Now showing items 1-11 of 11

    • Biologically Inspired Spatial Representation 

      Komer, Brent (University of Waterloo, 2020-10-08)
      In this thesis I explore a biologically inspired method of encoding continuous space within a population of neurons. This method provides an extension to the Semantic Pointer Architecture (SPA) to encompass Semantic Pointers ...
    • Biologically Plausible Cortical Hierarchical-Classifier Circuit Extensions in Spiking Neurons 

      Suma, Peter (University of Waterloo, 2018-01-09)
      Hierarchical categorization inter-leaved with sequence recognition of incoming stimuli in the mammalian brain is theorized to be performed by circuits composed of the thalamus and the six-layer cortex. Using these circuits, ...
    • Incorporating Biologically Realistic Neuron Models into the NEF 

      Duggins, Peter (University of Waterloo, 2017-09-18)
      Theoretical neuroscience is fundamentally concerned with the relationship between biological mechanisms, information processing, and cognitive abilities, yet current models often lack either biophysical realism or cognitive ...
    • An Integrated Model of Contex, Short-Term, and Long-Term Memory 

      Gosmann, Jan (University of Waterloo, 2018-07-27)
      I present the context-unified encoding (CUE) model, a large-scale spiking neural network model of human memory. It combines and integrates activity-based short-term memory with weight-based long-term memory. The ...
    • Learned Legendre Predictive State Estimator for Control 

      Jaworski, Pawel (University of Waterloo, 2022-09-26)
      This thesis introduces a novel method for system model identification, specifically for state estimation. The method uses a 2 or 3 layer neural network developed and trained with the methods of the Neural Engineering ...
    • Learning and Decision Making in Social Contexts: Neural and Computational Models 

      Duggins, Peter (University of Waterloo, 2023-04-19)
      Social interaction is one of humanity's defining features. Through it, we develop ideas, express emotions, and form relationships. In this thesis, we explore the topic of social cognition by building biologically-plausible ...
    • Learning and Leveraging Neural Memories 

      Aubin, Sean (University of Waterloo, 2018-09-26)
      Learning in the Neural Engineering Framework (NEF) and the Semantic Pointer Architecture (SPA) has been recently extended beyond the supervised Prescribed Error Sensitivity (PES) to include the unsupervised Vector Oja ...
    • Neural Plausibility of Bayesian Inference 

      Sharma, Sugandha (University of Waterloo, 2018-07-31)
      Behavioral studies have shown that humans account for uncertainty in a way that is nearly optimal in the Bayesian sense. Probabilistic models based on Bayes' theorem have been widely used for understanding human cognition, ...
    • Observe, Predict, Adapt: A Neural model of Adaptive Motor Control 

      Vaidyanathan, Natarajan (University of Waterloo, 2024-01-24)
      Biological control systems have evolved to perform efficiently in an environment characterized by high uncertainty and unexpected disturbances, while relying on noisy sensors and unreliable actuators. Despite these ...
    • Parallelizing Legendre Memory Unit Training 

      Chilkuri, Narsimha Reddy (University of Waterloo, 2021-07-14)
      Recently, a new recurrent neural network (RNN) named the Legendre Memory Unit (LMU) was proposed and shown to achieve state-of-the-art performance on several benchmark datasets. Here we leverage the linear time-invariant ...
    • Spiking Deep Neural Networks: Engineered and Biological Approaches to Object Recognition 

      Hunsberger, Eric (University of Waterloo, 2018-01-08)
      Modern machine learning models are beginning to rival human performance on some realistic object recognition tasks, but we still lack a full understanding of how the human brain solves this same problem. This thesis combines ...


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