An algorithm for analysing brain connectivity data identifies cell types and connections in simple (C. elegans) and complex (mouse) nervous systems, and can even resolve structure and connectivity in a man-made microprocessor.

Modelling differential roles for identified dopaminergic and output neurons of the fruit fly mushroom bodies, combined with a novel dopaminergic plasticity rule, explains neural and behavioural phenomena in olfactory learning tasks.

Semaphorin7A sculpts the collective arbor architecture of the lateral line nerve and facilitates neural contacts with the hair cells for synaptogenesis during neuromast development in the zebrafish.

Systematic and coordinated variations in morphology and connectivity can structurally tune a microcircuit's computation but non-systematic variably also exists, imparting "connection noise" that potentially limits processing performance.

The technique of serial blockface scanning electron microscopy, which permits the complete reconstruction of neuronal structures, allows comparison of the detailed "wiring diagrams" of lateral-line receptor organs in wild-type and mutant zebrafish.

A proof-of-principle is provided for the use of high-density neurophysiological recordings in assessing local circuit interactions within the macaque brain.

Artificial neural networks can faithfully recapitulate realistic neuronal behavior with dramatically accelerated simulation runtimes, empowering accessible large-scale realistic network simulations.

A principle from which the neuronal dynamics and synaptic plasticity in arbitrary network architectures can be inferred, so that output errors are online minimized while simultaneously processing sensory input streams.

Combining a multineuron patch-clamp system with an automated pipette pressure and cleaning device enables efficient screening of synaptic connectivity in human brain slices and allows inter-individual comparison.

A computational model shows that it's possible to learn and replay extended temporal sequences in a network of spiking neurons with a modular architecture and a biologically realistic learning rule.