Accurate population models are needed to build very large-scale neural models, but their
derivation is difficult for realistic networks of neurons, in particular when nonlinear properties …

Cortical slow oscillations (SO) of neural activity spontaneously emerge and propagate during
deep sleep and anesthesia and are also expressed in isolated brain slices and cortical …

We present a mean-field formalism able to predict the collective dynamics of large networks
of conductance-based interacting spiking neurons. We apply this formalism to several …

Sleep slow waves are known to participate in memory consolidation, yet slow waves occurring
under anesthesia present no positive effects on memory. Here, we shed light onto this …

The occurrence of sleep passed through the evolutionary sieve and is widespread in animal
species. Sleep is known to be beneficial to cognitive and mnemonic tasks, while chronic …

Slow oscillations (≲ 1 Hz), a hallmark of slow-wave sleep and deep anesthesia across
species, arise from spatiotemporal patterns of activity whose complexity increases as …

The brain exhibits capabilities of fast incremental learning from few noisy examples, as well
as the ability to associate similar memories in autonomously-created categories and to …

The brain can efficiently learn a wide range of tasks, motivating the search for biologically
inspired learning rules for improving current artificial intelligence technology. Most biological …

The field of recurrent neural networks is over-populated by a variety of proposed learning
rules and protocols. The scope of this work is to define a generalized framework, to move a …

Biological networks display a variety of activity patterns reflecting a web of interactions that
is complex both in space and time. Yet inference methods have mainly focused on …