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
Abstract Objective In Genetic Absence Epilepsy Rats From Strasbourg ( GAERS s), epileptogenesis takes place during brain maturation and correlates with increased mRNA expression of D3 dopamine receptors (D3R). Whether these alterations are the consequence of seizure repetition or contribute to the development of epilepsy remains to be clarified. Here, we addressed the involvement of the dopaminergic system in epilepsy onset in GAERS s. Methods Experiments were performed using rats at different stages of brain maturation from three strains according to their increasing propensity to develop absence seizures: nonepileptic control rats ( NEC s), Wistar Hannover rats, and GAERS s. Changes in dopaminergic neurotransmission were investigated using different behavioral and neurochemical approaches: autoradiography of D3R and dopamine transporter, single photon emission computed tomographic imaging, acute and chronic drug effects on seizure recordings (dopaminergic agonists and antagonists), quinpirole-induced yawns and dopamine synaptosomal uptake, microdialysis, brain tissue monoamines, and brain-derived neurotrophic factor quantification. Results Autoradiography revealed an increased expression of D3R in 14-day-old GAERS s, before absence seizure onset, that persists in adulthood, as compared to age-matched NEC s. This was confirmed by increased yawns, a marker of D3R activity, and increased seizures when animals were injected with quinpirole at low doses to activate D3R. We also observed a concomitant increase in the expression and activity of the dopamine transporter in GAERS s before seizure onset, consistent with both lowered dopamine basal level and increased phasic responses. Significance Our data show that the dopaminergic system is persistently altered in GAERS s, which may contribute not only to behavioral comorbidities but also as an etiopathogenic factor in the development of epilepsy. The data suggest that an imbalanced dopaminergic tone may contribute to absence epilepsy development and seizure onset, as its reversion by a chronic treatment with a dopamine stabilizer significantly suppressed epileptogenesis. Our data suggest a potential new target for antiepileptic therapies and/or improvement of quality of life of epileptic patients.
{"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":null,"pages":null},"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}
Yanqing Kang, Di Zhu, Haiyang Zhang, Enze Shi, Sigang Yu, Jinru Wu, Xuhui Wang, Xuan Liu, Geng Chen, Xi Jiang, Tuo Zhang, Shu Zhang
Studying influential nodes (I-nodes) in brain networks is of great significance in the field of brain imaging. Most existing studies consider brain connectivity hubs as I-nodes. However, this approach relies heavily on prior knowledge from graph theory, which may overlook the intrinsic characteristics of the brain network, especially when its architecture is not fully understood. In contrast, self-supervised deep learning can learn meaningful representations directly from the data. This approach enables the exploration of I-nodes for brain networks, which is also lacking in current studies. This paper proposes a Self-Supervised Graph Reconstruction framework based on Graph-Transformer (SSGR-GT) to identify I-nodes, which has three main characteristics. First, as a self-supervised model, SSGR-GT extracts the importance of brain nodes to the reconstruction. Second, SSGR-GT uses Graph-Transformer, which is well-suited for extracting features from brain graphs, combining both local and global characteristics. Third, multimodal analysis of I-nodes uses graph-based fusion technology, combining functional and structural brain information. The I-nodes we obtained are distributed in critical areas such as the superior frontal lobe, lateral parietal lobe, and lateral occipital lobe, with a total of 56 identified across different experiments. These I-nodes are involved in more brain networks than other regions, have longer fiber connections, and occupy more central positions in structural connectivity. They also exhibit strong connectivity and high node efficiency in both functional and structural networks. Furthermore, there is a significant overlap between the I-nodes and both the structural and functional rich-club. These findings enhance our understanding of the I-nodes within the brain network, and provide new insights for future research in further understanding the brain working mechanisms.
