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A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder
- Source :
- Frontiers in Neuroscience, 10, Frontiers in Neuroscience, Frontiers in Neuroscience, Vol 10 (2016)
- Publication Year :
- 2016
- Publisher :
- Frontiers Media, 2016.
-
Abstract
- Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive.<br />Frontiers in Neuroscience, 10<br />ISSN:1662-453X<br />ISSN:1662-4548
- Subjects :
- modular system
Computer science
bidirectional BMI, neuromorphic decoder, on-line learning, modular system, spiking neural network
02 engineering and technology
lcsh:RC321-571
spiking neural network
bidirectional BMI
03 medical and health sciences
0302 clinical medicine
0202 electrical engineering, electronic engineering, information engineering
lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry
Brain–computer interface
Original Research
10194 Institute of Neuroinformatics
Spiking neural network
Digital electronics
Modularity (networks)
business.industry
General Neuroscience
020208 electrical & electronic engineering
2800 General Neuroscience
Modular design
Chip
neuromorphic chip
Neuromorphic engineering
Embedded system
570 Life sciences
biology
on-line learning
Spiking neural network (SNN)
Bidirectional BMI
Modular system
Neuromorphic decoder
On-line learning
business
Encoder
030217 neurology & neurosurgery
Computer hardware
Neuroscience
neuromorphic decoder
Subjects
Details
- Language :
- English
- ISSN :
- 1662453X and 16624548
- Database :
- OpenAIRE
- Journal :
- Frontiers in Neuroscience, 10, Frontiers in Neuroscience, Frontiers in Neuroscience, Vol 10 (2016)
- Accession number :
- edsair.doi.dedup.....8ec0a00855d669d070ed011eb2c98ef1