Thesis (Ph. D.)--University of California, San Diego, 2002. / Vita. Includes bibliographical references.

Thesis (Ph. D.)--University of Virginia, 1998. / Spine title: Synaptic function & aging. Includes bibliographical references (p. 131-162). Also available online through Digital Dissertations.

Thesis (Ph. D.)--UCLA, 2008. / Vita. Includes bibliographical references (leaves 73-81).

This study investigated the role of nicotinic acetylcholine receptors (nAChR) in object recognition and spatial recognition memory using the spontaneous object recognition (SOR) and object-location (OL) tasks, respectively. Experiments 1 to 4, did not yield any consistent facilitative effects of systemic nAChR activation with nicotine using 24- and 48-hr delays. Using a 72-hr delay, experiments 5 and 8 revealed that systemic pre-sample nicotine dose-dependently facilitated SOR and OL performance, respectively. Experiments 6-7 and 9-10 investigated the potential involvement of the perirhinal cortex (PRh) and hippocampus (HPC) in these systemic effects, with activation of nAChR in both of these brain regions producing facilitative effects on SOR and OL performance. These results not only demonstrate that nAChR facilitate performance on SOR and OL memory tasks, but suggest these effects are mediated by nAChR action in both PRh and HPC. This study indicates that, even though PRh and HPC are functionally distinct, they can interact to enhance performance on tasks for which they are not entirely necessary. / This research was supported by NSERC and CFI, operating grants to Dr. Boyer Winters.

Damage precipitating cognitive decline in Alzheimer's disease (AD) begins long before behavioural alterations become clinically apparent. At this prodromal stage, communication between networks of neurons connecting different brain regions starts to break down; setting in motion a chain of events leading to clinical AD. A significant challenge facing Alzheimer's researchers today is finding a cheap, easy-to-perform test capable of detecting prodromal AD. Such a test would afford significant benefits to patients, including a chance of early intervention. Perhaps, more importantly, it would also aid development and testing of novel therapies aimed at combating AD before it causes irreversible damage. Since oscillations in electrical field activity are important for facilitating connectivity across the brain and have been seen to alter in AD, this work studied how oscillations and regional connectivity are affected in the AD brain. Specifically, local field oscillations were recorded from the hippocampus and prelimbic cortex (regions implicated in memory formation and maintenance) in a double transgenic AD model - the TASTPM mouse. Here, periods of predominant theta activity were assessed both spontaneously, under urethane anaesthesia and following electrical induction through dorsal periaqueductal gray (dPAG) stimulation. From these recordings, spectral power and connectivity between regions was assessed using both a traditional measure of functional connectivity (inter-region correlation) and through a novel information theoretic approach measuring effective connectivity (transfer entropy).Perhaps the most prominent finding from this study was the observation that young TASTPM mice, at an age prior to overt cognitive decline or plaque deposition, showed significant alterations in measures of both functional and effective connectivity. This suggests that such measures may be used as biomarkers predictive of prodromal AD and, as such, may be used to aid development of drugs targeted towards treatment of prodromal AD.This study also uncovered a number of interesting observations concerning hippocampal/prelimbic connectivity. Firstly, although spectral power and inter-regional correlation peaked at ∼ 3Hz, information flow between these structures was strongest at ∼6Hz. This suggests that low and high-band theta activity may fulfil separate functions. Secondly, at theta frequencies, information flowed predominantly from the prelimbic cortex to the hippocampus. However, during lower frequency activity, information flowed predominantly in the opposite direction. Suggesting that separate frequency bands may be important for routing information flow between these structures. Finally, the strength of information transfer was seen to oscillate at approximately double the frequency of its carrier signal, perhaps suggesting locking of information transfer to certain phases of an underlying oscillation. Therefore, oscillations may structure information transfer by temporal windowing and frequency-locked routing; processes which can be studied using measures of effective connectivity such as transfer entropy.

Diss. (sammanfattning) Stockholm : Karol. inst., 2004. / Härtill 6 uppsatser.

In this thesis we use computational neural network models to examine the dynamics and functionality of the CA3 region of the mammalian hippocampus. The emphasis of the project is to investigate how the dynamic control structures provided by inhibitory circuitry and cellular modification may effect the CA3 region during the recall of previously stored information. The CA3 region is commonly thought to work as a recurrent auto-associative neural network due to the neurophysiological characteristics found, such as, recurrent collaterals, strong and sparse synapses from external inputs and plasticity between coactive cells. Associative memory models have been developed using various configurations of mathematical artificial neural networks which were first developed over 40 years ago. Within these models we can store information via changes in the strength of connections between simplified model neurons (two-state). These memories can be recalled when a cue (noisy or partial) is instantiated upon the net. The type of information they can store is quite limited due to restrictions caused by the simplicity of the hard-limiting nodes which are commonly associated with a binary activation threshold. We build a much more biologically plausible model with complex spiking cell models and with realistic synaptic properties between cells. This model is based upon some of the many details we now know of the neuronal circuitry of the CA3 region. We implemented the model in computer software using Neuron and Matlab and tested it by running simulations of storage and recall in the network. By building this model we gain new insights into how different types of neurons, and the complex circuits they form, actually work. The mammalian brain consists of complex resistive-capacative electrical circuitry which is formed by the interconnection of large numbers of neurons. A principal cell type is the pyramidal cell within the cortex, which is the main information processor in our neural networks. Pyramidal cells are surrounded by diverse populations of interneurons which have proportionally smaller numbers compared to the pyramidal cells and these form connections with pyramidal cells and other inhibitory cells. By building detailed computational models of recurrent neural circuitry we explore how these microcircuits of interneurons control the flow of information through pyramidal cells and regulate the efficacy of the network. We also explore the effect of cellular modification due to neuronal activity and the effect of incorporating spatially dependent connectivity on the network during recall of previously stored information. In particular we implement a spiking neural network proposed by Sommer and Wennekers (2001). We consider methods for improving associative memory recall using methods inspired by the work by Graham and Willshaw (1995) where they apply mathematical transforms to an artificial neural network to improve the recall quality within the network. The networks tested contain either 100 or 1000 pyramidal cells with 10% connectivity applied and a partial cue instantiated, and with a global pseudo-inhibition.We investigate three methods. Firstly, applying localised disynaptic inhibition which will proportionalise the excitatory post synaptic potentials and provide a fast acting reversal potential which should help to reduce the variability in signal propagation between cells and provide further inhibition to help synchronise the network activity. Secondly, implementing a persistent sodium channel to the cell body which will act to non-linearise the activation threshold where after a given membrane potential the amplitude of the excitatory postsynaptic potential (EPSP) is boosted to push cells which receive slightly more excitation (most likely high units) over the firing threshold. Finally, implementing spatial characteristics of the dendritic tree will allow a greater probability of a modified synapse existing after 10% random connectivity has been applied throughout the network. We apply spatial characteristics by scaling the conductance weights of excitatory synapses which simulate the loss in potential in synapses found in the outer dendritic regions due to increased resistance. To further increase the biological plausibility of the network we remove the pseudo-inhibition and apply realistic basket cell models with differing configurations for a global inhibitory circuit. The networks are configured with; 1 single basket cell providing feedback inhibition, 10% basket cells providing feedback inhibition where 10 pyramidal cells connect to each basket cell and finally, 100% basket cells providing feedback inhibition. These networks are compared and contrasted for efficacy on recall quality and the effect on the network behaviour. We have found promising results from applying biologically plausible recall strategies and network configurations which suggests the role of inhibition and cellular dynamics are pivotal in learning and memory.

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