
NSF Org: |
CBET Division of Chemical, Bioengineering, Environmental, and Transport Systems |
Recipient: |
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Initial Amendment Date: | September 4, 2015 |
Latest Amendment Date: | September 4, 2015 |
Award Number: | 1555720 |
Award Instrument: | Standard Grant |
Program Manager: |
Aleksandr Simonian
asimonia@nsf.gov (703)292-2191 CBET Division of Chemical, Bioengineering, Environmental, and Transport Systems ENG Directorate for Engineering |
Start Date: | September 1, 2015 |
End Date: | August 31, 2018 (Estimated) |
Total Intended Award Amount: | $299,928.00 |
Total Awarded Amount to Date: | $299,928.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
520 LEE ENTRANCE STE 211 AMHERST NY US 14228-2577 (716)645-2634 |
Sponsor Congressional District: |
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Primary Place of Performance: |
Davis Hall Buffalo NY US 14260-4200 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | EFRI Research Projects |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.041 |
ABSTRACT
1555720(Jornet)
For many decades, the interaction between humans and machines has been restricted to the exchange of visual, auditory and tactile information. A conceptual analysis of the existing human-machine interfaces (HMIs) reveals that the amount of useful information that they can transfer is generally not limited by the capabilities of the human brain or those of the machine processor, but by the interfaces between them, such as the sensor organs that are required to handle visual, auditory and tactile information. This is especially true for people with developmental- and aging-related disabilities, whose sensor organs or musculoskeletal system further limit the functionality of traditional HMIs. To overcome such limitation, several brain-machine interfaces (BMIs), which establish a direct path between the brain and a remote machine, have been proposed in the last decade. For example, electroencephalogram (EEG) signals have been successfully utilized to control machines in a non-invasive way and with high temporal resolution. However, EEG-based BMIs cannot be utilized to read the activity from individual neurons, but only their collective response. Similarly, optogenetics-based BMIs, which rely on the use of light to interact with genetically modified neurons in the brain, can be utilized to more accurately read or control the neuronal activity in the brain. However, existing optical devices used for BMIs are highly invasive and difficult to interface with single neurons.
In this project, novel nanophotonic BMIs will be developed by leveraging the state of the art in nanophotonics, nanoelectronics and wireless communications. The proposed technology relies on the use of a distributed network of nano-devices to monitor and control the neuronal activity of the human brain with very high spatial and temporal accuracy and in a minimally invasive way. This technology is expected to significantly change the way in which humans interact with machines and can significantly improve the quality of life of people with disabilities, by providing them a transformative way to interact with the environment and restoring functional abilities as well as cognition capabilities.
The objective of the proposed project is to prove the feasibility of novel nanophotonic brain-machine interfaces based on the use of a distributed network of nano-devices to monitor and control the neuronal activity in the brain. The fundamental idea is to replace existing micro-led arrays and micro-photodetector arrays by a network of coordinated nano-devices, which are able to optically excite individual neurons and measure their activity. The benefits of this approach are several. First, the very small size of optical nano-antennas, below one micrometer in the largest dimension, enables the possibility to measure the neuronal activity in a single neuron with very high accuracy. In addition, the total size of each individual nano-device is expectedly below several tens of cubic micrometers, thus minimizing the invasiveness of this approach. Moreover, by operating at optical frequencies, a very high temporal resolution is possible, which can enable the measurement of high-frequency time-transients in the neuronal activity.
Within this long-term goal, the focus of this two-year EAGER project is on establishing the foundations of distributed neuronal activity monitoring with cooperative nano-devices for next-generation nanophotonic brain-machine interfaces. Contributions will be made along the following three main thrusts: i) Design of optical nano-antennas for efficient detection of visible electromagnetic radiation generated by neuronal activity; ii) Development of a neuronal platform for experimental optogenetics; and, iii) System-level design guidelines for nanophotonic BMIs.
