Bio-Inspired Controller on an FPGA Applied to Closed-Loop Diaphragmatic Stimulation

Abstract

Cervical spinal cord injury can disrupt connections between the brain respiratory network and the respiratory muscles which can lead to partial or complete loss of ventilatory control and require ventilatory assistance. Unlike current open-loop technology, a closed-loop diaphragmatic pacing system could overcome the drawbacks of manual titration as well as respond to changing ventilation requirements. We present an original bio-inspired assistive technology for real-time ventilation assistance, implemented in a digital configurable Field Programmable Gate Array (FPGA). The bio-inspired controller, which is a spiking neural network (SNN) inspired by the medullary respiratory network, is as robust as a classic controller while having a flexible, low-power and low-cost hardware design. The system was simulated in MATLAB with FPGA-specific constraints and tested with a computational model of rat breathing; the model reproduced experimentally collected respiratory data in eupneic animals. The open-loop version of the bio-inspired controller was implemented on the FPGA. Electrical test bench characterizations confirmed the system functionality. Open and closed-loop paradigm simulations were simulated to test the FPGA system real-time behavior using the rat computational model. The closed-loop system monitors breathing and changes in respiratory demands to drive diaphragmatic stimulation. The simulated results inform future acute animal experiments and constitute the first step toward the development of a neuromorphic, adaptive, compact, low-power, implantable device. The bio-inspired hardware design optimizes the FPGA resource and time costs while harnessing the computational power of spike-based neuromorphic hardware. Its real-time feature makes it suitable for in vivo applications.

Multimed: An Integrated, Multi-Application Platform for the Real-Time Recording and Sub-Millisecond Processing of Biosignals

“…Our architecture demonstrated its accuracy in real-time, long-term [ 4 , 21 , 33 , 34 , 35 , 39 ] experiments. Furthermore, current FPGA use is below its maximum processing potential, as only a few electrodes and simple feedback rules have been exploited.…”

“…In this experiment, no stimulation artifact was observed as no stimulation was actually performed on the culture. Closed-loop experiments using Multimed were recently performed in two research projects: the BRAINBOW project [ 33 ], exploring functional organization and the dynamics of damaged parts of the central nervous system, and the CENAVEX project [ 34 , 35 ], which implements a real-time control scheme for ventilation after spinal cord injury impairing respiratory functions.…”

Restoring ventilatory control using an adaptive bioelectronic system

“…Previous studies have suggested technology that can be used to synchronize artificial ventilation with intrinsic respiratory drive or to replicate its function. These methods include development of a controllable stimulator with an update frequency higher than the stimulation frequency and a real-time processing controller (Castelli et al, 2017), implementation of a bio-inspired spiking neural network model that follows intrinsic respiratory rate (Zbrzeski et al, 2016), predictive algorithms using body temperature and heart rate (Kimura et al, 1992), and breath-triggering through the use of genioglossus muscle activity (Mercier et al, 2017). However, these approaches have not yet been sufficiently developed or investigated.…”

Quantization of Map-Based Neuronal Model for Embedded Simulations of Neurobiological Networks in Real-Time

“…They mainly rely on the development and implementation of a spiking neuron model and synapses that replicate the dynamics of neurons as close to real biological neurons as possible that fits in computational hardware. Two main directions of these studies are the use of analog computing simulating the network with specially designed VLSI integrated circuits (Indiveri et al, 2006;Silver et al, 2007) and numerical simulation of the model with specially designed microchips, GPUs FPGAs or DSPs (Yavuz et al, 2016;Zbrzeski et al, 2016;Cheung et al, 2016;Brette et al, 2007 and references therein). This paper focuses on the design, implementation and validation of a neuronal model that could be used in real-time simulations of relatively large networks using off-the-shelf 32-bit microprocessors.…”