arXiv - QuanBio - Neurons and Cognition最新文献

{"title":"Early reduced dopaminergic tone mediated by D3 receptor and dopamine transporter in absence epileptogenesis","authors":"Fanny CavarecGIN, CHUGA, Philipp KraussGIN, CHUGA, Tiffany WitkowskiGIN, CHUGA, Alexis BroisatGIN, CHUGA, Catherine GhezziGIN, CHUGA, Stéphanie de GoisGIN, CHUGA, Bruno GirosGIN, CHUGA, Antoine DepaulisGIN, CHUGA, Colin DeransartGIN, CHUGA","doi":"arxiv-2409.11758","DOIUrl":"https://doi.org/arxiv-2409.11758","url":null,"abstract":"Abstract Objective In Genetic Absence Epilepsy Rats From Strasbourg ( GAERS\u0000s), epileptogenesis takes place during brain maturation and correlates with\u0000increased mRNA expression of D3 dopamine receptors (D3R). Whether these\u0000alterations are the consequence of seizure repetition or contribute to the\u0000development of epilepsy remains to be clarified. Here, we addressed the\u0000involvement of the dopaminergic system in epilepsy onset in GAERS s. Methods\u0000Experiments were performed using rats at different stages of brain maturation\u0000from three strains according to their increasing propensity to develop absence\u0000seizures: nonepileptic control rats ( NEC s), Wistar Hannover rats, and GAERS\u0000s. Changes in dopaminergic neurotransmission were investigated using different\u0000behavioral and neurochemical approaches: autoradiography of D3R and dopamine\u0000transporter, single photon emission computed tomographic imaging, acute and\u0000chronic drug effects on seizure recordings (dopaminergic agonists and\u0000antagonists), quinpirole-induced yawns and dopamine synaptosomal uptake,\u0000microdialysis, brain tissue monoamines, and brain-derived neurotrophic factor\u0000quantification. Results Autoradiography revealed an increased expression of D3R\u0000in 14-day-old GAERS s, before absence seizure onset, that persists in\u0000adulthood, as compared to age-matched NEC s. This was confirmed by increased\u0000yawns, a marker of D3R activity, and increased seizures when animals were\u0000injected with quinpirole at low doses to activate D3R. We also observed a\u0000concomitant increase in the expression and activity of the dopamine transporter\u0000in GAERS s before seizure onset, consistent with both lowered dopamine basal\u0000level and increased phasic responses. Significance Our data show that the\u0000dopaminergic system is persistently altered in GAERS s, which may contribute\u0000not only to behavioral comorbidities but also as an etiopathogenic factor in\u0000the development of epilepsy. The data suggest that an imbalanced dopaminergic\u0000tone may contribute to absence epilepsy development and seizure onset, as its\u0000reversion by a chronic treatment with a dopamine stabilizer significantly\u0000suppressed epileptogenesis. Our data suggest a potential new target for\u0000antiepileptic therapies and/or improvement of quality of life of epileptic\u0000patients.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248932","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}

{"title":"Identifying Influential nodes in Brain Networks via Self-Supervised Graph-Transformer","authors":"Yanqing Kang, Di Zhu, Haiyang Zhang, Enze Shi, Sigang Yu, Jinru Wu, Xuhui Wang, Xuan Liu, Geng Chen, Xi Jiang, Tuo Zhang, Shu Zhang","doi":"arxiv-2409.11174","DOIUrl":"https://doi.org/arxiv-2409.11174","url":null,"abstract":"Studying influential nodes (I-nodes) in brain networks is of great\u0000significance in the field of brain imaging. Most existing studies consider\u0000brain connectivity hubs as I-nodes. However, this approach relies heavily on\u0000prior knowledge from graph theory, which may overlook the intrinsic\u0000characteristics of the brain network, especially when its architecture is not\u0000fully understood. In contrast, self-supervised deep learning can learn\u0000meaningful representations directly from the data. This approach enables the\u0000exploration of I-nodes for brain networks, which is also lacking in current\u0000studies. This paper proposes a Self-Supervised Graph Reconstruction framework\u0000based on Graph-Transformer (SSGR-GT) to identify I-nodes, which has three main\u0000characteristics. First, as a self-supervised model, SSGR-GT extracts the\u0000importance of brain nodes to the reconstruction. Second, SSGR-GT uses\u0000Graph-Transformer, which is well-suited for extracting features from brain\u0000graphs, combining both local and global characteristics. Third, multimodal\u0000analysis of I-nodes uses graph-based fusion technology, combining functional\u0000and structural brain information. The I-nodes we obtained are distributed in\u0000critical areas such as the superior frontal lobe, lateral parietal lobe, and\u0000lateral occipital lobe, with a total of 56 identified across different\u0000experiments. These I-nodes are involved in more brain networks than other\u0000regions, have longer fiber connections, and occupy more central positions in\u0000structural connectivity. They also exhibit strong connectivity and high node\u0000efficiency in both functional and structural networks. Furthermore, there is a\u0000significant overlap between the I-nodes and both the structural and functional\u0000rich-club. These findings enhance our understanding of the I-nodes within the\u0000brain network, and provide new insights for future research in further\u0000understanding the brain working mechanisms.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248934","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}

