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CBL Alumni Talk: Nonlinear filtering as a unifying principle in neuroscience by Jean-Pascal Pfister

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If you have a question about this talk, please contact Elre Oldewage .

A remarkable property of the brain is its ability to perform robust computation while being made of unreliable elements. For example, synapses are highly stochastic elements that often fail to transmit the information across the synaptic cleft. Similarly, at the neuronal level, the action potential generation is best described by a stochastic process and is therefore not fully deterministic. It remains therefore unclear how the brain performs reliable computation with unreliable components. In this talk, I will argue that a fundamental task that the brain needs to solve is the dynamical extraction of relevant information from a continuous stream of unreliable observations. This task can be generically formulated as a nonlinear Bayesian filtering task. I will therefore reinterpret several phenomena in neuroscience from this nonlinear filtering principle. Short-term plasticity will be seen as a nonlinear filter that estimates the presynaptic membrane potential from observed spikes. Long-term plasticity will be seen as a nonlinear filter that estimates the dynamically changing ground truth weights. Finally neuronal dynamics will be seen as a nonlinear filter that dynamically extracts features from synaptic inputs.

This talk is part of the Machine Learning Reading Group @ CUED series.

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