Kondratyev, I. | Search bioRxiv

bioRxiv2026

Ataraxis: Bridging AI Coding Assistants and Scientific Hardware

AI coding assistants excel at software tasks but lack structured access to laboratory hardware, the physical instruments that define experimental science. We present AO_SCPLOWTARAXISC_SCPLOW, an open-source framework that provides hardware control capabilities spanning camera acquisition, microcontroller communication, precision timing, and inter-process coordination, while exposing these capabilities to AI agents through Model Context Protocol (MCP) servers and domain-specific skills. Critically, AO_SCPLOWTARAXISC_SCPLOW separates configuration-time AI assistance from runtime data acquisition, ensuring that experiments run deterministically regardless of AI service availability. We validate this architecture in a two-photon imaging and virtual reality rodent behavior platform, demonstrating up to order-of-magnitude reductions in hardware validation, integration, and personnel onboarding time. By bridging the gap between AI software capabilities and physical instrument control, AO_SCPLOWTARAXISC_SCPLOW offers a reusable blueprint for AI-assisted scientific instrumentation across experimental disciplines. All code is available at github.com/Sun-Lab-NBB/ataraxis.

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bioRxiv2022

Composition of a spatial engram in mouse dentate gyrus

The dentate gyrus (DG) of hippocampus is hypothesized to act as a pattern separator that distinguishes between similar input patterns during memory formation and retrieval. Sparse ensembles of DG cells associated with learning and memory, i.e. engrams, have been labeled and manipulated to recall novel context memories. Functional studies of DG cell activity have demonstrated the spatial specificity and stability of DG cells during navigation. To reconcile how the DG contributes to separating global context as well as individual navigational routes, we trained mice to perform a delayed-non-match-to-position (DNMP) T-maze task and labeled DG neurons during performance of this task on a novel T-maze. The following day, mice navigated a second environment: the same T-maze, the same T-maze with one route permanently blocked but still visible, or a novel open field. We found that the degree of engram reactivation across days differed based on the traversal of maze routes, such that mice traversing only one arm had higher ensemble overlap than chance but less overlap than mice running the full two-route task. Mice experiencing the open field had similar ensemble sizes to the other groups but only chance-level ensemble reactivation. Ensemble overlap differences could not be explained by behavioral variability across groups, nor did behavioral metrics correlate to degree of ensemble reactivation. Together, these results support the hypothesis that DG contributes to spatial navigation memory and that partially non-overlapping ensembles encode different routes within the context of different environments. HighlightsO_LIImmediate-early-gene labeling strategy revealed spatial navigation ensembles in DG C_LIO_LISub-ensembles encode separate maze routes within a larger task context C_LIO_LIEnsemble reactivation does not correlate with behavioral variables C_LI

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