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DTSTART;TZID=Europe/Berlin:20220106T160000
DTEND;TZID=Europe/Berlin:20220106T173000
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CREATED:20211213T101206Z
LAST-MODIFIED:20250604T092718Z
UID:11359-1641484800-1641490200@www.scienceofintelligence.de
SUMMARY:Lars Lewejohann (Science of Intelligence)\, “What’s on a Mouse’s Mind? Behavioral Measures To Understand Experiences and Needs of an Animal”
DESCRIPTION:What’s on a mouse’s mind? Behavioral measures to understand experiences and needs of an animal\nLars Lewejohann\, Freie Universität Berlin\, German Federal Institute for Risk Assessment (BfR)\, German Centre for the Protection of Laboratory Animals (Bf3R) \nMice\, as all other living creatures\, have adapted to specific living conditions in the course of evolution. From a human point of view\, the behavior of animals is therefore not always easy to understand. This applies not only to the question of whether mice are actually capable of behaving intelligently\, but also to the question of what is necessary for optimizing animal welfare of laboratory animals. In our work\, we are interested in both questions and follow an animal-centered approach and are giving mice their say. Of course mice cannot fill out questionnaires\, but we have developed a series of behavioral tests that allow to query the animals. In this lecture I will outline our approach with regard to improving housing and living conditions as well as the implications of using mice as a model species for the science of intelligence. \nThe Zoom Link will be sent the day before the lecture.
URL:https://www.scienceofintelligence.de/event/pi-lecture-with-lars-lewejohann-3/
CATEGORIES:PI Lecture
ATTACH;FMTTYPE=image/jpeg:https://www.scienceofintelligence.de/wp-content/uploads/2021/12/lewejohann_800.jpg
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DTSTART;TZID=Europe/Berlin:20220120T160000
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CREATED:20211213T101428Z
LAST-MODIFIED:20250604T092649Z
UID:11363-1642694400-1642699800@www.scienceofintelligence.de
SUMMARY:Henning Sprekeler (Science of Intelligence)\, “Harnessing Machine Learning To Model Biological Systems”
DESCRIPTION:“Harnessing machine learning to model biological systems” \nAbstract:\nClassically\, models of biological systems follow two different approaches. In bottom-up approaches\, biological data are used to constrain a phenomenological model of the system in question\, and the model is the studied to identify potential functions or potential consequences of the observations that flow into the model. Top-down approaches\, on the other hand\, start with a presumed function and ask how this question could be implemented in a biologically inspired architecture. Both approaches have been very successful\, but both suffer from their own kind of problems. Bottom-up approaches often suffer from (potentially many) parameters that cannot be sufficiently constrained from data. Top-down approaches were in the past hard to combine with the complexities of the biological system in question. Recent advances in machine learning (ML) software now offer a promising hybrid approach\, because they allow to optimize not only neural nets\, but basically any dynamical system by gradient descent. I will offer a few examples how we have used ML to study biological systems\, ranging from behavioral level (nature vs. nurture) down to the level of neural circuits (role of feedback for invariant sensory processing\, and\, time permitting\, the function of different cell classes). \nThe Zoom Link will be sent the day before the lecture.
URL:https://www.scienceofintelligence.de/event/pi-lecture-with-henning-sprekeler/
CATEGORIES:PI Lecture
ATTACH;FMTTYPE=image/jpeg:https://www.scienceofintelligence.de/wp-content/uploads/2021/12/sprekeler_800.jpg
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