arXiv - QuanBio - Neurons and Cognition最新文献_第2页

Turing Video-based Cognitive Tests to Handle Entangled Concepts 基于图灵视频的认知测试，处理纠缠不清的概念

arXiv - QuanBio - Neurons and Cognition Pub Date : 2024-09-13 DOI: arxiv-2409.08868

Diederik Aerts, Roberto Leporini, Sandro Sozzo

{"title":"Turing Video-based Cognitive Tests to Handle Entangled Concepts","authors":"Diederik Aerts, Roberto Leporini, Sandro Sozzo","doi":"arxiv-2409.08868","DOIUrl":"https://doi.org/arxiv-2409.08868","url":null,"abstract":"We have proved in both human-based and computer-based tests that natural\u0000concepts generally `entangle' when they combine to form complex sentences,\u0000violating the rules of classical compositional semantics. In this article, we\u0000present the results of an innovative video-based cognitive test on a specific\u0000conceptual combination, which significantly violates the\u0000Clauser--Horne--Shimony--Holt version of Bell's inequalities (`CHSH\u0000inequality'). We also show that collected data can be faithfully modelled\u0000within a quantum-theoretic framework elaborated by ourselves and a `strong form\u0000of entanglement' occurs between the component concepts. While the video-based\u0000test confirms previous empirical results on entanglement in human cognition,\u0000our ground-breaking empirical approach surpasses language barriers and\u0000eliminates the need for prior knowledge, enabling universal accessibility.\u0000Finally, this transformative methodology allows one to unravel the underlying\u0000connections that drive our perception of reality. As a matter of fact, we\u0000provide a novel explanation for the appearance of entanglement in both physics\u0000and cognitive realms.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring Biological Neuronal Correlations with Quantum Generative Models 用量子生成模型探索生物神经元相关性

arXiv - QuanBio - Neurons and Cognition Pub Date : 2024-09-13 DOI: arxiv-2409.09125

Vinicius Hernandes, Eliska Greplova

引用次数: 0

Exploring Action-Centric Representations Through the Lens of Rate-Distortion Theory 从速率失真理论的角度探索以动作为中心的表象

arXiv - QuanBio - Neurons and Cognition Pub Date : 2024-09-13 DOI: arxiv-2409.08892

Miguel de Llanza Varona, Christopher L. Buckley, Beren Millidge

{"title":"Exploring Action-Centric Representations Through the Lens of Rate-Distortion Theory","authors":"Miguel de Llanza Varona, Christopher L. Buckley, Beren Millidge","doi":"arxiv-2409.08892","DOIUrl":"https://doi.org/arxiv-2409.08892","url":null,"abstract":"Organisms have to keep track of the information in the environment that is\u0000relevant for adaptive behaviour. Transmitting information in an economical and\u0000efficient way becomes crucial for limited-resourced agents living in\u0000high-dimensional environments. The efficient coding hypothesis claims that\u0000organisms seek to maximize the information about the sensory input in an\u0000efficient manner. Under Bayesian inference, this means that the role of the\u0000brain is to efficiently allocate resources in order to make predictions about\u0000the hidden states that cause sensory data. However, neither of those frameworks\u0000accounts for how that information is exploited downstream, leaving aside the\u0000action-oriented role of the perceptual system. Rate-distortion theory, which\u0000defines optimal lossy compression under constraints, has gained attention as a\u0000formal framework to explore goal-oriented efficient coding. In this work, we\u0000explore action-centric representations in the context of rate-distortion\u0000theory. We also provide a mathematical definition of abstractions and we argue\u0000that, as a summary of the relevant details, they can be used to fix the content\u0000of action-centric representations. We model action-centric representations\u0000using VAEs and we find that such representations i) are efficient lossy\u0000compressions of the data; ii) capture the task-dependent invariances necessary\u0000to achieve successful behaviour; and iii) are not in service of reconstructing\u0000the data. Thus, we conclude that full reconstruction of the data is rarely\u0000needed to achieve optimal behaviour, consistent with a teleological approach to\u0000perception.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Yes, Prime Minister, question order does matter -- and it's certainly not classical! But is it quantum? 是的，首相，提问顺序确实很重要，而且它肯定不是经典的！但它是量子的吗？

arXiv - QuanBio - Neurons and Cognition Pub Date : 2024-09-13 DOI: arxiv-2409.08930

Dorje C. Brody

引用次数: 0

The Neuroscientific Basis of Flow: Learning Progress Guides Task Engagement and Cognitive Control 流动的神经科学基础：学习进度引导任务参与和认知控制

arXiv - QuanBio - Neurons and Cognition Pub Date : 2024-09-10 DOI: arxiv-2409.06592

