# Difference between revisions of "VS265: Syllabus"

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* Dynamical models | * Dynamical models | ||

− | ==== Nov. 18,20,25: Probabilistic models and inference ==== | + | ==== Nov. 18,20,25, Dec. 2: Probabilistic models and inference ==== |

* Probability theory and Bayes’ rule | * Probability theory and Bayes’ rule | ||

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* The mixture of Gaussians model | * The mixture of Gaussians model | ||

* Boltzmann machines | * Boltzmann machines | ||

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* Kalman filter model | * Kalman filter model | ||

* Energy-based models | * Energy-based models | ||

− | ==== Dec. | + | ==== Dec. 4: Guest lecture (Tony Bell) ==== |

+ | * Sparse coding and ‘ICA’ | ||

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+ | ==== Dec. 9: Neural implementations ==== | ||

* Integrate-and-fire model | * Integrate-and-fire model | ||

* Neural encoding and decoding | * Neural encoding and decoding | ||

* Limits of precision in neurons | * Limits of precision in neurons | ||

− | * Neural synchrony and phase-based coding | + | <!-- * Neural synchrony and phase-based coding --> |

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## Revision as of 07:05, 10 December 2014

## Contents

- 1 Syllabus
- 1.1 Aug. 28: Introduction
- 1.2 Sept. 2,4: Neuron models
- 1.3 Sept. 9,11: Guest lectures
- 1.4 Sept. 16,18: Supervised learning
- 1.5 Sept. 23,25: Unsupervised learning
- 1.6 Sept. 30, Oct. 2: Guest lecture
- 1.7 Oct. 7,9: Guest lectures
- 1.8 Oct. 14: Unsupervised learning (continued)
- 1.9 Oct. 16: Guest lecture
- 1.10 Oct. 21,23,28: Sparse, distributed coding
- 1.11 Oct. 30, Nov. 4: Plasticity and cortical maps
- 1.12 Nov. 6: Manifold learning
- 1.13 Nov. 13: Recurrent networks
- 1.14 Nov. 18,20,25, Dec. 2: Probabilistic models and inference
- 1.15 Dec. 4: Guest lecture (Tony Bell)
- 1.16 Dec. 9: Neural implementations

## Syllabus

#### Aug. 28: Introduction

- Theory and modeling in neuroscience
- Goals of AI/machine learning vs. theoretical neuroscience
- Turing vs. neural computation

#### Sept. 2,4: Neuron models

- Membrane equation, compartmental model of a neuron
- Linear systems: vectors, matrices, linear neuron models
- Perceptron model and linear separability

#### Sept. 9,11: Guest lectures

- Matlab/Python tutorial
- Paul Rhodes, Evolved Machines: Multi-compartment models; dendritic integration

#### Sept. 16,18: Supervised learning

- Perceptron learning rule
- Adaptation in linear neurons, Widrow-Hoff rule
- Objective functions and gradient descent
- Multilayer networks and backpropagation

#### Sept. 23,25: Unsupervised learning

- Linear Hebbian learning and PCA, decorrelation
- Winner-take-all networks and clustering

#### Sept. 30, Oct. 2: Guest lecture

- Fritz Sommer: Associative memories and attractor neural networks

#### Oct. 7,9: Guest lectures

- Jerry Feldman: Ecological utility and the mythical neural code
- Pentti Kanerva: Computing with 10,000 bits

#### Oct. 14: Unsupervised learning (continued)

#### Oct. 16: Guest lecture

- Tom Dean, Google: Connectomics

#### Oct. 21,23,28: Sparse, distributed coding

- Autoencoders
- Natural image statistics
- Projection pursuit

#### Oct. 30, Nov. 4: Plasticity and cortical maps

- Cortical maps
- Self-organizing maps, Kohonen nets
- Models of experience dependent learning and cortical reorganization

#### Nov. 6: Manifold learning

- Local linear embedding, Isomap

#### Nov. 13: Recurrent networks

- Hopfield networks, memories as 'basis of attraction'
- Line attractors and `bump circuits’
- Dynamical models

#### Nov. 18,20,25, Dec. 2: Probabilistic models and inference

- Probability theory and Bayes’ rule
- Learning and inference in generative models
- The mixture of Gaussians model
- Boltzmann machines
- Kalman filter model
- Energy-based models

#### Dec. 4: Guest lecture (Tony Bell)

- Sparse coding and ‘ICA’

#### Dec. 9: Neural implementations

- Integrate-and-fire model
- Neural encoding and decoding
- Limits of precision in neurons