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Recent Submissions
Symbols, Dynamics, and Maps: A Neurosymbolic Approach to Spatial Cognition
(University of Waterloo, 2025-03-12) Dumont, Nicole Sandra-Yaffa
The discovery of various spatially sensitive neurons in the hippocampal formation, such as place, grid, and boundary cells, has provided valuable insights into the neural mechanisms underlying spatial representation and navigation. However, neural activity and connectivity data alone cannot fully reveal the brain’s algorithms. Bridging this gap requires computational models that not only explain the low-level activity of spatially sensitive cells but also link it to higher-level symbolic representations manipulable within a cognitive framework – models capable of binding spatial representations to discrete abstractions, while also supporting hierarchical and probabilistic structures that enable reasoning and decision-making.
The Semantic Pointer Architecture (SPA; Eliasmith, 2013), in combination with the Neural Engineering Framework (NEF; Eliasmith et al., 2003), provides a mathematical and computational framework to represent symbols and implement dynamical systems in spiking neural networks. Spatial Semantic Pointers (SSPs; Komer et al., 2019), an extension to the SPA, encode continuous variables, such as spatial locations, while supporting the binding of spatial information with other features – continuous or discrete – into compressed, multi-domain representations. This flexibility allows SSPs to model diverse cognitive processes, ranging from spatial memory to abstract reasoning, offering a unified theory for how continuous variables might be represented and manipulated in the brain.
In this thesis, we leverage these tools to model key components of spatial cognition, including path integration, cognitive map creation, and reinforcement learning. Our contributions include the development of SSP-PI, a SSP-based path integration model that combines velocity controlled oscillators with attractor dynamics to integrate continuous spatial variables. We also introduce SSP-SLAM, a biologically inspired spiking neural SLAM system capable of constructing semantic cognitive maps that bind and associate spatial and non spatial features. Furthermore, we propose spiking RL models that demonstrate how SSP embeddings can effectively represent successor features, reward distributions, and stochastic policies. Finally, we use the SPA and SSPs to construct state embeddings for deep RL networks, demonstrating their utility in tasks requiring mixed semantic-spatial representations.
Our findings underscore the potential of SSPs to act as a unifying framework for understanding spatial representation in the brain while advancing biologically inspired approaches to navigation and learning in artificial systems. This work bridges theoretical neuroscience and artificial intelligence, laying the groundwork for future explorations of shared principles across spatial and abstract cognition.
Development of Novel Human Aggrecanse-2 Dual-Binding Bis-Squaramide Inhibitors
(University of Waterloo, 2025-03-12) Ratto, Amanda
Osteoarthritis (OA) is a degenerative joint disease that affects millions of individuals worldwide. OA is characterized by the breakdown of articular cartilage, including the proteoglycan aggrecan, which plays a crucial role in enabling cartilage to withstand compressive loads. A Disintegrin and Metalloproteinase with Thrombospondin Motifs-5 (ADAMTS-5; aggrecanase-2), has been reported to be the predominant aggrecanase in mice, and in vitro studies revealed ADAMTS-5 exhibits high efficiency at cleaving aggrecan. Although no disease modifying OA drugs have been developed, it is hypothesized that inhibitors against ADAMTS-5 could slow the progression of OA. Typical inhibitors of ADAMTS-5 include zinc-binding groups (ZBGs) that interact with the catalytic zinc. Recently, an exosite that inhibitors can target has been identified at a nearby domain, not within the catalytic site. Here we present the development of novel potential dual-binding inhibitors which aim to target both the catalytic site and exosite of ADAMTS-5.
The inhibitors investigated in this thesis incorporate a squaramide nucleus, which is an excellent molecular scaffold due to its ease of derivatization, known synthetic pathways, and commercial availability. To identify potential dual-binding bis-squaramide inhibitors, a large in silico library was constructed, consisting of the squaramide nucleus linking potential exosite binding groups and ZBGs. Numerous computational techniques were utilized to identify inhibitors, including molecular docking to evaluate potential interactions with both the binding pocket and exosite of ADAMTS-5, as well as molecular dynamics simulations to assess inhibitor stability and predict binding affinities. The four bis-squaramide molecules identified from the computational screening were successfully synthesized using a one-pot, microwave-assisted synthetic approach, which facilitated a high-throughput process through reaction automation. A range of bis-squaramide compounds were enzymatically screened with micromolar IC50’s for ADAMTS-5.
Harnessing Exosomes from Human Dermal Fibroblasts and Pirfenidone-exosomes as Innovative Strategies for Scarless Tissue Repair in Wound Healing
(University of Waterloo, 2025-03-11) Wang, Jin
The wound healing process often leads to scar formation that can negatively affect patients both physically and psychologically. The management and treatment of scars also place a considerable financial burden on healthcare systems. Significant efforts are being made to improve wound healing outcomes by accelerating closure while simultaneously minimizing scar formation. To facilitate scarless wound healing, developing an anti-scarring treatment that modulates dermal fibroblast activity is a promising strategy, with pirfenidone (PFD) showing potential due to its anti-fibrotic properties by targeting intracellular pathways that regulate collagen disposition. PFD, particularly when delivered via dermal fibroblast-derived exosomes, may further enhance therapeutic effectiveness and promote scarless healing.
