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  • Whence the Expected Free Energy?

    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

    authors: Millidge B,Tschantz A,Buckley CL

    更新日期:2021-01-05 00:00:00

  • NMDA Receptor Alterations After Mild Traumatic Brain Injury Induce Deficits in Memory Acquisition and Recall.

    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

    authors: Gabrieli D,Schumm SN,Vigilante NF,Meaney DF

    更新日期:2021-01-01 00:00:00

  • Analyzing and Accelerating the Bottlenecks of Training Deep SNNs With Backpropagation.

    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

    authors: Chen R,Li L

    更新日期:2020-12-01 00:00:00

  • Resonator Networks, 2: Factorization Performance and Capacity Compared to Optimization-Based Methods.

    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

    authors: Kent SJ,Frady EP,Sommer FT,Olshausen BA

    更新日期:2020-12-01 00:00:00

  • Active Learning for Enumerating Local Minima Based on Gaussian Process Derivatives.

    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

    authors: Inatsu Y,Sugita D,Toyoura K,Takeuchi I

    更新日期:2020-10-01 00:00:00

  • A Mean-Field Description of Bursting Dynamics in Spiking Neural Networks with Short-Term Adaptation.

    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

    authors: Gast R,Schmidt H,Knösche TR

    更新日期:2020-09-01 00:00:00

  • A Mathematical Analysis of Memory Lifetime in a Simple Network Model of Memory.

    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

    authors: Helson P

    更新日期:2020-07-01 00:00:00

  • The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models.

    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

    authors: Burkhart MC,Brandman DM,Franco B,Hochberg LR,Harrison MT

    更新日期:2020-05-01 00:00:00

  • Bayesian Filtering with Multiple Internal Models: Toward a Theory of Social Intelligence.

    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

    authors: Isomura T,Parr T,Friston K

    更新日期:2019-12-01 00:00:00

  • Capturing the Forest but Missing the Trees: Microstates Inadequate for Characterizing Shorter-Scale EEG Dynamics.

    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

    authors: Shaw SB,Dhindsa K,Reilly JP,Becker S

    更新日期:2019-11-01 00:00:00

  • Supervised Determined Source Separation with Multichannel Variational Autoencoder.

    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

    authors: Kameoka H,Li L,Inoue S,Makino S

    更新日期:2019-09-01 00:00:00

  • When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Time.

    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

    authors: Miller D,Wang Y,Kesidis G

    更新日期:2019-08-01 00:00:00

  • A Reservoir Computing Model of Reward-Modulated Motor Learning and Automaticity.

    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

    authors: Pyle R,Rosenbaum R

    更新日期:2019-07-01 00:00:00

  • Asynchronous Event-Based Motion Processing: From Visual Events to Probabilistic Sensory Representation.

    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

    authors: Khoei MA,Ieng SH,Benosman R

    更新日期:2019-06-01 00:00:00

  • Inhibition and Excitation Shape Activity Selection: Effect of Oscillations in a Decision-Making Circuit.

    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

    authors: Bose T,Reina A,Marshall JAR

    更新日期:2019-05-01 00:00:00

  • A Distributed Framework for the Construction of Transport Maps.

    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 P to another distribution

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/neco_a_01172

    authors: Mesa DA,Tantiongloc J,Mendoza M,Kim S,P Coleman T

    更新日期:2019-04-01 00:00:00

  • State-Space Representations of Deep Neural Networks.

    abstract::This letter deals with neural networks as dynamical systems governed by finite difference equations. It shows that the introduction of k -many skip connections into network architectures, such as residual networks and additive dense n...

    journal_title:Neural computation

    pub_type: 杂志文章

    doi:10.1162/neco_a_01165

    authors: Hauser M,Gunn S,Saab S Jr,Ray A

    更新日期:2019-03-01 00:00:00

  • Robust Closed-Loop Control of a Cursor in a Person with Tetraplegia using Gaussian Process Regression.

    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

    authors: Brandman DM,Burkhart MC,Kelemen J,Franco B,Harrison MT,Hochberg LR

    更新日期:2018-11-01 00:00:00

  • Adaptive Learning Algorithm Convergence in Passive and Reactive Environments.

