A simple regulatory architecture allows learning the statistical structure of a changing environment.

Bacteria live in environments that are continuously fluctuating and changing. Exploiting any predictability of such fluctuations can lead to an increased fitness. On longer timescales, bacteria can 'learn' the structure of these fluctuations through evolution.

Self-organized criticality in neural networks from activity-based rewiring.

Neural systems process information in a dynamical regime between silence and chaotic dynamics. This has lead to the criticality hypothesis, which suggests that neural systems reach such a state by self-organizing toward the critical point of a dynamical phase transition. Here, we study a minimal neural network model that exhibits self-organized criticality in the presence of stochastic noise using a rewiring rule which only utilizes local information.

Large systems of random linear equations with nonnegative solutions: Characterizing the solvable and the unsolvable phase.

Large systems of linear equations are ubiquitous in science. Quite often, e.g.

Self-tolerance and autoimmunity in a minimal model of the idiotypic network.

We consider self-tolerance and its failure-autoimmunity-in a minimal mathematical model of the idiotypic network. A node in the network represents a clone of B-lymphocytes and its antibodies of the same idiotype which is encoded by a bitstring. The links between nodes represent possible interactions between clones of almost complementary idiotype.