{"title":"Contrastive Learning in Memristor-based Neuromorphic Systems","authors":"Cory Merkel, Alexander Ororbia","doi":"arxiv-2409.10887","DOIUrl":"https://doi.org/arxiv-2409.10887","url":null,"abstract":"Spiking neural networks, the third generation of artificial neural networks,\u0000have become an important family of neuron-based models that sidestep many of\u0000the key limitations facing modern-day backpropagation-trained deep networks,\u0000including their high energy inefficiency and long-criticized biological\u0000implausibility. In this work, we design and investigate a proof-of-concept\u0000instantiation of contrastive-signal-dependent plasticity (CSDP), a neuromorphic\u0000form of forward-forward-based, backpropagation-free learning. Our experimental\u0000simulations demonstrate that a hardware implementation of CSDP is capable of\u0000learning simple logic functions without the need to resort to complex gradient\u0000calculations.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248935","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}

{"title":"Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identification","authors":"Jiaxing Xu, Kai He, Mengcheng Lan, Qingtian Bian, Wei Li, Tieying Li, Yiping Ke, Miao Qiao","doi":"arxiv-2409.10944","DOIUrl":"https://doi.org/arxiv-2409.10944","url":null,"abstract":"Understanding neurological disorder is a fundamental problem in neuroscience,\u0000which often requires the analysis of brain networks derived from functional\u0000magnetic resonance imaging (fMRI) data. Despite the prevalence of Graph Neural\u0000Networks (GNNs) and Graph Transformers in various domains, applying them to\u0000brain networks faces challenges. Specifically, the datasets are severely\u0000impacted by the noises caused by distribution shifts across sub-populations and\u0000the neglect of node identities, both obstruct the identification of\u0000disease-specific patterns. To tackle these challenges, we propose\u0000Contrasformer, a novel contrastive brain network Transformer. It generates a\u0000prior-knowledge-enhanced contrast graph to address the distribution shifts\u0000across sub-populations by a two-stream attention mechanism. A cross attention\u0000with identity embedding highlights the identity of nodes, and three auxiliary\u0000losses ensure group consistency. Evaluated on 4 functional brain network\u0000datasets over 4 different diseases, Contrasformer outperforms the\u0000state-of-the-art methods for brain networks by achieving up to 10.8%\u0000improvement in accuracy, which demonstrates its efficacy in neurological\u0000disorder identification. Case studies illustrate its interpretability,\u0000especially in the context of neuroscience. This paper provides a solution for\u0000analyzing brain networks, offering valuable insights into neurological\u0000disorders. Our code is available at\u0000url{https://github.com/AngusMonroe/Contrasformer}.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248933","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}

