Biology subjects

Kasuba, K. C.

Publications and source records attributed to Kasuba, K. C..

4 recordsLinked to original sources

bioRxiv2026

A hippocampal digital Twin reveals latent structure in freely flying bats

Concurrent advances in artificial intelligence (AI), large-scale neuronal recording, and behavioral tracking have enabled increasingly powerful predictive models of brain-behavior relationships. Yet predictive accuracy does not automatically yield interpretable principles of neural organization, particularly during natural behavior, where the relevant variables computed are often unknown in advance. Here, we developed a functional digital twin of the hippocampus, trained jointly on large-scale neural activity and self-directed three-dimensional flight in freely flying bats. A multimodal, multitask transformer accurately predicted both neural activity and behavior, outperformed standard approaches, and generalized across recording sessions. Interrogating the model's representations suggested a form of hippocampal generalization across self-selected trajectories that was not apparent in physical coordinates, but was plainly visible in the model's low-dimensional latent space. We confirmed this organization directly in the recorded neural activity: neurons fired at corresponding stages of spatially distinct paths connecting the same origin and destination, preserving their relative firing order even as their firing fields shifted with the path. Analysis of neuron-specific readouts further identified functional relationships among neurons that generalized from flight to waking rest and sleep, brain states never used for training, and differed systematically between the hippocampal subregions CA1 and CA3. Together, these results show how AI-based functional digital twins can complement conventional analysis by generating testable hypotheses about neural organization during natural behavior.

neuroscience↗

bioRxiv2026

Ultrafast Frame-Free Imaging of Neural Activity with Event Cameras

Frame-based fluorescence imaging has long defined how neural activity is optically measured. This approach requires acquiring all pixels within an image, regardless of whether they carry meaningful neural dynamics, thereby intrinsically coupling spatial and temporal resolution while increasing data output. Here, we introduce an entirely different, frame-free approach that leverages the sparse nature of neural activity using event-based cameras, which asynchronously report fluorescence changes as spatiotemporal events. Compared with a frame-based camera, our method preserves signal fidelity while eliminating the fixed trade-off between spatial resolution, temporal resolution and data rate, thereby reducing data output by orders of magnitude. Applied to hippocampal preparations we demonstrate that the frame-free approach can resolve both single action potentials and fast network dynamics over large fields of view at kilohertz rates, enabling scalable, ultrafast optical recordings.

neuroscience↗

bioRxiv2023

A detailed input-output characterization of single neurons reveals the synaptic basis of spontaneous spiking in recurrent networks

Self-sustained recurrent activity in cortical networks is thought to be important for multiple crucial processes, including circuit development and homeostasis. Yet, the precise relationship between synaptic input patterns and the spiking output of individual neurons remains largely unresolved. Here, we developed, validated and applied a novel in vitro experimental platform and analytical procedures that provide - for individual neurons - simultaneous excitatory and inhibitory synaptic activity estimates during recurrent network activity. Our approach combines whole-network high-density microelectrode array (HD-MEA) recordings from rat neuronal cultures with patch clamping and enables a comprehensive mapping and characterization of active incoming connections to single postsynaptic neurons. We found that, during network states with excitation(E)-inhibition(I) balance, postsynaptic spiking often coincided with the maxima of fast fluctuations in the input E/I ratio. These spike-associated E/I ratio escalations were largely due to a rapid bidirectional change in synaptic inhibition that was modulated by the network-activity level. Our approach also uncovered the underlying circuit architecture and we show that individual neurons received a few key inhibitory connections - often from special hub neurons - that were instrumental in controlling postsynaptic spiking. Balanced network theory predicts dynamical regimes governed by small and rapid input fluctuation and featuring a fast neuronal responsiveness. Our findings - obtained in self-organized neuronal cultures - suggest that the emergence of these favorable regimes and associated network architectures is an inherent property of cortical networks in general.

neuroscience↗

bioRxiv2019

Nuclear pores as versatile reference standards for quantitative superresolution microscopy

Quantitative fluorescence and superresolution microscopy are often limited by insufficient data quality or artifacts. In this context, it is essential to have biologically relevant control samples to benchmark and optimize the quality of microscopes, labels and imaging conditions.\n\nHere we exploit the stereotypic arrangement of proteins in the nuclear pore complex as in situ reference structures to characterize the performance of a variety of microscopy modalities. We created four genome edited cell lines in which we endogenously labeled the nucleoporin Nup96 with mEGFP, SNAP-tag or HaloTag or the photoconvertible fluorescent protein mMaple. We demonstrate their use a) as 3D resolution standards for calibration and quality control, b) to quantify absolute labeling efficiencies and c) as precise reference standards for molecular counting.\n\nThese cell lines will enable the broad community to assess the quality of their microscopes and labels, and to perform quantitative, absolute measurements.

biophysics↗