To achieve this goal, we began by isolating high-purity exosomes from in vitro cultured human dermal fibroblasts. Two common isolation methods—PEG precipitation and affinity-based techniques—were compared to identify the most efficient approach for obtaining high-purity and relatively homogenous exosomes. A range of characterization techniques, including transmission electron microscopy (TEM), atomic force microscopy (AFM), antibody arrays, and enzyme-linked immunosorbent assays (ELISA), confirmed the successful isolation of high-purity exosomes. The affinity-based method demonstrated superior performance, yielding well-dispersed and highly pure exosomes. Due to the difficulties in achieving efficient drug encapsulation in exosomes, the following chapter specifically focused on the encapsulation and formulation optimization of the antifibrotic compound PFD and explored the use of exosomes as a drug delivery platform. We optimized an active drug loading method using sonication to enhance encapsulation efficiency (EE%) and loading efficiency (LE%), ensuring that careful control of the sonication process maintained exosome integrity. The optimal formulation of PFD-exosomes achieved an EE% of 11.14% ± 1.27% and an LE of 10.01% ± 1.03%, with a particle recovery rate of exosomes at 64.21% ± 8.49%. Then, we investigated how to harness exosomes from dermal fibroblasts and PFD-exosomes as innovative strategies for achieving scarless tissue repair in wound healing. Our findings showed that exosomes enhanced fibroblast migration and proliferation through an autocrine mechanism, highlighting their potential as a stand-alone cell-free therapy for wound healing. Additionally, this study was ground-breaking in demonstrating that exosomes can improve the efficacy of PFD as a drug carrier, amplifying its anti-fibrotic effects in both in vitro and in vivo models. The in vivo results indicated that PFD-exosomes accelerated wound healing while organizing the extracellular matrix (ECM) by reducing excessive collagen deposition. Overall, PFD-exosomes present an innovative strategy for pre-scarring interventions, offering benefits of enhanced wound healing outcomes while minimizing scarring.
Perspectives of Graph Diffusion: Computation, Local Partitioning, Statistical Recovery, and Applications
(University of Waterloo, 2025-03-06) Yang, Shenghao
Diffusion describes the process of mass moving from one region to another. In the con- text of graph, the diffusing mass spreads from nodes to nodes along the edges of the graph. Broadly speaking, this includes a number of stochastic and deterministic processes such as random walk, heat diffusion, network flow and electrical flow on graphs. Graph diffusion is a highly important primitive, and has been shown to have a variety of surprising properties both theoretically and practically. In this thesis, we present several new perspectives of graph diffusion, with an emphasis on how diffusion algorithms uncover the local clustering structure of the input data without necessarily exploring the entire graph.
In the first two parts of the thesis, we introduce a new class of graph diffusion methods that are provably better at extracting the local clustering structure of a graph or a hy- pergraph. Here, diffusion is formulated as a pair of primal and dual convex optimization problems, based on the idea of spreading mass in the graph while minimizing a p-norm net- work flow cost. The primal solution of the diffusion problem provides an intuitive physical interpretation where paint (i.e. mass) spills from the source nodes, spreads over the graph, and there is a sink at each node where up to a certain amount of paint can settle. The dual solution embeds the nodes on the non-negative real line and is considered as the output of diffusion. We will show that the dual variables nicely encode the local clustering structure around a given set of seed nodes. In particular, assume the existence of a cluster C of low conductance Φ(C), the sweep cut procedure on the dual variables returns a cluster whose conductance is not too much larger than Φ(C).
In the next two parts of the thesis, we introduce a weighted diffusion mechanism which allows any existing diffusion method to take into account additional node information such as node attributes and labels. The method weighs the edges of the graph based on the attributes or the labels of each node. Depending on the nature and availability of additional node information, two simple yet effective edge-weighting schemes are introduced and analyzed. Over contextual random graphs generated by a local variant of the stochastic block model with noisy node information, we will show that, if the additional information contains enough signal about the ground-truth cluster, then employing existing diffusion algorithms in the weighted graph can more accurately recover the ground-truth cluster than employing diffusion in the original graph without edge weights. In particular, statistical recovery guarantees in terms of precision and F1 score will be derived and compared.
All of the results are supplemented with extensive experiments on both synthetic and real-world data to illustrate the technical results and the effectiveness of the new methods in practice. The code is open-source on GitHub.
Introducing the INSPIRE Framework Guidelines From Expert Librarians for Search and Selection in HCI Literature
(Oxford University Press on behalf of The British Computer Society., 2025-02-01) Joseph Tu; Lennart Nacke; Katja Rogers
Formalized literature reviews are crucial in human–computer interaction (HCI) because they synthesize research and identify unsolved problems. However, current practices lack transparency when reporting details of a literature search. This restricts replicability. This paper introduces the INSPIRE framework for HCI research. It focuses on the search stage in literature reviews to support a search that prioritizes transparency and quality-of-fit to a research question. It was developed based on guiding principles for successful searches and precautions advised by librarian experts in HCI (n=8) for search strategies in (primarily systematic) literature reviews. We discuss how their advice aligns with the HCI field and their concerns about computational AI tools assisting or automating these reviews. Based on their advice, the framework outlines pivotal stages in conducting a literature search. These essential stages are: (1) defining research goals, (2) navigating relevant databases and (3) using searching techniques (like divergent and convergent searching) to identify a set of relevant studies. The framework also emphasizes the importance of team involvement, transparent reporting, and a flexible, iterative approach to refining the search terms.