SEMANA 1

Introduction & Basic Neurobiology (Rajesh Rao)

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

6 videos, 6 leituras, 2 testes para praticar

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
Vídeo: 1.2 Computational Neuroscience: Descriptive Models
Vídeo: 1.3 Computational Neuroscience: Mechanistic and Interpretive Models
Vídeo: 1.4 The Electrical Personality of Neurons
Vídeo: 1.5 Making Connections: Synapses
Vídeo: 1.6 Time to Network: Brain Areas and their Function
Practice Quiz: Matlab/Octave Programming
Practice Quiz: Python Programming

SEMANA 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 videos, 3 leituras

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

Nota atribuída: Spike Triggered Averages: A Glimpse Into Neural Encoding

SEMANA 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 videos, 2 leituras

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

Nota atribuída: Neural Decoding

SEMANA 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 videos, 2 leituras

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

Nota atribuída: Information Theory & Neural Coding

SEMANA 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 videos, 2 leituras

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

Nota atribuída: Computing in Carbon

SEMANA 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 videos, 2 leituras

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

Nota atribuída: Computing with Networks

SEMANA 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 videos, 2 leituras

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

Nota atribuída: Networks that Learn

SEMANA 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 videos, 2 leituras

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

Nota atribuída: Learning from Supervision and Rewards

Nível	Básico
Idioma	English
Como ser aprovado	Seja aprovado em todas as tarefas para concluir o curso.
Classificação do usuário	4.6 estrelas Classificação média do usuário 4.6Veja o que os aprendizes disseram

Neurociência Computacional

Neurociência Computacional