研究大脑网络中的影响节点（I 节点）在大脑成像领域具有重要意义。现有研究大多将大脑连接枢纽视为 I 节点。然而，这种方法在很大程度上依赖于图论的先验知识，可能会忽略大脑网络的内在特征，尤其是在对其架构不甚了解的情况下。相比之下，自监督深度学习可以直接从数据中学习有意义的表征。这种方法可以探索大脑网络的 I 节点，这也是当前研究中所缺乏的。本文提出了一种基于图变换器（Graph-Transformer，SSGR-GT）的自监督图重构框架（Self-Supervised Graph Reconstruction frameworkbased on Graph-Transformer，SSGR-GT）来识别 I 节点，它有三个主要特点。首先，作为一个自监督模型，SSGR-GT 提取了大脑节点对重建的重要性。其次，SSGR-GT 使用了图变换器（Graph-Transformer），它非常适合从钎图中提取特征，同时结合了局部和全局特征。第三，I 节点的多模态分析使用了基于图的融合技术，将大脑功能和结构信息结合起来。我们获得的 I 节点分布在额叶上部、顶叶外侧和枕叶外侧等关键区域，在不同实验中共识别出 56 个。与其他区域相比，这些 I 节点参与了更多的大脑网络，具有更长的纤维连接，并占据了更多的中心位置，具有指示性连接。在功能网络和结构网络中，它们也表现出较强的连接性和较高的节点效率。此外，I 节点与结构网络和功能网络之间都有显著的重叠。这些发现加深了我们对脑网络中 I 节点的理解，为今后进一步了解大脑工作机制的研究提供了新的视角。
{"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":null,"pages":null},"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}
Spiking neural networks, the third generation of artificial neural networks, have become an important family of neuron-based models that sidestep many of the key limitations facing modern-day backpropagation-trained deep networks, including their high energy inefficiency and long-criticized biological implausibility. In this work, we design and investigate a proof-of-concept instantiation of contrastive-signal-dependent plasticity (CSDP), a neuromorphic form of forward-forward-based, backpropagation-free learning. Our experimental simulations demonstrate that a hardware implementation of CSDP is capable of learning simple logic functions without the need to resort to complex gradient calculations.
{"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":null,"pages":null},"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}
Understanding neurological disorder is a fundamental problem in neuroscience, which often requires the analysis of brain networks derived from functional magnetic resonance imaging (fMRI) data. Despite the prevalence of Graph Neural Networks (GNNs) and Graph Transformers in various domains, applying them to brain networks faces challenges. Specifically, the datasets are severely impacted by the noises caused by distribution shifts across sub-populations and the neglect of node identities, both obstruct the identification of disease-specific patterns. To tackle these challenges, we propose Contrasformer, a novel contrastive brain network Transformer. It generates a prior-knowledge-enhanced contrast graph to address the distribution shifts across sub-populations by a two-stream attention mechanism. A cross attention with identity embedding highlights the identity of nodes, and three auxiliary losses ensure group consistency. Evaluated on 4 functional brain network datasets over 4 different diseases, Contrasformer outperforms the state-of-the-art methods for brain networks by achieving up to 10.8% improvement in accuracy, which demonstrates its efficacy in neurological disorder identification. Case studies illustrate its interpretability, especially in the context of neuroscience. This paper provides a solution for analyzing brain networks, offering valuable insights into neurological disorders. Our code is available at url{https://github.com/AngusMonroe/Contrasformer}.
{"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":null,"pages":null},"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}
The appearance of an object triggers an orienting gaze movement toward its location. The movement consists of a rapid rotation of the eyes, the saccade, which is accompanied by a head rotation if the target eccentricity exceeds the oculomotor range, by a slow eye movement if it moves. Completing a previous report, we explain the numerous points that lead to questioning the validity of a one-to-one correspondence relation between measured physical values of gaze or head orientation and neuronal activity. Conflating kinematic (or dynamic) numerical values with neurophysiological recordings carries the risk of believing that central neuron activity directly encodes gaze or head orientation rather than mediating changes in extraocular and neck muscle contraction. Rather than reducing mismatches between extrinsic physical parameters (such as position or velocity errors), eye and head movements are behavioral expressions of intrinsic processes that restore a poly-equilibrium, i.e., balances of activities opposing antagonistic visuomotor channels. Past results obtained in the cat and monkey left a treasure allowing a synthesis, which illustrates the tremendous complexity underlying the changes in the orientations of the eyes and head. Its aim is to serve as a guide for further investigations in marmosets or for comparison with other species.
