Approximate Hubel-Wiesel Modules and the Data Structures of Neural Computation

This paper describes a framework for modeling the interface between perception and memory on the algorithmic level of analysis. It is consistent with phenomena associated with many different brain regions. These include view-dependence (and invariance) effects in visual psychophysics [1, 2] and inferotemporal cortex physiology [3, 4], as well as episodic memory recall interference effects associated with the medial temporal lobe [5, 6]. The perspective developed here relies on a novel interpretation of Hubel and Wiesel’s conjecture for how receptive fields tuned to complex objects, and invariant to details, could be achieved [7]. It complements existing accounts of two-speed learning systems in neocortex and hippocampus (e.g., [8, 9]) while significantly expanding their scope to encompass a unified view of the entire pathway from V1 to hippocampus.