False memory and delusions in Alzheimer's disease

Aims: This thesis aimed to investigate the relationship between memory errors and delusions in Alzheimer’s disease (AD), in order to further elucidate the mechanisms underlying delusion formation. This was achieved by undertaking narrative and systematic review of relevant literature, by exploring the relationship between performance on memory and metamemory tasks and delusions in AD patient populations and by investigating the neuroanatomical correlates of memory errors and delusions in AD patient populations. // Methods: I recruited 27 participants with and without delusions in AD and compared performance on measures of context memory, false memory and metamemory. I explored statistically significant behavioural findings further in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort of participants with AD (n = 733). I then conducted hypothesis-driven region of interest and exploratory voxel-based morphometric analyses to determine the relationship between false memory and delusions and regional brain volume in the ADNI cohort. This informed similar analyses of neuroimaging data in my own participants (n = 8). // Results: In both samples, individuals with delusions in AD had higher false recognition rates on recognition memory tasks than those without delusions. False recognition was inversely correlated with volume of medial temporal lobe, ventral visual stream and prefrontal cortex in both samples. In the ADNI sample, false recognition was also inversely correlated with anterior cingulate cortex (ACC) volume bilaterally. Participants with delusions had reduced volume of right ACC and increased volume of right parahippocampal gyrus compared to the control group. // Conclusions: These two complementary studies provide evidence of specific memory impairments associated with both delusions and a distinct pattern of brain atrophy in AD. Simple cognitive interventions can reduce false recognition rates in AD. Given the significant risks associated with antipsychotic drug treatment of delusions, exploring how these non-pharmacological interventions potentially affect psychosis symptoms in AD is an important next step

Boolean Weightless Neural Network Architectures

A collection of hardware weightless Boolean elements has been developed. These form fundamental building blocks which have particular pertinence to the field of weightless neural networks. They have also been shown to have merit in their own right for the design of robust architectures. A major element of this is a collection of weightless Boolean sum and threshold techniques. These are fundamental building blocks which can be used in weightless architectures particularly within the field of weightless neural networks. Included in these is the implementation of L-max also known as N point thresholding. These elements have been applied to design a Boolean weightless hardware version of Austin’s ADAM neural network. ADAM is further enhanced by the addition of a new learning paradigm, that of non-Hebbian Learning. This new method concentrates on the association of ‘dis-similarity’, believing this is as important as areas of similarity. Image processing using hardware weightless neural networks is investigated through simulation of digital filters using a Type 1 Neuroram neuro-filter. Simulations have been performed using MATLAB to compare the results to a conventional median filter. Type 1 Neuroram has been tested on an extended collection of noise types. The importance of the threshold has been examined and the effect of cascading both types of filters was examined. This research has led to the development of several novel weightless hardware elements that can be applied to image processing. These patented elements include a weightless thermocoder and two weightless median filters. These novel robust high speed weightless filters have been compared with conventional median filters. The robustness of these architectures has been investigated when subjected to accelerated ground based generated neutron radiation simulating the atmospheric radiation spectrum experienced at commercial avionic altitudes. A trial investigating the resilience of weightless hardware Boolean elements in comparison to standard weighted arithmetic logic is detailed, examining the effects on the operation of the function when implemented on hardware experiencing high energy neutron bombardment induced single event effects. Further weightless Boolean elements are detailed which contribute to the development of a weightless implementation of the traditionally weighted self ordered map

A new class of neural architectures to model episodic memory : computational studies of distal reward learning

A computational cognitive neuroscience model is proposed, which models episodic memory based on the mammalian brain. A computational neural architecture instantiates the proposed model and is tested on a particular task of distal reward learning. Categorical Neural Semantic Theory informs the architecture design. To experiment upon the computational brain model, embodiment and an environment in which the embodiment exists are simulated. This simulated environment realizes the Morris Water Maze task, a well established biological experimental test of distal reward learning. The embodied neural architecture is treated as a virtual rat and the environment it acts in as a virtual water tank. Performance levels of the neural architectures are evaluated through analysis of embodied behavior in the distal reward learning task. Comparison is made to biological rat experimental data, as well as comparison to other published models. In addition, differences in performance are compared between the normal and categorically informed versions of the architecture

Learning as a Nonlinear Line of Attraction for Pattern Association, Classification and Recognition

Development of a mathematical model for learning a nonlinear line of attraction is presented in this dissertation, in contrast to the conventional recurrent neural network model in which the memory is stored in an attractive fixed point at discrete location in state space. A nonlinear line of attraction is the encapsulation of attractive fixed points scattered in state space as an attractive nonlinear line, describing patterns with similar characteristics as a family of patterns. It is usually of prime imperative to guarantee the convergence of the dynamics of the recurrent network for associative learning and recall. We propose to alter this picture. That is, if the brain remembers by converging to the state representing familiar patterns, it should also diverge from such states when presented by an unknown encoded representation of a visual image. The conception of the dynamics of the nonlinear line attractor network to operate between stable and unstable states is the second contribution in this dissertation research. These criteria can be used to circumvent the plasticity-stability dilemma by using the unstable state as an indicator to create a new line for an unfamiliar pattern. This novel learning strategy utilizes stability (convergence) and instability (divergence) criteria of the designed dynamics to induce self-organizing behavior. The self-organizing behavior of the nonlinear line attractor model can manifest complex dynamics in an unsupervised manner. The third contribution of this dissertation is the introduction of the concept of manifold of color perception. The fourth contribution of this dissertation is the development of a nonlinear dimensionality reduction technique by embedding a set of related observations into a low-dimensional space utilizing the result attained by the learned memory matrices of the nonlinear line attractor network. Development of a system for affective states computation is also presented in this dissertation. This system is capable of extracting the user\u27s mental state in real time using a low cost computer. It is successfully interfaced with an advanced learning environment for human-computer interaction

