Active systems are driven out of equilibrium by exchanging energy and momentum with their
environment. This endows them with anomalous mechanical properties that are reviewed in …

Stochastic gradient descent (SGD) is widely believed to perform implicit regularization when
used to train deep neural networks, but the precise manner in which this occurs has thus far …

The brain displays a remarkable ability to sustain stable memories, allowing animals to
execute precise behaviors or recall stimulus associations years after they were first learned …

Despite tremendous success of the stochastic gradient descent (SGD) algorithm in deep
learning, little is known about how SGD finds generalizable solutions at flat minima of the …

The developmental dynamics of multicellular organisms is a process that takes place in a
multi-stable system in which each attractor state represents a cell type, and attractor …

In theoretical biology, we are often interested in random dynamical systems—like the brain—
that appear to model their environments. This can be formalized by appealing to the …

We present an effective field theory for the nonlinear fluctuating hydrodynamics of a single
conserved charge with or without time-reversal symmetry, based on the Martin-Siggia-Rose …

We study the heat transfer between two nanoparticles held at different temperatures that
interact through nonreciprocal forces, by combining molecular dynamics simulations with …

There is a whole range of emergent phenomena in a complex network such as robustness,
adaptiveness, multiple-equilibrium, hysteresis, oscillation and feedback. Those non …

We summarize the original formulation of the free energy principle and highlight some
technical issues. We discuss how these issues affect related results involving generalised …