{"title":"Orienting gaze toward a visual target: Neurophysiological synthesis with epistemological considerations","authors":"Laurent GoffartCGGG","doi":"arxiv-2409.10189","DOIUrl":"https://doi.org/arxiv-2409.10189","url":null,"abstract":"The appearance of an object triggers an orienting gaze movement toward its\u0000location. The movement consists of a rapid rotation of the eyes, the saccade,\u0000which is accompanied by a head rotation if the target eccentricity exceeds the\u0000oculomotor range, by a slow eye movement if it moves. Completing a previous\u0000report, we explain the numerous points that lead to questioning the validity of\u0000a one-to-one correspondence relation between measured physical values of gaze\u0000or head orientation and neuronal activity. Conflating kinematic (or dynamic)\u0000numerical values with neurophysiological recordings carries the risk of\u0000believing that central neuron activity directly encodes gaze or head\u0000orientation rather than mediating changes in extraocular and neck muscle\u0000contraction. Rather than reducing mismatches between extrinsic physical\u0000parameters (such as position or velocity errors), eye and head movements are\u0000behavioral expressions of intrinsic processes that restore a poly-equilibrium,\u0000i.e., balances of activities opposing antagonistic visuomotor channels. Past\u0000results obtained in the cat and monkey left a treasure allowing a synthesis,\u0000which illustrates the tremendous complexity underlying the changes in the\u0000orientations of the eyes and head. Its aim is to serve as a guide for further\u0000investigations in marmosets or for comparison with other species.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248941","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}

{"title":"Hippocampal synchronization in a realistic CA1 neuron model","authors":"Alessandro Fiasconaro, Michele Migliore","doi":"arxiv-2409.10431","DOIUrl":"https://doi.org/arxiv-2409.10431","url":null,"abstract":"This work delves into studying the synchronization in two realistic neuron\u0000models using Hodgkin-Huxley dynamics. Unlike simplistic point-like models,\u0000excitatory synapses are here randomly distributed along the dendrites,\u0000introducing strong stochastic contributions into their signal propagation. To\u0000focus on the role of different excitatory positions, we use two copies of the\u0000same neuron whose synapses are located at different distances from the soma and\u0000are exposed to identical Poissonian distributed current pulses. The\u0000synchronization is investigated through a specifically defined spiking\u0000correlation function, and its behavior is analyzed as a function of several\u0000parameters: inhibition weight, distance from the soma of one synaptic group,\u0000excitatory inactivation delay, and weight of the excitatory synapses.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248938","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}

{"title":"Effects of synapse location, delay and background stochastic activity on synchronising hippocampal CA1 neurons","authors":"Alessandro Fiasconaro, Michele Migliore","doi":"arxiv-2409.10460","DOIUrl":"https://doi.org/arxiv-2409.10460","url":null,"abstract":"We study the synchronisation of neurons in a realistic model under the\u0000Hodgkin-Huxley dynamics. To focus on the role of the different locations of the\u0000excitatory synapses, we use two identical neurons where the set of input\u0000signals is grouped at two different distances from the soma. The system is\u0000intended to represent a CA1 hippocampal neuron in which the synapses arriving\u0000from the CA3 neurons of the trisynaptic pathway appear to be localised in the\u0000apical dendritic region and are, in principle, either proximal or distal to the\u0000soma. Synchronisation is studied using a specifically defined spiking\u0000correlation function as a function of various parameters such as the distance\u0000from the soma of one of the synaptic groups, the inhibition weight and the\u0000associated activation delay. We found that the neurons' spiking activity\u0000depends nonmonotonically on the relative dendritic location of the synapses and\u0000their inhibitory weight, whereas the synchronisation measure always decreases\u0000with inhibition, and strongly depends on its activation time delay. The\u0000background activity on the somas results essentially independent on the\u0000fluctuation intensity and strongly support the importance of the balance\u0000between inhibition and excitation for neuronal synchronization.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248937","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}

{"title":"Self-Attention Limits Working Memory Capacity of Transformer-Based Models","authors":"Dongyu Gong, Hantao Zhang","doi":"arxiv-2409.10715","DOIUrl":"https://doi.org/arxiv-2409.10715","url":null,"abstract":"Recent work on Transformer-based large language models (LLMs) has revealed\u0000striking limits in their working memory capacity, similar to what has been\u0000found in human behavioral studies. Specifically, these models' performance\u0000drops significantly on N-back tasks as N increases. However, there is still a\u0000lack of mechanistic interpretability as to why this phenomenon would arise.\u0000Inspired by the executive attention theory from behavioral sciences, we\u0000hypothesize that the self-attention mechanism within Transformer-based models\u0000might be responsible for their working memory capacity limits. To test this\u0000hypothesis, we train vanilla decoder-only transformers to perform N-back tasks\u0000and find that attention scores gradually aggregate to the N-back positions over\u0000training, suggesting that the model masters the task by learning a strategy to\u0000pay attention to the relationship between the current position and the N-back\u0000position. Critically, we find that the total entropy of the attention score\u0000matrix increases as N increases, suggesting that the dispersion of attention\u0000scores might be the cause of the capacity limit observed in N-back tasks.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248936","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}

