Biology subjects

Hira, R.

Publications and source records attributed to Hira, R..

13 recordsLinked to original sources

bioRxiv2026

Recurrent Spiking Neural Networks for Continuous Motor Control: A Biologically Plausible Model of Motor Cortex

Motor cortex acquires skilled movements through recurrent spiking dynamics and experience-dependent synaptic plasticity, yet how such circuits learn continuous motor control using biologically local signals remains unclear. Here, we develop a recurrent spiking neural network model of motor cortex that addresses both the computational and biological requirements of motor learning. We first analytically derived the recurrent firing-rate state directly from synaptic input and trained the network by implicit differentiation, thereby avoiding backpropagation through long spike trajectories. We then introduced State-Carry Initialization, which enables closed-loop motor control by continuously carrying the ongoing recurrent state across successive actions. Building on this formulation, we devised a learning rule termed Feedback-Spike Nudging (FSN), in which teaching signals are delivered as spikes through a feedback pathway. Synaptic changes depend only on presynaptic activity and the feedback-evoked change in postsynaptic firing, consistent with biological mechanisms of synaptic plasticity in motor cortex. The resulting networks learned continuous motor control through both supervised and reinforcement learning, which could be mapped respectively onto interactions of motor cortex with the cerebellum and basal ganglia. Moreover, the FSN learning rule was consistent with experimental observations of structural spine plasticity in mouse motor cortex. Thus, our study lays a foundation for recurrent spiking models of motor cortex that are biologically grounded at both the functional and anatomical levels.

neuroscience↗

bioRxiv2026

Thalamocortical credit routing from the basal ganglia and cerebellum: anatomical constraints and circuit principles

Animals learn to select actions that maximize reward in complex environments. A central challenge in this process is credit routing: behavioral outcomes must be routed back to the neural populations that contributed to generating the relevant actions. One possible route is through thalamocortical pathways conveying learning signals from the basal ganglia and cerebellum, where plasticity is shaped by reward-prediction errors. However, how these pathways route such signals back to specific cortical circuits remains unclear. To address this question, we combined anatomical circuit mapping in mice with closed-loop circuit modeling. Anterograde transsynaptic tracing revealed that thalamic neurons receiving input from the substantia nigra pars reticulata or the lateral cerebellar nucleus were largely segregated, with partial overlap in the ventromedial thalamus. Nevertheless, their cortical axons shared a pronounced enrichment in layer 1a, most prominently in secondary motor cortex. In a brain- wide synergistic loop-circuit model, learning was enhanced when both pathways delivered additive and gain-modulating feedback aligned with the cortical action readout. Local activity- based calibration established this alignment, whereas dendritic-spike- and reward-prediction- error-dependent thalamocortical plasticity further refined behavioral output. Module-specific feedback was essential for a brain-machine interface task requiring the simultaneous conditioning of multiple target neurons. Together, these findings define a credit-routing architecture in which laminar convergence, plastic alignment, and modular feedback link parallel subcortical learning systems to cortical action representations.

neuroscience↗

bioRxiv2025

Post-translational modifications within fibrinaloid microclot complexes distinguish Pre-COVID-19 Postural Orthostatic Tachycardia Syndrome, Long COVID, and Long COVID-POTS and reveal disease-specific molecular pathways

