Human Brain I - Neural Nets

Since the beginning of time people have been interested in finding out how the brain works. This project will investigate neural network software for which there are a lot of packages available. Your task here is to try several free/shareware packages, choose one for experimentation, and then conduct the following experiments. Most of the research and applications of neural networks involves feed-forward networks trained by the back-propagation algorithm, and the design of these networks does not correspond with our knowledge of the human brain. In experiments 2 and 3 below we design neural networks that simulate, at least to a reasonable extent, important aspects of the human visual system. Some programming will be required but not much if you can interface with an appropriate neural network package. This team will explore the available neural network software and conduct the following experiments:
  1. Your initial experiment (just to test the neural-network software packages) is to show that a two-layer feed-forward Perceptron network (one input-unit layer, one output-unit layer, and one set of trainable weights between them) is not sufficient to separate the exclusive OR function, separating (0, 1) and (1, 0) from (0, 0) and (1, 1). This neural network has two binary input units and one output unit that is trained to be positive for (0, 1) and (1, 0) and negative for (0, 0) and (1, 1). Then show that a three-layer network (add another layer of, say, two units between the input and output layer) that has two sets of adjustable weights easily solves this problem.
  2. You first real experiment is to create a simple model of the visual cortex by designing Hubel/Wiesel-like cells (see also the The Visual Cortex article) and to perform a character recognition study that compares this model against a simple three-layer Perceptron. This experiment should be completed by the halfway point of the semester and the results presented at our second in-class meeting. The model of the visual cortex is as follows:
  3. Your second real experiment is elaborate on the previous experiment as follows:
  4. Another experiment, a real-world classification problem, might be added later.