Introduction to Neural Networks
U. of Minnesota
Mid-term Study Guide, Fall, 2001

The mid-term will cover material in Lectures 1-13
and the Anderson book, mainly chapters 1-9.
You are encouraged to research any chapters of the text or lecture notes
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	heteroassociation	coarse coding	spontaneous activity
leaky integrator	EPSP/IPSP	projective field	summed vector memory
dendrite	classical conditioning	receptive field	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 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 anatomy of the generic neuron, and the slow-potential model.

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.

Discuss the pros and cons of distributed vs. localized representations with examples from theoretical considerations and neurophysiology.

Contrast "connectionist" computational schemes with traditional serial computing.

Describe physiological results supporting the biological basis of Hebbian synaptic modification.

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.