BackgroundPre-COVID-19 Postural orthostatic tachycardia syndrome (PC-POTS), Long COVID, and their overlap (LC-POTS) are chronic post-viral conditions marked by debilitating symptoms despite frequently normal routine laboratory results. We previously identified insoluble fibrinaloid microclot complexes (FMCs) in Long COVID. It is not known whether FMCs are also present in PC-POTS, or whether post-translational modifications (PTMs) within FMC-entrapped proteins contribute to disease mechanisms. MethodsPlatelet-poor plasma from healthy controls, PC-POTS patients (collected prior to the COVID-19 pandemic), Long COVID (without POTS) and LC-POTS patients underwent fluorescence imaging flow cytometry to quantify FMCs. Proteomic analyses were performed on insoluble protein fractions using a double trypsin digestion strategy and data-independent liquid chromatography-tandem mass spectrometry (LC-MS/MS). Differential protein abundance, PTMs, and amyloidogenicity were compared across groups. ResultsMeasured with imaging flowcytometry in objects/mL, higher levels of FMCs were present in disease groups compared to controls, although not statistically significant. Statistically significant differences potentially lay within FMC sizes and composition. Furthermore, despite only a few dysregulated proteins, FMC proteomics revealed extensive and disease-specific peptides with PTM dysregulation across coagulation, immune, and metabolic pathways. Long COVID displayed FMCs with PTMs of coagulation proteins including prominent advanced glycation end-products (AGE)- and oxidation-based modifications of fibrinogen subunits, particularly fibrinogen subunit A (FIBA), resembling diabetic glycation profiles. FMCs in PC-POTS showed fewer fibrinogen PTMs but markedly increased modifications in metabolic proteins, including oxidised apoA1 and apoB, and immune patterns with complement-related proteins (C3, C4A/B, IC1), immunoglobulin G1 (IGG1) and alpha 2 macroglobulin (A2MG). LC-POTS shared coagulation pathology with Long COVID and immune pathology with PC-POTS. Many dysregulated peptides were determined by in silco methods to be highly amyloidogenic, consistent with FMCs as {beta}-sheet-rich aggregates. Protein-level differences were minimal compared with PTM changes. ConclusionsThis study provides the first evidence that post-translational modifications (PTMs) within fibrinaloid microclots complexes (FMCs) uniquely distinguish pre-COVID-19 POTS, Long COVID, and Long COVID-POTS. Because PC-POTS samples were collected before SARS-CoV-2, their PTM patterns reflect intrinsic disease biology, allowing a clear separation from Long COVID-related changes. PTM profiling revealed pro-coagulant fibrinogen modifications in Long COVID and LC-POTS, metabolic-oxidative disruptions in Long COVID and PC-POTS, and immune dysregulation in PC-POTS and LC-POTS. None of these is detectable with routine assays, and all are independent of protein abundance. The consistent presence of amyloidogenic peptides suggests a contribution to microvascular dysfunction. These findings define disease-specific PTM landscapes and support new diagnostic and therapeutic avenues across autonomic and post-viral disorders.

physiology↗

bioRxiv2025

Ground tilt representation in the rodent cerebral cortex

Information about ground tilt at ones location is indispensable for integrating the body with its surrounding environment. However, how ground-tilt information, together with movement and posture information, is represented in the cerebral cortex remains unclear. To address this issue, we developed a new six-degree-of-freedom platform that can rapidly and precisely impart ground motion to head-fixed mice. Using the Diesel2p large field-of-view two-photon mesoscope, we performed calcium imaging to simultaneously capture activity dynamics across broad cortical areas from the frontal to the parietal cortex while driving the platform through various motion patterns. We found a core area for the ground-tilt information at the parietal cortex, although tilt-direction neurons were found across the dorsal cortex. Neuronal populations preserved tilt-direction information within a common low-dimensional manifold across three distinct motion conditions, indicating a condition-invariant tilt code. Furthermore, to investigate shared dynamics across multiple cortical areas, we introduced broadcast- subspace analysis as an extension of communication subspace analysis. This extended framework revealed that tilt information occupied the second principal shared axis, following movement information. These results reveal the core cortical area for processing tilt information, its universal format, and its sharing across the dorsal cortex, thereby providing a framework for cortical information processing that integrates movement, posture, and environment.

neuroscience↗

bioRxiv2025

Independence and Coherence in Temporal Sequence Computation across the Fronto-Parietal Network

Time processing requires distributed and coordinated cortical dynamics. Flexible yet robust temporal representations can arise from two distinct computational modes: a coherence mode, where multiple cortical areas hold the same elapsed-time estimate, and an independence mode, where each area maintains its own local estimate. However, how the brain switches between these modes has remained unknown. Using mesoscale two-photon calcium imaging, we simultaneously recorded neuronal populations in the secondary motor cortex (M2) and posterior parietal cortex (PPC) of mice performing a novel alternating-interval timing task. Both areas encoded elapsed time through similar high-dimensional sequential activity. Decoding analyses revealed that the fronto-parietal network has both independent and coherent temporal codes. Communication-subspace analysis showed that temporal information was distributed across multiple low-variance subspaces, whereas the largest subspace preferentially encoded behaviour. A twin recurrent neural network (RNN) model with sparse inter-RNN connections and shared high-variance noise reproduced these experimental findings. Moreover, perturbations applied along the dominant shared subspace paradoxically enhanced independence between the two networks. Through a mathematical formalization based on the local Lyapunov exponents, we uncovered how perturbations along different subspaces selectively evoke either independent or coherent communication mode. Together, these results reveal a principle by which fronto-parietal circuits achieve robust yet flexible computation through the interplay of sparse coupling and shared global fluctuations.

neuroscience↗

bioRxiv2025

Open-source modular FPGA system for two-photon mesoscope enabling multi-layer, multi-depth neural activity recording and lifetime imaging

