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Duenki, T.

Publications and source records attributed to Duenki, T..

2 recordsLinked to original sources

Repetitive stimulation modifies network characteristics of neural organoid circuits

Neural organoids form complex networks but lack external stimuli and hierarchical structures crucial for refining functional microcircuits. In this study, we modeled the hierarchical and modular network organization by connecting multiple organoids and tested if the connection enhances the external stimuli-induced network refinement. We cultured networks of one, two, or three organoids on high-density microelectrode arrays, applied repetitive stimulation at two input locations from the microelectrodes, and monitored emergence of output signals that can decode the stimulus locations with machine learning algorithms. After two weeks of daily stimulation, networks of three organoids showed significantly higher stimulus decoding capability compared to the simpler one- or two-organoid networks. Long-term stimulation induced pronounced changes in the three-organoid networks response patterns, spontaneous activity, and inter- and intra-organoid functional connectivity. These findings underscore the importance of hierarchical network organization, e.g. creating distinct subnetworks with specialized roles, for stimuli-induced formation of circuits with robust input-output functionality.

bioengineering↗

BioemuS: A new tool for neurological disorders studies through real-time emulation and hybridization using biomimetic SpikingNeural Network

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.

neuroscience↗