano, instale o OpenPose e use-o para processar o vídeo desejado. As capacidades da Caixa de Ferramentas deepbehavior foram demonstradas em vídeos de ratos realizando uma tarefa de alcance de pelotas de alimentos.Suas patas direitas foram rotuladas e o movimento foi rastreado com câmeras de visão frontal e lateral. Após o pós-processamento com calibração da câmera, foram obtidas trajetórias 3D do alcance. As saídas do YOLOv3 são várias caixas delimitadoras porque vários objetos podem ser rastreados.As caixas delimitadoras estão ao redor dos objetos de interesse que podem ser partes do corpo. No OpenPose, a rede detectou as posições articulares e após o pós-processamento com calibração da câmera, foi criado um modelo 3D do assunto. Um passo crítico não abordado neste protocolo é garantir que seu dispositivo tenha as versões e dependências Python apropriadas, bem como um dispositivo configurado por GPU antes de começar.Depois de obter com sucesso o comportamento da faixa a partir do processamento adicional de postagem da rede pode ser feito para analisar melhor a cinemática e padrões do comportamento. Por que a Caixa de Ferramentas deepbehavior é aplicável para abordagens diagnósticas em modelos de doenças de roedores e seres humanos não é benefício terapêutico direto. O uso dessas técnicas como ferramenta diagnóstica ou prognóstica está em pesquisa ativa em nosso laboratório.Esta técnica está sendo usada para investigar os mecanismos neurais do comportamento motor qualificado em roedores, bem como sendo utilizada em estudos clínicos para avaliar a recuperação motora em pacientes com doenças 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.

Contextual e Cued Medo Condicionado Teste Usando um sistema de análise de vídeo em Ratos

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 ...

Comportamento

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.

Avaliação da Cognição Social em primatas não-humanos usando uma rede de Computadorizado dispositivo automático de Aprendizagem Sistemas (ALDM) teste

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.

Quantificar Aprendizagem em lactentes jovens: Ações Perna Rastreamento Durante uma tarefa de aprendizagem-Discovery

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.

Um método para a atividade neural remotamente Silenciando em Roedores Durante fases distintas de Aprendizagem

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.

Um método geral para avaliar os efeitos Estimulação Profunda do Cérebro em metanfetamina intravenosa auto-administração

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 Recolha de Dados e Análise Aplicada à Tecnologia Análise Expressão Facial automatizada e Análise Temporal de Avaliação 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).

Usando uma escada rolante Split-cinto para avaliar a generalização de adaptação de locomoção humana

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.

Análise automatizada de um ensaio de preferência quimiossensorial baseado na população de um 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.

Análise de Paw-Print de gravações contrastadas (PrAnCER): um sistema automatizado de análise de marcha de baixo custo e acesso aberto para avaliação de déficits 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.

Uma metodologia para capturar a atenção visual conjunta usando 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 ...