Neural Computation最新文献_第3页

Nearly Optimal Learning Using Sparse Deep ReLU Networks in Regularized Empirical Risk Minimization With Lipschitz Loss 基于稀疏深度ReLU网络的带Lipschitz损失正则化经验风险最小化的近最优学习。

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2025-03-18 DOI: 10.1162/neco_a_01742

Ke Huang;Mingming Liu;Shujie Ma

{"title":"Nearly Optimal Learning Using Sparse Deep ReLU Networks in Regularized Empirical Risk Minimization With Lipschitz Loss","authors":"Ke Huang;Mingming Liu;Shujie Ma","doi":"10.1162/neco_a_01742","DOIUrl":"10.1162/neco_a_01742","url":null,"abstract":"We propose a sparse deep ReLU network (SDRN) estimator of the regression function obtained from regularized empirical risk minimization with a Lipschitz loss function. Our framework can be applied to a variety of regression and classification problems. We establish novel nonasymptotic excess risk bounds for our SDRN estimator when the regression function belongs to a Sobolev space with mixed derivatives. We obtain a new, nearly optimal, risk rate in the sense that the SDRN estimator can achieve nearly the same optimal minimax convergence rate as one-dimensional nonparametric regression with the dimension involved in a logarithm term only when the feature dimension is fixed. The estimator has a slightly slower rate when the dimension grows with the sample size. We show that the depth of the SDRN estimator grows with the sample size in logarithmic order, and the total number of nodes and weights grows in polynomial order of the sample size to have the nearly optimal risk rate. The proposed SDRN can go deeper with fewer parameters to well estimate the regression and overcome the overfitting problem encountered by conventional feedforward neural networks.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 4","pages":"815-870"},"PeriodicalIF":2.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143607153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Context-Sensitive Processing in a Model Neocortical Pyramidal Cell With Two Sites of Input Integration 具有两个输入整合位点的新皮质锥体细胞模型的上下文敏感加工。

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2025-03-18 DOI: 10.1162/neco_a_01739

Bruce P. Graham;Jim W. Kay;William A. Phillips

{"title":"Context-Sensitive Processing in a Model Neocortical Pyramidal Cell With Two Sites of Input Integration","authors":"Bruce P. Graham;Jim W. Kay;William A. Phillips","doi":"10.1162/neco_a_01739","DOIUrl":"10.1162/neco_a_01739","url":null,"abstract":"Neocortical layer 5 thick-tufted pyramidal cells are prone to exhibiting burst firing on receipt of coincident basal and apical dendritic inputs. These inputs carry different information, with basal inputs coming from feedforward sensory pathways and apical inputs coming from diverse sources that provide context in the cortical hierarchy. We explore the information processing possibilities of this burst firing using computer simulations of a noisy compartmental cell model. Simulated data on stochastic burst firing due to brief, simultaneously injected basal and apical currents allow estimation of burst firing probability for different stimulus current amplitudes. Information-theory-based partial information decomposition (PID) is used to quantify the contributions of the apical and basal input streams to the information in the cell output bursting probability. Four different operating regimes are apparent, depending on the relative strengths of the input streams, with output burst probability carrying more or less information that is uniquely contributed by either the basal or apical input, or shared and synergistic information due to the combined streams. We derive and fit transfer functions for these different regimes that describe burst probability over the different ranges of basal and apical input amplitudes. The operating regimes can be classified into distinct modes of information processing, depending on the contribution of apical input to output bursting: apical cooperation, in which both basal and apical inputs are required to generate a burst; apical amplification, in which basal input alone can generate a burst but the burst probability is modulated by apical input; apical drive, in which apical input alone can produce a burst; and apical integration, in which strong apical or basal inputs alone, as well as their combination, can generate bursting. In particular, PID and the transfer function clarify that the apical amplification mode has the features required for contextually modulated information processing.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 4","pages":"588-634"},"PeriodicalIF":2.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143607147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced EEG Forecasting: A Probabilistic Deep Learning Approach 增强脑电图预测：一种概率深度学习方法。

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2025-03-18 DOI: 10.1162/neco_a_01743