Hairong Lu, Dimitri van der Linden, Arnold B. Bakker

{"title":"The Neuroscientific Basis of Flow: Learning Progress Guides Task Engagement and Cognitive Control","authors":"Hairong Lu, Dimitri van der Linden, Arnold B. Bakker","doi":"arxiv-2409.06592","DOIUrl":"https://doi.org/arxiv-2409.06592","url":null,"abstract":"People often strive for deep engagement in activities which is usually\u0000associated with feelings of flow: a state of full task absorption accompanied\u0000by a sense of control and fulfillment. The intrinsic factors driving such\u0000engagement and facilitating subjective feelings of flow remain unclear.\u0000Building on computational theories of intrinsic motivation, this study examines\u0000how learning progress predicts engagement and directs cognitive control.\u0000Results showed that task engagement, indicated by feelings of flow and\u0000distractibility, is a function of learning progress. Electroencephalography\u0000data further revealed that learning progress is associated with enhanced\u0000proactive preparation (e.g., reduced pre-stimulus contingent negativity\u0000variance and parietal alpha desynchronization) and improved feedback processing\u0000(e.g., increased P3b amplitude and parietal alpha desynchronization). The\u0000impact of learning progress on cognitive control is observed at the task-block\u0000and goal-episode levels, but not at the trial level. This suggests that\u0000learning progress shapes cognitive control over extended periods as progress\u0000accumulates. These findings highlight the critical role of learning progress in\u0000sustaining engagement and cognitive control in goal-directed behavior.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Explainable Metrics for the Assessment of Neurodegenerative Diseases through Handwriting Analysis 通过笔迹分析评估神经退行性疾病的可解释指标

arXiv - QuanBio - Neurons and Cognition Pub Date : 2024-09-10 DOI: arxiv-2409.08303

Thomas Thebaud, Anna Favaro, Casey Chen, Gabrielle Chavez, Laureano Moro-Velazquez, Ankur Butala, Najim Dehak

{"title":"Explainable Metrics for the Assessment of Neurodegenerative Diseases through Handwriting Analysis","authors":"Thomas Thebaud, Anna Favaro, Casey Chen, Gabrielle Chavez, Laureano Moro-Velazquez, Ankur Butala, Najim Dehak","doi":"arxiv-2409.08303","DOIUrl":"https://doi.org/arxiv-2409.08303","url":null,"abstract":"Motor changes are early signs of neurodegenerative diseases (NDs) such as\u0000Parkinson's disease (PD) and Alzheimer's disease (AD), but are often difficult\u0000to detect, especially in the early stages. In this work, we examine the\u0000behavior of a wide array of explainable metrics extracted from the handwriting\u0000signals of 113 subjects performing multiple tasks on a digital tablet. The aim\u0000is to measure their effectiveness in characterizing and assessing multiple NDs,\u0000including AD and PD. To this end, task-agnostic and task-specific metrics are\u0000extracted from 14 distinct tasks. Subsequently, through statistical analysis\u0000and a series of classification experiments, we investigate which metrics\u0000provide greater discriminative power between NDs and healthy controls and among\u0000different NDs. Preliminary results indicate that the various tasks at hand can\u0000all be effectively leveraged to distinguish between the considered set of NDs,\u0000specifically by measuring the stability, the speed of writing, the time spent\u0000not writing, and the pressure variations between groups from our handcrafted\u0000explainable metrics, which shows p-values lower than 0.0001 for multiple tasks.\u0000Using various classification algorithms on the computed metrics, we obtain up\u0000to 87% accuracy to discriminate AD and healthy controls (CTL), and up to 69%\u0000for PD vs CTL.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Non-exchangeable networks of integrate-and-fire neurons: spatially-extended mean-field limit of the empirical measure 整合-发射神经元的非交换网络：经验测量的空间扩展平均场极限

arXiv - QuanBio - Neurons and Cognition Pub Date : 2024-09-10 DOI: arxiv-2409.06325

Pierre-Emmanuel Jabin, Valentin Schmutz, Datong Zhou

{"title":"Non-exchangeable networks of integrate-and-fire neurons: spatially-extended mean-field limit of the empirical measure","authors":"Pierre-Emmanuel Jabin, Valentin Schmutz, Datong Zhou","doi":"arxiv-2409.06325","DOIUrl":"https://doi.org/arxiv-2409.06325","url":null,"abstract":"The dynamics of exchangeable or spatially-structured networks of $N$\u0000interacting stochastic neurons can be described by deterministic population\u0000equations in the mean-field limit $Ntoinfty$, when synaptic weights scale as\u0000$O(1/N)$. This asymptotic behavior has been proven in several works but a\u0000general question has remained unanswered: does the $O(1/N)$ scaling of synaptic\u0000weights, by itself, suffice to guarantee the convergence of network dynamics to\u0000a deterministic population equation, even when networks are not assumed to be\u0000exchangeable or spatially structured? In this work, we consider networks of\u0000stochastic integrate-and-fire neurons with arbitrary synaptic weights\u0000satisfying only a $O(1/N)$ scaling condition. Borrowing results from the theory\u0000of dense graph limits (graphons), we prove that, as $Ntoinfty$, and up to the\u0000extraction of a subsequence, the empirical measure of the neurons' membrane\u0000potentials converges to the solution of a spatially-extended mean-field partial\u0000differential equation (PDE). Our proof requires analytical techniques that go\u0000beyond standard propagation of chaos methods. In particular, we introduce a\u0000weak metric that depends on the dense graph limit kernel and we show how the\u0000weak convergence of the initial data can be obtained by propagating the\u0000regularity of the limit kernel along the dual-backward equation associated with\u0000the spatially-extended mean-field PDE. Overall, this result invites us to\u0000re-interpret spatially-extended population equations as universal mean-field\u0000limits of networks of neurons with $O(1/N)$ synaptic weight scaling.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predictive Coding with Spiking Neural Networks: a Survey 利用尖峰神经网络进行预测编码：一项调查

