Here is a list of our projects.
Computational Neuroscience:
Dan Gartner, Lauren Jones, and Lowell McLinskey are all helping Professor Jensen by exploring the nuances of software that simulates neurons, the building blocks of the nervous system. One such package is called Genesis. The Book of Genesis, by James M. Bower and David Beeman states that Genesis(or General NEural SImulation System) was designed to use a wide variety of simulations for neurons or collections of neurons, either from existing code that came with Genesis, or simulations that are defined by the person using it. It is considered by Bower and Beeman to be the first general simulator that could support realistic models that didn't recquire specialized code. Lauren is working with Genesis to study what happens to neurons when they are randomly driven. Dan is approaching the same problem but using Mathematica for his studies. Lowell is getting a second simulator called NEURON up and running in the Zoo, our computer lab, as well as on Condor, the Unix Academic Computing Server.
Quantum Chaos:
When most people talk about chaos, they are generally talking about phenomena that take place in what is known as the classical universe, things like the weather, a pendulum, or billiard balls. These are all objects that are macroscopic and can be described using Newton's laws. However, when the objects studied become molecules or atoms, classical theory generally does not accurately predict the behavior of these small objects. Quantum Mechanics predicts the microscopic world accurately. One very important link between the two is called the Correspondence Principle. It basically says that if you describe a macroscopic case in quantum mechanics it should take the form of the familiar classical description. So what happens when you look at things right on the boundary? In particular, will quantum mechanics describe chaos if the classical description predicts it? That is something that John Debes is investigating for Professor Jensen. He is taking the classical description for a hydrogen and helium atom and seeing what happens to the atoms when they are exposed to different kinds of radiation.
John is approaching this by using computer models to look at when the atoms ionize under linearly and cirularly polarized radiation. The radiation fields push and pull on the outer electron of the atoms and at certain field strengths the atoms ionize. The hydrogen atom has been studied extensively and been described, but so far the helium atom has not been tackled. This is because the addition of another electron in the atom provides a new force that can potentially make the orbits of the electrons even more chaotic and complicated.
Super Fonts:
John Jacobson's current research in computational neuroscience focuses on early processes in letter recognition. So far he has modeled neuronal representations of letters and preliminary data suggests that this model can be used to predict the frequency of inter-letter confusions with a correlational coefficient of about .67. Using this model he is developing techniques for generating more legible typefaces and is testing the effectiveness of these superfonts on human subjects.
The Making of a Textbook:
For her masters thesis, Jessica Leiken wrote a textbook that taught students Mathematica 2.2 while they were learning about Waves and Oscillations. Now that Wolfram Research has come out with version 3.0 for Mathematica, Jessica is revising her text to fit in with the changes in the new version.
Healing of the Mind:
Sam Sober is using computational artificial neural networks to examine how the mind heals itself. Specifically he is trying to recreate data gathered by Dr. Dwayne Yamasaki,who studied how macaques recover from focal lesions to cortical area MT, which is involved in the perception of visual motion. Sam believes that the healing process is two-fold: the first step involves changes in physiological processes resulting from the removal of dead neurons, followed by a Hebbian process of synaptic modification. A Hebbian process is the method by which neurons alter their connection with other neurons. In this way undamaged neurons can "learn" the tasks that the damaged neurons performed. Sam's hypothesis seems well-founded; last summer his computational model, which only included the first healing process, accutately reproduced experimental data taken from the first few hours following a lesion.
Currently Sam is working on two projects. The fist is to study the possible mechanisms of Hebbian recovery following lesion, focusing especially on the formation of cell assemblies and their reorganization after brain damage. The second project concerns a more detailed model of initial lesion. Although last summer's model accurately described physiological phenomena, it did not make predictions about the monkey's altered perception of the visual world following lesions, which included a scotoma(or blind spot) in which information about visual motion was processed in accurately. Sam's current model attempts to explain pathological perceptual defects while preserving physiological realism, and therefore retaining the falsifiability of more biological network simulations.