Change is ubiquitous in living beings. In particular, the connectome and neural representations
can change. Nevertheless, behaviors and memories often persist over long times. In a …

The ability of humans and animals to quickly adapt to novel tasks is difficult to reconcile with
the standard paradigm of learning by slow synaptic weight modification. Here, we show that …

Autonomous, randomly coupled, neural networks display a transition to chaos at a critical
coupling strength. Here, we investigate the effect of a time-varying input on the onset of chaos …

Experiments in various neural systems found avalanches: bursts of activity with characteristics
typical for critical dynamics. A possible explanation for their occurrence is an underlying …

Jellyfish nerve nets provide insight into the origins of nervous systems, as both their taxonomic
position and their evolutionary age imply that jellyfish resemble some of the earliest …

Noise is ubiquitous in neural systems due to intrinsic stochasticity or external drive. For
deterministic dynamics, neural networks of randomly coupled units display a transition to chaos …

Neural circuits exhibit complex activity patterns, both spontaneously and evoked by external
stimuli. Information encoding and learning in neural circuits depend on how well time-…

Neural networks of the brain form one of the most complex systems we know. Many qualitative
features of the emerging collective phenomena, such as correlated activity, stability, …

Change is ubiquitous in living beings. In particular, the connectome and neural representations
can change. Nevertheless, behaviors and memories often persist over long times. In a …

Abstract Information encoding in neural circuits depends on how well time-varying stimuli
are encoded by neural populations. Slow neuronal timescales, noise and network chaos can …