arXiv - QuanBio - Neurons and Cognition Pub Date : 2024-09-09 DOI: arxiv-2409.05386

Antony W. N'dri, William Gebhardt, Céline Teulière, Fleur Zeldenrust, Rajesh P. N. Rao, Jochen Triesch, Alexander Ororbia

{"title":"Predictive Coding with Spiking Neural Networks: a Survey","authors":"Antony W. N'dri, William Gebhardt, Céline Teulière, Fleur Zeldenrust, Rajesh P. N. Rao, Jochen Triesch, Alexander Ororbia","doi":"arxiv-2409.05386","DOIUrl":"https://doi.org/arxiv-2409.05386","url":null,"abstract":"In this article, we review a class of neuro-mimetic computational models that\u0000we place under the label of spiking predictive coding. Specifically, we review\u0000the general framework of predictive processing in the context of neurons that\u0000emit discrete action potentials, i.e., spikes. Theoretically, we structure our\u0000survey around how prediction errors are represented, which results in an\u0000organization of historical neuromorphic generalizations that is centered around\u0000three broad classes of approaches: prediction errors in explicit groups of\u0000error neurons, in membrane potentials, and implicit prediction error encoding.\u0000Furthermore, we examine some applications of spiking predictive coding that\u0000utilize more energy-efficient, edge-computing hardware platforms. Finally, we\u0000highlight important future directions and challenges in this emerging line of\u0000inquiry in brain-inspired computing. Building on the prior results of work in\u0000computational cognitive neuroscience, machine intelligence, and neuromorphic\u0000engineering, we hope that this review of neuromorphic formulations and\u0000implementations of predictive coding will encourage and guide future research\u0000and development in this emerging research area.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sparse learning enabled by constraints on connectivity and function 通过对连接性和功能的限制实现稀疏学习

arXiv - QuanBio - Neurons and Cognition Pub Date : 2024-09-08 DOI: arxiv-2409.04946

Mirza M. Junaid Baig, Armen Stepanyants

引用次数: 0

Hybrid Spiking Neural Networks for Low-Power Intra-Cortical Brain-Machine Interfaces 用于低功耗皮层内脑机接口的混合尖峰神经网络

arXiv - QuanBio - Neurons and Cognition Pub Date : 2024-09-06 DOI: arxiv-2409.04428

Alexandru Vasilache, Jann Krausse, Klaus Knobloch, Juergen Becker

{"title":"Hybrid Spiking Neural Networks for Low-Power Intra-Cortical Brain-Machine Interfaces","authors":"Alexandru Vasilache, Jann Krausse, Klaus Knobloch, Juergen Becker","doi":"arxiv-2409.04428","DOIUrl":"https://doi.org/arxiv-2409.04428","url":null,"abstract":"Intra-cortical brain-machine interfaces (iBMIs) have the potential to\u0000dramatically improve the lives of people with paraplegia by restoring their\u0000ability to perform daily activities. However, current iBMIs suffer from\u0000scalability and mobility limitations due to bulky hardware and wiring. Wireless\u0000iBMIs offer a solution but are constrained by a limited data rate. To overcome\u0000this challenge, we are investigating hybrid spiking neural networks for\u0000embedded neural decoding in wireless iBMIs. The networks consist of a temporal\u0000convolution-based compression followed by recurrent processing and a final\u0000interpolation back to the original sequence length. As recurrent units, we\u0000explore gated recurrent units (GRUs), leaky integrate-and-fire (LIF) neurons,\u0000and a combination of both - spiking GRUs (sGRUs) and analyze their differences\u0000in terms of accuracy, footprint, and activation sparsity. To that end, we train\u0000decoders on the \"Nonhuman Primate Reaching with Multichannel Sensorimotor\u0000Cortex Electrophysiology\" dataset and evaluate it using the NeuroBench\u0000framework, targeting both tracks of the IEEE BioCAS Grand Challenge on Neural\u0000Decoding. Our approach achieves high accuracy in predicting velocities of\u0000primate reaching movements from multichannel primary motor cortex recordings\u0000while maintaining a low number of synaptic operations, surpassing the current\u0000baseline models in the NeuroBench framework. This work highlights the potential\u0000of hybrid neural networks to facilitate wireless iBMIs with high decoding\u0000precision and a substantial increase in the number of monitored neurons, paving\u0000the way toward more advanced neuroprosthetic technologies.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0