VS265: Syllabus: Difference between revisions

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==== Aug. 28: Introduction ====
==== Aug. 28: Introduction ====
# Theory and modeling in neuroscience
* Theory and modeling in neuroscience
# Goals of AI/machine learning vs. theoretical neuroscience
* Goals of AI/machine learning vs. theoretical neuroscience
# Turing vs. neural computation
* Turing vs. neural computation


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


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


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


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


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


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


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


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


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


# TBD
* Fritz Sommer: Associative memories and attractor neural networks


==== Oct. 2: Sparse, distributed coding ====
==== Oct. 7,9: Guest lectures ====


# Autoencoders
* Jerry Feldman: Ecological utility  and the mythical neural code
# Natural image statistics
* Pentti Kanerva: Computing with 10,000 bits
# Projection pursuit


==== Oct. 7: Plasticity and cortical maps ====
==== Oct. 14: Unsupervised learning (continued) ====


# Cortical maps
==== Oct. 16: Guest lecture ====
# Self-organizing maps, Kohonen nets
# Models of experience dependent learning and cortical reorganization


==== Oct. 9:  Guest lecture ====
* Tom Dean, Google:  Connectomics


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


==== Oct. 14:  Manifold learning ====
* Autoencoders
* Natural image statistics
* Projection pursuit


# Local linear embedding, Isomap
==== Oct. 30, Nov. 4:  Plasticity and cortical maps ====


==== Oct. 16:  Guest lecture ====
* Cortical maps
* Self-organizing maps, Kohonen nets
* Models of experience dependent learning and cortical reorganization


# Tom Dean, Google:  Connectomics
==== Nov. 6:  Manifold learning ====


==== Oct. 21,23,28,30:  Recurrent networks ====
* Local linear embedding, Isomap
# Hopfield networks
# Models of associative memory, pattern completion
# Line attractors and `bump circuits’
# Dynamical models


==== Nov. 4,6,13,18,20,25:  Probabilistic models and inference ====
==== Nov. 13:  Recurrent networks ====


# Probability theory and Bayes’ rule
* Hopfield networks, memories as 'basis of attraction'
# Learning and inference in generative models
* Line attractors and `bump circuits’
# The mixture of Gaussians model
* Dynamical models
# Boltzmann machines
# Sparse coding and ‘ICA’
# Kalman filter model
# Energy-based models


==== Dec. 2,4:  Neural implementations ====
==== Nov. 18,20,25, Dec. 2:  Probabilistic models and inference ====


# Integrate-and-fire model
* Probability theory and Bayes’ rule
# Neural encoding and decoding
* Learning and inference in generative models
# Limits of precision in neurons
* The mixture of Gaussians model
# Neural synchrony and phase-based coding
* Boltzmann machines
* Kalman filter model
* Energy-based models


==== Dec. 9,11:  Guest lectures ====
==== Dec. 4:  Guest lecture (Tony Bell) ====
* Sparse coding and ‘ICA’


# TBD
==== Dec. 9:  Neural implementations ====
# TBD
 
* Integrate-and-fire model
* Neural encoding and decoding
* Limits of precision in neurons
<!-- * Neural synchrony and phase-based coding -->
 
==== Dec. 11:  Guest lecture (Tony Bell) ====
* Levels and loops

Latest revision as of 07:06, 10 December 2014

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

Dec. 11: Guest lecture (Tony Bell)

  • Levels and loops
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