Large field-of-view (FOV) two-photon microscopy makes it possible to record a large number of neural activities from multiple brain regions simultaneously. However, the larger the field of view, the longer it takes to scan the entire FOV. To increase imaging speed, we have developed open-source software to digitize analogue signals from a photomultiplier tube using a field-programmable gate array (FPGA) at a rate of 3.2 GS/s. By combining this with a newly developed a circular delay-path module for a custom two-photon mesoscope (Diesel2p), we succeeded in simultaneous recording of >10,000 neurons from the entire bilateral dorsal cortex at up to four depths. We also demonstrated large FOV lifetime imaging using the same system. Our modular, open-source FPGA system can be readily integrated into any type of two-photon microscope and will accelerate the biomedical application of multi-scale two-photon imaging in a wide range of pathophysiological investigations.

neuroscience↗

bioRxiv2025

Sperrfy the brain: A data-driven realization of Sperry's Chemoaffinity theory in the neural connectome

Understanding how brain-wide neural circuits are genetically wired remains a fundamental question in neuroscience. While Sperrys chemoaffinity theory posits that molecular gradients provide positional cues for axonal projections, its application has been largely limited to localized sensory systems. Here, we present SPERRFY (Spatial Positional Encoding for Reconstructing Rules of axonal Fiber connectivitY), a data-driven framework that operationalizes Sperrys theory at the whole-brain scale. By integrating connectomic data with spatial transcriptomic profiles from the Allen Mouse Brain Atlas, SPERRFY deciphers latent positional gradients that underlie axonal wiring. Using canonical correlation analysis (CCA), we extract top gradient pairs that align with observed neural connectivity patterns, capturing both global (inter-regional) and local (intra-regional) organizational principles. Connectivity reconstruction based on these gradients achieves high predictive performance (AUC = 0.90), and permutation-based null models confirm the biological relevance of the inferred structures. Furthermore, SPERRFY identifies candidate genes that may contribute to positional wiring information, providing molecular insight into the developmental logic of brain-wide circuitry. Our results extend Sperrys foundational theory beyond the sensory domain, offering a unified, data-driven framework for understanding genetically encoded connectivity across the entire brain.

neuroscience↗

bioRxiv2024

Dynamic integration of cortical activity in the deep layer of the anterolateral superior colliculus

The superior colliculus (SC) receives inputs from various brain regions in a layer- and radial location-specific manner, but whether the SC exhibits location-specific dynamics remains unclear. To address this issue, we recorded the spiking activity of single SC neurons while photoactivating cortical areas in awake head-fixed Thy1-ChR2 rats. We classified 309 neurons that responded significantly into 8 clusters according to the response dynamics. Among them, neurons with monophasic excitatory responses (7-12 ms latency) that returned to baseline within 20 ms were commonly observed in the optic and intermediate gray layers of centromedial and centrolateral SC. In contrast, neurons with complex polyphasic responses were commonly observed in the deep layers of the anterolateral SC. Cross-correlation analysis suggested that the complex pattern could be only partly explained by an internal circuit of the deep gray layer. Our results indicate that medial to centrolateral SC neurons simply relay cortical activity, whereas neurons in the deep layers of the anterolateral SC dynamically integrate inputs from the cortex, SNr, CN, and local circuits. These findings suggest a spatial gradient in SC integration, with a division of labor between simple relay circuits and those integrating complex dynamics.

neuroscience↗

bioRxiv2024

Synergistic reinforcement learning by cooperation of the cerebellum and basal ganglia

The cerebral cortex, cerebellum and basal ganglia are essential for flexible learning in mammals. Although traditionally thought to operate under different learning rules, recent evidence suggests that both the basal ganglia and the cerebellum may employ reinforcement learning mechanisms. This raises the question of how these structures coordinate when a common reward prediction error mechanism is active. To address this issue, we first examined output signals from the basal ganglia and cerebellum following the activity of the cerebral cortex. We recorded single-neuron activity from the output regions of the cerebellum and basal ganglia - the cerebellar nuclei (CN) and substantia nigra pars reticulata (SNr) - in both male and female ChR2 transgenic rats. Neurons in the CN and SNr exhibited distinct temporal response patterns; notably, the fast excitatory response in the CN, driven by mossy fiber input, was synchronized with the inhibitory response in the SNr, mediated via the direct pathway. Using these experimental findings together with connectome data, we developed both a semi-realistic spiking network model and a reservoir-based reinforcement learning model. In the latter model, successful learning depended on synaptic plasticity in both the cerebellum and basal ganglia with a temporal precision on the order of 10 ms. Furthermore, cortical {beta}-oscillations enhanced learning and optimal reinforcement learning occurred when the output of cerebellar and basal ganglia signal phase-locked at the frequency of cortical oscillation. Taken together, our results suggest that the coordinated output of the cerebellum and basal ganglia, driven by tightly tuned cortical input, underlies brain-wide synergistic reinforcement learning. Significance StatementThe cerebral cortex, cerebellum, and basal ganglia support learning. Recent research suggests that both the basal ganglia and cerebellum use a similar learning process called reinforcement learning, which involves predicting rewards. To understand how these brain regions work together, we recorded brain activity in rats while photo-stimulating the cerebral cortex. We found that two types of responses in the cerebellum and basal ganglia were synchronized, which might help activate the cerebral cortex. A computer model showed that precise timing of signals from both the cerebellum and basal ganglia is important for learning. This timing was important only when the cerebral cortex worked in a specific frequency range. Our findings suggest that coordinated brain activity enhances learning.

