A Neuromorphic Prosthesis to Restore Communication in Neuronal Networks

Abstract

Summary Recent advances in bioelectronics and neural engineering allowed the development of brain machine interfaces and neuroprostheses, capable of facilitating or recovering functionality in people with neurological disability. To realize energy-efficient and real-time capable devices, neuromorphic computing systems are envisaged as the core of next-generation systems for brain repair. We demonstrate here a real-time hardware neuromorphic prosthesis to restore bidirectional interactions between two neuronal populations, even when one is damaged or missing. We used in vitro modular cell cultures to mimic the mutual interaction between neuronal assemblies and created a focal lesion to functionally disconnect the two populations. Then, we employed our neuromorphic prosthesis for bidirectional bridging to artificially reconnect two disconnected neuronal modules and for hybrid bidirectional bridging to replace the activity of one module with a real-time hardware neuromorphic Spiking Neural Network. Our neuroprosthetic system opens avenues for the exploitation of neuromorphic-based devices in bioelectrical therapeutics for health care.

Neurostimulation stabilizes spiking neural networks by disrupting seizure-like oscillatory transitions

“…Additional future work could investigate how in vitro neuromodulatory stimuli can be optimized so as not to induce oscillatory activity, with our analysis here providing initial evidence www.nature.com/scientificreports/ that stimulation with a sufficiently high frequency may accomplish this through selectively activating inhibitory cells in a non-rhythmic fashion. The use of bio-hybrid systems [92][93][94][95] could provide an useful tool in such research. A paramount use of these mathematical findings is towards the articulation of a theoretical understanding of how neuromodulation acts to mitigate seizure onset, and in turn potential pathways through which these treatments might be improved.…”

Plasticity and Adaptation in Neuromorphic Biohybrid Systems

“…The Coronet project shows activity propagation in alternating BNN and ANN populations in a closed loop. In contrast, the Brainbow project aims at the replacement of BNN areas by ANN ( Buccelli et al., 2019 ). As an alternative application to this neuroprosthetic orientation, ANNs are also used to perform biomimetic stimulations for driving BNN dynamics.…”

“…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.…”

“…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.…”

Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics

“…Nevertheless, they are outside the scope of this perspective, and we will limit ourselves only to the CMOS-compatible approaches that are ready for the integration into existing technological workflows dedicated to practical applications. The focus on metal-oxide memristive electronics will allow going beyond the traditional neuromorphic chips as parts of neurohybrid systems (Hogri et al, 2015;Boi et al, 2016;Buccelli et al, 2019).…”