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Energy efficiency

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Hippocampal granule cell (GC) models exhibit degeneracy with different ion channel parameters resulting in comparable functional behaviour [1,2]. However, it is unknown how the degenerate models are further constrained by energetic considerations and known computational functions of GCs. Here, we use Pareto optimality to investigate this question. The Pareto perspective is particularly useful for multi-objective optimisation with multiple trade-offs [3]. We assume that evolution constrains and simplifies the ion channel parameter space by favouring Pareto optimal neurons, which cannot improve

ePoster · Computational Neuroscience

What can we further learn from the brain for artificial intelligence?

Kenji Doya · Okinawa Institute of Science and Technology

…Energy efficiency and data efficiency are the major features of the brain and human cognition that today’s deep learning has yet to deliver. The brain can be seen as a multi-agent system of heterogeneous learners using different representations and algorithms. The flexible use of reactive, model-free control and model-based “mental simulation” appears to be the basis for computational and d

Fri, Sep 11, 2020 · 15:00 UTC

Seminar · Artificial Intelligence