VS265: Syllabus

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. 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