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

• TBD
• 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: Guest lecture

• TBD

Oct. 2: Sparse, distributed coding

• Autoencoders
• Natural image statistics
• Projection pursuit

Oct. 7: Plasticity and cortical maps

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

Oct. 9: Guest lecture

• TBD

Oct. 14: Manifold learning

• Local linear embedding, Isomap

Oct. 16: Guest lecture

• Tom Dean, Google: Connectomics

Oct. 21,23,28,30: Recurrent networks

• 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

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

Dec. 2,4: Neural implementations

• Integrate-and-fire model
• Neural encoding and decoding
• Limits of precision in neurons
• Neural synchrony and phase-based coding

Dec. 9,11: Guest lectures

• TBD
• TBD