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| Home > Publications Database > High-resolution CMOS-based biosensor for assessing hippocampal circuit dynamics in experience-dependent plasticity. |
| Home > Publications Database > High-resolution CMOS-based biosensor for assessing hippocampal circuit dynamics in experience-dependent plasticity. |
| Journal Article | DZNE-2023-00797 |
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2023
Elsevier Science
Amsterdam [u.a.]
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Please use a persistent id in citations: doi:10.1016/j.bios.2023.115471
Abstract: Experiential richness creates tissue-level changes and synaptic plasticity as patterns emerge from rhythmic spatiotemporal activity of large interconnected neuronal assemblies. Despite numerous experimental and computational approaches at different scales, the precise impact of experience on network-wide computational dynamics remains inaccessible due to the lack of applicable large-scale recording methodology. We here demonstrate a large-scale multi-site biohybrid brain circuity on-CMOS-based biosensor with an unprecedented spatiotemporal resolution of 4096 microelectrodes, which allows simultaneous electrophysiological assessment across the entire hippocampal-cortical subnetworks from mice living in an enriched environment (ENR) and standard-housed (SD) conditions. Our platform, empowered with various computational analyses, reveals environmental enrichment's impacts on local and global spatiotemporal neural dynamics, firing synchrony, topological network complexity, and large-scale connectome. Our results delineate the distinct role of prior experience in enhancing multiplexed dimensional coding formed by neuronal ensembles and error tolerance and resilience to random failures compared to standard conditions. The scope and depth of these effects highlight the critical role of high-density, large-scale biosensors to provide a new understanding of the computational dynamics and information processing in multimodal physiological and experience-dependent plasticity conditions and their role in higher brain functions. Knowledge of these large-scale dynamics can inspire the development of biologically plausible computational models and computational artificial intelligence networks and expand the reach of neuromorphic brain-inspired computing into new applications.
Keyword(s): Mice (MeSH) ; Animals (MeSH) ; Artificial Intelligence (MeSH) ; Biosensing Techniques (MeSH) ; Neurons: physiology (MeSH) ; Hippocampus (MeSH) ; Cerebral Cortex (MeSH) ; CMOS-MEAs ; Connectome ; Enriched environment ; Graph theory ; Large-scale biosensors ; Neural circuit
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