  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.
