Introduction to Neural Networks
U. of Minnesota
Mid-term Study Guide, Fall, 2007

The mid-term will cover material in Lectures 1-13
You are encouraged to research the lecture notes and supplementary material
that may provide insight into the concepts and questions below.

Sample short answer questions
Define and describe the relation of the following key words or phrases to neural networks. Provide examples where appropriate.
(8 items drawn from set below; 3 points each).

eigenvector	linear associator	autoassociator	synaptic modification
Hebbian rule	heteroassociation	superposition
leaky (or forgetful) integrate and fire model	EPSP/IPSP	Hodgkin-Huxley model	summed vector memory
dendrite	classical conditioning	operant conditioning	lateral inhibition
spike	linear independence	grandmother cell	perceptron
McCulloch-Pitts	distinctive features	cross-correlation	supervised learning
recurrent inhibition	pseudoinverse	compartmental model	symmetric matrix
WTA	least sum of mean squares	linear system	orthogonality
Widrow-Hoff error correction	diameter-limited	linear discriminant	generative model
outer product learning	perceptron learning rule	topic vs. stress positions	generic neural network neuron

Sample essay questions
(choice of 2 essays drawn from a subset of those listed below; 12 points each).

Describe the slow-potential model and discuss it in the context of current views of the computational power of single neurons (see articles by Koch & Segev, 2000; Poirazi, Brannon & Mel, 2003).

Discuss a linear model of either auto- or hetero-associative learning. Give one example of its application.

Describe a neural network model for the lateral eye of the limulus. Discuss the relationship between the performance of feedforward and feedback models of lateral inhibition. (See chapter by Hartline & Ratliff, 1972.)

Explain why XOR can't be computed with a simple TLU. Explain how it can be solved by using an augmented input to a TLU. Describe how a error-back propagation network can be configured to learn to solve the problem.

Problem
(One problem, 3 to 6 points)

You should be able to compute inner and outer products, multiply matrices and vectors, calculate the transpose, know how to find eigenvectors and eigenvalues, measure the "similarity" between vectors, and find the inverse of small (e.g. 2x2) matrices.