Gradient-based methods

We introduce a statistical physics framework for learning in neural architectures composed of single or interconnected asymmetric attractor networks. These systems can exhibit a manifold of global fixed points capable of implementing sophisticated input-output mappings, which we characterize analytically. Learning from extensive datasets is achieved through the stabilization of fixed points via a fully distributed and local learning process, implemented at the single-neuron level. This simple mechanism yields performance comparable to that of conventional feedforward deep neural networks train

The ability to reason compositionally is a hallmark of intelligent behavior. This algebraic capacity to assemble new concepts from novel combinations of existing components lies at the heart of many human intellectual endeavors: language, math, science, philosophy, and much more. Further, recent discoveries in animal cognition indicate that compositional reasoning may not be a uniquely human trait --examples include numeracy in non-human primates, spatial navigation using cognitive maps in rodents, and even the use of basic syntax in songbirds (Tervo, Tenenbaum, & Gershman 2016) suggesting