Computational Neuroscience Essentials
Explore mathematical models of brain function in this advanced 6-week course on neuronal dynamics and cognition.
Explore mathematical models of brain function in this advanced 6-week course on neuronal dynamics and cognition.
Delve into the fascinating world of computational neuroscience with this advanced 6-week course. Uncover the mathematical and computational models that explain how thousands of neurons interact to produce cognition. You'll explore key topics such as decision-making, memory formation, and perception through the lens of theoretical neuroscience. The course covers advanced concepts including mean-field theory, non-linear differential equations, and cortical field models. By the end, you'll be able to analyze connected neural networks, understand models of memory and decision processes, and formalize biological facts into mathematical frameworks. This course is ideal for those with a strong background in calculus and differential equations, offering a deep dive into the quantitative aspects of brain function and cognition.
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What you'll learn
Analyze connected neural networks using mean-field theory
Develop mathematical models of memory formation in the brain
Understand the dynamics of decision-making processes through computational models
Explore cortical field models and their role in perception
Learn to formalize biological facts into rigorous mathematical frameworks
Apply non-linear differential equations to model neuronal interactions
Skills you'll gain
This course includes:
PreRecorded video
Online exercises, quizzes, final exam
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 6 modules in this course
This advanced course in computational neuroscience focuses on the mathematical and computational models used to understand cognitive processes in the brain. The curriculum is structured around six key topics: associative memory and the Hopfield model, attractor networks and spiking neurons, neuronal populations and mean-field theory, perception and cortical field models, decision-making and competitive dynamics, and synaptic plasticity and learning. Students will learn to analyze connected networks in the mean-field limit, formalize biological facts into mathematical models, and understand complex models of memory formation, decision processes, and perception. The course emphasizes the collective dynamics of thousands of interacting neurons and uses advanced mathematical techniques such as non-linear differential equations to model these interactions. Throughout the course, students will engage with online exercises, quizzes, and a final exam to reinforce their understanding of these complex concepts.
Associative Memory and Hopfield Model
Module 1
Attractor networks and spiking neurons
Module 2
Neuronal populations and mean-field theory
Module 3
Perception and cortical field models
Module 4
Decision making and competitive dynamics
Module 5
Synaptic Plasticity and learning
Module 6
Fee Structure
Instructor
2 Courses
Pioneer in Computational Neuroscience and Neural Networks
Wulfram Gerstner is a Full Professor at École polytechnique fédérale de Lausanne (EPFL), holding a joint appointment in Computer Science and Life Sciences since 2006. After studying physics at Tübingen and Munich, followed by research at UC Berkeley, he earned his Ph.D. in theoretical physics from TU Munich in 1993, focusing on associative memory in networks of spiking neurons. Since joining EPFL in 1996, he has directed the Laboratory of Computational Neuroscience, making significant contributions to understanding neural networks, spike-timing dependent plasticity, and reward-based learning in spiking neurons. His research bridges theoretical neuroscience with practical applications, influencing fields from artificial intelligence to neurobiology. Gerstner has served on editorial boards of prestigious journals including Science and the Journal of Computational Neuroscience, and received the Valentino Braitenberg Award for Computational Neuroscience in 2018. His work continues to shape our understanding of neural computation and learning mechanisms in biological and artificial systems.
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