A Decade of Neural Networks: Practical Applications and Prospects

The Jet Propulsion Laboratory Neural Network Workshop, sponsored by NASA and DOD, brings together sponsoring agencies, active researchers, and the user community to formulate a vision for the next decade of neural network research and application prospects. While the speed and computing power of microprocessors continue to grow at an ever-increasing pace, the demand to intelligently and adaptively deal with the complex, fuzzy, and often ill-defined world around us remains to a large extent unaddressed. Powerful, highly parallel computing paradigms such as neural networks promise to have a major impact in addressing these needs. Papers in the workshop proceedings highlight benefits of neural networks in real-world applications compared to conventional computing techniques. Topics include fault diagnosis, pattern recognition, and multiparameter optimization

Characterising the effect of semantic and perceptual similarity in episodic memory

This thesis investigated the contribution of semantic and perceptual similarity to mnemonic discrimination, defined as the ability to recognize previously encountered events and to discriminate them from similar ones to avoid false recognition. When mnemonic discriminations fails, false recognition of similar events is typically attributed to shared memory representations (gist) typically characterized in terms of meaning. In two experiments with young adults, I investigated the effects of multiple semantic and perceptual relations in a mnemonic discrimination task involving pictures of objects. I hypothesised that semantic meaning could be processed at least at two different levels: at the concept level, semantic similarity captures features shared by different concepts. I derived this continuous measure from the Conceptual Structure Account, a feature-based model of semantic memory. At the item level, rated semantic similarity between a depicted exemplar and people’s internal representation of that concept captures the degree to which an item activates its individual representation. For each item, I also measured indexes of visual form and colour similarity. In all the experiments of the current thesis, the study phase was followed by a recognition memory test including studied items, similar lures, and novel items. In Experiment 1 and 2 of Chapter 2 participants studied single and multiple exemplars for each basic-level concept, respectively. Participants were less likely to recognize studied items with high concept confusability, but also to falsely recognize these lures. Thus, greater emphasis on coarse processing of shared features relative to fine-grained processing of individual concepts weakened the basic-level semantic gist shared between studied items and lures. In contrast, false recognition was higher for those lures judged more similar to people’s internal representation of that concept, suggesting reactivation of the studied concept at test by a different exemplar. False recognition was also more frequent for more visually confusable lures. These results suggest that multiple levels of semantic and perceptual similarity contribute to mnemonic discrimination. In Chapter 3, to understand whether initial processing of semantic and perceptual information at encoding is responsible for later true and false recognition, I ran an fMRI study that combined representational similarity analysis (RSA) with the subsequent memory paradigm. To index semantic and perceptual processing that I found relevant in Chapter 2, I created two semantic models to index coarse-grained taxonomic categories and specific object features, respectively, and two perceptual models defining visual properties, like line orientation and colour attributes. Participants encoded images of objects during fMRI scanning. Both perceptual and semantic models predicted later true memory. The strength of the neural patterns corresponding to low-level visual representations in the early visual and inferotemporal cortex was stronger for items successfully recognized versus forgotten. Similarly, greater alignment of neural patterns with object-specific semantic representations in the inferotemporal cortex also predicted true recognition. However, the strength of the neural patterns reflecting nonspecific taxonomic information was stronger for items later forgotten than remembered in ventral anterior temporal lobe, left inferior frontal gyrus and, preliminary, in the left perirhinal cortex. In contrast, inefficient visual processing in posterior regions was associated with false recognition of similar lures. The results suggest that fine-grained semantic as well as visual analysis contributes to accurate later recognition, while processing visual image detail is critical for avoiding false recognition. To conclude, in Chapter 4 I investigated whether the same semantic and perceptual variables that I found to be significant predictors of young adults’ ability to recognize previously studied items, as well as misrecognize similar lures, have a greater impact on older adults’ performance on the same task. Thus, I focused on age-related differences. Contrary to our predictions, Experiment 1 did not show any age-related differences in true and false recognition, or semantic and perceptual modulations on recognition memory. However, Experiment 2 revealed an increase in true and false recognition for more confusable concepts in the older relative to young group when multiple exemplars of each basic-level concept were presented at study. I suggested this effect to be due to a reduction of processing semantic relations across concepts. This result is consistent with gist-based theories of aging that assume that semantic gist between studied items and lures can promote true and false recognition. Altogether, the studies described in the current thesis provide evidence that multiple semantic and perceptual relations can contribute to true and false recognition of objects and that these effects are in part mediated by operation that occur during the encoding phase