BiœmuS: A new tool for neurological disorders studies through real-time emulation and hybridization using biomimetic Spiking Neural Network

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 led to the design of the new generation of neuroprostheses for brain repair.However, its complete development requires deeper understanding and expertise in biohybrid interaction. Here, we show a novel real-time, biomimetic, cost-effective and user-friendly neural network for bio-hybrid experiments and real-time emulation. Our system allows investigation and reproduction of biophysically detailed neural network dynamics while promoting cost-efficiency, flexibility and ease of use. We showcase the feasibility of conducting biohybrid experiments using standard biophysical interfaces and various biological cells as well as real-time emulation of complex models. We anticipate our system to be a step towards developing neuromorphicbased neuroprostheses for bioelectrical therapeutics by enabling communication with biological networks on a similar time scale, facilitated by an easy-to-use and accessible embedded real-time system. Our real-time device further enhances its potential for practical applications in biohybrid experiments.Competing Interest StatementThe authors have declared no competing interest.

Andrea Combette

Physics Student

I’m a master’s physics student at the École Normale Supérieure in France interst in biophysics, high energy physics, machine Learning, computational Physics.