Searching for principles of brain computation

•Hints for computational principles from experimental data.•Computational role of diverse network components.•Emergence and computational role of assemblies.•Probabilistic inference through stochastic network dynamics.•Ongoing network rewiring and compensation through synaptic sampling.

Experimental methods in neuroscience, such as calcium-imaging and recordings with multi-electrode arrays, are advancing at a rapid pace. They produce insight into the simultaneous activity of large numbers of neurons, and into plasticity processes in the brains of awake and behaving animals. These new data constrain models for neural computation and network plasticity that underlie perception, cognition, behavior, and learning. I will discuss in this short article four such constraints: inherent recurrent network activity and heterogeneous dynamic properties of neurons and synapses, stereotypical spatio-temporal activity patterns in networks of neurons, high trial-to-trial variability of network responses, and functional stability in spite of permanently ongoing changes in the network. I am proposing that these constraints provide hints to underlying principles of brain computation and learning.