neuroscience↗

bioRxiv2023

Mesoscale functional architecture in medial posterior parietal cortex

The posterior parietal cortex (PPC) in mice has various functions including multisensory integration1-3, vision-guided behaviors4-6, working memory7-13, and posture control14,15. However, an integrated understanding of these functions and their cortical localizations in and around the PPC and higher visual areas (HVAs) has not been completely elucidated. Here we simultaneously imaged the activity of thousands of neurons within a 3 x 3 mm2 field-of-view, including eight cortical areas around the PPC, during behavior with a two-photon mesoscope16. Mice performed both a vision-guided task and a choice history-dependent task, and the imaging results revealed distinct, localized, behavior-related functions of two medial PPC areas. Neurons in the anteromedial (AM) HVA responded to both vision and choice information, and thus AM is a locus of association between these channels. By contrast, the anterior (A) HVA stores choice history with sequential dynamics and represents posture. Mesoscale correlation analysis on the intertrial variability of neuronal activity demonstrated that neurons in AM exhibited diverse, area-dependent interactions, while neurons in area A shared fluctuations with the primary somatosensory area. Pairwise interareal interactions among neurons were precisely predicted by the anatomical input correlations, with the exception of some global interactions. Thus, the medial PPC has two distinct modules, areas AM and A, with each having distinctive modes of cortical communication. These medial PPC modules can serve separate higher-order functions: area AM for multisensory and cognitive integration with locally processed signals and area A for transmission of information including posture, movement, and working memory.

neuroscience↗

bioRxiv2023

Working memory-based and -free reward prediction in a dual dopamine system in the basal ganglia

Animals optimize their actions by predicting and verifying their outcomes (e.g., rewards). Reward prediction often requires working memory (WM)-based information. To elucidate the neural basis of WM-based reward prediction, we compared the activity of dopamine (DA) neurons in the ventral tegmental area (VTA) in an alternate reward condition (WM-based) with that in a random (WM-free) reward condition in rats and mice. Positron emission tomography revealed greater VTA activation in the WM-based than the WM-free condition. Lateral and medial VTA neurons displayed differential electrophysiological spike activities reflecting WM-based and WM-free reward prediction error, respectively. Furthermore, phasic DA release in the dorsal and ventral striatum changed as WM-based and WM-free classical conditioning progressed. Consistent with our WM-based model, reward acquisition caused a DA dip only in the dorsal striatum. Thus a dual DA system processes WM-based and WM-free reward prediction in parallel.

neuroscience↗

bioRxiv2020

Diesel2p mesoscope with dual independent scan engines for flexible capture of dynamics in distributed neural circuitry

Imaging the activity of neurons that are widely distributed across brain regions deep in scattering tissue at high speed remains challenging. Here, we introduce an open-source system with Dual Independent Enhanced Scan Engines for Large Field-of-view Two-Photon imaging (Diesel2p). Combining novel optical design, adaptive optics, and temporal multiplexing, the system offers subcellular resolution over a large field-of-view ([~] 25 mm2) with independent scan engines. We demonstrate the flexibility and various use cases of this system for calcium imaging of neurons in the living brain.

neuroscience↗

bioRxiv2017

Mice use robust and common strategies to discriminate natural scenes

Mice use vision to navigate and avoid predators in natural environments. However, the spatial resolution of mouse vision is poor compared to primates, and mice lack a fovea. Thus, it is unclear how well mice can discriminate ethologically relevant scenes. Here, we examined natural scene discrimination in mice using an automated touch-screen system. We estimated the discrimination difficulty using the computational metric structural similarity (SSIM), and constructed psychometric curves. However, the performance of each mouse was better predicted by the population mean than SSIM. This high inter-mouse agreement indicates that mice use common and robust strategies to discriminate natural scenes. We tested several other image metrics to find an alternative to SSIM for predicting discrimination performance. We found that a simple, primary visual cortex (V1)-inspired model predicted mouse performance with fidelity approaching the inter-mouse agreement. The model involved convolving the images with Gabor filters, and its performance varied with the orientation of the Gabor filter. This orientation dependence was driven by the stimuli, rather than an innate biological feature. Together, these results indicate that mice are adept at discriminating natural scenes, and their performance is well predicted by simple models of V1 processing.

neuroscience↗