Now showing items 1-19 of 19

    • Biologically inspired methods in speech recognition and synthesis: closing the loop 

      Bekolay, Trevor (University of Waterloo, 2016-02-18)
      Current state-of-the-art approaches to computational speech recognition and synthesis are based on statistical analyses of extremely large data sets. It is currently unknown how these methods relate to the methods that ...
    • 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, ...
    • Computational Mechanisms of Language Understanding and Use in the Brain and Behaviour 

      Kajic, Ivana (University of Waterloo, 2020-10-15)
      Linguistic communication is a unique characteristic of intelligent behaviour that distinguishes humans from non-human animals. Natural language is a structured, complex communication system supported by a variety of ...
    • Continuous Spatial and Temporal Representations in Machine Vision 

      Lu, Thomas (University of Waterloo, 2021-06-02)
      This thesis explores continuous spatial and temporal representations in machine vision. For spatial representations, we explore the Spatial Semantic Pointer as a biologically plausible representation of continuous space ...
    • Dynamical Systems in Spiking Neuromorphic Hardware 

      Voelker, Aaron Russell (University of Waterloo, 2019-05-10)
      Dynamical systems are universal computers. They can perceive stimuli, remember, learn from feedback, plan sequences of actions, and coordinate complex behavioural responses. The Neural Engineering Framework (NEF) provides ...
    • The Free Self: What Separates Us From Machines 

      Ross, Mitchell (University of Waterloo, 2023-08-29)
      Could a machine ever achieve consciousness? Will it ever make sense to hold a machine morally responsible? In this thesis, I argue that the architecture of SPAUN - the largest WIP functioning brain model currently in ...
    • Harnessing Neural Dynamics as a Computational Resource 

      Stöckel, Andreas (University of Waterloo, 2022-01-10)
      Researchers study nervous systems at levels of scale spanning several orders of magnitude, both in terms of time and space. While some parts of the brain are well understood at specific levels of description, there are few ...
    • 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 ...
    • Inferential Role Semantics for Natural Language 

      Blouw, Peter (University of Waterloo, 2017-08-22)
      The most general goal of semantic theory is to explain facts about language use. In keeping with this goal, I introduce a framework for thinking about linguistic expressions in terms of (a) the inferences they license, (b) ...
    • 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, ...
    • 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 ...
    • Spaun 2.0: Extending the World’s Largest Functional Brain Model 

      Choo, Feng-Xuan (University of Waterloo, 2018-05-17)
      Building large-scale brain models is one method used by theoretical neuroscientists to understand the way the human brain functions. Researchers typically use either a bottom-up approach, which focuses on the detailed ...
    • 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 ...
    • A spiking neural network of state transition probabilities in model-based reinforcement learning 

      Shein, Mariah (University of Waterloo, 2017-10-23)
      The development of the field of reinforcement learning was based on psychological studies of the instrumental conditioning of humans and other animals. Recently, reinforcement learning algorithms have been applied to ...


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