{"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":null,"pages":null},"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}
This work delves into studying the synchronization in two realistic neuron models using Hodgkin-Huxley dynamics. Unlike simplistic point-like models, excitatory synapses are here randomly distributed along the dendrites, introducing strong stochastic contributions into their signal propagation. To focus on the role of different excitatory positions, we use two copies of the same neuron whose synapses are located at different distances from the soma and are exposed to identical Poissonian distributed current pulses. The synchronization is investigated through a specifically defined spiking correlation function, and its behavior is analyzed as a function of several parameters: inhibition weight, distance from the soma of one synaptic group, excitatory inactivation delay, and weight of the excitatory synapses.
{"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":null,"pages":null},"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}
We study the synchronisation of neurons in a realistic model under the Hodgkin-Huxley dynamics. To focus on the role of the different locations of the excitatory synapses, we use two identical neurons where the set of input signals is grouped at two different distances from the soma. The system is intended to represent a CA1 hippocampal neuron in which the synapses arriving from the CA3 neurons of the trisynaptic pathway appear to be localised in the apical dendritic region and are, in principle, either proximal or distal to the soma. Synchronisation is studied using a specifically defined spiking correlation function as a function of various parameters such as the distance from the soma of one of the synaptic groups, the inhibition weight and the associated activation delay. We found that the neurons' spiking activity depends nonmonotonically on the relative dendritic location of the synapses and their inhibitory weight, whereas the synchronisation measure always decreases with inhibition, and strongly depends on its activation time delay. The background activity on the somas results essentially independent on the fluctuation intensity and strongly support the importance of the balance between inhibition and excitation for neuronal synchronization.
{"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":null,"pages":null},"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}
Recent work on Transformer-based large language models (LLMs) has revealed striking limits in their working memory capacity, similar to what has been found in human behavioral studies. Specifically, these models' performance drops significantly on N-back tasks as N increases. However, there is still a lack of mechanistic interpretability as to why this phenomenon would arise. Inspired by the executive attention theory from behavioral sciences, we hypothesize that the self-attention mechanism within Transformer-based models might be responsible for their working memory capacity limits. To test this hypothesis, we train vanilla decoder-only transformers to perform N-back tasks and find that attention scores gradually aggregate to the N-back positions over training, suggesting that the model masters the task by learning a strategy to pay attention to the relationship between the current position and the N-back position. Critically, we find that the total entropy of the attention score matrix increases as N increases, suggesting that the dispersion of attention scores might be the cause of the capacity limit observed in N-back tasks.
最近对基于变换器的大型语言模型（LLMs）的研究发现，这些模型的工作记忆能力有惊人的极限，这与人类行为研究中发现的情况类似。具体来说，随着 N 的增加，这些模型在 N 回溯任务中的表现会明显下降。受行为科学中执行注意理论的启发，我们假设基于变形金刚的模型中的自我注意机制可能是造成其工作记忆容量限制的原因。为了验证这一假设，我们训练香草解码器转换器执行N-后退任务，结果发现注意力分数在训练过程中逐渐聚集到N-后退位置，这表明模型通过学习一种策略来掌握任务，即注意当前位置和N-后退位置之间的关系。重要的是，我们发现注意力分数矩阵的总熵随着N的增加而增加，这表明注意力分数的分散可能是在N-back任务中观察到的容量限制的原因。
{"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":null,"pages":null},"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}
Emotion and fairness play a key role in mediating socioeconomic decisions in humans; however, the underlying neurocognitive mechanism remains largely unknown. In this study, we explored the interplay between proposers' emotions and fairness of offer magnitudes in rational decision-making. Employing a time-bound UG paradigm, 40 (male, age: 18-20) participants were exposed to three distinct proposers' emotions (Happy, Neutral, and Disgusted) followed by one of the three offer ranges (Low, Intermediate, Maximum). Our findings show a robust influence of fairness of offer on acceptance rates, with the impact of emotions obtained only within the low offer range. The increment of the offer amount resulted in shorter reaction times, while emotional stimuli resulted in prolonged reaction times. A multilevel generalized linear model showed offer as the dominant predictor of trial-specific responses. Subsequent agglomerative clustering grouped participants into five primary clusters based on responses modulated by emotions/offers. The Drift Diffusion Model based on the clustering further corroborated our findings. Emotion-sensitive markers, including N170 and LPP, demonstrated the participants' effect on facial expressions; however, facial emotions had minimal effect on subsequent socioeconomic decisions. Our study suggests that, in general, participants gave more preference to the fairness of the offer with a slight effect of emotions in decision-making. We show that though emotion is perceived and has an effect on decision-making time, people mostly prioritise financial gain and fairness of offer. Moreover, it establishes a connection between reaction time and responses and further dives deep into individualistic decision-making processes revealing different cognitive strategies.
