Reproducibility and replicability in zebrafish behavioral neuroscience research

2019 ◽  
Vol 178 ◽  
pp. 30-38 ◽  
Author(s):  
Robert Gerlai
Keyword(s):  
Behavioral Neuroscience ◽  
Neuroscience Research

Application of microarray technology in primate behavioral neuroscience research

Methods ◽  
2006 ◽  
Vol 38 (3) ◽  
pp. 227-234 ◽  
Author(s):  
Adriaan M. Karssen ◽  
Jun Z. Li ◽  
Song Her ◽  
Paresh D. Patel ◽  
Fan Meng ◽  
...  
Keyword(s):  
Behavioral Neuroscience ◽  
Microarray Technology ◽  
Neuroscience Research

scholarly journals Mice tracking using the YOLO algorithm

2019 ◽  
Author(s):  
Helton Maia Peixoto ◽  
Richardson Santiago Teles ◽  
John Victor Alves Luiz ◽  
Aron Miranda Henriques-Alves ◽  
Rossana Moreno Santa Cruz
Keyword(s):  
New Technologies ◽  
Good Alternative ◽  
Behavioral Neuroscience ◽  
Experimental Designs ◽  
Training Set ◽  
Computational Tools ◽  
Common System ◽  
Neuroscience Research ◽  
The Mean ◽  
System Errors

The development of computational tools is essential for the development of new technologies, including experimental designs needed for behavioral neuroscience research. The computational tool developed in this study is based on the convolutional neural networks and the You Only Look Once (YOLO) algorithm for detecting and tracking mice in videos recorded during behavioral neuroscience experiments. The task of mice detection consists of determining the location in the image where the animals are present, for each frame acquired. In this work, we propose mice tracking using the YOLO algorithm, running on an NVIDIA GeForce GTX 1060 GPU. We analyzed a set of data composed of 13622 images, made up of behavioral videos of three important researches in this area. The training set used 50% of the images, 25% for validation and 25% for the tests. The results show that the mean Average Precision (mAP) reached by the developed system was 90.79% and 90.75% for the Full and Tiny versions of YOLO, respectively. It has also been found that the use of the Tiny version is a good alternative for experimental designs that require real-time response. Considering the high accuracy of the results, the developed work allows the experimentalists to perform mice tracking in a reliable and non-evasive way, avoiding common system errors that require delimitations of regions of interest (ROI) or even evasive luminous identifiers such as LED for tracking the animals.

scholarly journals The Future Is Open: Open-Source Tools for Behavioral Neuroscience Research

eNeuro ◽  
2019 ◽  
Vol 6 (4) ◽  
pp. ENEURO.0223-19.2019 ◽  
Author(s):  
Samantha R. White ◽  
Linda M. Amarante ◽  
Alexxai V. Kravitz ◽  
Mark Laubach
Keyword(s):  
Open Source ◽  
Behavioral Neuroscience ◽  
Neuroscience Research ◽  
The Future

scholarly journals Mice tracking using the YOLO algorithm

2019 ◽  
Author(s):  
Helton Maia Peixoto ◽  
Richardson Santiago Teles ◽  
John Victor Alves Luiz ◽  
Aron Miranda Henriques-Alves ◽  
Rossana Moreno Santa Cruz
Keyword(s):  
New Technologies ◽  
Good Alternative ◽  
Behavioral Neuroscience ◽  
Experimental Designs ◽  
Training Set ◽  
Computational Tools ◽  
Common System ◽  
Neuroscience Research ◽  
The Mean ◽  
System Errors

The development of computational tools is essential for the development of new technologies, including experimental designs needed for behavioral neuroscience research. The computational tool developed in this study is based on the convolutional neural networks and the You Only Look Once (YOLO) algorithm for detecting and tracking mice in videos recorded during behavioral neuroscience experiments. The task of mice detection consists of determining the location in the image where the animals are present, for each frame acquired. In this work, we propose mice tracking using the YOLO algorithm, running on an NVIDIA GeForce GTX 1060 GPU. We analyzed a set of data composed of 13622 images, made up of behavioral videos of three important researches in this area. The training set used 50% of the images, 25% for validation and 25% for the tests. The results show that the mean Average Precision (mAP) reached by the developed system was 90.79% and 90.75% for the Full and Tiny versions of YOLO, respectively. It has also been found that the use of the Tiny version is a good alternative for experimental designs that require real-time response. Considering the high accuracy of the results, the developed work allows the experimentalists to perform mice tracking in a reliable and non-evasive way, avoiding common system errors that require delimitations of regions of interest (ROI) or even evasive luminous identifiers such as LED for tracking the animals.

Social Psychology and Noninvasive Electrical Stimulation

2016 ◽  
Vol 21 (1) ◽  
pp. 30-40 ◽  
Author(s):  
Paulo S. Boggio ◽  
Gabriel G. Rêgo ◽  
Lucas M. Marques ◽  
Thiago L. Costa
Keyword(s):  
Social Psychology ◽  
Social Neuroscience ◽  
Special Focus ◽  
Social Pain ◽  
Substantial Evidence ◽  
Social Decision ◽  
Correlational Research ◽  
Neuroscience Research ◽  
Social Decision Making ◽  
Brain Behavior

Abstract. Social neuroscience and psychology have made substantial advances in the last few decades. Nonetheless, the field has relied mostly on behavioral, imaging, and other correlational research methods. Here we argue that transcranial direct current stimulation (tDCS) is an effective and relevant technique to be used in this field of research, allowing for the establishment of more causal brain-behavior relationships than can be achieved with most of the techniques used in this field. We review relevant brain stimulation-aided research in the fields of social pain, social interaction, prejudice, and social decision-making, with a special focus on tDCS. Despite the fact that the use of tDCS in Social Neuroscience and Psychology studies is still in its early days, results are promising. As better understanding of the processes behind social cognition becomes increasingly necessary due to political, clinical, and even philosophical demands, the fact that tDCS is arguably rare in Social Neuroscience research is very noteworthy. This review aims at inspiring researchers to employ tDCS in the investigation of issues within Social Neuroscience. We present substantial evidence that tDCS is indeed an appropriate tool for this purpose.

Behavioral Neuroscience: Editor.

2003 ◽  
Vol 117 (4) ◽  
pp. C2-C2
Author(s):  
No authorship indicated
Keyword(s):  
Behavioral Neuroscience
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