Download Two unsupervised principles to learn place cells from grid

Saturday evening, Poster III-28
Cosyne
Two Unsupervised Learning Principles to Learn Place Cells
from Grid Cells
Alexis Guanella1 and Paul F. M. J. Verschure1,2
1
2
Swiss Federal Institute of Technology (ETH), Zürich, Switzerland
University Pompeu Fabra (UPF), Barcelona, Spain
The study of spatial cognition in mammals has revealed the existence of neurons with spatially localized
activity. Firstly, the place cells of the hippocampal formation are activated when the animal’s position
correlates with unique regions in an environment, the so-called place fields [1]. Secondly, the grid cells of
the medial entorhinal cortex (MEC) show higher firing frequency at multiple regions in space, or subfields,
that are arranged in regular triangular tessellations [2]. The entorhinal cortex is found one synapse upstream
of the hippocampus, which suggests that place cells could be learned from the activity of grid cells.
Here, we investigate this hypothesis by using a model of place cells that was shown to extract invariant
features from a continuous input video stream leading to the formation of place fields as observed in the
hippocampus [3]. We simulate the activity of 10×10 entorhinal grid cells based on the physiological distributions of the grid parameters (i.e. grid spacing, orientation and phase) and driven by the position of a
virtual rat exploring randomly a one square meter arena. The unique position information of 4×4 place
cells is extracted from the grid cell activity by a gradient ascent on two objective functions, i.e. stability
and decorrelation. The stability objective allows extracting unique spatial representations, with a smooth
increase of the cell firing rate as the rat approaches the center of a place field. The decorrelation objective
prevents the formation of place fields at identical locations. Our results show that the resulting activity of the
output layer of the model is consistent in good detail with experimental characteristics observed in physiological recordings of place cells. Additionally, we quantify the difficulty of the learning problem by varying
different environmental and model parameters and show in particular that the two computational principles
of stability and decorrelation are complementary in the learning process. Our results further suggest that
grid cells may provide a fundamental substrate of spatial cognition, by showing firstly that they can support
the formation of place fields and secondly that this process can be learned by itself.
a
b
Figure 1: a. Mean activity maps of four representative simulated grid cells (network input). Odd columns:
normalized mean activity as a function of the position of the virtual rat. Red and blue regions correspond
to a high and low cell activity. Even columns: normalized mean activity as a function of the rat’s orientation. Black contours and gray regions represent normalized mean cell activity ± standard deviation. b.
Representative output of 4 place cells.
References
[1] O’Keefe, J. and Nadel, L. The hippocampus as a cognitive map. Clarendon Press, Oxford, (1978).
[2] Hafting, T., Fyhn, M., Molden, S., Moser, M.-B. and Moser, E. I. Microstructure of a spatial map in the
entorhinal cortex. Nature 436(7052), 801-6, Aug (2005).
[3] Wyss, T., König, P. and Verschure, P.F.M.J. A model of the ventral visual system based on temporal
stability and local memory. PLoS Biology 4(5), e120, May (2006).
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