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Slowness and sparseness lead to place, head-direction, and spatial-view cells
Franzius, Mathias
Sprekeler, Henning
Collaborator: Mathias Franzius, Henning Sprekeler

If a rat runs around in a cage with textured walls and floor, its visual input varyies quickly while its position varies relatively slowly. If the visual environment is rich enough, there is a one-to-one mapping between the visual input and the position. Thus, it should be possible to use the slowness principle to extract from the quickly varying visual input the more slowly varying position of the rat. Applying slow feature analysis to the visual input of a simulated rat indeed extracts spatial information resembling grid cells. Applying a linear transformation maximizing sparseness, see project page Sparseness yields place cells from grid cells, then leads to units similar to place cells. With the same network but with different movement statistics one can similarly get units resembling head-direction cells and spatial-view cells.

architecture overview (70 kB)Figure 1: Overview of the simulation setup and network architecture. Left: Top view of the simulated rat (red arrow) in the textured cage. Bottom: Visual input of the rat at the current position. Right: Architecture of the hierarchical SFA-network for learning grid-like units and subsequently place-like units.

simulated place cells (79 kB)

Figure 2: Place cell receptive fields. Top: Place fields emerging from the network simulation driven only by visual input like that in figure 1. Bottom: Place fields from an experiment (Wilson & McNaughton), arranged such that they optimally match the simulation results.

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

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