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Exploiting semantic information in a spiking neural SLAM system

To navigate in new environments, an animal must be able to keep track of its position while simultaneously creating and updating an internal map of features in the environment, a problem formulated as simultaneous localization and mapping (SLAM) in the field of robotics. This requires integrating in...

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Autores principales: Dumont, Nicole Sandra-Yaffa, Furlong, P. Michael, Orchard, Jeff, Eliasmith, Chris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10354246/
https://www.ncbi.nlm.nih.gov/pubmed/37476829
http://dx.doi.org/10.3389/fnins.2023.1190515
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author Dumont, Nicole Sandra-Yaffa
Furlong, P. Michael
Orchard, Jeff
Eliasmith, Chris
author_facet Dumont, Nicole Sandra-Yaffa
Furlong, P. Michael
Orchard, Jeff
Eliasmith, Chris
author_sort Dumont, Nicole Sandra-Yaffa
collection PubMed
description To navigate in new environments, an animal must be able to keep track of its position while simultaneously creating and updating an internal map of features in the environment, a problem formulated as simultaneous localization and mapping (SLAM) in the field of robotics. This requires integrating information from different domains, including self-motion cues, sensory, and semantic information. Several specialized neuron classes have been identified in the mammalian brain as being involved in solving SLAM. While biology has inspired a whole class of SLAM algorithms, the use of semantic information has not been explored in such work. We present a novel, biologically plausible SLAM model called SSP-SLAM—a spiking neural network designed using tools for large scale cognitive modeling. Our model uses a vector representation of continuous spatial maps, which can be encoded via spiking neural activity and bound with other features (continuous and discrete) to create compressed structures containing semantic information from multiple domains (e.g., spatial, temporal, visual, conceptual). We demonstrate that the dynamics of these representations can be implemented with a hybrid oscillatory-interference and continuous attractor network of head direction cells. The estimated self-position from this network is used to learn an associative memory between semantically encoded landmarks and their positions, i.e., an environment map, which is used for loop closure. Our experiments demonstrate that environment maps can be learned accurately and their use greatly improves self-position estimation. Furthermore, grid cells, place cells, and object vector cells are observed by this model. We also run our path integrator network on the NengoLoihi neuromorphic emulator to demonstrate feasibility for a full neuromorphic implementation for energy efficient SLAM.
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spelling pubmed-103542462023-07-20 Exploiting semantic information in a spiking neural SLAM system Dumont, Nicole Sandra-Yaffa Furlong, P. Michael Orchard, Jeff Eliasmith, Chris Front Neurosci Neuroscience To navigate in new environments, an animal must be able to keep track of its position while simultaneously creating and updating an internal map of features in the environment, a problem formulated as simultaneous localization and mapping (SLAM) in the field of robotics. This requires integrating information from different domains, including self-motion cues, sensory, and semantic information. Several specialized neuron classes have been identified in the mammalian brain as being involved in solving SLAM. While biology has inspired a whole class of SLAM algorithms, the use of semantic information has not been explored in such work. We present a novel, biologically plausible SLAM model called SSP-SLAM—a spiking neural network designed using tools for large scale cognitive modeling. Our model uses a vector representation of continuous spatial maps, which can be encoded via spiking neural activity and bound with other features (continuous and discrete) to create compressed structures containing semantic information from multiple domains (e.g., spatial, temporal, visual, conceptual). We demonstrate that the dynamics of these representations can be implemented with a hybrid oscillatory-interference and continuous attractor network of head direction cells. The estimated self-position from this network is used to learn an associative memory between semantically encoded landmarks and their positions, i.e., an environment map, which is used for loop closure. Our experiments demonstrate that environment maps can be learned accurately and their use greatly improves self-position estimation. Furthermore, grid cells, place cells, and object vector cells are observed by this model. We also run our path integrator network on the NengoLoihi neuromorphic emulator to demonstrate feasibility for a full neuromorphic implementation for energy efficient SLAM. Frontiers Media S.A. 2023-07-05 /pmc/articles/PMC10354246/ /pubmed/37476829 http://dx.doi.org/10.3389/fnins.2023.1190515 Text en Copyright © 2023 Dumont, Furlong, Orchard and Eliasmith. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Dumont, Nicole Sandra-Yaffa
Furlong, P. Michael
Orchard, Jeff
Eliasmith, Chris
Exploiting semantic information in a spiking neural SLAM system
title Exploiting semantic information in a spiking neural SLAM system
title_full Exploiting semantic information in a spiking neural SLAM system
title_fullStr Exploiting semantic information in a spiking neural SLAM system
title_full_unstemmed Exploiting semantic information in a spiking neural SLAM system
title_short Exploiting semantic information in a spiking neural SLAM system
title_sort exploiting semantic information in a spiking neural slam system
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10354246/
https://www.ncbi.nlm.nih.gov/pubmed/37476829
http://dx.doi.org/10.3389/fnins.2023.1190515
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