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.

A 0.086-mm<formula> <tex>$^2$</tex> </formula> 12.7-pJ/SOP 64k-Synapse 256-Neuron Online-Learning Digital Spiking Neuromorphic Processor in 28nm CMOS

“…Information processing is entirely eventdriven, leading to sparse low-power computation [5]. This two-fold paradigm shift could lead to new bio-inspired and power-efficient neuromorphic computing devices, whose sparse event-driven data acquisition and processing appear to be particularly suited for distributed autonomous smart sensors for the IoT relying on energy harvesting [1], [3], closed sensorimotor loops for autonomous embedded systems and robots with strict battery requirements [7], [8], brain-machine interfaces [9], [10] and neuroscience experimentation or biohybrid platforms [11], [12]. However, despite recent advances in neuroscience, detailed understanding of the computing and operating principles of the brain is still out of reach [13].…”

Memristive synapses connect brain and silicon spiking neurons

“…Currently, spike processing is typically managed by digital Von Neumann-based hardware running statistical algorithms. However, neuromorphic electronic devices and architectures represent a fascinating computational alternative, by virtue of relying on near-biological spike signals and processing strategies [4][5][6] . In this context, recent findings that nanoscale memristors can emulate plasticity properties of synapses 7,8 have, on the one hand, boosted hopes of delivering computing systems that are closer to the brain circuits in terms of computation capacity and power efficiency 9,10 .…”

State-Dependent Decoding Algorithms Improve the Performance of a Bidirectional BMI in Anesthetized Rats

“…We took the within trajectory variance, shortened to wtv and defined as $\sqrt{{C_x}^2+{C_y}^2}$ where C x and C y is the covariance of the distribution of per-step displacement along the x and y axis, respectively (Boi et al, 2016), as a measure of how the trial-to-trial variability is reflected into the shape of the generated trajectories. Results of how wtv varies when considering state-independent or state-dependent BMIs are reported in Figure 7.…”