High-resolution CMOS-based biosensor for assessing hippocampal circuit dynamics in experience-dependent plasticity.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Bioengineering

Background:

• Experiential richness induces synaptic plasticity and tissue-level changes in neuronal assemblies.
• Current large-scale recording methods limit understanding of experience's impact on network-wide computational dynamics.

Purpose of the Study:

• To develop and utilize a novel large-scale biohybrid brain circuitry biosensor for high-resolution electrophysiological assessment.
• To investigate the impact of environmental enrichment on hippocampal-cortical subnetworks' spatiotemporal dynamics and network properties.

Main Methods:

• Development of a 4096-microelectrode on-CMOS-based biosensor for simultaneous electrophysiological recording.
• Comparison of neural activity in mice housed in enriched (ENR) versus standard (SD) environments.
• Application of computational analyses to assess local/global dynamics, synchrony, network complexity, and connectome.

Main Results:

• Environmental enrichment significantly impacts spatiotemporal neural dynamics, firing synchrony, and network topology.
• Enriched environments enhance multiplexed dimensional coding and improve error tolerance/resilience in neuronal ensembles.
• The study reveals distinct large-scale network differences between ENR and SD conditions.

Conclusions:

• High-density, large-scale biosensors are critical for understanding experience-dependent plasticity and brain function.
• Prior experience profoundly influences neural coding, network resilience, and higher brain functions.
• Findings can inform biologically plausible computational models and neuromorphic computing applications.