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NCSO Review
by Thomas Breuel
# NCSO Review

## Outline

### neurons

### associative
memories

### computation with neurons

### classification and learning

### visual object recognition

### theory

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basic structure

membrane potentials and channels

dendritic computations

generation and propagation of the action potential

structure and function of synapses

Willshaw's associative memory

Hopfield networks

Linear Associative Memories

linear threshold units

constructing boolean circuits out of neurons

dynamic computations with networks of neurons

rule 110, Turing equivalence

Hebb's rule

linear threshold units and perceptron learning algorithms

logistic regression and gradient descent

multi-layer perceptrons and backpropagation

posterior probabilities and multilayer perceptrons

PCA and "neural" PCA

k-means algorithm, EM algorithms, SOM

gradient descent version of k-means

convolutional neural networks

multilayer convolutional neural networks

feature hierarchies

convolutional neural networks

the HMAX model

experimental results on databases

view-based or component based recognition

experiments and theory by Bulthoff

experiments and theory by Biederman

attention, salience, grouping

Bayesian decision theory

loss functions

risk minimization

Bayesian parameter estimation

ML, MAP, Bayesian methods

game theory

agent theory

evolution