Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 8 of 8 for “"spiking neuron model"”.
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Characterization of a Spiking Neuron Model via a Linear Approach
<p>In the past decade, characterizing spiking neuron models has been extensively researched as an essential issue in computational neuroscience. In this thesis, we examine the estimation problem of two different neuron models. In Chapter 2, We propose a modified Izhikevich model with an adaptive …
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Learning temporal representations in cortical networks through reward dependent expression of synaptic plasticity
… is long-term synaptic potentiation between neurons in a recurrent network. Analytical and numerical techniques are used to demonstrate that the model is sufficient to allow näive networks of both linear and non-linear neurons to encode and reliably represent durations specified by external …
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Biosignal Recording with Integrated Circuits
… an improved quadratic-integrate-and-fire neuron fabricated in CMOS technology, capable of mimicking various biologically inspired spike patterns. This neuron, in combination with a piezo-FET tactile sensor, shows promising results for future applications in biologically plausible spiking …
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Massively parallel neural computation
… of large neural networks using the Izhikevich spiking neuron model. Neural computation has been described as “embarrassingly parallel” as each neuron can be thought of as an independent system, with behaviour described by a mathematical model. However, the real challenge lies in modelling …
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Spiking Neural Network Framework for Brain Computer Interfaces
… However, with EEG being an accumulation of spiking neural activity, reverse engineering it to a spiking version could establish new directions in improving BCI systems, which is the focus of this thesis. Spiking neural network (SNN) communicates via spikes and is a suitable model to study …
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Dynamics and precursor signs for phase transitions in neural systems
… to state transition. We use theoretical neural modelling (single spiking neurons, a network of these, and a mean-field continuum limit) and in vitro experiments to address this question. Dynamically realistic equations of motion for thalamic relay neuron, reticular nuclei, cortical pyramidal and …
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Learning and memory in chaotic spiking neural models
… This has been done in the context of chaotic spiking neural networks (CSNNs). Chaos provides many interesting properties that can be used to achieve computational tasks. Such properties are sensitivity to initial conditions, space filling, control and synchronization. Biological research …
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Predicting and identifying signs of criticality near neuronal phase transition
… neural states associated with the transition to neuron spiking and with the induction of anaesthesia. First, mathematical and electronic models of a single spiking neuron are investigated, focusing on stochastic subthreshold dynamics on close approach to spiking and to depolarisation-blocked …