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


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Recent Articles

Learning quantum models from quantum or classical data

Kappen H.J., 2020

Atom-by-atom construction of attractors in a tunable finite size spin array

Kolmus A., Khajetoorians A.A., Kappen H.J., 2020

An atomic Boltzmann machine capable of on-chip learning

Kiraly B, Knol EJ, Kappen H.J., Khajetoorians A.A., 2020

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