Navigation and the efficiency of spatial coding: insights from closed-loop simulations

Spatial learning is critical for survival and its underlying neuronal mechanisms have been studied extensively. These studies have revealed a wealth of information about the neural representations of space, such as place cells and boundary cells. While many studies have focused on how these represen...

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Bibliographic Details
Published in:Brain Structure and Function Vol. 229; no. 3; pp. 577 - 592
Main Authors: Ghazinouri, Behnam, Nejad, Mohammadreza Mohagheghi, Cheng, Sen
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2024
Springer Nature B.V
Subjects:
Biomedical and Life Sciences
Biomedicine
Brain
Cell Biology
Cells
Computational neuroscience
Efficiency
Firing pattern
Firing rate
Hippocampus - physiology
Hypotheses
Models, Neurological
Motivation
Neural coding
Neural networks
Neurology
Neurons - physiology
Neurosciences
Original Article
Simulation
Spatial discrimination learning
Spatial Learning
Spatial Navigation - physiology
Place cells
Efficient coding hypothesis
Spiking neural networks
Fisher information
ISSN:1863-2661, 1863-2653, 1863-2661, 0340-2061
Online Access:Get full text
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Summary:Spatial learning is critical for survival and its underlying neuronal mechanisms have been studied extensively. These studies have revealed a wealth of information about the neural representations of space, such as place cells and boundary cells. While many studies have focused on how these representations emerge in the brain, their functional role in driving spatial learning and navigation has received much less attention. We extended an existing computational modeling tool-chain to study the functional role of spatial representations using closed-loop simulations of spatial learning. At the heart of the model agent was a spiking neural network that formed a ring attractor. This network received inputs from place and boundary cells and the location of the activity bump in this network was the output. This output determined the movement directions of the agent. We found that the navigation performance depended on the parameters of the place cell input, such as their number, the place field sizes, and peak firing rate, as well as, unsurprisingly, the size of the goal zone. The dependence on the place cell parameters could be accounted for by just a single variable, the overlap index, but this dependence was nonmonotonic. By contrast, performance scaled monotonically with the Fisher information of the place cell population. Our results therefore demonstrate that efficiently encoding spatial information is critical for navigation performance.
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ISSN:1863-2661
1863-2653
1863-2661
0340-2061
DOI:10.1007/s00429-023-02637-8