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Nelms Institute Contact: Swarup Bhunia
Storage and retrieval of data in a computer memory plays a major role in system performance. Traditionally, computer memory organization is static – i.e., they do not change based on the application-specific characteristics in memory access behavior during system operation. Specifically, the association of a data block with a search pattern (or cues) as well as the granularity of a stored data do not evolve. Such a static nature of computer memory, we observe, not only limits the amount of data we can store in a given physical storage, but it also misses the opportunity for dramatic performance improvement in various applications. On the contrary, human memory is characterized by seemingly infinite plasticity in storing and retrieving data – as well as dynamically creating/updating the associations between data and corresponding cues. In this paper, we introduce Neural Storage (NS), a brain-inspired learning memory paradigm that organizes the memory as a flexible neural memory network. In NS, the network structure, strength of associations, and granularity of the data adjust continuously during system operation, providing unprecedented plasticity and performance benefits. We present the associated storage/retrieval/ retention algorithms in NS, which integrate a formalized learning process. Using a full-blown operational model, we demonstrate that NS achieves an order of magnitude improvement in memory access performance for two representative applications when compared to traditional content-based memory.



Nelms Institute Contact: Ruogu Fang
Human emotions are dynamic, multidimensional responses to challenges and opportunities, which emerge from network interactions in the brain. Disruptions of these network interactions underlie emotional dysregulation in many mental disorders including anxiety and depression. In the process of carrying out our current NIH funded research on how human brain processes emotional information, we recognize the limitation of empirical studies, including not being able to manipulate the system to establish the causal basis for the observed relationship between brain and behavior. Creating an AI-based model system that is informed and validated by known biological findings and that can be used to carry out causal manipulations and allow the testing of the consequences against human imaging data will thus be a highly significant development in the short term. In the long term, the model can be further enriched and expanded so that it becomes a platform for testing a wider range of normal brain functions, as well as a platform for testing for how various pathologies affect these functions in mental disorders.



Nelms Institute Contact: Ruogu Fang
The three distinct neurodegenerative disorders — Parkinson’s disease; multiple system atrophy Parkinsonian variant, or MSAp; and progressive supranuclear palsy, or PSP — can be difficult to differentiate because they share overlapping motor and non-motor features, such as changes in gait. But they also have important differences in pathology and prognosis, and obtaining an accurate diagnosis is key to determining the best possible treatment for patients as well as developing improved therapies of the future. Previous research has shown that accuracy of diagnosis in early Parkinson’s can be as low as 58%, and more than half of misdiagnosed patients actually have one of the two variants. Testing of the new AI tool, which will include MRI images from 315 patients at 21 sites across North America, builds upon more than a decade of research in the laboratory of David Vaillancourt, Ph.D., a professor and chair of the UF College of Health & Human Performance’s department of applied physiology and kinesiology, whose work is focused on improving the lives of more than 6 million people with Parkinson’s disease and Parkinson’s-like syndromes. To differentiate between the forms of Parkinsonism, Vaillancourt’s lab has developed a novel, noninvasive biomarker technique using diffusion-weighted MRI, which measures how water molecules diffuse in the brain and helps identify where neurodegeneration is occurring. Vaillancourt’s team demonstrated the effectiveness of the technique in an international, 1,002- patient study published in The Lancet Digital Health in 2019.



Nelms Institute Contact: Ruogu Fang
The temporal dynamics of blood flows through the network of cerebral arteries and veins provides a window into the health of the human brain. Since the brain is vulnerable to disrupted blood supply, brain dynamics serves as a crucial indicator for many kinds of neurological diseases such as stroke, brain cancer, and Alzheimer’s disease. Existing efforts at characterizing brain dynamics have predominantly centered on ‘isolated’ models in which data from single-voxel, single-modality, and single-subject are characterized. However, the brain is a vast network, naturally connected on structural and functional levels, and multimodal imaging provides complementary information on this natural connectivity. Thus, the current isolated models are deemed not capable of offering the platform necessary to enable many of the potential advancements in understanding, diagnosing, and treating neurological and cognitive diseases, leaving a critical gap between the current computational modeling capabilities and the needs in brain dynamics analysis. This project aims to bridge this gap by exploiting multi-scale structural (voxel, vasculature, tissue) connectivity and multi-modal (anatomical, angiography, perfusion) connectivity to develop an integrated connective computational paradigm for characterizing and understanding brain dynamics.



Nelms Institute Contact: Shreya Saxena
Personalized neurostimulation using data-driven models has enormous potential to restore neural activity towards health. However, the inference of individualized high- dimensional dynamical models from data remains challenging due to their under-constrained nature. Moreover, the design of stimulation strategies requires exploration of extremely large parameter spaces. To address these issues, we will leverage the wealth of data that multi-subject experiments provide, as well as the computational resources newly available at UF. We will develop new AI methods that utilize in-vivo neural responses to design and implement personalized stimulation in real-time. These will be developed using functional Magnetic Resonance Imaging (fMRI) datasets collected in-house to examine neural activity related to memory / cognition. We will (Aim 1) build recurrent neural networks of memory-related neural activity, and (Aim 2) design personalized brain stimulation to achieve a memory-enhanced neural response. Promising stimulation strategies will be validated in- silico on multiple datasets and finally in-vivo using an fMRI-compatible neural probe.