A neuroprosthetic system to restore neuronal communication in modular networks

Abstract

48Recent advances in neurotechnology allow neurological impairments to be treated or 49 reduced by brain machine interfaces and neuroprostheses. To develop energy-efficient and 50 3 real-time capable devices, neuromorphic computing systems are envisaged as the core of 51 next-generation 'neurobiohybrid' systems for brain repair. We demonstrate here the first 52 exploitation of a neuromorphic prosthesis to restore bidirectional interactions between two 53 neuronal populations, even when one is damaged or completely missing. We used in vitro 54 modular cell cultures to mimic the mutual interaction between neuronal assemblies and 55 created a focal lesion to functionally disconnect the two populations. Then, we employed 56 our neuromorphic prosthesis for two specific applications with future clinical 57 implications: bidirectional bridging to artificially reconnect two disconnected neuronal 58 modules and hybrid bidirectional bridging to replace the activity of one module with a 59 neuromorphic spiking neural network. Our neuroprosthetic system opens up new avenues 60 for the development of novel bioelectrical therapeutics for human applications. 61 62 65 the greatest impact carried by stroke (1) and traumatic brain injury (2), brain disorders are among 66 the leading causes of disabilities worldwide. Due to recent advances in neural and neuromorphic 67 engineering, direct interfacing of artificial circuits with large neuronal networks is possible to 68 develop novel 'neurobiohybrid' systems (such as neuroprostheses (3)), which are envisaged as 69 potentially interesting clinical applications for brain lesions (4). In this paper, we introduce an 70 innovative neuroprosthetic system based on a neuromorphic real-time interface that can re-71 establish the communication between two disconnected neuronal assemblies. 72 Neural interfaces are promising solutions for brain repair (5). Modern neural interfaces are mainly 73 designed to restore lost motor functions in only one direction, i.e., from the brain to the body (6) 74 or from the body to the brain (7). Additionally, recent neuroprosthetic developments have shown 75 4 the enormous potential of neural interfaces to aid and accelerate functional recovery (8, 9). 76 However, a major obstacle in developing novel neuroprosthetic devices for bidirectional 77 communication with and within the brain is the complex nature of interactions among different 78 brain areas, which in turn presents a challenge for the development of appropriate stimulation 79 protocols as well as for testing such devices using in vivo models (10). 80 Despite very recent technological progress (11, 12), in vivo models still have two main 81 bottlenecks. The first bottleneck is the technical challenge to faithfully reproduce specific/focal 82 network lesions (mainly due to their complexity) that the neuroprosthesis aims to treat, whereas 83 the second is the difficulty in disentangling the actual effect of the adopted electrical therapy from 84 the complex activity of a brain constantly pr...

Optimized Real-Time Biomimetic Neural Network on FPGA for Bio-hybridization

“…Another point is to increase the degree of precision by designing multi-compartmental neurons and give a real-time tool to neuroscientist to simulate their neuron structure with different parameters. To conclude, this biomimetic SNN platform can provide better solutions for biomedical application especially in neuroprosthesis (Buccelli et al, 2019) and therapeutics protocol for neurological diseases.…”

Snake Robot Controlled by Biomimetic CPGs

“…We previously showed that our system can be connected to biological neurons [22,23]. Another advantage is that the real-time systems can simulate multiple CPG topologies and simulate different hypothesis and protocol treatments for biomedical applications such as spinal cord injury [24] and neuroprosthetics [25,26].…”

Plasticity and Adaptation in Neuromorphic Biohybrid Systems

“…These signals express important biomarkers symptomatic of various neurodegenerative diseases, e.g., Parkinson disease (Lehmkuhle et al, 2009), and provide great opportunities for clinical applications. Moreover, especially in the interaction with biological central pattern generators, biohybrid systems profit from this type of recording, as illustrated by Buccelli et al (2019), where the authors use bursts to trigger spiking activity in the ANN side of their biohybrid. In situations where either spatial or temporal resolution prohibits the focus on single unit activity, signals undergo extensive signal conditioning before feature extraction.…”

“…The most straightforward approach here is the triggering of single stimulus waveforms whenever an event occurs in the output population of the ANN. This, however, requires the experimental selection of parameters in the stimulation waveform (Buccelli et al, 2019;Jung et al, 2001). With the argument that high stimulation frequencies have adverse fatigue effects Eftekhar et al, 2007 focus on a modulation of the stimulation current amplitude, rather than a direct translation of spike to stimulus.…”

“…More recently, neurorehabilitative applications have also shifted into focus, where the close coupling of artificial systems and nervous tissue promises approaches to treat brain (Guggenmos et al, 2013) and spinal cord injury (Wagner et al, 2018). However, a major obstacle in developing novel neuroprostheses based on bidirectional communication with and within the brain is the complex nature of interactions among different brain areas, which in turn presents a challenge for the development of appropriate stimulation protocols as well as for testing those devices using in vivo models (Buccelli et al, 2019). As suggested by Steve Potter (Potter and DeMarse, 2001), a possible alternative methodology consists of exploiting biohybrid systems composed by simpler experimental preparations such as in vitro neuronal cultures, which can be easily controlled, manipulated, and monitored, thus reducing the experimental variability and facilitating the interpretation of results.…”