Various algorithms for optimization and learning in adaptive systems

This thesis discusses various methods for learning and optimization in adaptive systems. Overall, it emphasizes the relationship between optimization, learning, and adaptive systems; and it illustrates the influence of underlying hardware upon the construction of efficient algorithms for learning and optimization. Chapter 1 provides a summary and an overview.

Chapter 2 discusses a method for using feed-forward neural networks to filter the noise out of noise-corrupted signals. The networks use back-propagation learning, but they use it in a way that qualifies as unsupervised learning. The networks adapt based only on the raw input data-there are no external teachers providing information on correct operation during training. The chapter contains an analysis of the learning and develops a simple expression that, based only on the geometry of the network, predicts performance.

Chapter 3 explains a simple model of the piriform cortex, an area in the brain involved in the processing of olfactory information. The model was used to explore the possible effect of acetylcholine on learning and on odor classification. According to the model, the piriform cortex can classify odors better when acetylcholine is present during learning but not present during recall. This is interesting since it suggests that learning and recall might be separate neurochemical modes (corresponding to whether or not acetylcholine is present). When acetylcholine is turned off at all times, even during learning, the model exhibits behavior somewhat similar to Alzheimer's disease, a disease associated with the degeneration of cells that distribute acetylcholine.

Chapters 4, 5, and 6 discuss algorithms appropriate for adaptive systems implemented entirely in analog hardware. The algorithms inject noise into the systems and correlate the noise with the outputs of the systems. This allows them to estimate gradients and to implement noisy versions of gradient descent, without having to calculate gradients explicitly. The methods require only noise generators, adders, multipliers, integrators, and differentiators; and the number of devices needed scales linearly with the number of adjustable parameters in the adaptive systems. With the exception of one global signal, the algorithms require only local information exchange.

Surface markers of regionalization in the vertebrate nervous system

In order to identify new molecules that might play a role in regional specification of the nervous system, we generated and characterized monoclonal antibodies (mAbs) that have positionally-restricted labeling patterns.

The FORSE-1 mAb was generated using a strategy designed to produce mAbs against neuronal cell surface antigens that might be regulated by regionally-restricted transcription factors in the developing central nervous system (CNS). FORSE-1 staining is enriched in the forebrain as compared to the rest of the CNS until E18. Between E11.5-E13.5, only certain areas of the forebrain are labeled. There is also a dorsoventrally-restricted region of labeling in the hindbrain and spinal cord. The mAb labels a large proteoglycan-like cell-surface antigen (>200 kD). The labeling pattern of FORSE-1 is conserved in various mammals and in chick.

To determine whether the FORSE-1 labeling pattern is similar to that of known transcription factors, the expression of BF-1 and Dlx-2 was compared with FORSE-1. There is a striking overlap between BF-1 and FORSE-1 in the telencephalon. In contrast, FORSE-1 and Dlx-2 have very different patterns of expression in the forebrain, suggesting that regulation by Dlx-2 alone cannot explain the distribution of FORSE-1. They do, however, share some sharp boundaries in the diencephalon. In addition, FORSE-1 identifies some previously unknown boundaries in the developing forebrain. Thus, FORSE-1 is a new cell surface marker that can be used to subdivide the embryonic forebrain into regions smaller than previously described, providing further complexity necessary for developmental patterning.

I also studied the expression of the cell surface protein CD9 in the developing and adult rat nervous system. CD9 is implicated in intercellular signaling and cell adhesion in the hematopoetic system. In the nervous system, CD9 may perform similar functions in early sympathetic ganglia, chromaffin cells, and motor neurons, all of which express the protein. The presence of CD9 on the surfaces of Schwann cells and axons at the appropriate time may allow the protein to participate in the cellular interactions involved in myelination.

Development of Microfluidic Devices with the Use of Nanotechnology to Aid in the Analysis of Biological Systems Including Membrane Protein Separation, Single Cell Analysis, and Genetic Markers

Computation technology has dramatically changed the world around us; you can hardly find an area where cell phones have not saturated the market, yet there is a significant lack of breakthroughs in the development to integrate the computer with biological environments. This is largely the result of the incompatibility of the materials used in both environments; biological environments and experiments tend to need aqueous environments. To help aid in these development chemists, engineers, physicists and biologists have begun to develop microfluidics to help bridge this divide. Unfortunately, the microfluidic devices required large external support equipment to run the device. This thesis presents a series of several microfluidic methods that can help integrate engineering and biology by exploiting nanotechnology to help push the field of microfluidics back to its intended purpose, small integrated biological and electrical devices. I demonstrate this goal by developing different methods and devices to (1) separate membrane bound proteins with the use of microfluidics, (2) use optical technology to make fiber optic cables into protein sensors, (3) generate new fluidic devices using semiconductor material to manipulate single cells, and (4) develop a new genetic microfluidic based diagnostic assay that works with current PCR methodology to provide faster and cheaper results. All of these methods and systems can be used as components to build a self-contained biomedical device.