Doctor of Philosophy (PhD)
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Jean H. Andrian
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Neuron models are the elementary units which determine the performance of an artificial spiking neural network (ASNN). This study introduces a new Generalized Leaky Integrate-and-Fire (GLIF) neuron model with variable leaking resistor and bias current in order to reproduce accurately the membrane voltage dynamics of a biological neuron. The accuracy of this model is ensured by adjusting its parameters to the statistical properties of the Hodgkin-Huxley model outputs; while the speed is enhanced by introducing a Generalized Exponential Moving Average method that converts the parameterized kernel functions into pre-calculated lookup tables based on an analytic solution of the dynamic equations of the GLIF model.
Spike encoding is the initial yet crucial step for any application domain of ASNN. However, current encoding methods are not suitable to process complex temporal signal. Motivated by the modulation relationship found between afferent synaptic currents in biological neurons, this study proposes a biologically plausible spike phase encoding method based on a novel spiking neuron model which could perform wavelet decomposition of the input signal, and encode the wavelet spectrum into synchronized output spike trains. The spike delays in each synchronizing period represent the spectrum amplitudes. The encoding method was tested in encoding of human voice records for speech recognition purposes. Empirical evaluations confirm that encoded spike trains constitute a good representation of the continuous wavelet transform of the original signal.
Interictal spike (IS) is a type of transient discharge commonly found in the electroencephalography (EEG) records from epilepsy patients. The detection of IS remains an essential task for 3D source localization as well as in developing algorithms for essential in seizure prediction and guided therapy. We present in this work a new IS detection technology method using the phase encoding method with customized wavelet sensor neuron and a specially designed ASNN structure. The detection results confirm the ability of such ASNN to capture IS automatically from multichannel EEG records.
Wang, Zhenzhong, "System Design and Implementation of a Fast and Accurate Bio-Inspired Spiking Neural Network" (2015). FIU Electronic Theses and Dissertations. 2227.
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