Context Modulation Enables Multi-tasking and Resource Efficiency in Liquid State Machines

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dc.contributor.authorHelfer, Peter
dc.contributor.authorTeeter, Corinne
dc.contributor.authorHill, Aaron
dc.contributor.authorVineyard, Craig M.
dc.contributor.authorAimone, James B.
dc.contributor.authorKudithipudi, Dhireesha
dc.date.accessioned2023-11-28T17:21:47Z
dc.date.available2023-11-28T17:21:47Z
dc.date.issued2023-08-28
dc.description.abstractMemory storage and retrieval are context-sensitive in both humans and animals; memories are more accurately retrieved in the context where they were acquired, and similar stimuli can elicit different responses in different contexts. Researchers have suggested that such effects may be underpinned by mechanisms that modulate the dynamics of neural circuits in a context-dependent fashion. Based on this idea, we design a mechanism for context-dependent modulation of a liquid state machine, a recurrent spiking artificial neural network. We find that context modulation enables a single network to multitask and requires fewer neurons than when several smaller networks are used to perform the tasks individually.
dc.description.departmentElectrical and Computer Engineering
dc.description.departmentComputer Science
dc.description.sponsorshipThis work was supported by the Laboratory Directed Research and Development program at Sandia National Laboratories, a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc. for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
dc.identifier.citationHelfer, P., Teeter, C., Hill, A., Vineyard, C. M., Aimone, J. B., & Kudithipudi, D. (2023). Context Modulation Enables Multi-tasking and Resource Efficiency in Liquid State Machines. Paper presented at the 2023 International Conference on Neuromorphic Systems, Santa Fe, NM, USA. https://doi.org/10.1145/3589737.3605975
dc.identifier.isbn979-8-4007-0175-7
dc.identifier.otherhttps://doi.org/10.1145/3589737.3605975
dc.identifier.urihttps://hdl.handle.net/20.500.12588/2253
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.rightsAttribution 3.0 United Statesen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/
dc.subjectcontext modulation
dc.subjectneuromorphic
dc.subjectspiking neural network
dc.subjectliquid state machine
dc.titleContext Modulation Enables Multi-tasking and Resource Efficiency in Liquid State Machines
dc.typeArticle

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