Neuroprostheses are neuroengineering devices that have an interface with the nervous system and supplement or substitute functionality in people with disabilities. In the collective imagination, neuroprostheses are mostly used to restore sensory or motor capabilities, but in recent years, new devices directly acting at the brain level have been proposed. In order to design the next-generation of neuroprosthetic devices for brain repair, we foresee the increasing exploitation of closed-loop systems enabled with neuromorphic elements due to their intrinsic energy efficiency, their capability to perform real-time data processing, and of mimicking neurobiological computation for an improved synergy between the technological and biological counterparts. In this manuscript, after providing definitions of key concepts, we reviewed the first exploitation of a real-time hardware neuromorphic prosthesis to restore the bidirectional communication between two neuronal populations in vitro. Starting from that ‘case-study’, we provide perspectives on the technological improvements for real-time interfacing and processing of neural signals and their potential usage for novel in vitro and in vivo experimental designs. The development of innovative neuroprosthetics for translational purposes is also presented and discussed. In our understanding, the pursuit of neuromorphic-based closed-loop neuroprostheses may spur the development of novel powerful technologies, such as ‘brain-prostheses’, capable of rewiring and/or substituting the injured nervous system.

Characterization and modeling of biological neural networks has emerged as a field driving significant advancements in our understanding of brain function and related pathologies. As of today, pharmacological treatments for neurological disorders remain limited, pushing the exploration of promising alternative approaches such as electroceutics. Recent research in bioelectronics and neuromorphic engineering have fostered the development of the new generation of neuroprostheses for brain repair. However, achieving their full potential necessitates a deeper understanding of biohybrid interaction. In this study, we present a novel real-time, biomimetic, cost-effective and user-friendly neural network capable of real-time emulation for biohybrid experiments. Our system facilitates the investigation and replication of biophysically detailed neural network dynamics while prioritizing cost-efficiency, flexibility and ease of use. We showcase the feasibility of conducting biohybrid experiments using standard biophysical interfaces and a variety of biological cells as well as real-time emulation of diverse network configurations. We envision our system as a crucial step towards the development of neuromorphic-based neuroprostheses for bioelectrical therapeutics, enabling seamless communication with biological networks on a comparable timescale. Its embedded real-time functionality enhances practicality and accessibility, amplifying its potential for real-world applications in biohybrid experiments.

Activity dependent stimulation (ADS) is a closed loop stimulation technique whose neurophysiological effects have not been deeply investigated. Here we explored how Local field Potentials (LFP) are impacted by a focal ischemic lesion and, subsequently, by ADS treatment. Intracortical microelectrode arrays were implanted in the rostral forelimb area (RFA) and in the primary somatosensory area (S1) of anaesthetized rats. An ischemic injury was induced in the caudal forelimb area through microinjections of Endothelin-1. The lesion induced an acute depressive trend in LFP power in RFA (evaluated in 6 bands of interest: Delta (1–4Hz), Theta (4–8Hz), Alpha (8-11Hz), Beta (11–30Hz), LowGamma (30–55Hz) and HighGamma (55-80)) followed by a noticeable significant rebound in both areas. Applying ADS induced an overall decrease of power. The lesion impacted the connectivity in a frequency specific manner, resulting in widespread increase in connectivity in Delta both between and within areas. Two hours after the lesion, without stimulation, correlated activity between areas increased in Beta and Gamma. After stimulation, inter-area connectivity increased in Delta, Theta and Alpha, while considerably dropping within RFA in highGamma. By computing phase-amplitude coupling, we found that the lesion produced an incremental increase in the coupling between (Theta) Alpha phase and (lowGamma) highGamma amplitude within RFA, while S1 had a more generalized increase. Likewise, coupling between Theta phase and lowGamma/highGamma amplitudes increased between areas after lesion. ADS induced a similar increase, but greater in magnitude both within and between RFA and S1. These results have important implications on the emerging field of closed-loop adaptive stimulation promoting ADS as an innovative tool for the treatment of neurological disorders.

This study addresses the inherent difficulties in the creation of neuroengineering devices for closed-loop stimulation, a task typically characterized by intricate and technically demanding processes. Beneath the substantial hardware advancements in neurotechnology, there is often rather complex low-level code that poses challenges in terms of development, documentation, and long-term maintenance. To overcome these obstacles, we adopted an alternative strategy centered on Model-based Design (MBD) as a means to simplify the creation of closed-loop systems and reduce the entry barriers. MBD offers distinct advantages by streamlining the development workflow and facilitating the implementation of intricate systems. In this study, we applied MBD techniques to implement a spike detection algorithm on a Field-Programmable Gate Array (FPGA) using commercially available hardware that combines neural probe electronics with programmable FPGA-based hardware. The entire process of data handling and data processing was designed within the Simulink® environment, with subsequent generation of HDL code tailored to the FPGA hardware. The validation of our approach was conducted through in vivo experiments involving six animals. We have made all project code files open-source, thereby providing free access to fellow scientists interested in the development of closed-loop systems.

Sleep plays a critical role in neuroplasticity related processes of learning and memory consolidation. Moreover, its importance in recovery after stroke is known. Yet, how ischemic lesions affect the sleep-wake cycle (SWC) neural dynamics remains poorly explored, with most studies not addressing the multi-scale extent of this pathology effects in brain function. Here, we performed chronic wideband electrophysiological recordings in behaving rats to investigate ischemic stroke impact in the primary motor cortex on SWC organization in connected, non-lesioned, areas of the sensorimotor loop. We combined behavioral assessment with multi-scale analyses spanning global SWC architecture, network level events of privileged functional reshaping during sleep, and single-unit activity. Lesioned animals, exhibiting sharp motor impairments, displayed clear alterations of SWC macro-architecture, including reduced latency to slow-wave sleep and correlations between spectral features and motor asymmetry. At meso-scale, ischemia disrupted the physiological coordination between slow oscillations and spindles, and the latter showed shift in spectral power distribution during intermediate sleep. In contrast, units firing and coupling with spindles were largely preserved. These findings reveal that ischemia perturbs the fine coordination of sleep-related processes across scales stage-specifically. The study lays solid evidence to be possibly leveraged for innovative protocols of neurorehabilitation targeting privileged windows of neuroplasticity.