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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Vydáno v:	Brain Structure and Function Ročník 229; číslo 3; s. 577 - 592
Hlavní autoři:	Ghazinouri, Behnam, Nejad, Mohammadreza Mohagheghi, Cheng, Sen
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2024 Springer Nature B.V
Témata:	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
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Abstract	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.
AbstractList	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. 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.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.
Author	Ghazinouri, Behnam Nejad, Mohammadreza Mohagheghi Cheng, Sen
Author_xml	– sequence: 1 givenname: Behnam surname: Ghazinouri fullname: Ghazinouri, Behnam organization: Faculty of Computer Science, Institute for Neural Computation, Ruhr University Bochum – sequence: 2 givenname: Mohammadreza Mohagheghi surname: Nejad fullname: Nejad, Mohammadreza Mohagheghi organization: Faculty of Computer Science, Institute for Neural Computation, Ruhr University Bochum – sequence: 3 givenname: Sen surname: Cheng fullname: Cheng, Sen email: sen.cheng@rub.de organization: Faculty of Computer Science, Institute for Neural Computation, Ruhr University Bochum
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CitedBy_id	crossref_primary_10_1016_j_neubiorev_2025_106282
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Keywords	Place cells Efficient coding hypothesis Spiking neural networks Fisher information
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SubjectTerms	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
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Title	Navigation and the efficiency of spatial coding: insights from closed-loop simulations
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