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
Keywords
Python, NeuroAI, Deep Learning, Perception, Behavior, Motor Control and Learning, Skill Learning
Learning Prerequisites
Important concepts to start the course
Programming in Python, good mathematical background
Learning Outcomes
By the end of the course, the student must be able to:
- Formulate models of brain function
- Hypothesize about potential mechanisms that give rise to behavior
- Design models of brain functions
- Characterize the models
Transversal skills
- Demonstrate the capacity for critical thinking
- Summarize an article or a technical report.
- Write a scientific or technical report.
- Set objectives and design an action plan to reach those objectives.
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, participate in quizzes, the modeling project and read scientific articles. Complete problem sets and take the final exam.
Assessment methods
The final mark is a combination of three evaluations: modeling 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: 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
- 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
Reference week
Mo | Tu | We | Th | Fr | |
8-9 | |||||
9-10 | |||||
10-11 | |||||
11-12 | |||||
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16-17 | |||||
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20-21 | |||||
21-22 |
Légendes:
Lecture
Exercise, TP
Project, Lab, other