Volume 11 Supplement 1
How good is grid coding versus place coding for navigation using noisy, spiking neurons?
© Herz et al; licensee BioMed Central Ltd. 2010
Published: 20 July 2010
Our understanding of how the brain encodes navigation through space underwent a revolution with the remarkable discovery of grid cells in the medial entorhinal cortex (MEC) of rodents . A grid cell builds a hexagonal lattice representation of physical space, such that the cell fires whenever the rodent moves through a lattice point. In contrast, place cells in the hippocampus proper fire only at a single, specific location in space.
After these interpretations we set out to investigate the spatial resolution of the place code and the grid code on a limited, one-dimensional space with a finite amount of cells and families of tuning curves. Therefore, we built a stochastic population coding model as in Bethge . The family of tuning curves convert the position into firing rates for statistically independent Poisson neurons. To compare the two coding schemes we calculate the maximum likelihood position estimate and compute the mean square error of the population code. We believe that the grid and place code should enable real-time readout of the rat's position while it is moving. For this reason, we have to consider short decoding times and hence, since Fisher information methods based on the Cramér Rao bound fail to estimate the mean error in such cases , we make use of Monte Carlo integration methods.
Under the condition of noisy, spiking neurons, we demonstrate that the grid code, if it is organized as in the interval nesting hypothesis outperforms the place code for any tuning width. On the other hand, if it is organized as in the modular arithmetic hypothesis, i.e. spatial periods that are far shorter than the length of space the grid code has a lower distortion than the best place codes.
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This article is published under license to BioMed Central Ltd.