Classical theoretical approaches to studying the cerebellar cortex are generalized to a broader set of learning tasks, revealing that the optimal value for the sparsity of granule cell activity is task-dependent.

Different features of an odor can be represented in mouse olfactory cortex using the particular ensemble of responsive neurons to represent odor identity and the synchrony of the ensemble activity to represent odor intensity.

Prediction of future input explains diverse neural tuning properties in sensory cortex.

Morphologically-mature granule cells with complex dendritic trees contribute to a sparse representation of space in the rat dentate gyrus.

Computational analyses of in-vivo cerebral activity show that gamma oscillations in the olfactory cortex originate from local feedback inhibition following each sniff and serve to segregate competing odor representations through a winner-take-all process, providing an optimal window to decode odor.

An accurate and efficient biologically plausible statistical model of the spiking activity of neural populations shows computational benefits of homeostatic synaptic scaling in learning large neural population codes.

Sensory innervation density in the insect associative learning center is set by postsynaptic cells and accomplished by flexible allocation of processes by presynaptic cells.

Two-photon imaging reveals super-sparse responses to natural images in primate V1, which carry sufficient information for discrimination of the input natural images with high accuracy.

Young neurons of the adult hippocampus are synaptically activated by a small group of non-overlapping afferent excitatory fibers, due to high synaptic gain and sparse connectivity, important for sparse and orthogonal coding during hippocampal information processing.

Targeted optogenetic activation of small ensembles of neurons is sufficient to trigger a behavioral report while recruiting matched network suppression, suggesting exquisite sensitivity despite network mechanisms that maintain sparseness.