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theses

Astrocytes 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.

ETD_9670

M.S.

Includes bibliographical references

doi:10.7282/t3-cjkr-p983

ETD graduate

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