Modular device for wireless optically controlled neuromodulation in free behaving models
Author(s)
Sands, Joanna(Joanna M.)
Download1227508022-MIT.pdf (6.388Mb)
Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Anantha Chandrakasan.
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This work presents a modular, light-weight head- borne neuromodulation platform that achieves low-power wireless neuromodulation and allows real-time programmability of the stimulation parameters such as the frequency, duty cycle, and intensity. This platform is comprised of two parts: the main device and the optional intensity module. The main device is functional independently, however, the intensity control module can be introduced on demand. The stimulation is achieved through the use of energy-efficient [mu]LEDs directly integrated in the custom-drawn fiber-based probes. Our platform can control up to 4 devices simultaneously and each device can control multiple LEDs in a given subject. Using the multiple LED channels, the platform can also be used to recording in-vivo temperatures to prevent damage to the couple neuron. Our hardware uses off-the-shelf components and has a plug and play structure, which allows for fast turn-over time and eliminates the need for complex surgeries. The rechargeable, battery-powered wireless platform uses Bluetooth Low Energy (BLE) and is capable of providing stable power and communication regardless of orientation. This presents a potential advantage over the battery-free, fully implantable systems that rely on wireless power transfer, which is typically direction-dependent, requires sophisticated implantation surgeries, and demands complex custom-built experimental apparatuses. Although the battery life is limited to several hours, this is sufficient to complete the majority of behavioral neuroscience experiments. Our platform consumes an average power of 0.5 mW, has a battery life of 12 hours.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020 Cataloged from student-submitted PDF of thesis. Includes bibliographical references (pages 55-57).
Date issued
2020Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer SciencePublisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.