Introduction to Organoid Intelligence
Lecture Notes on Computer Science
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This text provides a comprehensive introduction to Organoid Intelligence (OI), an emerging and interdisciplinary field at the intersection of biology, computation, neuroscience, and engineering. These notes are intended to act as a course companion (which may be designed based on the following syllabus), not necessarily a standalone text for self-study unless the reader already has some familiarity with studying computational neuroscience and artificial intelligence. It is designed for advanced undergraduate and graduate students (e.g., a 500/600-level course) who are already familiar with lin...
This text provides a comprehensive introduction to Organoid Intelligence (OI), an emerging and interdisciplinary field at the intersection of biology, computation, neuroscience, and engineering. These notes are intended to act as a course companion (which may be designed based on the following syllabus), not necessarily a standalone text for self-study unless the reader already has some familiarity with studying computational neuroscience and artificial intelligence. It is designed for advanced undergraduate and graduate students (e.g., a 500/600-level course) who are already familiar with linear algebra, programming, and AI concepts such as machine learning and deep learning. The text begins by establishing the necessary biological background, covering essential topics like cell biology, neurogenesis, computational neuroscience, and the development of brain organoids. From there, it explores how these biological foundations intersect with computational models to form the basis of OI. Through practical projects using the PyOrganoid Python library, students will learn to create and simulate Deep Organoid Learning models, applying theoretical concepts in a hands-on environment. Furthermore, the text is designed to prepare students for more advanced studies and experimental work in Organoid Intelligence, particularly in contexts where real brain organoids are combined with AI models for cutting-edge research and applications.
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