Manifold Learning via Memory and Context

Given a memory with infinite capacity, can we solve the learning problem? Apparently, nature has solved this problem as evidenced by the evolution of mammalian brains. Inspired by the organizational principles underlying hippocampal-neocortical systems, we present a navigation-based approach to manifold learning using memory and context. The key insight is to navigate on the manifold and memorize the positions of each route as inductive/design bias of direct-fit-to-nature. We name it navigation-based because our approach can be interpreted as navigating in the latent space of sensorimotor learning via memory (local maps) and context (global indexing). The indexing to the library of local maps within global coordinates is collected by an associative memory serving as the librarian, which mimics the coupling between the hippocampus and the neocortex. In addition to breaking from the notorious bias-variance dilemma and the curse of dimensionality, we discuss the biological implementation of our navigation-based learning by episodic and semantic memories in neural systems. The energy efficiency of navigation-based learning makes it suitable for hardware implementation on non-von Neumann architectures, such as the emerging in-memory computing paradigm, including spiking neural networks and memristor neural networks.