Hanna Pankka;Jaakko Lehtinen;Risto J. Ilmoniemi;Timo Roine

{"title":"Enhanced EEG Forecasting: A Probabilistic Deep Learning Approach","authors":"Hanna Pankka;Jaakko Lehtinen;Risto J. Ilmoniemi;Timo Roine","doi":"10.1162/neco_a_01743","DOIUrl":"10.1162/neco_a_01743","url":null,"abstract":"Forecasting electroencephalography (EEG) signals, that is, estimating future values of the time series based on the past ones, is essential in many real-time EEG-based applications, such as brain–computer interfaces and closed-loop brain stimulation. As these applications are becoming more and more common, the importance of a good prediction model has increased. Previously, the autoregressive model (AR) has been employed for this task; however, its prediction accuracy tends to fade quickly as multiple steps are predicted. We aim to improve on this by applying probabilistic deep learning to make robust longer-range forecasts. For this, we applied the probabilistic deep neural network model WaveNet to forecast resting-state EEG in theta- (4–7.5 Hz) and alpha-frequency (8–13 Hz) bands and compared it to the AR model. WaveNet reliably predicted EEG signals in both theta and alpha frequencies 150 ms ahead, with mean absolute errors of 1.0 ± 1.1 µV (theta) and 0.9 ± 1.1 µV (alpha), and outperformed the AR model in estimating the signal amplitude and phase. Furthermore, we found that the probabilistic approach offers a way of forecasting even more accurately while effectively discarding uncertain predictions. We demonstrate for the first time that probabilistic deep learning can be used to forecast resting-state EEG time series. In the future, the developed model can enhance the real-time estimation of brain states in brain–computer interfaces and brain stimulation protocols. It may also be useful for answering neuroscientific questions and for diagnostic purposes.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 4","pages":"793-814"},"PeriodicalIF":2.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143607149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spiking Neuron-Astrocyte Networks for Image Recognition 脉冲神经元-星形胶质细胞网络用于图像识别。

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2025-03-18 DOI: 10.1162/neco_a_01740

Jhunlyn Lorenzo;Juan-Antonio Rico-Gallego;Stéphane Binczak;Sabir Jacquir

{"title":"Spiking Neuron-Astrocyte Networks for Image Recognition","authors":"Jhunlyn Lorenzo;Juan-Antonio Rico-Gallego;Stéphane Binczak;Sabir Jacquir","doi":"10.1162/neco_a_01740","DOIUrl":"10.1162/neco_a_01740","url":null,"abstract":"From biological and artificial network perspectives, researchers have started acknowledging astrocytes as computational units mediating neural processes. Here, we propose a novel biologically inspired neuron-astrocyte network model for image recognition, one of the first attempts at implementing astrocytes in spiking neuron networks (SNNs) using a standard data set. The architecture for image recognition has three primary units: the preprocessing unit for converting the image pixels into spiking patterns, the neuron-astrocyte network forming bipartite (neural connections) and tripartite synapses (neural and astrocytic connections), and the classifier unit. In the astrocyte-mediated SNNs, an astrocyte integrates neural signals following the simplified Postnov model. It then modulates the integrate-and-fire (IF) neurons via gliotransmission, thereby strengthening the synaptic connections of the neurons within the astrocytic territory. We develop an architecture derived from a baseline SNN model for unsupervised digit classification. The spiking neuron-astrocyte networks (SNANs) display better network performance with an optimal variance-bias trade-off than SNN alone. We demonstrate that astrocytes promote faster learning, support memory formation and recognition, and provide a simplified network architecture. Our proposed SNAN can serve as a benchmark for future researchers on astrocyte implementation in artificial networks, particularly in neuromorphic systems, for its simplified design.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 4","pages":"635-665"},"PeriodicalIF":2.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143607154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamics of Continuous Attractor Neural Networks With Spike Frequency Adaptation 具有尖峰频率自适应的连续吸引子神经网络动力学。

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2025-03-14 DOI: 10.1162/neco_a_01757

