解剖学和形态学
麻醉学
听力与言语-语言病理学
行为科学
心脏和心血管系统
细胞和组织工程学
临床神经病学
危重症监护医学
牙科,口腔外科和医学
皮肤病学
急诊医学
内分泌学和新陈代谢
肠胃学和肝脏学
老人病学和老年医学
卫生保健科学和服务
血液学
免疫学
传染病
综合和补充性医学
医学伦理学
医学信息学
医学实验室技术
医学,全科和内科
医学,法律
医学,研究和试验
神经系统科学
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营养学和饮食学
产科医学和妇科医学
肿瘤学
眼科学
整形外科学
耳鼻喉科学
病理学
儿科学
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药理学和药剂学
生理学
基本医疗保健
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公共、环境和职业卫生
放射学,核医学和医学成像
康复学
生殖生物学
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风湿病学
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热带医学
泌尿学和肾脏学
病毒学
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健康政策和服务
心理学,临床
abstract::The expected free energy (EFE) is a central quantity in the theory of active inference. It is the quantity that all active inference agents are mandated to minimize through action, and its decomposition into extrinsic and intrinsic value terms is key to the balance of exploration and exploitation that active inference...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01354
更新日期:2021-01-05 00:00:00
abstract::Mild traumatic brain injury (mTBI) presents a significant health concern with potential persisting deficits that can last decades. Although a growing body of literature improves our understanding of the brain network response and corresponding underlying cellular alterations after injury, the effects of cellular disru...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01343
更新日期:2021-01-01 00:00:00
abstract::Spiking neural networks (SNNs) with the event-driven manner of transmitting spikes consume ultra-low power on neuromorphic chips. However, training deep SNNs is still challenging compared to convolutional neural networks (CNNs). The SNN training algorithms have not achieved the same performance as CNNs. In this letter...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01319
更新日期:2020-12-01 00:00:00
abstract::We develop theoretical foundations of resonator networks, a new type of recurrent neural network introduced in Frady, Kent, Olshausen, and Sommer (2020), a companion article in this issue, to solve a high-dimensional vector factorization problem arising in Vector Symbolic Architectures. Given a composite vector formed...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01329
更新日期:2020-12-01 00:00:00
abstract::We study active learning (AL) based on gaussian processes (GPs) for efficiently enumerating all of the local minimum solutions of a black-box function. This problem is challenging because local solutions are characterized by their zero gradient and positive-definite Hessian properties, but those derivatives cannot be ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01307
更新日期:2020-10-01 00:00:00
abstract::Bursting plays an important role in neural communication. At the population level, macroscopic bursting has been identified in populations of neurons that do not express intrinsic bursting mechanisms. For the analysis of phase transitions between bursting and non-bursting states, mean-field descriptions of macroscopic...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01300
更新日期:2020-09-01 00:00:00
abstract::We study the learning of an external signal by a neural network and the time to forget it when this network is submitted to noise. The presentation of an external stimulus to the recurrent network of binary neurons may change the state of the synapses. Multiple presentations of a unique signal lead to its learning. Th...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01286
更新日期:2020-07-01 00:00:00
abstract::The Kalman filter provides a simple and efficient algorithm to compute the posterior distribution for state-space models where both the latent state and measurement models are linear and gaussian. Extensions to the Kalman filter, including the extended and unscented Kalman filters, incorporate linearizations for model...
journal_title:Neural computation
pub_type: 信件
doi:10.1162/neco_a_01275
更新日期:2020-05-01 00:00:00
abstract::To exhibit social intelligence, animals have to recognize whom they are communicating with. One way to make this inference is to select among internal generative models of each conspecific who may be encountered. However, these models also have to be learned via some form of Bayesian belief updating. This induces an i...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01239
更新日期:2019-12-01 00:00:00
abstract::The brain is known to be active even when not performing any overt cognitive tasks, and often it engages in involuntary mind wandering. This resting state has been extensively characterized in terms of fMRI-derived brain networks. However, an alternate method has recently gained popularity: EEG microstate analysis. Pr...
