Abstract
Medial entorhinal grid cells and hippocampal place cells provide neural correlates of spatial representation in the brain. A place cell typically fires whenever an animal is present in one or more spatial regions, or places, of an environment. A grid cell typically fires in multiple spatial regions that form a regular hexagonal grid structure extending throughout the environment. Different grid and place cells prefer spatially offset regions, with their firing fields increasing in size along the dorsoventral axes of the medial entorhinal cortex and hippocampus. The spacing between neighboring fields for a grid cell also increases along the dorsoventral axis. This article presents a neural model whose spiking neurons operate in a hierarchy of self-organizing maps, each obeying the same laws. This spiking GridPlaceMap model simulates how grid cells and place cells may develop. It responds to realistic rat navigational trajectories by learning grid cells with hexagonal grid firing fields of multiple spatial scales and place cells with one or more firing fields that match neurophysiological data about these cells and their development in juvenile rats. The place cells represent much larger spaces than the grid cells, which enable them to support navigational behaviors. Both self-organizing maps amplify and learn to categorize the most frequent and energetic co-occurrences of their inputs. The current results build upon a previous rate-based model of grid and place cell learning, and thus illustrate a general method for converting rate-based adaptive neural models, without the loss of any of their analog properties, into models whose cells obey spiking dynamics. New properties of the spiking GridPlaceMap model include the appearance of theta band modulation. The spiking model also opens a path for implementation in brain-emulating nanochips comprised of networks of noisy spiking neurons with multiple-level adaptive weights for controlling autonomous adaptive robots capable of spatial navigation.
Highlights
How our brains acquire stable cognitive maps of the spatial environments that we explore is an outstanding scientific question, and one with immense potential for technological applications
AMPA-gated channels, which regulate the fast components of excitatory postsynaptic potentials (EPSPs), are not explicitly included because there are no clear data on the NMDA/AMPA receptor density ratios for entorhinal stellate cells and hippocampal pyramidal cells before postnatal development of the spatial representation maps begins
Understanding how the entorhinal-hippocampal system learns grid and place cells is needed as a foundation for developing a comprehensive theory of how spatial cognition works in humans and higher animals, as well as for developing controllers of autonomous adaptive mobile robots that use only locally available signals to navigate to remembered locations of valued goal objects
Summary
How our brains acquire stable cognitive maps of the spatial environments that we explore is an outstanding scientific question, and one with immense potential for technological applications. This knowledge can be applied in designing autonomous agents that are capable of spatial cognition and navigation in a GPS signal-impoverished environment without the need for human teleoperation. It should be noted that place cells can have multiple fields in a large space, they do not exhibit any noticeable spatial periodicity in their responses [5,7]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
