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Some insects have the capability to detect and track small moving objects, often against cluttered moving backgrounds. Determining how this task is performed is an intriguing challenge, both from a physiological and computational perspective. Previous research has characterized higher-order neurons within the fly brain known as 'small target motion detectors‘ (STMD) that respond selectively to targets, even within complex moving surrounds. Interestingly, these cells still respond robustly when the velocity of the target is matched to the velocity of the background (i.e. with no relative motion cues). We performed intracellular recordings from intermediate-order neurons in the fly visual system (the medulla). These full-wave rectifying, transient cells (RTC) reveal independent adaptation to luminance changes of opposite signs (suggesting separate 'on‘ and 'off‘ channels) and fast adaptive temporal mechanisms (as seen in some previously described cell types). We show, via electrophysiological experiments, that the RTC is temporally responsive to rapidly changing stimuli and is well suited to serving an important function in a proposed target-detecting pathway. To model this target discrimination, we use high dynamic range (HDR) natural images to represent 'real-world‘ luminance values that serve as inputs to a biomimetic representation of photoreceptor processing. Adaptive spatiotemporal high-pass filtering (1st-order interneurons) shapes the transient 'edge-like‘ responses, useful for feature discrimination. Following this, a model for the RTC implements a nonlinear facilitation between the rapidly adapting, and independent polarity contrast channels, each with centre-surround antagonism. The recombination of the channels results in increased discrimination of small targets, of approximately the size of a single pixel, without the need for relative motion cues. This method of feature discrimination contrasts with traditional target and background motion-field computations. We show that our RTC-based target detection model is well matched to properties described for the higher-order STMD neurons, such as contrast sensitivity, height tuning and velocity tuning. The model output shows that the spatiotemporal profile of small targets is sufficiently rare within natural scene imagery to allow our highly nonlinear 'matched filter‘ to successfully detect many targets from the background. The model produces robust target discrimination across a biologically plausible range of target sizes and a range of velocities. We show that the model for small target motion detection is highly correlated to the velocity of the stimulus but not other background statistics, such as local brightness or local contrast, which normally influence target detection tasks. From an engineering perspective, we examine model elaborations for improved target discrimination via inhibitory interactions from correlation-type motion detectors, using a form of antagonism between our feature correlator and the more typical motion correlator. We also observe that a changing optimal threshold is highly correlated to the value of observer ego-motion. We present an elaborated target detection model that allows for implementation of a static optimal threshold, by scaling the target discrimination mechanism with a model-derived velocity estimation of ego-motion. Finally, we investigate the physiological relevance of this target discrimination model. We show that via very subtle image manipulation of the visual stimulus, our model accurately predicts dramatic changes in observed electrophysiological responses from STMD neurons. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1368818 / Thesis (Ph.D.) - University of Adelaide, School of Molecular and Biomedical Science, 2009

A terrestrial robotic electrophysiology platform has been developed that can hold a moth (<italic>Manduca sexta</italic>), record signals from its brain or muscles, and use these signals to control the rotation of the robot. All signal processing (electrophysiology, spike detection, and robotic control) was performed onboard the robot with custom designed electronic circuits. Wireless telemetry allowed remote communication with the robot. In this study, we interfaced directionally-sensitive visual neurons and pleurodorsal steering muscles of the mesothorax with the robot and used the spike rate of these signals to control its rotation, thereby emulating the classical optomotor response known from studies of the fly visual system. The interfacing of insect and machine can contribute to our understanding of the neurobiological processes underlying behavior and also suggest promising advancements in biosensors and human brain-machine interfaces.

The cellular mechanisms by which nervous systems evolve to match evolutionary changes occurring in the rest of the body remain largely unexplored. In a distal visual neuropil of a previously unexamined ancient dipteran family, Stratiomyidae, homologues of all of the periodic neurons known already from more recent Diptera can be recognized, occupying the same locations within the unit structure. This points to extreme developmental stasis for more than 200 million years, conserving both cell identity and position. The arborizations that some neurons make also have remained conservative, but others show marked differences between families in both size and branching patterns. At the electron-microscopical level, extensive differences in synaptic connectivity are found, some sufficient to radically redefine the systems roles of particular neurons. The findings bear out an earlier prediction that changes in the connectivity matrix linking conserved neurons may have been a major factor in implementing evolutionary change in the nervous system.

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System identification finds nowadays application in various areas of biological research as a tool of empiric mathematical modeling and model individualization. A fundamental challenge of system identification in biology awaits in the form of response variability. Furthermore, biological systems tend to exhibit high degree of nonlinearity as well as significant time delays. This thesis covers system identification approaches developed for the applications within two particular biomedical fields: neuroscience and endocrinology. The first topic of the thesis is parameter estimation of the classical Elementary Motion Detector (EMD) model in insect vision. There are two important aspects to be taken care of in the identification approach, namely the nonlinear dynamics of the individual EMD and the spatially distributed structure of multiple detectors producing a measurable neural response. Hence, the suggested identification method is comprised of two consecutive stages addressing each of the above aspects. Furthermore, visual stimulus design for high spatial excitation order has been investigated. The second topic is parameter estimation of mathematical model for testosterone regulation in the human male. The main challenges of this application are in the unavailability of input signal measurements and the presence of an unknown pulsatile feedback in the system resulting in a highly nonlinear closed-loop dynamics. Semi-blind identification method has been developed based on a recently proposed pulse-modulated model of pulsatile endocrine regulation. The two system identification problems treated in the thesis bear some resemblance in the sense that both involve measured signals that can be seen as square-integrable functions of time. This property is handled by transforming the signals into the Laguerre domain, i.e. by equivalently representing the functions with their infinite Laguerre series.