journal_title:Neural computation
pub_type: 信件
doi:10.1162/neco_a_01229
更新日期:2019-11-01 00:00:00
abstract::This letter proposes a multichannel source separation technique, the multichannel variational autoencoder (MVAE) method, which uses a conditional VAE (CVAE) to model and estimate the power spectrograms of the sources in a mixture. By training the CVAE using the spectrograms of training examples with source-class label...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01217
更新日期:2019-09-01 00:00:00
abstract::A significant threat to the recent, wide deployment of machine learning-based systems, including deep neural networks (DNNs), is adversarial learning attacks. The main focus here is on evasion attacks against DNN-based classifiers at test time. While much work has focused on devising attacks that make small perturbati...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01209
更新日期:2019-08-01 00:00:00
abstract::Reservoir computing is a biologically inspired class of learning algorithms in which the intrinsic dynamics of a recurrent neural network are mined to produce target time series. Most existing reservoir computing algorithms rely on fully supervised learning rules, which require access to an exact copy of the target re...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01198
更新日期:2019-07-01 00:00:00
abstract::In this work, we propose a two-layered descriptive model for motion processing from retina to the cortex, with an event-based input from the asynchronous time-based image sensor (ATIS) camera. Spatial and spatiotemporal filtering of visual scenes by motion energy detectors has been implemented in two steps in a simple...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01191
更新日期:2019-06-01 00:00:00
abstract::Decision making is a complex task, and its underlying mechanisms that regulate behavior, such as the implementation of the coupling between physiological states and neural networks, are hard to decipher. To gain more insight into neural computations underlying ongoing binary decision-making tasks, we consider a neural...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01185
更新日期:2019-05-01 00:00:00
abstract::The need to reason about uncertainty in large, complex, and multimodal data sets has become increasingly common across modern scientific environments. The ability to transform samples from one distribution journal_title:Neural computation pub_type: 杂志文章 doi:10.1162/neco_a_01172 更新日期:2019-04-01 00:00:00
abstract::This letter deals with neural networks as dynamical systems governed by finite difference equations. It shows that the introduction of
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01165
更新日期:2019-03-01 00:00:00
abstract::Intracortical brain computer interfaces can enable individuals with paralysis to control external devices through voluntarily modulated brain activity. Decoding quality has been previously shown to degrade with signal nonstationarities-specifically, the changes in the statistics of the data between training and testin...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01129
更新日期:2018-11-01 00:00:00
abstract::Although the number of artificial neural network and machine learning architectures is growing at an exponential pace, more attention needs to be paid to theoretical guarantees of asymptotic convergence for novel, nonlinear, high-dimensional adaptive learning algorithms. When properly understood, such guarantees can g...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01117
更新日期:2018-10-01 00:00:00
abstract::A hippocampal prosthesis is a very large scale integration (VLSI) biochip that needs to be implanted in the biological brain to solve a cognitive dysfunction. In this letter, we propose a novel low-complexity, small-area, and low-power programmable hippocampal neural network application-specific integrated circuit (AS...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01107
更新日期:2018-09-01 00:00:00
abstract::We formulate an equivalence between machine learning and the formulation of statistical data assimilation as used widely in physical and biological sciences. The correspondence is that layer number in a feedforward artificial network setting is the analog of time in the data assimilation setting. This connection has b...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01094
更新日期:2018-08-01 00:00:00
abstract::It has been suggested that reactivation of previously acquired experiences or stored information in declarative memories in the hippocampus and neocortex contributes to memory consolidation and learning. Understanding memory consolidation depends crucially on the development of robust statistical methods for assessing...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01090
更新日期:2018-08-01 00:00:00
abstract::We present a comprehensive framework of search methods, such as simulated annealing and batch training, for solving nonconvex optimization problems. These methods search a wider range by gradually decreasing the randomness added to the standard gradient descent method. The formulation that we define on the basis of th...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01089
更新日期:2018-07-01 00:00:00
abstract::In this letter, we perform a complete and in-depth analysis of Lorentzian noises, such as those arising from [Formula: see text] and [Formula: see text] channel kinetics, in order to identify the source of [Formula: see text]-type noise in neurological membranes. We prove that the autocovariance of Lorentzian noise de...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_01067
更新日期:2018-07-01 00:00:00
abstract::A neuronal population is a computational unit that receives a multivariate, time-varying input signal and creates a related multivariate output. These neural signals are modeled as stochastic processes that transmit information in real time, subject to stochastic noise. In a stationary environment, where the input sig...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01057
更新日期:2018-04-01 00:00:00