Yujun Li;Tianhao Chu;Si Wu

{"title":"Dynamics of Continuous Attractor Neural Networks With Spike Frequency Adaptation","authors":"Yujun Li;Tianhao Chu;Si Wu","doi":"10.1162/neco_a_01757","DOIUrl":"10.1162/neco_a_01757","url":null,"abstract":"Attractor neural networks consider that neural information is stored as stationary states of a dynamical system formed by a large number of interconnected neurons. The attractor property empowers a neural system to encode information robustly, but it also incurs the difficulty of rapid update of network states, which can impair information update and search in the brain. To overcome this difficulty, a solution is to include adaptation in the attractor network dynamics, whereby the adaptation serves as a slow negative feedback mechanism to destabilize what are otherwise permanently stable states. In such a way, the neural system can, on one hand, represent information reliably using attractor states, and on the other hand, perform computations wherever rapid state updating is involved. Previous studies have shown that continuous attractor neural networks with adaptation (A-CANNs) exhibit rich dynamical behaviors accounting for various brain functions. In this review, we present a comprehensive view of the rich diverse dynamics of A-CANNs. Moreover, we provide a unified mathematical framework to understand these different dynamical behaviors and briefly discuss their biological implications.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 6","pages":"1057-1101"},"PeriodicalIF":2.7,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144026069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neural Code Translation With LIF Neuron Microcircuits 用LIF神经元微电路进行神经代码翻译。

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2025-03-14 DOI: 10.1162/neco_a_01754

Ville Karlsson;Joni Kämäräinen

{"title":"Neural Code Translation With LIF Neuron Microcircuits","authors":"Ville Karlsson;Joni Kämäräinen","doi":"10.1162/neco_a_01754","DOIUrl":"10.1162/neco_a_01754","url":null,"abstract":"Spiking neural networks (SNNs) provide an energy-efficient alternative to traditional artificial neural networks, leveraging diverse neural encoding schemes such as rate, time-to-first-spike (TTFS), and population-based binary codes. Each encoding method offers distinct advantages: TTFS enables rapid and precise transmission with minimal energy use, rate encoding provides robust signal representation, and binary population encoding aligns well with digital hardware implementations. This letter introduces a set of neural microcircuits based on leaky integrate-and-fire neurons that enable translation between these encoding schemes. We propose two applications showcasing the utility of these microcircuits. First, we demonstrate a number comparison operation that significantly reduces spike transmission by switching from rate to TTFS encoding. Second, we present a high-bandwidth neural transmitter capable of encoding and transmitting binary population-encoded data through a single axon and reconstructing it at the target site. Additionally, we conduct a detailed analysis of these microcircuits, providing quantitative metrics to assess their efficiency in terms of neuron count, synaptic complexity, spike overhead, and runtime. Our findings highlight the potential of LIF neuron microcircuits in computational neuroscience and neuromorphic computing, offering a pathway to more interpretable and efficient SNN designs.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 6","pages":"1124-1153"},"PeriodicalIF":2.7,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144046411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamics and Bifurcation Structure of a Mean-Field Model of Adaptive Exponential Integrate-and-Fire Networks 自适应指数型积分网络平均场模型的动力学和分岔结构。

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2025-03-14 DOI: 10.1162/neco_a_01758

Lionel Kusch;Damien Depannemaecker;Alain Destexhe;Viktor Jirsa

{"title":"Dynamics and Bifurcation Structure of a Mean-Field Model of Adaptive Exponential Integrate-and-Fire Networks","authors":"Lionel Kusch;Damien Depannemaecker;Alain Destexhe;Viktor Jirsa","doi":"10.1162/neco_a_01758","DOIUrl":"10.1162/neco_a_01758","url":null,"abstract":"The study of brain activity spans diverse scales and levels of description and requires the development of computational models alongside experimental investigations to explore integrations across scales. The high dimensionality of spiking networks presents challenges for understanding their dynamics. To tackle this, a mean-field formulation offers a potential approach for dimensionality reduction while retaining essential elements. Here, we focus on a previously developed mean-field model of adaptive exponential integrate and fire (AdEx) networks used in various research work. We observe qualitative similarities in the bifurcation structure but quantitative differences in mean firing rates between the mean-field model and AdEx spiking network simulations. Even if the mean-field model does not accurately predict phase shift during transients and oscillatory input, it generally captures the qualitative dynamics of the spiking network’s response to both constant and varying inputs. Finally, we offer an overview of the dynamical properties of the AdExMF to assist future users in interpreting their results of simulations.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 6","pages":"1102-1123"},"PeriodicalIF":2.7,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144045166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Memory States From Almost Nothing: Representing and Computing in a Nonassociative Algebra 记忆状态：非结合代数的表示与计算。

