PDF(Pubmed)
Abstract：
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.
摘要：
生物神经网络的表征和建模已经成为推动我们对脑功能和相关病理的理解取得重大进展的领域。截至今天,神经系统疾病的药物治疗仍然有限，推动对有前途的替代方法的探索，如电算。最近在生物电子学和神经形态工程方面的研究促进了用于脑修复的新一代神经假体的开发。然而,实现它们的全部潜力需要对生物杂交相互作用有更深入的了解。在这项研究中,我们提出了一个新的实时，仿生,具有成本效益和用户友好的神经网络，能够实时仿真生物混合实验。我们的系统有助于研究和复制生物物理详细的神经网络动力学，同时优先考虑成本效益，灵活性和易用性。我们展示了使用标准生物物理接口和各种生物细胞以及各种网络配置的实时仿真进行生物混合实验的可行性。我们将我们的系统视为朝着开发用于生物电治疗的基于神经形态的神经假体迈出的关键一步，在可比的时间尺度上实现与生物网络的无缝通信。其嵌入式实时功能增强了实用性和可访问性，放大其在生物混合实验中的实际应用潜力。
