At SNN particular topics are approximate methods for probabilistic ′Bayesian′ inference, control theory, computational neuroscience and data analysis.



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Recent Articles

Supermodeling: Synchronization of Alternative Dynamical Models of a Single Objective Process

Duane G.S., Wiegerinck W.A.J.J., Selten F., Shen M.L, Keenlyside N., 2018

Learning effective state-feedback controllers through efficient multilevel importance samplers

Menschon SA, Kappen H.J., 2018

Role of Synaptic Stochasticity in Training Low-Precision Neural Netwtorks

Baldassi C, Gerace F., Kappen H.J., Lucibello C., Saglietti L., Tartaglione E., Zecchina R., 2018

All publications by SNN

SNN Adaptive Intelligence

We conduct research on the computational principles that underlie natural and artificial intelligence. Intelligent behavior is learned by adapting to the environment; it requires integration of sensory data with prior knowledge; and it must be robust to noise.

The research goal is to provide theoretical insights, models and methods that address these issues combining methods from machine learning and neuroscience. Particular topics are approximate methods for probabilistic ′Bayesian′ inference, control theory, neural networks and data analysis. The research results are applied in diverse fields outside of science together with our spin-off company SMART Research BV.

More about SNN and SMART Research BV