{"title":"Fairness, not Emotion, Drives Socioeconomic Decision Making","authors":"Rudra Mukhopadhyay, Sourin Chatterjee, Koel Das","doi":"arxiv-2409.10322","DOIUrl":"https://doi.org/arxiv-2409.10322","url":null,"abstract":"Emotion and fairness play a key role in mediating socioeconomic decisions in\u0000humans; however, the underlying neurocognitive mechanism remains largely\u0000unknown. In this study, we explored the interplay between proposers' emotions\u0000and fairness of offer magnitudes in rational decision-making. Employing a\u0000time-bound UG paradigm, 40 (male, age: 18-20) participants were exposed to\u0000three distinct proposers' emotions (Happy, Neutral, and Disgusted) followed by\u0000one of the three offer ranges (Low, Intermediate, Maximum). Our findings show a\u0000robust influence of fairness of offer on acceptance rates, with the impact of\u0000emotions obtained only within the low offer range. The increment of the offer\u0000amount resulted in shorter reaction times, while emotional stimuli resulted in\u0000prolonged reaction times. A multilevel generalized linear model showed offer as\u0000the dominant predictor of trial-specific responses. Subsequent agglomerative\u0000clustering grouped participants into five primary clusters based on responses\u0000modulated by emotions/offers. The Drift Diffusion Model based on the clustering\u0000further corroborated our findings. Emotion-sensitive markers, including N170\u0000and LPP, demonstrated the participants' effect on facial expressions; however,\u0000facial emotions had minimal effect on subsequent socioeconomic decisions. Our\u0000study suggests that, in general, participants gave more preference to the\u0000fairness of the offer with a slight effect of emotions in decision-making. We\u0000show that though emotion is perceived and has an effect on decision-making\u0000time, people mostly prioritise financial gain and fairness of offer. Moreover,\u0000it establishes a connection between reaction time and responses and further\u0000dives deep into individualistic decision-making processes revealing different\u0000cognitive strategies.","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248939","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}

{"title":"Towards a Quantitative Theory of Digraph-Based Complexes and its Applications in Brain Network Analysis","authors":"Heitor Baldo","doi":"arxiv-2409.09862","DOIUrl":"https://doi.org/arxiv-2409.09862","url":null,"abstract":"In this work, we developed new mathematical methods for analyzing network\u0000topology and applied these methods to the analysis of brain networks. More\u0000specifically, we rigorously developed quantitative methods based on complexes\u0000constructed from digraphs (digraph-based complexes), such as path complexes and\u0000directed clique complexes (alternatively, we refer to these complexes as\u0000\"higher-order structures,\" or \"higher-order topologies,\" or \"simplicial\u0000structures\"), and, in the case of directed clique complexes, also methods based\u0000on the interrelations between the directed cliques, what we called \"directed\u0000higher-order connectivities.\" This new quantitative theory for digraph-based\u0000complexes can be seen as a step towards the formalization of a \"quantitative\u0000simplicial theory.\" Subsequently, we used these new methods, such as\u0000characterization measures and similarity measures for digraph-based complexes,\u0000to analyze the topology of digraphs derived from brain connectivity estimators,\u0000specifically the estimator known as information partial directed coherence\u0000(iPDC), which is a multivariate estimator that can be considered a\u0000representation of Granger causality in the frequency-domain, particularly\u0000estimated from electroencephalography (EEG) data from patients diagnosed with\u0000left temporal lobe epilepsy, in the delta, theta and alpha frequency bands, to\u0000try to find new biomarkers based on the higher-order structures and\u0000connectivities of these digraphs. In particular, we attempted to answer the\u0000following questions: How does the higher-order topology of the brain network\u0000change from the pre-ictal to the ictal phase, from the ictal to the post-ictal\u0000phase, at each frequency band and in each cerebral hemisphere? Does the\u0000analysis of higher-order structures provide new and better biomarkers for\u0000seizure dynamics and also for the laterality of the seizure focus than the\u0000usual graph theoretical analyses?","PeriodicalId":501517,"journal":{"name":"arXiv - QuanBio - Neurons and Cognition","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248943","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}