published_or_final_version / abstract / Physics / Doctoral / Doctor of Philosophy

published_or_final_version / Biochemistry / Master / Master of Philosophy

published_or_final_version / Paediatrics / Master / Master of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

published_or_final_version / Physics / Master / Master of Philosophy

Many recent studies on the segmental motor control system have employed spike-triggered-averaging (STA) and other forms of cross-correlation to either attribute CNS, reflex, or direct motor effects to the impulses of a single (reference) neuronal spike train or to explore conditions under which pairs of neural units show temporal correlations in their discharge. Our experience with these techniques suggested the need for a control procedure that tests for synchrony between the reference and other spike trains such as to: (1) either preclude that the observed effects are due to spike trains other than or in addition to the reference train; or (2) give insight into the conditions leading to correlated discharge between two units. A motor unit synchronization test based on analysis of EMG waveforms has already been described. We have modified this test for the detection of synchrony between either afferent or efferent signals by analysis of averaged muscle nerve signals rather than EMG waveforms. Our procedure involves use of a multi-unit muscle nerve recording that serves as the input to a signal averager triggered by a spike train from either: (1) a motor unit's EMG; (2) a dorsal root filament or ganglion cell; or (3) a ramdom trigger source. With appropriate delay of the muscle nerve signal input, the non-rectified average of the trigger signal's waveform is compared to the rectified average which contains this waveform together with contributions of all other active unitary events. Additionally, the rectified average is compared to a "randomly" triggered average of the same input signal. On the basis of these recordings, it can be determined, within certain boundary conditions, whether or not any other unitary events are in synchrony with the reference event. Such synchronization is expressed quantitatively in the form of a synchronization index (SI). We evaluated the efficacy of the SI by electronic simulation procedures and by comparing its use to that of a cross-correlation procedure that tests for synchrony on the basis of crosscorrelograms computed between two simultaneously recorded spindle afferent spike trains during brief stretch of a passive muscle at progressively increasing amplitudes (5 - 100um). These experiments revealed that the SI is a sensitive test of afferent synchrony in the passive muscle provided the spike trains of interest have a signal-to-noise (S/N) ratio > 0.2 in the muscle nerve recording and that it is recognized that the detectable degree of synchronization of a non-reference event is a function of its S/N ratio. For tests on the active muscle, the force levels must remain low. Otherwise increased neuronal activity in the muscle nerve recording decreases the S/N ratio of individual spike trains. Thus, despite restrictive (but predictable) boundary conditions, the SI test can contribute importantly to select conclusions drawn from cross-correlation studies.

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Μια σύντομη αναφορά σε κάποια γνωστά μοντέλα Νευρωνικών Δικτύων, περιγραφή της αρχιτεκτονικής τους και εφαρμογές. Παραδείγματα και εφαρμογές Δυναμικών Νευρωνικών Δικτύων. Γενικό πλαίσιο λειτουργίας των CNN, ιδιότητες και εφαρμογές. / A short reference in Neural Networks, architecture description and applications. Implementation of Dynamic Neural Networks. CNN (cellular neural networks) paradigm, attributes and examples.

Η εργασία ασχολείται με την ομαδοποίηση δεδομένων ανθρώπινου γονιδιόματος με την χρήση αλγόριθμων ομαδοποίοησης και νευρωνικών δικτύων για τον διαχωρισμό του δείγματος σε ομάδες με βάση το αν έχουν κάποιο είδος ασθένειας ή όχι ή για τον καθορισμό του τύπου της ασθένειας. Παρουσιάζονται κάποια πειράματα που έγιναν με την χρήση και των δύο μεθόδων. / This master thesis is an application of clustering algorithms and artificial neural networks on human dna data in order to cluster the data in groups depending on wether a person has or hasn't an illness or what type of ilness one has. The thesis shows the results of some experiments conducted using either technique (clustering, ANNs) and a combination of both.