A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder - Boi, Fabio; Moraitis, Timoleon; De Feo, Vito; Diotalevi, Francesco; Bartolozzi, Chiara; Indiveri, Giacomo; Vato, Alessandro (Frontiers in Neuroscience, 2016)
Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link 4 between the brain and the external world. A decoder translates recorded neural activity into motor 5 commands and an encoder delivers sensory information collected from the environment directly 6 to the brain creating a closed-loop system. These two modules are typically integrated in bulky 7 external devices. However, the clinical support of patients with severe motor and sensory deficits 8 requires compact, low-power, and fully implantable systems that can decode neural signals to 9 control external devices. As a first step toward this goal, we developed a modular bidirectional BMI 10 setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented 11 a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. 12 On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn 13 to decode neural signals recorded from the brain into motor outputs controlling the movements 14 of an external device. The modularity of the BMI allowed us to tune the individual components 15 of the setup without modifying the whole system. In this paper we present the features of 16 this modular BMI, and describe how we configured the network of spiking neuron circuits to 17 implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm 18 that connects bidirectionally the brain of an anesthetized rat with an external object. We show that 19 the chip learned the decoding task correctly, allowing the interfaced brain to control the object’s 20 trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is 21 mature enough for the development of BMI modules that are sufficiently low-power and compact, 22 while being highly computationally powerful and adaptive.