Laboratory of Computational Neuroscience
LCN - Brain Mind - I&C - Life Sciences - EPFL

Computational Neuroscience - Neuronal Dynamics of Cognition

Video Lectures on Cognitive Models in Computational Neuroscience:

by Wulfram Gerstner

SEVERAL ONLINE -Lectures per week of about 5-20 Minutes each over 6 weeks (45 lectures total).
This course, designed for Physicists and mathematically trained Life Scientists focuses on Computational Neuroscience of Cognition. It covers classical topics of computational neuroscience such as the Hopfield model, neural field models, and Hebbian learning, but also includes modern developments in the field of computational neuroscience.
Mathematical prerequisites:
- Calculus
- Differential Equations
- Probability
Link to EdX-EPFL page
For some reason out of my control, the lectures of the first 3 weeks have been marked as 'private'. However you can still access them by signing up for FREE as an auditor to the edX-course at EPFL that covers this material. It is a self-paced class which means you can start at any moment.
Enrol free of charge as auditor for Computational Neuroscience at EPFL edX

Week 1. Associative Memory in a Network of Neurons (57 min)
Part 1 - Introduction: human memory and networks of neurons (4 min)
Part 2 - Classification by similarity (5 min)
Part 3 - Detour: Magnetic Materials (9 min)
Part 4 - Hopfield Model (15 min)
Part 5 - Learning of Associations (9 min)
Part 6 - Storage Capacity (15 min)
Week 2. Attractor Networks and Generalizations of the Hopfield model (62 min)
Part 1 - Attractor Networks (8 min)
Part 2 - Stochastic Hopfield model(18 min)
Part 3 - Energy Landscape (14 min)
Part 4 - Toward biology 1: Low-activity patterns (6 min)
Part 5 - Toward biology 2: Spiking neurons (16 min)
Week 3. Neuronal Populations
Part 1 - Population activity (9 min)
Part 2 - Cortical populations: Columns and receptive fields (7 min)
Part 3 - Connectivity - in cortex and in models(11 min)
Part 4a - Asynchronous state (13 min)
Part 4b - Mean-field argument (10 min)
Part 5 - Stationary mean-field and asynchronous state (16 min)
Part 6 - Random Networks and balanced state (21 min)
Week 4: Continuum models: Cortical Fields and Perception
Part 1 - Aims and challenges for this chapter (7 min)
Part 2 - Transients (16 min)
Part 3 - Spatial Continuum (Cortex) (3 min)
Part 4 - Spatial continuum (model) (18 min)
Part 5 - Solution types (8 min)
Part 6 - Perception(10 min)
Week 5: Decision models: Competitive Dynamics
Part 1 - Introduction: Aims and challenges for this chapter (18 min)
Part 2 - Perceptual Decision Making (15 min)
Part 3 - Theory of Decision Dynamics (Cortex) (11 min)
Part 4 - Solutions: symmetric case and biased case (8 min)
Part 5 - Simulations and Experiments(8 min)
Part 6 - Decisions, actions, volition (6 min)
Week 6: Synaptic plasticity and learning
Part 1 - Synaptic plasticity: motivation and aims (6 min)
Part 2 - Classification of plasticity (17 min)
Part 3 - Model of Short-Term Plasticity (see other MOOC) (1 min)
Part 4 - Models of Long-Term Plasticity: Hebbian learning and Bienenstock-Cooper-Munro rule (17 min)
Part 5 - STDP: Spike-Timining Dependent Models of Plasticity (11 min)
Part 6 - From Spiking Models to Rate Modles (9 min)
Part 6b - From Spiking Models to Rate Modles (math, 27 min)
Part 7 - Triplet STDP Model(11 min)
Part 8 - Online Learning of Memories (15 min)

Recommanded text book

NEURONAL DYNAMICS - From single neurons to networks and models of Cognition; by W. Gerstner, W. Kistler, R. Naud, L. Paninski (Cambridge University Press, July 2014).

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