Biomedical Engineering - Theses
Permanent URI for this collection
Search Results
1 - 2 of 2
-
ItemTracking neural dynamics( 2014)Epilepsy affects around 1% of the world's population, of which roughly a third are refractory to treatment. A major impediment to the development of effective treatments for these patients is the lack of understanding of the mechanisms underlying the disorder. In this thesis, a model-based approach is developed to provide some insight into these mechanisms. Initially, it is demonstrated that parameters linked to physiology from nonlinear models of the brain can be estimated accurately under various conditions. An animal model of epilepsy is used to illustrate that this computational model of the brain can be used to demonstrate variability in seizure mechanisms between animals, and over time. In particular, it is demonstrated that seizures group across days in the animal model, and that for each group, different physiological mechanisms are involved. This provides evidence that in the animal model studied seizures evolve, and that this evolution is linked to seizure grouping. With the knowledge that seizures are subject-specific and evolve over time, it may be possible to develop techniques that use control system theory to continuously alter therapy based on estimated physiological parameters inferred from recorded EEG, to provide treatments that are more efficacious. odel of epilepsy is used to illustrate that this computational model of the brain can be used to demonstrate variability in seizure mechanisms between animals, and over time. In particular, it is demonstrated that seizures group across days in the animal model, and that for each group, different physiological mechanisms are involved. This provides evidence that in the animal model studied seizures evolve, and that this evolution is linked to seizure grouping. With the knowledge that seizures are subject-specific and evolve over time, it may be possible to develop techniques that use control system theory to continuously alter therapy based on estimated physiological parameters inferred from recorded EEG, to provide treatments that are more efficacious.
-
ItemMathematical modeling of brain networks: from synaptic plasticity to behavior( 2014)A fundamental goal of neuroscience is to understand how the brain encodes and processes information and how the networks and structures involved are formed. In this thesis, we use theoretical approaches to further our understanding of brain function. First, we investigate how experimentally-based learning rules lead to the formation of different network structures, through unsupervised learning. Second, we investigate how different experimentally-based neural models and network structures enable different types of information processing, such as goal-directed, top-down processing. Third, we consider how reinforcement learning arising from synaptic plasticity mechanisms can coexist with unsupervised learning during the operant conditioning of neural firing rates. The unsupervised learning rule spiking-timing-dependent plasticity (STDP) has been shown to selectively potentiate feed-forward connections with specific axonal delays, enabling functions such as sound localization in the auditory brainstem of the barn owl. We demonstrate a similar selective potentiation for the recurrent connections in a network with axonal delays corresponding to the period of incoming oscillatory activity with frequencies in the range of 100-300Hz. For lower frequency oscillations, such as gamma (60Hz), we show that multiple, recurrently connected groups of neurons could encode not only the oscillation frequency but also a time lag between different sets of oscillations. These results have the potential to help explain missing fundamental pitch perception in the auditory brainstem and the formation of neuronal ensembles (or cell assemblies) in the cortex, respectively. Neural systems are able to perform top-down processing of stimulus information and flexibly select behaviors appropriate to the environment and present goals. Based upon previous experimental and theoretical studies, we propose that information in higher-level areas of the cortex, such as the prefrontal cortex, is encoded in the amplitude and phase of neural oscillations, such as gamma oscillations, and that this activity is gated by two mechanisms: top-down feedback and coherence between these oscillations. By forming these units into circuits that can perform logic operations, we identify the different ways in which operations can be initiated and manipulated by top-down feedback. We demonstrate that more sophisticated and flexible top-down control is possible when the gain of units is modulated by two mechanisms. We explore how different network properties affect top-down control and make predictions about the likely connectivities between certain brain regions. Typical and well-studied examples of behavioral learning are those in which the firing rates of individual cortical neurons in monkeys are increased using rewards. These results have been reproduced using reinforcement learning rules, such as a variant of STDP called reward-modulated spike-timing-dependent plasticity (RSTDP). However, these previous models have assumed that no unsupervised learning is present (i.e., no learning occurs without, or independent of, rewards). We show that these models cannot elicit firing rate reinforcement while exhibiting both reward learning and ongoing, stable unsupervised learning. To address this issue, we propose a new RSTDP model of synaptic plasticity, based upon the observed effects that dopamine has on long-term potentiation and depression, that is able to exhibit unsupervised learning and lead to firing rate reinforcement.