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BEOL Electro-Biological Interface for 1024-Channel TFT Neurostimulator with Cultured DRG Neurons arxiv.org/abs/2412.01834

Eye dominance and testing order effects in the circularly-oriented macular pigment optical density measurements that rely on the perception of structured light-based stimuli arxiv.org/abs/2412.01836

Eye dominance and testing order effects in the circularly-oriented macular pigment optical density measurements that rely on the perception of structured light-based stimuli

Psychophysical discrimination of structured light (SL) stimuli may be useful in screening for various macular disorders, including degenerative macular diseases. The circularly-oriented macular pigment optical density (coMPOD), calculated from the discrimination performance of SL-induced entoptic phenomena, may reveal a novel functional biomarker of macular health. In this study, we investigated the potential influence of eye dominance and testing order effects on SL-based stimulus perception, factors that potentially influence the sensitivity of screening tests based on SL technology. A total of 28 participants (aged 18-38 years) were selected for the study after undergoing a comprehensive eye examination. A psychophysical task was performed where various SL-based entoptic images with multiple azimuthal fringes rotating with a specific temporal frequency were projected onto the participants' retinas. By occluding the central areas of entoptic images, we measured the retinal eccentricity ($R$) of the perceivable area of the stimuli. The slope of the coMPOD profile ($a$-value) was calculated for each participant using a spatiotemporal sensitivity model that takes into account the perceptual threshold measurements of structured light stimuli with varying spatial densities and temporal frequencies. The Pearson correlation coefficient between eye dominance and testing order effects was $r=0.8$ ($p<0.01$). The Bland-Altman plots for both factors indicated zero bias. The results indicate repeatable measurements for both eyes, implying minimal impact from eye dominance and testing order on SL-based stimulus perception. The results provide a foundation for future studies exploring the clinical utility of SL tools in eye health.

arXiv.org

Fluctuations and the limit of predictability in protein evolution arxiv.org/abs/2412.01969

Fluctuations and the limit of predictability in protein evolution

Protein evolution involves mutations occurring across a wide range of time scales [Di Bari et al., PNAS 121, e2406807121 (2024)]. In analogy with other disordered systems, this dynamical heterogeneity suggests strong correlations between mutations happening at distinct sites and times. To quantify these correlations, we examine the role of various fluctuation sources in protein evolution, simulated using a data-driven epistatic landscape. By applying spatio-temporal correlation functions inspired by statistical physics, we disentangle fluctuations originating from the ancestral protein sequence from those driven by stochastic mutations along independent evolutionary paths. Our analysis shows that, in diverse protein families, fluctuations from the ancestral sequence predominate at shorter time scales. This allows us to identify a time scale over which ancestral sequence information persists, enabling its reconstruction. We link this persistence to the strength of epistatic interactions: ancestral sequences with stronger epistatic signatures impact evolutionary trajectories over extended periods. At longer time scales, however, ancestral influence fades as epistatically constrained sites evolve collectively. To confirm this idea, we apply a standard ancestral sequence reconstruction algorithm and verify that the time-dependent recovery error is influenced by the properties of the ancestor itself.

arXiv.org

Hierarchical feature extraction on functional brain networks for autism spectrum disorder identification with resting-state fMRI data arxiv.org/abs/2412.02424

Hierarchical feature extraction on functional brain networks for autism spectrum disorder identification with resting-state fMRI data

Autism spectrum disorder (ASD) is a pervasive developmental disorder of the central nervous system, which occurs most frequently in childhood and is characterized by unusual and repetitive ritualistic behaviors. Currently, diagnostic methods primarily rely on questionnaire surveys and behavioral observation, which may lead to misdiagnoses due to the subjective evaluation and measurement used in these traditional methods. With the advancement in medical imaging, MR imaging-based diagnosis has become an alternative and more objective approach. In this paper, we propose a Hybrid neural Network model for ASD identification, termded ASD-HNet, to hierarchically extract features on the functional brain networks based on resting-state functional magnetic resonance imaging data. This hierarchical method can better extract brain representations, improve the diagnostic accuracy, and help us better locate brain regions related to ASD. Specifically, features are extracted from three scales: local regions of interest (ROIs) scale, community-clustering scale, and the whole-communities scale. For the ROI scale, graph convolution is used to transfer features between ROIs. At the community cluster scale, functional gradients are introduced, the clustering algorithm K-Means is used to automatically cluster ROIs with similar functional gradients into several communities, and features of ROIs belonging to the same community are extracted to characterize these communities. At global information integration scale, we extract global features from community-scale brain networks to characterize the whole brain networks. We validate the effectiveness of our method using the public dataset of Autism Brain Imaging Data Exchange I (ABIDE I), and elucidate the interpretability of the method. Experimental results demonstrate that the proposed ASD-HNet can yield superior performance than compared methods.

arXiv.org

The Underlying Dynamics of Life and Its Evolution: A Prigogine-Inspired Informational Dissipative System arxiv.org/abs/2412.02459

The Underlying Dynamics of Life and Its Evolution: A Prigogine-Inspired Informational Dissipative System

Life is fundamentally a scientific enigma. The interplay between chaos, entropy dynamics, and Prigogine's dissipative systems offers profound insights into the emergence, stabilization, and eventual collapse of far-from-equilibrium systems. This study proposes that, alongside thermodynamic dissipative systems as highlighted by Ilya Prigogine, informational dissipative systems actively contribute to granting inanimate matter properties characteristic of living systems, such as autopoiesis. By examining cyclic entropy flows between water topology (Shannon space) and molecular systems (Boltzmann space), the work emphasizes the pivotal role of disquisotropic entropy, an informational entropy reservoir arising from imperfections in molecular structures. The analysis demonstrates that chaos functions as a stabilizing force, enhancing resilience, adaptability, and longevity by delaying thermodynamic equilibrium. This research connects foundational thermodynamic principles with the emergent behavior of chaotic systems, paving the way for a deeper understanding of complexity in natural and technological contexts. By exploring the relationship between chaos, entropy, and dissipative dynamics, the study advances a paradigm where disorder becomes a mechanism to sustain order, a hallmark of life and complex systems.

arXiv.org

Multi-timescale synaptic plasticity on analog neuromorphic hardware arxiv.org/abs/2412.02515

Multi-timescale synaptic plasticity on analog neuromorphic hardware

As numerical simulations grow in complexity, their demands on computing time and energy increase. Hardware accelerators offer significant efficiency gains in many computationally intensive scientific fields, but their use in computational neuroscience remains limited. Neuromorphic substrates, such as the BrainScaleS architectures, offer significant advantages, especially for studying complex plasticity rules that require extended simulation runtimes. This work presents the implementation of a calcium-based plasticity rule that integrates calcium dynamics based on the synaptic tagging-and-capture hypothesis on the BrainScaleS-2 system. The implementation of the plasticity rule for a single synapse involves incorporating the calcium dynamics and the plasticity rule equations. The calcium dynamics are mapped to the analog circuits of BrainScaleS-2, while the plasticity rule equations are numerically solved on its embedded digital processors. The main hardware constraints include the speed of the processors and the use of integer arithmetic. By adjusting the timestep of the numerical solver and introducing stochastic rounding, we demonstrate that BrainScaleS-2 accurately emulates a single synapse following a calcium-based plasticity rule across four stimulation protocols and validate our implementation against a software reference model.

arXiv.org

Active learning of neural population dynamics using two-photon holographic optogenetics arxiv.org/abs/2412.02529

Active learning of neural population dynamics using two-photon holographic optogenetics

Recent advances in techniques for monitoring and perturbing neural populations have greatly enhanced our ability to study circuits in the brain. In particular, two-photon holographic optogenetics now enables precise photostimulation of experimenter-specified groups of individual neurons, while simultaneous two-photon calcium imaging enables the measurement of ongoing and induced activity across the neural population. Despite the enormous space of potential photostimulation patterns and the time-consuming nature of photostimulation experiments, very little algorithmic work has been done to determine the most effective photostimulation patterns for identifying the neural population dynamics. Here, we develop methods to efficiently select which neurons to stimulate such that the resulting neural responses will best inform a dynamical model of the neural population activity. Using neural population responses to photostimulation in mouse motor cortex, we demonstrate the efficacy of a low-rank linear dynamical systems model, and develop an active learning procedure which takes advantage of low-rank structure to determine informative photostimulation patterns. We demonstrate our approach on both real and synthetic data, obtaining in some cases as much as a two-fold reduction in the amount of data required to reach a given predictive power. Our active stimulation design method is based on a novel active learning procedure for low-rank regression, which may be of independent interest.

arXiv.org

Topological analysis of brain dynamical signals reveals signatures of seizure susceptibility arxiv.org/abs/2412.01911 .soc-ph

Topological analysis of brain dynamical signals reveals signatures of seizure susceptibility

Epilepsy is known to drastically alter brain dynamics during seizures (ictal periods). However, whether epilepsy may alter brain dynamics during background (non-ictal) periods is less understood. To investigate this, we analyzed the brain activity of epileptic zebrafish as animal models, for two genetic conditions and two fishlines. The recordings were automatically segmented and labeled with machine learning, and then analyzed using Persistent Homology, a method from Topological Data Analysis, which reveals patterns in the topology of brain dynamics in a noise-robust and networkbased manner. We find that ictal and non-ictal periods can be distinguished from the topology of their dynamics, regardless of fishline or genetic condition, which validates our method. Additionally, within a single fishline wild type, we can distinguish the non-ictal periods of seizure-prone and seizure-free individuals. This suggests the presence of topological signatures of the epileptic brain, even during non-ictal periods. In general, our results suggest that Topological Data Analysis can be used as a general quantitative method to screen for dynamical markers of seizure susceptibility also in other species.

arXiv.org

The Copernican Argument for Alien Consciousness; The Mimicry Argument Against Robot Consciousness arxiv.org/abs/2412.00008

How reproducible are data-driven subtypes of Alzheimer's disease atrophy? arxiv.org/abs/2412.00160

Stochastic Dynamics and Probability Analysis for a Generalized Epidemic Model with Environmental Noise arxiv.org/abs/2412.00405

LLaMA-Gene: A General-purpose Gene Task Large Language Model Based on Instruction Fine-tuning arxiv.org/abs/2412.00471

Mapping, modeling, and reprogramming cell-fate decision making systems arxiv.org/abs/2412.00667

Dynamic Indicators of Adherence and Retention in Digital Health Studies: Insights from the Brighten Study arxiv.org/abs/2412.00942

The influence of chromosomal inversions on genetic variation and clinal patterns in genomic data of Drosophila melanogaster arxiv.org/abs/2412.01352

New Graphs at the braingraph.org Website for Studying the Aging Brain Circuitry arxiv.org/abs/2412.01418

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