We consider a $K$-layer hetero-associative neural-network and we carry out a statistical mechanical analysis. Our findings show that these networks exhibit spontaneous information processing capabilities that go far beyond those of auto-associative counterparts. In particular, they can perform frequency modulation and, when presented with a spurious state (e.g., a symmetric mixture made of $K$...
Recurrent neural networks (RNNs) based on model neurons that communicate via continuous signals have been widely used to study how neural circuits perform cognitive tasks. Training such networks to perform tasks that require information maintenance over a brief period (working memory tasks) remains a challenge. Inspired by the robust information maintenance observed in higher cortical areas...
Adaptive behavior relies on activity-dependent synaptic plasticity to sculpt internal models of the world. I introduce two complementary frameworks for how the brain encodes abstraction and probability. Regarding abstract representations, we first propose a three-factor plasticity rule for nonlinear dimensionality reduction in a three-layer network inspired by the Drosophila olfactory circuit....
Quantum integrable systems are characterised by an infinite number of conserved charges and stable quasi-particle excitations. When integrability is broken, interactions between quasi-particles are introduced, opening the way for a novel kinetic theory that incorporates both integrable and non-integrable processes. In this talk I will review recent advances in the development of such a kinetic...
