Phasic attractors for flexible and adaptive working memory in spiking recurrent neural networks
Working memory serves as a crucial building block in cognitive systems due to its fundamental role in information processing and decision-making. Acting as a temporary storage and manipulation mechanism, working memory enables individuals to actively hold and manipulate relevant information essentia...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Neuromorphic Computing and Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2634-4386/ade7ab |
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| author | Willian S Girão Thomas F Tiotto Jelmer P Borst Niels A Taatgen Elisabetta Chicca |
| author_facet | Willian S Girão Thomas F Tiotto Jelmer P Borst Niels A Taatgen Elisabetta Chicca |
| author_sort | Willian S Girão |
| collection | DOAJ |
| description | Working memory serves as a crucial building block in cognitive systems due to its fundamental role in information processing and decision-making. Acting as a temporary storage and manipulation mechanism, working memory enables individuals to actively hold and manipulate relevant information essential for ongoing tasks. This cognitive function is pivotal for various complex processes, including problem-solving, language comprehension, and decision-making. This paper introduces a novel working memory model designed as a spiking recurrent neural network of excitatory and inhibitory neurons. The proposed model incorporates biological mechanisms that allows attractors to be active phasically, thereby reducing the energy budget associated with maintaining attractor states. The core innovation of the model lies in its ability to leverage phasic attractors for state-dependent computation in a probabilistic manner. By modulating the activity of attractors via synaptic delays, the model demonstrates context-sensitive information processing. The results showcase the efficiency gains achieved by the proposed phasic attractor mechanism and highlight the model’s capacity for flexible and adaptive information processing. |
| format | Article |
| id | doaj-art-96ac35c15c9944428fce00d449692309 |
| institution | Kabale University |
| issn | 2634-4386 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Neuromorphic Computing and Engineering |
| spelling | doaj-art-96ac35c15c9944428fce00d4496923092025-08-20T03:51:25ZengIOP PublishingNeuromorphic Computing and Engineering2634-43862025-01-015303400410.1088/2634-4386/ade7abPhasic attractors for flexible and adaptive working memory in spiking recurrent neural networksWillian S Girão0https://orcid.org/0009-0002-7139-8346Thomas F Tiotto1https://orcid.org/0000-0002-2290-3409Jelmer P Borst2Niels A Taatgen3Elisabetta Chicca4https://orcid.org/0000-0002-5518-8990Bio-Inspired Circuits and Systems (BICS) Lab, Zernike Institute for Advanced Materials, University of Groningen , Groningen, The Netherlands; Groningen Cognitive Systems and Materials Center (CogniGron), University of Groningen , Groningen, The Netherlands; Zernike Institute for Advanced Materials, University of Groningen , Groningen, NetherlandGroningen Cognitive Systems and Materials Center (CogniGron), University of Groningen , Groningen, The Netherlands; Artificial Intelligence, Bernoulli Institute, University of Groningen , Groningen, The NetherlandsGroningen Cognitive Systems and Materials Center (CogniGron), University of Groningen , Groningen, The Netherlands; Artificial Intelligence, Bernoulli Institute, University of Groningen , Groningen, The NetherlandsGroningen Cognitive Systems and Materials Center (CogniGron), University of Groningen , Groningen, The Netherlands; Artificial Intelligence, Bernoulli Institute, University of Groningen , Groningen, The NetherlandsBio-Inspired Circuits and Systems (BICS) Lab, Zernike Institute for Advanced Materials, University of Groningen , Groningen, The Netherlands; Groningen Cognitive Systems and Materials Center (CogniGron), University of Groningen , Groningen, The Netherlands; Zernike Institute for Advanced Materials, University of Groningen , Groningen, NetherlandWorking memory serves as a crucial building block in cognitive systems due to its fundamental role in information processing and decision-making. Acting as a temporary storage and manipulation mechanism, working memory enables individuals to actively hold and manipulate relevant information essential for ongoing tasks. This cognitive function is pivotal for various complex processes, including problem-solving, language comprehension, and decision-making. This paper introduces a novel working memory model designed as a spiking recurrent neural network of excitatory and inhibitory neurons. The proposed model incorporates biological mechanisms that allows attractors to be active phasically, thereby reducing the energy budget associated with maintaining attractor states. The core innovation of the model lies in its ability to leverage phasic attractors for state-dependent computation in a probabilistic manner. By modulating the activity of attractors via synaptic delays, the model demonstrates context-sensitive information processing. The results showcase the efficiency gains achieved by the proposed phasic attractor mechanism and highlight the model’s capacity for flexible and adaptive information processing.https://doi.org/10.1088/2634-4386/ade7abattractor networksspiking neural networksworking memorysynaptic plasticityintrinsic plasticityfinite state machines |
| spellingShingle | Willian S Girão Thomas F Tiotto Jelmer P Borst Niels A Taatgen Elisabetta Chicca Phasic attractors for flexible and adaptive working memory in spiking recurrent neural networks Neuromorphic Computing and Engineering attractor networks spiking neural networks working memory synaptic plasticity intrinsic plasticity finite state machines |
| title | Phasic attractors for flexible and adaptive working memory in spiking recurrent neural networks |
| title_full | Phasic attractors for flexible and adaptive working memory in spiking recurrent neural networks |
| title_fullStr | Phasic attractors for flexible and adaptive working memory in spiking recurrent neural networks |
| title_full_unstemmed | Phasic attractors for flexible and adaptive working memory in spiking recurrent neural networks |
| title_short | Phasic attractors for flexible and adaptive working memory in spiking recurrent neural networks |
| title_sort | phasic attractors for flexible and adaptive working memory in spiking recurrent neural networks |
| topic | attractor networks spiking neural networks working memory synaptic plasticity intrinsic plasticity finite state machines |
| url | https://doi.org/10.1088/2634-4386/ade7ab |
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