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Stonis, R.

Publications and source records attributed to Stonis, R..

2 recordsLinked to original sources

Dorsoventral gradient of theta sweeps in medial entorhinal cortex

During random foraging, the positional signal decoded from entorhinal grid cells exhibits left-right theta sweeps, alternating from one side of the heading direction to the other across successive theta cycles. Here, we report that theta sweeps are topographically organised along the dorsoventral axis of the medial entorhinal cortex, with the angular deviation from heading direction increasing gradually from dorsal (smaller scale) to ventral (larger scale) modules. This gradient coexists with a corresponding dorsoventral increase in angular deviation decoded from theta-modulated directional cells, which drive grid-cell theta sweeps. These phenomena parallel a broadening of head direction tuning and increasing occurrence of theta cycle skipping in single cell firing along the dorsoventral axis. Computational modelling demonstrates that these patterns are consistent with continuous attractor dynamics and a dorsoventral gradient in firing rate adaptation. These results highlight how theta sweeps can simultaneously represent multiple potential future locations and reveal a clear neural mechanism underlying this process.

neuroscience↗

Fast photostimulus optimization for holographic control of neural ensemble activity in vivo

Determining the intricate structure and function of neural circuits requires the ability to precisely manipulate circuit activity. Two-photon holographic optogenetics has emerged as a powerful tool for achieving this via flexible excitation of user-defined neural ensembles. However, the precision of two-photon optogenetics has been constrained by off-target stimulation, an effect where proximal non-target neurons can be unintentionally activated due to imperfect spatial confinement of light onto target neurons. Here, we introduce a real-time computational method to mitigating off-target stimulation that first empirically samples each neurons sensitivity to stimulation at proximal locations, and then optimizes stimulation sites using a fast, interpretable model based on adaptive non-negative basis function regression (NBFR). NBFR is highly scalable, completing model fitting for hundreds of neurons in just a few seconds and then optimizing stimulation sites in several hundred milliseconds per stimulus - fast enough for most closed-loop behavioral experiments. We characterize the performance of our approach in both simulations and in vivo experiments in mouse hippocampus, showing its efficacy under realistic experimental conditions. Our results thus establish NBFR-based photostimulus optimization as an important addition to an emerging computational toolkit for precise yet scalable holographic optogenetics.

neuroscience↗