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2025-03-14 DOI: 10.1162/neco_a_01755

Stefan Reimann

{"title":"Memory States From Almost Nothing: Representing and Computing in a Nonassociative Algebra","authors":"Stefan Reimann","doi":"10.1162/neco_a_01755","DOIUrl":"10.1162/neco_a_01755","url":null,"abstract":"This letter presents a nonassociative algebraic framework for the representation and computation of information items in high-dimensional space. This framework is consistent with the principles of spatial computing and with the empirical findings in cognitive science about memory. Computations are performed through a process of multiplication-like binding and nonassociative interference-like bundling. Models that rely on associative bundling typically lose order information, which necessitates the use of auxiliary order structures, such as position markers, to represent sequential information that is important for cognitive tasks. In contrast, the nonassociative bundling proposed allows the construction of sparse representations of arbitrarily long sequences that maintain their temporal structure across arbitrary lengths. In this operation, noise is a constituent element of the representation of order information rather than a means of obscuring it. The nonassociative nature of the proposed framework results in the representation of a single sequence by two distinct states. The L-state, generated through left-associative bundling, continuously updates and emphasizes a recency effect, while the R-state, formed through right-associative bundling, encodes finite sequences or chunks, capturing a primacy effect. The construction of these states may be associated with activity in the prefrontal cortex in relation to short-term memory and hippocampal encoding in long-term memory, respectively. The accuracy of retrieval is contingent on a decision-making process that is based on the mutual information between the memory states and the cue. The model is able to replicate the serial position curve, which reflects the empirical recency and primacy effects observed in cognitive experiments.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 6","pages":"1154-1170"},"PeriodicalIF":2.7,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Low-Rank, High-Order Tensor Completion via t- Product-Induced Tucker (tTucker) Decomposition 通过t积诱导塔克（tTucker）分解的低秩、高阶张量补全。

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2025-03-14 DOI: 10.1162/neco_a_01756

Yaodong Li;Jun Tan;Peilin Yang;Guoxu Zhou;Qibin Zhao

引用次数: 0

Replay as a Basis for Backpropagation Through Time in the Brain 回放作为大脑中穿越时间反向传播的基础。

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2025-02-14 DOI: 10.1162/neco_a_01735

Huzi Cheng;Joshua W. Brown

{"title":"Replay as a Basis for Backpropagation Through Time in the Brain","authors":"Huzi Cheng;Joshua W. Brown","doi":"10.1162/neco_a_01735","DOIUrl":"10.1162/neco_a_01735","url":null,"abstract":"How episodic memories are formed in the brain is a continuing puzzle for the neuroscience community. The brain areas that are critical for episodic learning (e.g., the hippocampus) are characterized by recurrent connectivity and generate frequent offline replay events. The function of the replay events is a subject of active debate. Recurrent connectivity, computational simulations show, enables sequence learning when combined with a suitable learning algorithm such as backpropagation through time (BPTT). BPTT, however, is not biologically plausible. We describe here, for the first time, a biologically plausible variant of BPTT in a reversible recurrent neural network, R2N2, that critically leverages offline replay to support episodic learning. The model uses forward and backward offline replay to transfer information between two recurrent neural networks, a cache and a consolidator, that perform rapid one-shot learning and statistical learning, respectively. Unlike replay in standard BPTT, this architecture requires no artificial external memory store. This approach outperforms existing solutions like random feedback local online learning and reservoir network. It also accounts for the functional significance of hippocampal replay events. We demonstrate the R2N2 network properties using benchmark tests from computer science and simulate the rodent delayed alternation T-maze task.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 3","pages":"403-436"},"PeriodicalIF":2.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142958829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0