SEMAINE 1

Introduction & Basic Neurobiology (Rajesh Rao)

This module includes an Introduction to Computational Neuroscience, along with a primer on Basic Neurobiology.

6 vidéos, 6 lectures, 2 quiz pour s'exercer

Reading: Welcome Message & Course Logistics
Reading: About the Course Staff
Reading: Syllabus and Schedule
Reading: Matlab & Octave Information and Tutorials
Reading: Python Information and Tutorials
Reading: Week 1 Lecture Notes
Vidéo: 1.2 Computational Neuroscience: Descriptive Models
Vidéo: 1.3 Computational Neuroscience: Mechanistic and Interpretive Models
Vidéo: 1.4 The Electrical Personality of Neurons
Vidéo: 1.5 Making Connections: Synapses
Vidéo: 1.6 Time to Network: Brain Areas and their Function
Practice Quiz: Matlab/Octave Programming
Practice Quiz: Python Programming

SEMAINE 2

What do Neurons Encode? Neural Encoding Models (Adrienne Fairhall)

This module introduces you to the captivating world of neural information coding. You will learn about the technologies that are used to record brain activity. We will then develop some mathematical formulations that allow us to characterize spikes from neurons as a code, at increasing levels of detail. Finally we investigate variability and noise in the brain, and how our models can accommodate them.

8 vidéos, 3 lectures

Reading: Welcome Message
Reading: Week 2 Lecture Notes and Tutorials
Vidéo: 2.2 Neural Encoding: Simple Models
Vidéo: 2.3 Neural Encoding: Feature Selection
Vidéo: 2.4 Neural Encoding: Variability
Vidéo: Vectors and Functions (by Rich Pang)
Vidéo: Convolutions and Linear Systems (by Rich Pang)
Vidéo: Change of Basis and PCA (by Rich Pang)
Vidéo: Welcome to the Eigenworld! (by Rich Pang)
Reading: IMPORTANT: Quiz Instructions

Noté: Spike Triggered Averages: A Glimpse Into Neural Encoding

SEMAINE 3

Extracting Information from Neurons: Neural Decoding (Adrienne Fairhall)

In this module, we turn the question of neural encoding around and ask: can we estimate what the brain is seeing, intending, or experiencing just from its neural activity? This is the problem of neural decoding and it is playing an increasingly important role in applications such as neuroprosthetics and brain-computer interfaces, where the interface must decode a person's movement intentions from neural activity. As a bonus for this module, you get to enjoy a guest lecture by well-known computational neuroscientist Fred Rieke.

6 vidéos, 2 lectures

Reading: Welcome Message
Reading: Week 3 Lecture Notes and Supplementary Material
Vidéo: 3.2 Population Coding and Bayesian Estimation
Vidéo: 3.3 Reading Minds: Stimulus Reconstruction
Vidéo: Fred Rieke on Visual Processing in the Retina
Vidéo: Gaussians in One Dimension (by Rich Pang)
Vidéo: Probability distributions in 2D and Bayes' Rule (by Rich Pang)

Noté: Neural Decoding

SEMAINE 4

Information Theory & Neural Coding (Adrienne Fairhall)

This module will unravel the intimate connections between the venerable field of information theory and that equally venerable object called our brain.

5 vidéos, 2 lectures

Reading: Welcome Message
Reading: Week 4 Lecture Notes and Supplementary Material
Vidéo: 4.2 Calculating Information in Spike Trains
Vidéo: 4.3 Coding Principles
Vidéo: What's up with entropy? (by Rich Pang)
Vidéo: Information theory? That's crazy! (by Rich Pang)

Noté: Information Theory & Neural Coding

SEMAINE 5

Computing in Carbon (Adrienne Fairhall)

This module takes you into the world of biophysics of neurons, where you will meet one of the most famous mathematical models in neuroscience, the Hodgkin-Huxley model of action potential (spike) generation. We will also delve into other models of neurons and learn how to model a neuron's structure, including those intricate branches called dendrites.

7 vidéos, 2 lectures

Reading: Welcome Message
Reading: Week 5 Lecture Notes and Supplementary Material
Vidéo: 5.2 Spikes
Vidéo: 5.3 Simplified Model Neurons
Vidéo: 5.4 A Forest of Dendrites
Vidéo: Eric Shea-Brown on Neural Correlations and Synchrony
Vidéo: Dynamical Systems Theory Intro Part 1: Fixed points (by Rich Pang)
Vidéo: Dynamical Systems Theory Intro Part 2: Nullclines (by Rich Pang)

Noté: Computing in Carbon

SEMAINE 6

Computing with Networks (Rajesh Rao)

This module explores how models of neurons can be connected to create network models. The first lecture shows you how to model those remarkable connections between neurons called synapses. This lecture will leave you in the company of a simple network of integrate-and-fire neurons which follow each other or dance in synchrony. In the second lecture, you will learn about firing rate models and feedforward networks, which transform their inputs to outputs in a single "feedforward" pass. The last lecture takes you to the dynamic world of recurrent networks, which use feedback between neurons for amplification, memory, attention, oscillations, and more!

3 vidéos, 2 lectures

Reading: Welcome Message
Reading: Week 6 Lecture Notes and Tutorials
Vidéo: 6.2 Introduction to Network Models
Vidéo: 6.3 The Fascinating World of Recurrent Networks

Noté: Computing with Networks

SEMAINE 7

Networks that Learn: Plasticity in the Brain & Learning (Rajesh Rao)

This module investigates models of synaptic plasticity and learning in the brain, including a Canadian psychologist's prescient prescription for how neurons ought to learn (Hebbian learning) and the revelation that brains can do statistics (even if we ourselves sometimes cannot)! The next two lectures explore unsupervised learning and theories of brain function based on sparse coding and predictive coding.

4 vidéos, 2 lectures

Reading: Welcome Message
Reading: Week 7 Lecture Notes and Tutorials
Vidéo: 7.2 Introduction to Unsupervised Learning
Vidéo: 7.3 Sparse Coding and Predictive Coding
Vidéo: Gradient Ascent and Descent (by Rich Pang)

Noté: Networks that Learn

SEMAINE 8

Learning from Supervision and Rewards (Rajesh Rao)

In this last module, we explore supervised learning and reinforcement learning. The first lecture introduces you to supervised learning with the help of famous faces from politics and Bollywood, casts neurons as classifiers, and gives you a taste of that bedrock of supervised learning, backpropagation, with whose help you will learn to back a truck into a loading dock.The second and third lectures focus on reinforcement learning. The second lecture will teach you how to predict rewards à la Pavlov's dog and will explore the connection to that important reward-related chemical in our brains: dopamine. In the third lecture, we will learn how to select the best actions for maximizing rewards, and examine a possible neural implementation of our computational model in the brain region known as the basal ganglia. The grand finale: flying a helicopter using reinforcement learning!

4 vidéos, 2 lectures

Reading: Welcome Message and Concluding Remarks
Reading: Week 8 Lecture Notes and Supplementary Material
Vidéo: 8.2 Reinforcement Learning: Predicting Rewards
Vidéo: 8.3 Reinforcement Learning: Time for Action!
Vidéo: Eb Fetz on Bidirectional Brain-Computer Interfaces

Noté: Learning from Supervision and Rewards

Niveau	Débutant
Langue	English
Comment réussir	Réussissez tous les devoirs notés pour terminer le cours.
Notes des utilisateurs	4.6 étoiles Note moyenne des utilisateurs 4.6Voir ce que disent les étudiants