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Development of an astrocytic module for spiking neural networks on neuromorphic hardware
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Shah, Arpit. Development of an astrocytic module for spiking neural networks on neuromorphic hardware. Retrieved from https://doi.org/doi:10.7282/t3-cjkr-p983

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TitleDevelopment of an astrocytic module for spiking neural networks on neuromorphic hardware
NameShah, Arpit (author); Michmizos, Konstantinos P (chair); Metaxas, Dimitris (internal member); Imielinski, Tomasz (internal member); Rutgers University; School of Graduate Studies
Date Created2019
Other Date2019-05 (degree)
SubjectSpiking neural networks, Computer Science, Neural networks (Computer science), Astrocytes
Extent1 online resource (viii, 33 pages) : illustrations
DescriptionAstrocytes have long been neglected in application to neuronal networks due to being electrically silent. While these glial cells have been hypothesized to serve as a support for neurons, recent research suggests that they may have a role in learning through spatial and temporal modulation of neurons. Astrocytes may form their own networks and communicate amongst themselves through calcium signaling. They have so far been absent in the Spiking neural networks (SNNs) and consequently, they have not been incorporated into neuromorphic chips such as Intel's Loihi. In this work, we discuss a new astrocytic module to extend the capabilities of Loihi to facilitate the inclusion of astrocytes in SNNs. This transformation from SNNs to Spiking Neural-Astrocytic Networks (SNANs) would enable researchers to both explore and leverage the capabilities of astrocytes in neuromorphic hardware. The module serves as a higher-level interface on top of Intel's NxSDK to allocate resources which serve as internal components of our astrocyte model to inject Slow Inward Current (SIC) and then introduce synchronous activity in the postsynaptic neurons. In addition, this work also addresses an additional project focused on the Unidimensional SLAM problem where we focus on solely the orientation of a robot placed in a variety of environments. We show that the spike-based algorithm implemented on Loihi requires approximately 100 times less power than the state-of-the-art GMapping algorithm implemented on a CPU. This work demonstrates the viability of Spiking Neural Networks running on Loihi as an alternative solution for mobile robots.
NoteM.S.
NoteIncludes bibliographical references
Genretheses, ETD graduate
Persistent URLhttps://doi.org/doi:10.7282/t3-cjkr-p983
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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