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Course Description

The basis of intelligence – how the brain produces intelligent behavior and how to endow machines with human-like intelligence – is arguably the greatest problem in science and technology. To solve it, we will need to understand how natural intelligence emerges from computations in neural circuits, with rigor sufficient to reproduce similar intelligent behavior in machines. Success in this endeavor will ultimately enable us to understand ourselves better, to produce smarter machines, and perhaps even to make ourselves smarter. Today’s AI technologies are impressive but quite different from human intelligence. We still do not understand the mechanisms underlying the robustness, the generalization, and the continual learning capabilities of biological intelligence. The synergistic combination of cognitive science, neurobiology, engineering, mathematics, and computer science holds the promise of significant progress. Elucidating how human intelligence works will in turn lead to more sophisticated AI algorithms. The goal of this course is to help produce a community of leaders that is equally knowledgeable in neuroscience, cognitive science, and computer science and will lead the scientific understanding of intelligence and the development of true biologically inspired AI.

The class discussions will cover a range of topics, including:

• Neuroscience: neurons and models
• Computational vision
• Biological vision
• Machine learning
• Bayesian inference
• Planning and motor control
• Memory
• Social cognition
• Inverse problems & well-posedness
• Audition and speech processing
• Natural language processing

These discussions will be complemented in the first week by MathCamps and NeuroCamps, to refresh the necessary background. Throughout the course, students will participate in workshops and tutorials to gain hands-on experience with these topics. Lectures will be followed by afternoons of computational labs, with additional evening research seminars.

All students must attend the entire three weeks of the course, participate in all course lectures, participate in a research project, and present their research project at the conclusion of the course.

The course will culminate with student presentations of their projects. These projects provide the opportunity for students to work closely with the resident faculty, to develop ideas that grow out of the lectures and seminars, and to connect these ideas with problems from the students’ own research at their home institutions.

This course aims to cross-educate computer engineers and neuroscientists; it is appropriate for graduate students, postdocs, and faculty in computer science or neuroscience. Students are expected to have a strong background in at least one discipline (such as neurobiology, physics, engineering, and mathematics). Our goal is to develop the science and the technology of intelligence and to help train a new generation of scientists that will leverage the progress in neuroscience, cognitive science, and computer science.