In terms of broader impact, the project is expected to pave the way for the development of high-throughput nanophotonic BMIs. The proposed approach can significantly simplify and reduce the cost of existing single-neuron monitoring and control platforms, with increased spatial and temporal resolutions. Nanophotonic BMIs have the potential to significantly improve the quality of life of people with disabilities, by providing them a new way to bidirectionally interact with machines and, ultimately, their environment. In particular, the creation of a "direct-path" between the brain and external machines can help to overcome the limitations of people with general or aging-related disabilities and restore human functional abilities and even cognition. For example, neural signals from the brain could be utilized to directly control a computer or even an exoskeleton. Similarly, the proposed technology could help to develop transformative treatments for many developmental- and aging-related diseases, such as Alzheimer's disease or Schizophrenia, whose origin lies at communication problems between consecutive neurons.
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PROJECT OUTCOMES REPORT
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This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
For many decades, the interaction between humans and machines has been restricted to the exchange of visual, auditory and tactile information. The analysis of the existing human-machine interfaces (HMIs) reveals that the amount of useful information that can be exchanged between humans and machines is not limited by the capabilities of the human brain or those of the machine processor, but by the interfaces between them (e.g., the speed at which we can type on a keyboard or read from a screen). To overcome such limitation, direct brain-machine interfaces (BMIs) have been developed within the last few years. Such interfaces generally rely on the use of electrodes to measure brain activity, through which control an external computer. However, electrical BMIs cannot be utilized to reversely control the brain from the computer. Recently, the use of light to control neurons and read their activity has been demonstrated thanks to the developments in the fields of biophotonics and optogenetics. Nevertheless, such optical BMIs rely on traditional optical components, which are large and cannot simply be integrated in the brain.
The work completed during this two-year project symbolizes the first steps towards novel nanophotonic BMIs. The fundamental idea is to replace the conventional macro-sized optical devices currently utilized in existing optogenetic platforms, by a network of coordinated nano-devices, which are able both to excite individual neurons as well as to measure their activity. Each nano-device is equipped with an optical nano-transceiver and nano-antenna, which is able to both emit and detect optical radiation at a pre-established frequency or wavelength. The proposed platform enables bi-directional BMIs. In one direction, the network of nano-devices is utilized to excite the neurons with specific temporal and spatial patterns needed to trigger the desired response of the brain. In the other direction, the information from the neurons collected by the nano-devices is aggregated and relayed to an external controller for signal processing and interfacing with a machine.
The key project outcomes of this effort are summarized as follows. First, we have developed a framework to design plasmonic nano-antennas that allow us to interact with optical signals in the same way conventional antennas allow us to interact with electromagnetic waves at radio- and micro-wave frequencies. The developed nano-antennas, when working in conjunction with nano-lasers and nano-photodetectors, can be utilized to both excite and measure the response of neurons in living systems. Second, we developed an experimental platform consisting of genetically-modified light-sensitive neuronal cells, in which the optical nano-devices have been tested. With this platform, we have been able to experimentally demonstrate and study different type of biological processes controlled by light. Finally, we have we have proposed a new architecture for nanophotonic BMIs, named wireless optogenetic neural dust, in which miniature motes, integrated by a nano-laser, nano-antenna, nano-detector, a control unit and a piezoelectric nano-generator are powered by means of ultra-sounds and able to control and monitor neurons through light. We have also studied how light propagates between the optical devices and the actual neurons to be activated, as well as how acoustic signals propagate between a brain mounted powering device and the brain implanted energy-harvesting nano-devices.
This proposed nanophotonic BMIS are expected to significantly change the way in which humans interact with machines and can significantly improve the quality of life of people with disabilities, by providing them a transformative way to interact with the environment and restoring functional abilities and even cognition. For example, the direct control of machines from the brain can help to overcome the limitations in the ?interfaces? between them, namely, the sensor organs or locomotion apparatus. At the same time, the proposed technology can help to broaden the understanding of the developmental- and aging-related diseases, such as Schizophrenia or Alzheimer, whose origin lies at communication problems between consecutive neurons, and, ultimately, enable transformative treatments.
Last Modified: 12/05/2018
Modified by: Josep M Jornet
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