Brain-like computation and intelligence
Summary
Recent advances in machine learning have contributed to the emergence of powerful models of animal perception and behavior. In this course we will compare the behavior and underlying mechanisms in these models as well as brains.
Content
This comparison will be done based on contemporary models of vision, audition, touch, proprioception, motor control, language, and cognition.
Content
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Classical models of sensory, motor and cognitive function
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Goal-driven and data-driven brain modeling
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Hierarchical and recurrent neural network models
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Comparing models to neural data
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Comparing modes to behavioral data
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Modern tools for quantifying behavior
Keywords
Python, NeuroAI, Deep Learning, Perception, Behavior, Motor Control and Learning
Learning Prerequisites
Recommended courses
CS-433 (strongly recommended)
Important concepts to start the course
Programming in Python, good mathematics and machine learning background
Learning Outcomes
By the end of the course, the student must be able to:
- Formulate models of brain function
- Hypothesize potential mechanisms that give rise to behavior
- Design models of brain functions
- Characterize current models of brain function
Transversal skills
- Set objectives and design an action plan to reach those objectives.
- Demonstrate the capacity for critical thinking
- Write a scientific or technical report.
- Summarize an article or a technical report.
Teaching methods
Lectures and exercises to discuss and work on problem sets (both numerical and analytical). There will be one project as part of this class, which is partially done outside of the classroom.
Expected student activities
Attend lectures and take notes during lectures, participate in quizzes and read scientific articles. Solve the problem sets and take the final exam.
Assessment methods
The final mark is a combination of three evaluations: class project (30%), quizzes (20%), final exam (50%).
Supervision
| Office hours | Yes |
| Assistants | Yes |
| Forum | Yes |
In the programs
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Brain-like computation and intelligence
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: mandatory
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Brain-like computation and intelligence
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: mandatory
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Brain-like computation and intelligence
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Brain-like computation and intelligence
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Brain-like computation and intelligence
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Brain-like computation and intelligence
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Brain-like computation and intelligence
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional