ArXiv最新文献 - Book学术

Metastability in networks of nonlinear stochastic integrate-and-fire neurons. 非线性随机整合-发射神经元网络中的转移性

ArXiv Pub Date : 2024-12-12

Siddharth Paliwal, Gabriel Koch Ocker, Braden A W Brinkman

{"title":"Metastability in networks of nonlinear stochastic integrate-and-fire neurons.","authors":"Siddharth Paliwal, Gabriel Koch Ocker, Braden A W Brinkman","doi":"","DOIUrl":"","url":null,"abstract":"Neurons in the brain continuously process the barrage of sensory inputs they receive from the environment. A wide array of experimental work has shown that the collective activity of neural populations encodes and processes this constant bombardment of information. How these collective patterns of activity depend on single-neuron properties is often unclear. Single-neuron recordings have shown that individual neurons' responses to inputs are nonlinear, which prevents a straightforward extrapolation from single neuron features to emergent collective states. Here, we use a field-theoretic formulation of a stochastic leaky integrate-and-fire model to study the impact of single-neuron nonlinearities on macroscopic network activity. In this model, a neuron integrates spiking output from other neurons in its membrane voltage and emits spikes stochastically with an intensity depending on the membrane voltage, after which the voltage resets. We show that the interplay between nonlinear spike intensity functions and membrane potential resets can i) give rise to metastable active firing rate states in recurrent networks, and ii) can enhance or suppress mean firing rates and membrane potentials in the same or paradoxically opposite directions.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11213153/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141473422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the linear scaling of entropy vs. energy in human brain activity, the Hagedorn temperature and the Zipf law. 人脑活动的统计热力学、哈格多恩温度和齐普夫定律。

ArXiv Pub Date : 2024-12-11

Dante R Chialvo, Romuald A Janik

{"title":"On the linear scaling of entropy vs. energy in human brain activity, the Hagedorn temperature and the Zipf law.","authors":"Dante R Chialvo, Romuald A Janik","doi":"","DOIUrl":"","url":null,"abstract":"It is well established that the brain spontaneously traverses through a very large number of states. Nevertheless, despite its relevance to understanding brain function, a formal description of this phenomenon is still lacking. To this end, we introduce a machine learning based method allowing for the determination of the probabilities of all possible states at a given coarse-graining, from which all the thermodynamics can be derived. This is a challenge not unique to the brain, since similar problems are at the heart of the statistical mechanics of complex systems. This paper uncovers a linear scaling of the entropies and energies of the brain states, a behaviour first conjectured by Hagedorn to be typical at the limiting temperature in which ordinary matter disintegrates into quark matter. Equivalently, this establishes the existence of a Zipf law scaling underlying the appearance of a wide range of brain states. Based on our estimation of the density of states for large scale functional magnetic resonance imaging (fMRI) human brain recordings, we observe that the brain operates asymptotically at the Hagedorn temperature. The presented approach is not only relevant to brain function but should be applicable for a wide variety of complex systems.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11275682/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141790316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Timing consistency of T cell receptor activation in a stochastic model combining kinetic segregation and proofreading. 结合动力学分离和校对的随机模型中 T 细胞受体激活的时间一致性

ArXiv Pub Date : 2024-12-09

Thorsten Prüstel, Martin Meier-Schellersheim

引用次数: 0

Predictive Strategies for the Control of Complex Motor Skills: Recent Insights into Individual and Joint Actions.

ArXiv Pub Date : 2024-12-05

Marta Russo, Antonella Maselli, Dagmar Sternad, Giovanni Pezzulo

引用次数: 0

Brain Morphology Normative modelling platform for abnormality and Centile estimation: Brain MoNoCle. 用于异常和百分位估计的脑形态规范建模平台：Brain MoNoCle.

ArXiv Pub Date : 2024-12-05

Bethany Little, Nida Alyas, Alexander Surtees, Gavin P Winston, John S Duncan, David A Cousins, John-Paul Taylor, Peter Taylor, Karoline Leiberg, Yujiang Wang

{"title":"Brain Morphology Normative modelling platform for abnormality and Centile estimation: Brain MoNoCle.","authors":"Bethany Little, Nida Alyas, Alexander Surtees, Gavin P Winston, John S Duncan, David A Cousins, John-Paul Taylor, Peter Taylor, Karoline Leiberg, Yujiang Wang","doi":"","DOIUrl":"","url":null,"abstract":"Normative models of brain structure estimate the effects of covariates such as age and sex using large samples of healthy controls. These models can then be applied to e.g. smaller clinical cohorts to distinguish disease effects from other covariates. However, these advanced statistical modelling approaches can be difficult to access, and processing large healthy cohorts is computationally demanding. Thus, accessible platforms with pre-trained normative models are needed. We present such a platform for brain morphology analysis as an open-source web application (https://cnnplab.shinyapps.io/BrainMoNoCle/), with six key features: (i) user-friendly web interface, (ii) individual and group outputs, (iii) multi-site analysis, (iv) regional and whole-brain analysis, (v) integration with existing tools, and (vi) featuring multiple morphology metrics. Using a diverse sample of 3,276 healthy controls across 21 sites, we pre-trained normative models on various metrics. We validated the models with a small sample of individuals with bipolar disorder, showing outputs that aligned closely with existing literature only after applying our normative modelling. Using a cohort of people with temporal lobe epilepsy, we showed that individual-level abnormalities were in line with seizure lateralisation. Finally, with the ability to investigate multiple morphology measures in the same framework, we found that biological covariates are better explained in specific morphology measures, and for applications, only some measures are sensitive to the disease process. Our platform offers a comprehensive framework to analyse brain morphology in clinical and research settings. Validations confirm the superiority of normative models and the advantage of investigating a range of brain morphology metrics together.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11177948/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Motion-Guided Deep Image Prior for Cardiac MRI.

ArXiv Pub Date : 2024-12-05

Marc Vornehm, Chong Chen, Muhammad Ahmad Sultan, Syed Murtaza Arshad, Yuchi Han, Florian Knoll, Rizwan Ahmad

{"title":"Motion-Guided Deep Image Prior for Cardiac MRI.","authors":"Marc Vornehm, Chong Chen, Muhammad Ahmad Sultan, Syed Murtaza Arshad, Yuchi Han, Florian Knoll, Rizwan Ahmad","doi":"","DOIUrl":"","url":null,"abstract":"Cardiovascular magnetic resonance imaging is a powerful diagnostic tool for assessing cardiac structure and function. Traditional breath-held imaging protocols, however, pose challenges for patients with arrhythmias or limited breath-holding capacity. We introduce Motion-Guided Deep Image prior (M-DIP), a novel unsupervised reconstruction framework for accelerated real-time cardiac MRI. M-DIP employs a spatial dictionary to synthesize a time-dependent template image, which is further refined using time-dependent deformation fields that model cardiac and respiratory motion. Unlike prior DIP-based methods, M-DIP simultaneously captures physiological motion and frame-to-frame content variations, making it applicable to a wide range of dynamic applications. We validate M-DIP using simulated MRXCAT cine phantom data as well as free-breathing real-time cine and single-shot late gadolinium enhancement data from clinical patients. Comparative analyses against state-of-the-art supervised and unsupervised approaches demonstrate M-DIP's performance and versatility. M-DIP achieved better image quality metrics on phantom data, as well as higher reader scores for in-vivo patient data.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11643223/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142829986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A mathematical language for linking fine-scale structure in spikes from hundreds to thousands of neurons with behaviour. 将数百到数千个神经元尖峰的精细结构与行为联系起来的数学语言。

ArXiv Pub Date : 2024-12-05

Alexandra N Busch, Roberto C Budzinski, Federico W Pasini, Ján Mináč, Jonathan A Michaels, Megan Roussy, Roberto A Gulli, Ben C Corrigan, J Andrew Pruszynski, Julio Martinez-Trujillo, Lyle E Muller

{"title":"A mathematical language for linking fine-scale structure in spikes from hundreds to thousands of neurons with behaviour.","authors":"Alexandra N Busch, Roberto C Budzinski, Federico W Pasini, Ján Mináč, Jonathan A Michaels, Megan Roussy, Roberto A Gulli, Ben C Corrigan, J Andrew Pruszynski, Julio Martinez-Trujillo, Lyle E Muller","doi":"","DOIUrl":"","url":null,"abstract":"Recent advances in neural recording technology allow simultaneously recording action potentials from hundreds to thousands of neurons in awake, behaving animals. However, characterizing spike patterns in the resulting data, and linking these patterns to behaviour, remains a challenging task. The lack of a rigorous mathematical language for variable numbers of events (spikes) emitted by multiple agents (neurons) is an important limiting factor. We introduce a new mathematical operation to decompose complex spike patterns into a set of simple, structured elements. This creates a mathematical language that allows comparing spike patterns across trials, detecting sub-patterns, and making links to behaviour via a clear distance measure. We apply the method to dual Utah array recordings from macaque prefrontal cortex, where this technique reveals previously unseen structure that can predict both memory-guided decisions and errors in a virtual-reality working memory task. These results demonstrate that this technique provides a powerful new approach to understand structure in the spike times of neural populations, at a scale that will continue to grow more and more rapidly in upcoming years.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11643227/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142831101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adversarial Attacks on Large Language Models in Medicine. 对医学大型语言模型的对抗性攻击。

ArXiv Pub Date : 2024-12-05

Yifan Yang, Qiao Jin, Furong Huang, Zhiyong Lu

{"title":"Adversarial Attacks on Large Language Models in Medicine.","authors":"Yifan Yang, Qiao Jin, Furong Huang, Zhiyong Lu","doi":"","DOIUrl":"","url":null,"abstract":"The integration of Large Language Models (LLMs) into healthcare applications offers promising advancements in medical diagnostics, treatment recommendations, and patient care. However, the susceptibility of LLMs to adversarial attacks poses a significant threat, potentially leading to harmful outcomes in delicate medical contexts. This study investigates the vulnerability of LLMs to two types of adversarial attacks in three medical tasks. Utilizing real-world patient data, we demonstrate that both open-source and proprietary LLMs are susceptible to manipulation across multiple tasks. This research further reveals that domain-specific tasks demand more adversarial data in model fine-tuning than general domain tasks for effective attack execution, especially for more capable models. We discover that while integrating adversarial data does not markedly degrade overall model performance on medical benchmarks, it does lead to noticeable shifts in fine-tuned model weights, suggesting a potential pathway for detecting and countering model attacks. This research highlights the urgent need for robust security measures and the development of defensive mechanisms to safeguard LLMs in medical applications, to ensure their safe and effective deployment in healthcare settings.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11468488/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Photon-Counting CT in Cancer Radiotherapy: Technological Advances and Clinical Benefits. 癌症放疗中的光子计数 CT：技术进步与临床效益。

ArXiv Pub Date : 2024-12-04

Keyur D Shah, Jun Zhou, Justin Roper, Anees Dhabaan, Hania Al-Hallaq, Amir Pourmorteza, Xiaofeng Yang

{"title":"Photon-Counting CT in Cancer Radiotherapy: Technological Advances and Clinical Benefits.","authors":"Keyur D Shah, Jun Zhou, Justin Roper, Anees Dhabaan, Hania Al-Hallaq, Amir Pourmorteza, Xiaofeng Yang","doi":"","DOIUrl":"","url":null,"abstract":"Photon-counting computed tomography (PCCT) marks a significant advancement over conventional energy-integrating detector (EID) CT systems. This review highlights PCCT's superior spatial and contrast resolution, reduced radiation dose, and multi-energy imaging capabilities, which address key challenges in radiotherapy, such as accurate tumor delineation, precise dose calculation, and treatment response monitoring. PCCT's improved anatomical clarity enhances tumor targeting while minimizing damage to surrounding healthy tissues. Additionally, metal artifact reduction (MAR) and quantitative imaging capabilities optimize workflows, enabling adaptive radiotherapy and radiomics-driven personalized treatment. Emerging clinical applications in brachytherapy and radiopharmaceutical therapy (RPT) show promising outcomes, although challenges like high costs and limited software integration remain. With advancements in artificial intelligence (AI) and dedicated radiotherapy packages, PCCT is poised to transform precision, safety, and efficacy in cancer radiotherapy, marking it as a pivotal technology for future clinical practice.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11581100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142689966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

mdCATH: A Large-Scale MD Dataset for Data-Driven Computational Biophysics. mdCATH：用于数据驱动计算生物物理学的大规模 MD 数据集。

ArXiv Pub Date : 2024-12-03

Antonio Mirarchi, Toni Giorgino, Gianni De Fabritiis

引用次数: 0