scholarly journals Behavioral features of motivated response to alcohol in Drosophila

2020 ◽  
Author(s):  
Jamie L. Catalano ◽  
Nicholas Mei ◽  
Reza Azanchi ◽  
Sophia Song ◽  
Tyler Blackwater ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Operant Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Ethanol Reinforcement ◽  
Behavioral Expression ◽  
Behavioral Features ◽  
Seeking Behavior ◽  
Physiological Reactions

AbstractAnimals avoid predators and find the best food and mates by learning from the consequences of their behavior. However, reinforcers are not always uniquely appetitive or aversive but can have complex properties. Most intoxicating substances fall within this category; provoking aversive sensory and physiological reactions while simultaneously inducing overwhelming appetitive properties. Here we describe the subtle behavioral features associated with continued seeking for alcohol despite aversive consequences. We developed an automated runway apparatus to measure how Drosophila respond to consecutive exposures of a volatilized substance. Behavior within this Behavioral Expression of Ethanol Reinforcement Runway (BEER Run) demonstrated a defined shift from aversive to appetitive responses to volatilized ethanol. Behavioral metrics attained by combining computer vision and machine learning methods, reveal that a subset of 9 classified behaviors and component behavioral features associate with this shift. We propose this combination of 9 behaviors can be used to navigate the complexities of operant learning to reveal motivated goal-seeking behavior.

scholarly journals Lane Detection Based on Connection of Various Feature Extraction Methods

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Mingfa Li ◽  
Yuanyuan Li ◽  
Min Jiang
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Feature Detection ◽  
Extraction Methods ◽  
Lane Detection ◽  
New Method ◽  
Machine Learning Techniques ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Learning Techniques

Lane detection is a challenging problem. It has attracted the attention of the computer vision community for several decades. Essentially, lane detection is a multifeature detection problem that has become a real challenge for computer vision and machine learning techniques. Although many machine learning methods are used for lane detection, they are mainly used for classification rather than feature design. But modern machine learning methods can be used to identify the features that are rich in recognition and have achieved success in feature detection tests. However, these methods have not been fully implemented in the efficiency and accuracy of lane detection. In this paper, we propose a new method to solve it. We introduce a new method of preprocessing and ROI selection. The main goal is to use the HSV colour transformation to extract the white features and add preliminary edge feature detection in the preprocessing stage and then select ROI on the basis of the proposed preprocessing. This new preprocessing method is used to detect the lane. By using the standard KITTI road database to evaluate the proposed method, the results obtained are superior to the existing preprocessing and ROI selection techniques.

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