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Recent developments in pulse coupled neural networks techniques provide an opportunity to extend the toolbox available for exploring new approaches to biosensor applications. This paper presents a demonstration of properties and limitations of new computational intelligence (CI) techniques as shown by and related to an application. New pulse coupled neural networks (PCNN) techniques are supplemented by combination with wavelet analysis and fine- tuned by radial basis functions. This toolbox is exercised to demonstrate its properties and limitations as related to the development of biosensor applications. The approach selected employs abstractions of biological models of peripheral vision and relates them to analysis of time series generated by biosensors such as chemosensors or motion detectors. Detection of targets (rare or interesting events) is facilitated by PCNN multi-scale image factorization. Interpretation of the resulting image set is aided by contrast enhancement and by segmentation using standard PCNNs. Wavelet coefficients provide supplemental discrimination and lead to characteristic sets of numbers useful in identifying image factors of interest. To complete the transition from acquisition of a complex, noisy image to recognition of targets of interest, radial basis function (RBF) analysis is appended. This five- step process (odor image generation, image factoring, PCNN analysis, wavelet analysis and RBF interpretation) was recently suggested, but is expanded and fully implemented here for the first time. This paper explores the properties and limitations of this approach for simulation of biosensors using small, incomplete sets of real-world data. The relationship between selection of appropriate design parameters and the need for supplementing the available data by simulation is investigated. Evolutionary computation is employed off line to explore and evaluate the possibilities and limitations. Sensor fault detection and RBF training vector generation are addressed. Results are analyzed to provide recommendations for further experimentation and collection of needed additional data without extraneous effort. This methodology is recommended for use in real-world applications where experimental data is difficult, expensive or time consuming to obtain.
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