Biology subjects

Duenki, T.

Publications and source records attributed to Duenki, T..

3 recordsLinked to original sources

bioRxiv2026

A sensorimotor brain circuit for transforming aversive experiences into emotional states

Innately aversive experiences produce emotional states which control behavior as well as long term associative memories1-3. Brainstem regions engage innate defensive reactions1,3-7, but it is unclear how the nervous system transduces external aversive experiences into higher order, emotional representations in the forebrain to instruct learning and modulate behavior. The prevailing view is that the sensory properties of aversive experiences activate forebrain emotional processing1,3,8-10, but classical theories proposed that internal bodily reactions to unpleasant events produce feeling states11,12. Here we identify a brainstem cuneiform (CnF) circuit which conveys information about both the external sensory causes and internal motor reactions related to aversive events to the lateral/basal nuclei of the amygdala (LA/B), a brain region which stores emotional memories, to enhance ongoing defensive behaviors and regulate associative memory formation. Glutamatergic CnF neurons project directly to LA/B and receive afferent inputs from aversive-sensory and aversive-motor brain regions. Notably, LA/B neurons encode a sensorimotor state in response to noxious stimuli, with both sensory and motor features transmitted through the CnF-to-LA/B pathway. Finally, optogenetic perturbation experiments showed that during aversive experiences, the CnF-LA/B circuit instructs emotional memory formation and enhances the intensity of ongoing defensive behaviors. These findings reveal how the nervous system constructs an aversive sensorimotor state in the amygdala used for regulating the strength of emotional behaviors and producing memory formation. This demonstrates a clinically relevant brain mechanism for the regulation of emotional processing by both external traumatic events and the accompanying bodily reactions.

neuroscience↗

bioRxiv2025

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↗

bioRxiv2023

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↗