Neural populations may depend on balanced recurrent connectivity to produce an efficient stimulus representation while also maintaining an accurate stimulus encoding despite the variability introduced by adapting neural responses.

Spiking networks compensate the loss of neurons instantaneously, when restoration of excitatory/inhibitory balance becomes equivalent to restoration of functionality.

Critical networks show maximum pre-stimulus phase and amplitude regulation of stimulus-evoked responses.

Neural oscillations are a necessary consequence of efficient coding of sensory signals by a spiking neural network, limited by synaptic delays and noise.

Combining experimental and theoretical approaches reveals the interplay between optogenetic manipulation and recurrent interactions in mouse cortex.

The interplay of recurrent excitation and short-term plasticity enables nonlinear transient amplification, an ideal mechanism for selective amplification, pattern completion, and pattern separation in recurrent neural networks.

Short-ranged and random connectivity are sufficient to explain complex, long-range activity patterns observed in macaque motor cortex that are, moreover, flexibly adaptable to behavior.

Multistability and regime shifts are common and species diversity is high in microbial communities when nutrient supplies are balanced and competing species have different stoichiometries of essential nutrients.

Neuronal participation in generation of motor patterns in the spinal circuits is lognormal, which is an indication of a rich diversity of activity within the mean-driven as well as the fluctuation-driven regimes.