Video-Based Tracking and Incremental Learning Applied to Rodent Behavioral Activity Under Near-Infrared Illumination

Abstract-This paper describes a noninvasive video tracking system for measurement of rodent behavioral activity under near-infrared (NIR) illumination, where the rodent is of a similar color to the background. This novel method allows position tracking in the dark, when rodents are generally most active, or under visible light. It also improves current video tracking methods under low-contrast conditions. We also manually extracted rodent features and classified three common behaviors (sitting, walking, and rearing) using an inductive algorithm-a decision tree (ID3). In addition, we proposed the use of a time-spatial incremental decision tree (ID5R), with which new behavior instances can be used to update the existing decision tree in an online manner. These were implemented using incremental tree induction. Open-field locomotor activity was investigated under "visible" (460.5−561.1 nm), 880-and 940-nm wavelengths of NIR, as well as a "dark" condition consisting of a very small level of NIR illumination. A widely used NIR crossbeam-based tracking system (Activity Monitor, MED Associates, Inc., Georgia, VT) was used to record simultaneous position data for validation of the video tracking system. The classification accuracy for the set of new test data was 81.3%

Automated home-cage behavioral phenotyping of mice

We describe a trainable computer vision system enabling the automated analysis of complex mouse behaviors. We provide software and a very large manually annotated video database used for training and testing the system. Our system outperforms leading commercial software and performs on par with human scoring, as measured from the ground-truth manual annotations of thousands of clips of freely behaving animals. We show that the home-cage behavior profiles provided by the system is sufficient to accurately predict the strain identity of individual animals in the case of two standard inbred and two non-standard mouse strains. Our software should complement existing sensor-based automated approaches and help develop an adaptable, comprehensive, high-throughput, fine-grained, automated analysis of rodent behavior

Mouse Behavior Recognition with The Wisdom of Crowd

In this thesis, we designed and implemented a crowdsourcing system to annotatemouse behaviors in videos; this involves the development of a novel clip-based video labeling tools, that is more efficient than traditional labeling tools in crowdsourcing platform, as well as the design of probabilistic inference algorithms that predict the true labels and the workers' expertise from multiple workers' responses. Our algorithms are shown to perform better than majority vote heuristic. We also carried out extensive experiments to determine the effectiveness of our labeling tool, inference algorithms and the overall system

Mouse behavior recognition with the wisdom of crowd

Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 67-72).In this thesis, we designed and implemented a crowdsourcing system to annotate mouse behaviors in videos; this involves the development of a novel clip-based video labeling tools, that is more efficient than traditional labeling tools in crowdsourcing platform, as well as the design of probabilistic inference algorithms that predict the true labels and the workers' expertise from multiple workers' responses. Our algorithms are shown to perform better than majority vote heuristic. We also carried out extensive experiments to determine the effectiveness of our labeling tool, inference algorithms and the overall system.by Yuzhao Ni.S.M

Mice in a labyrinth: Rapid learning, sudden insight, and efficient exploration

Animals learn certain complex tasks remarkably fast, sometimes after a single experience. What behavioral algorithms support this efficiency? Many contemporary studies based on two-alternative-forced-choice (2AFC) tasks observe only slow or incomplete learning. As an alternative, we study the unconstrained behavior of mice in a complex labyrinth and measure the dynamics of learning and the behaviors that enable it. A mouse in the labyrinth makes ~2000 navigation decisions per hour. The animal quickly discovers the location of a reward in the maze and executes correct 10-bit choices after only 10 reward experiences – a learning rate 1000-fold higher than in 2AFC experiments. Many mice improve discontinuously from one minute to the next, suggesting moments of sudden insight about the structure of the labyrinth. The underlying search algorithm does not require a global memory of places visited and is largely explained by purely local turning rules

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 388)

This bibliography lists 132 reports, articles and other documents introduced into the NASA Scientific and Technical Information Database. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

Biological Intelligence: From Behavior to Learning Theory

Knowing how to learn, think, and act is not just a hallmark of intelligence, but a necessity of survival for many organisms. Behavior, the complete set of actions of species, allows us to glimpse into the minds of humans and animals, and by extension, intelligence itself. Biological intelligence is characterized by fast adaptation to changes and challenges, which is what allows species to survive in natural environments from starvation and predation. To study learning in a controlled setting, we can observe the behavior evoked through decision-making tasks that make it possible to quantify and analyze learning. By modeling the extracted behavioral features, we could start to understand the possible underlying mechanisms by proposing neural theory models, and look for those signals in the brain. Understanding the neural mechanisms of learning also strengthens the basis for building intelligent machines that are flexible and adaptive to the nonstationary world we live in. In this thesis, I present works in (1) automating behavioral setups and modeling suboptimal behavior in a traditional decision-making task, (2) using an ethological navigation task to characterize fast-sequence learning, and (3) how neural theory can explain some core behavioral phenomena in (2), and be used to solve a central problem in graph search.</p

Visible light positioning system based on a quadrant photodiode and encoding techniques

Visible light positioning systems (VLPSs) are a feasible alternative to local positioning systems due to the technology improvement and massive use of light-emitting diodes (LEDs). Compared to other technologies, VLPSs can provide significant advantages, such as the achieved accuracy, although they still present some issues, mainly related to the reduced coverage area or the high computational load. This article proposes the design of a VLPS based on four LED lamps as transmitters and a quadrant photodiode angular diversity aperture (QADA) as a receiver. As the shape of the QADA is circular and the aperture to be installed over it is square, we derive the corresponding general equations to obtain the currents through the different pads of the QADA, regarding the angle of incidence of the light (and, inversely, how to estimate the angle of incidence from the measured currents). An encoding scheme based on 1023-bit Kasami sequences is proposed for every transmission from the LED lamps, thus providing multiple access capability and robustness against low signal-to-noise ratios and harsh conditions, such as multipath and near-far effect. A triangulation technique has been applied to estimate the receiver's position, by means of the least-squares estimator (LSE), together with some geometrical considerations. The proposal has been validated by simulation and by experimental tests, obtaining 3-D positioning average errors below 13 and 5.5 cm for separations between the transmitters' plane and the receiver of 2 and 1 m, respectively