title: A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder.
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.
authors.affiliation: Neural Computation Laboratory, Istituto Italiano di Tecnologia Rovereto, Italy.
authors.forename: Fabio
authors.initials: F
authors.lastname: Boi
authors.affiliation: Institute of Neuroinformatics, University of Zurich and ETH Zurich Zurich, Switzerland.
authors.forename: Timoleon
authors.initials: T
authors.lastname: Moraitis
authors.affiliation: Neural Computation Laboratory, Istituto Italiano di Tecnologia Rovereto, Italy.
authors.forename: Vito
authors.initials: V
authors.lastname: De Feo
authors.affiliation: Robotics, Brain and Cognitive Sciences, Istituto Italiano di Tecnologia Genova, Italy.
authors.forename: Francesco
authors.initials: F
authors.lastname: Diotalevi
authors.affiliation: iCub Facility, Istituto Italiano di Tecnologia Genova, Italy.
authors.forename: Chiara
authors.initials: C
authors.lastname: Bartolozzi
authors.affiliation: Institute of Neuroinformatics, University of Zurich and ETH Zurich Zurich, Switzerland.
authors.forename: Giacomo
authors.initials: G
authors.lastname: Indiveri
authors.affiliation: Neural Computation Laboratory, Istituto Italiano di Tecnologia Rovereto, Italy.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Vato
keywords: bidirectional BMI; modular system; neuromorphic decoder; on-line learning; spiking neural network