abstract::We extend the neural Turing machine (NTM) model into a dynamic neural Turing machine (D-NTM) by introducing trainable address vectors. This addressing scheme maintains for each memory cell two separate vectors, content and address vectors. This allows the D-NTM to learn a wide variety of location-based addressing stra...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01060
更新日期:2018-04-01 00:00:00
abstract::Under the goal-driven paradigm, Yamins et al. ( 2014 ; Yamins & DiCarlo, 2016 ) have shown that by optimizing only the final eight-way categorization performance of a four-layer hierarchical network, not only can its top output layer quantitatively predict IT neuron responses but its penultimate layer can also automat...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01039
更新日期:2018-02-01 00:00:00
abstract::A key problem in computational neuroscience is to find simple, tractable models that are nevertheless flexible enough to capture the response properties of real neurons. Here we examine the capabilities of recurrent point process models known as Poisson generalized linear models (GLMs). These models are defined by a s...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01021
更新日期:2017-12-01 00:00:00
abstract::High-density electrocorticogram (ECoG) electrodes are capable of recording neurophysiological data with high temporal resolution with wide spatial coverage. These recordings are a window to understanding how the human brain processes information and subsequently behaves in healthy and pathologic states. Here, we descr...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01009
更新日期:2017-12-01 00:00:00
abstract::The statistical dependencies that independent component analysis (ICA) cannot remove often provide rich information beyond the linear independent components. It would thus be very useful to estimate the dependency structure from data. While such models have been proposed, they have usually concentrated on higher-order...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01006
更新日期:2017-11-01 00:00:00
abstract::Knowledge of synaptic input is crucial for understanding synaptic integration and ultimately neural function. However, in vivo, the rates at which synaptic inputs arrive are high, so that it is typically impossible to detect single events. We show here that it is nevertheless possible to extract the properties of the ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00975
更新日期:2017-07-01 00:00:00
abstract::Lossy compression and clustering fundamentally involve a decision about which features are relevant and which are not. The information bottleneck method (IB) by Tishby, Pereira, and Bialek ( 1999 ) formalized this notion as an information-theoretic optimization problem and proposed an optimal trade-off between throwin...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00961
更新日期:2017-06-01 00:00:00
abstract::Memory models that store new memories by forgetting old ones have memory lifetimes that are rather short and grow only logarithmically in the number of synapses. Attempts to overcome these deficits include "complex" models of synaptic plasticity in which synapses possess internal states governing the expression of syn...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00956
更新日期:2017-06-01 00:00:00
abstract::This review examines the relevance of parameter identifiability for statistical models used in machine learning. In addition to defining main concepts, we address several issues of identifiability closely related to machine learning, showing the advantages and disadvantages of state-of-the-art research and demonstrati...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00947
更新日期:2017-05-01 00:00:00
abstract::We show that Langevin Markov chain Monte Carlo inference in an energy-based model with latent variables has the property that the early steps of inference, starting from a stationary point, correspond to propagating error gradients into internal layers, similar to backpropagation. The backpropagated error is with resp...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00934
更新日期:2017-03-01 00:00:00
abstract::Much experimental evidence suggests that during decision making, neural circuits accumulate evidence supporting alternative options. A computational model well describing this accumulation for choices between two options assumes that the brain integrates the log ratios of the likelihoods of the sensory inputs given th...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00917
更新日期:2017-02-01 00:00:00
abstract::Function approximation in online, incremental, reinforcement learning needs to deal with two fundamental problems: biased sampling and nonstationarity. In this kind of task, biased sampling occurs because samples are obtained from specific trajectories dictated by the dynamics of the environment and are usually concen...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00906
更新日期:2017-01-01 00:00:00
abstract::Linear-nonlinear (LN) models and their extensions have proven successful in describing transformations from stimuli to spiking responses of neurons in early stages of sensory hierarchies. Neural responses at later stages are highly nonlinear and have generally been better characterized in terms of their decoding perfo...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00890
更新日期:2016-11-01 00:00:00
abstract::Integrate-and-express models of synaptic plasticity propose that synapses integrate plasticity induction signals before expressing synaptic plasticity. By discerning trends in their induction signals, synapses can control destabilizing fluctuations in synaptic strength. In a feedforward perceptron framework with binar...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00889
更新日期:2016-11-01 00:00:00
abstract::Neuromorphic engineering combines the architectural and computational principles of systems neuroscience with semiconductor electronics, with the aim of building efficient and compact devices that mimic the synaptic and neural machinery of the brain. The energy consumptions promised by neuromorphic engineering are ext...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00882
更新日期:2016-10-01 00:00:00