情感和公平性在调解人类的社会经济决策中起着关键作用；然而，其背后的神经认知机制在很大程度上仍不为人所知。在本研究中，我们探讨了理性决策中提议者的情绪和提议幅度的公平性之间的相互作用。我们采用有时间限制的 UG 范式，让 40 名参与者（男性，年龄：18-20 岁）在三种不同的提议者情绪（快乐、中性和厌恶）以及三种提议范围（低、中、高）中的一种情绪下进行决策。我们的研究结果表明，提议的公平性对接受率的影响很大，只有在低提议范围内才会受到情绪的影响。报价金额的增加导致反应时间缩短，而情绪刺激则导致反应时间延长。多层次广义线性模型显示，提议是预测特定试验反应的主要因素。随后的聚类分析根据受情绪/提议影响的反应将参与者分为五个主要群组。基于聚类的漂移扩散模型进一步证实了我们的发现。包括 N170 和 LPP 在内的情绪敏感标记显示了参与者对面部表情的影响；然而，面部情绪对后续社会经济决策的影响微乎其微。我们的研究表明，总体而言，参与者更倾向于考虑报价的公平性，情绪对决策的影响微乎其微。这表明，虽然情绪会被感知并对决策时间产生影响，但人们大多会优先考虑经济收益和报价的公平性。此外，该研究还建立了反应时间与反应之间的联系，并进一步深入研究了个体化决策过程，揭示了不同的认知策略。
{"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":null,"pages":null},"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}
In this work, we developed new mathematical methods for analyzing network topology and applied these methods to the analysis of brain networks. More specifically, we rigorously developed quantitative methods based on complexes constructed from digraphs (digraph-based complexes), such as path complexes and directed clique complexes (alternatively, we refer to these complexes as "higher-order structures," or "higher-order topologies," or "simplicial structures"), and, in the case of directed clique complexes, also methods based on the interrelations between the directed cliques, what we called "directed higher-order connectivities." This new quantitative theory for digraph-based complexes can be seen as a step towards the formalization of a "quantitative simplicial theory." Subsequently, we used these new methods, such as characterization measures and similarity measures for digraph-based complexes, to analyze the topology of digraphs derived from brain connectivity estimators, specifically the estimator known as information partial directed coherence (iPDC), which is a multivariate estimator that can be considered a representation of Granger causality in the frequency-domain, particularly estimated from electroencephalography (EEG) data from patients diagnosed with left temporal lobe epilepsy, in the delta, theta and alpha frequency bands, to try to find new biomarkers based on the higher-order structures and connectivities of these digraphs. In particular, we attempted to answer the following questions: How does the higher-order topology of the brain network change from the pre-ictal to the ictal phase, from the ictal to the post-ictal phase, at each frequency band and in each cerebral hemisphere? Does the analysis of higher-order structures provide new and better biomarkers for seizure dynamics and also for the laterality of the seizure focus than the usual graph theoretical analyses?
{"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":null,"pages":null},"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}
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