Jean-Pascal Pfister

Wednesday - 28 November 2018

Time: 4.00pm

Ground Floor Seminar Room

25 Howland Street, London, W1T 4JG

The filtering brain hypothesis

An ever-increasing body of experimental literature shows that the brain integrates sensory information consistently with Bayes’ rule leading to the so-called Bayesian brain hypothesis. However, most of those evidences come from experiments with static stimuli whereas natural stimuli are intrinsically dynamic. Indeed, the brain is remarkably good at dynamically estimating hidden features from on-going stimuli such as tracking the position of the prey from on-going visual inputs or identify the voice of a person in noisy crowd. The Bayesian optimal way to perform this on-going feature extraction from dynamical stimuli is known as nonlinear filtering. Here, I propose a generalised hypothesis - the (Bayesian) filtering brain hypothesis - which generalises the Bayesian brain hypothesis to dynamic stimuli. In this talk, I will show that this hypothesis can be seen as a guiding computational principle at different levels of descriptions. At the single synapse level, both short-term and long-term plasticity can be seen as a nonlinear filtering processes [1]. At the neural network level, we proposed a new particle filter called the Neural Particle Filter which can be implemented by a neural network [2] and does not suffer from the curse of dimensionality unlike traditional particle filters [3]. All together, those studies advocate for a tighter link between the fields of nonlinear filtering and neuroscience.

[1] Pfister, J.-P., Dayan, P., & Lengyel, M. (2010). Synapses with short-term plasticity are optimal estimators of presynaptic membrane potentials. Nature Neuroscience, 13(10), 1271–1275. [2] Kutschireiter, A., Surace, S. C., Sprekeler, H., & Pfister, J.-P. (2017). Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception. Nature Scientific Reports, 7(1), 8722. [3] Surace, S. C., Kutschireiter, A., & Pfister, J.-P. (2018). How to avoid the curse of dimensionality: scalability of particle filters with and without importance weights. SIAM Review, in Press, arXiv:1703.07879