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PhD
University of Waterloo and National Research Center Canada
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Schedule
Wednesday, July 15, 2020
6:00 PM Europe/Berlin
Domain
NeuroscienceHost
BCCN Berlin lectures series
Duration
70 minutes
The Neural Engineering Framework (and the associated software tool Nengo) provide a general method for converting algorithms into neural networks with an adjustable level of biological plausibility. I will give an introduction to this approach, and then focus on recent developments that have shown new insights into how brains represent time and space. This will start with the underlying mathematical formulation of ideal methods for representing continuous time and continuous space, then show how implementing these in neural networks can improve Machine Learning tasks, and finally show how the resulting systems compare to temporal and spatial representations in biological brains.
Terry Stewart
PhD
University of Waterloo and National Research Center Canada
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