Abstract
The entorhinal-hippocampal system plays a crucial role in spatial cognition and navigation. Since the discovery of grid cells in layer II of medial entorhinal cortex (MEC), several types of models have been proposed to explain their development and operation; namely, continuous attractor network models, oscillatory interference models, and self-organizing map (SOM) models. Recent experiments revealing the in vivo intracellular signatures of grid cells (Domnisoru et al., 2013; Schmidt-Heiber and Hausser, 2013), the primarily inhibitory recurrent connectivity of grid cells (Couey et al., 2013; Pastoll et al., 2013), and the topographic organization of grid cells within anatomically overlapping modules of multiple spatial scales along the dorsoventral axis of MEC (Stensola et al., 2012) provide strong constraints and challenges to existing grid cell models. This article provides a computational explanation for how MEC cells can emerge through learning with grid cell properties in modular structures. Within this SOM model, grid cells with different rates of temporal integration learn modular properties with different spatial scales. Model grid cells learn in response to inputs from multiple scales of directionally-selective stripe cells (Krupic et al., 2012; Mhatre et al., 2012) that perform path integration of the linear velocities that are experienced during navigation. Slower rates of grid cell temporal integration support learned associations with stripe cells of larger scales. The explanatory and predictive capabilities of the three types of grid cell models are comparatively analyzed in light of recent data to illustrate how the SOM model overcomes problems that other types of models have not yet handled.
Highlights
During navigation in the external world, the brains of many animals are able to update representations of their current position, or place
The tuning widths increase with the spatial scale. These results provide further support to our previously described hypothesis that the rate of temporal integration of entorhinal map cells determines the subset of input stripe scales to which they can get tuned, and thereby the development of their regular hexagonal grid fields (Grossberg and Pilly, 2012)
Color coding from blue to red is used for the rate map, and from blue (−1) to red (1) for the autocorrelogram
Summary
During navigation in the external world, the brains of many animals are able to update representations of their current position, or place. While the underlying neural computations involved still remain to be fully elucidated, place cells in the hippocampus (O’Keefe and Dostrovsky, 1971) and grid cells in parahippocampal areas, including the medial entorhinal cortex (MEC) (Hafting et al, 2005; Sargolini et al, 2006), presubiculum (PrS), and parasubiculum (PaS) (Boccara et al, 2010), are understood to play critical roles These space-encoding cells respond both to displacements from a reference position as well as to environmental sensory stimuli. Stensola et al (2012) recently performed a comprehensive study of the anatomical organization of two-dimensional grid scales in layers II and III of MEC, both within and across animals Their experiments showed that grid cells along the dorsoventral axis of MEC have a modular organization; namely, that grid cells along the dorsoventral axis “cluster into a small number of layerspanning anatomically overlapping modules with distinct scale, orientation, asymmetry, and theta-frequency modulation” This article describes a neural model that proposes how such a modular organization may arise through learning as an animal navigates in its environment during postnatal development
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