    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

    authors: Golden RM

    更新日期:2018-10-01 00:00:00

  • ASIC Implementation of a Nonlinear Dynamical Model for Hippocampal Prosthesis.

    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

    authors: Qiao Z,Han Y,Han X,Xu H,Li WXY,Song D,Berger TW,Cheung RCC

    更新日期:2018-09-01 00:00:00

  • Machine Learning: Deepest Learning as Statistical Data Assimilation Problems.

    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

    authors: Abarbanel HDI,Rozdeba PJ,Shirman S

    更新日期:2018-08-01 00:00:00

  • Methods for Assessment of Memory Reactivation.

    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

    authors: Liu S,Grosmark AD,Chen Z

    更新日期:2018-08-01 00:00:00

  • Why Does Large Batch Training Result in Poor Generalization? A Comprehensive Explanation and a Better Strategy from the Viewpoint of Stochastic Optimization.

    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

    authors: Takase T,Oyama S,Kurihara M

    更新日期:2018-07-01 00:00:00

  • Hidden Quantum Processes, Quantum Ion Channels, and 1/ fθ-Type Noise.

    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

    authors: Paris A,Vosoughi A,Berman SA,Atia G

    更新日期:2018-07-01 00:00:00

  • A Unifying Framework of Synaptic and Intrinsic Plasticity in Neural Populations.

    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

    authors: Leugering J,Pipa G

    更新日期:2018-04-01 00:00:00

  • Dynamic Neural Turing Machine with Continuous and Discrete Addressing Schemes.

    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

    authors: Gulcehre C,Chandar S,Cho K,Bengio Y

    更新日期:2018-04-01 00:00:00

  • Statistics of Visual Responses to Image Object Stimuli from Primate AIT Neurons to DNN Neurons.

    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

    authors: Dong Q,Wang H,Hu Z

    更新日期:2018-02-01 00:00:00

  • Capturing the Dynamical Repertoire of Single Neurons with Generalized Linear Models.

    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

    authors: Weber AI,Pillow JW

    更新日期:2017-12-01 00:00:00

  • Delay Differential Analysis of Seizures in Multichannel Electrocorticography Data.

    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

    authors: Lainscsek C,Weyhenmeyer J,Cash SS,Sejnowski TJ

    更新日期:2017-12-01 00:00:00

  • Simultaneous Estimation of Nongaussian Components and Their Correlation Structure.

    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

    authors: Sasaki H,Gutmann MU,Shouno H,Hyvärinen A

    更新日期:2017-11-01 00:00:00

  • Extraction of Synaptic Input Properties in Vivo.

    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

    authors: Puggioni P,Jelitai M,Duguid I,van Rossum MCW

    更新日期:2017-07-01 00:00:00

  • The Deterministic Information Bottleneck.

    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

    authors: Strouse DJ,Schwab DJ

    更新日期:2017-06-01 00:00:00

  • Mean First Passage Memory Lifetimes by Reducing Complex Synapses to Simple Synapses.

    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

    authors: Elliott T

    更新日期:2017-06-01 00:00:00

  • Parameter Identifiability in Statistical Machine Learning: A Review.

    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

    authors: Ran ZY,Hu BG

    更新日期:2017-05-01 00:00:00

  • STDP-Compatible Approximation of Backpropagation in an Energy-Based Model.

    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

    authors: Bengio Y,Mesnard T,Fischer A,Zhang S,Wu Y

    更新日期:2017-03-01 00:00:00

  • Neural Circuits Trained with Standard Reinforcement Learning Can Accumulate Probabilistic Information during Decision Making.

    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

    authors: Kurzawa N,Summerfield C,Bogacz R

    更新日期:2017-02-01 00:00:00

  • Online Reinforcement Learning Using a Probability Density Estimation.

    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

    authors: Agostini A,Celaya E

    更新日期:2017-01-01 00:00:00

  • Neural Quadratic Discriminant Analysis: Nonlinear Decoding with V1-Like Computation.

    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

    authors: Pagan M,Simoncelli EP,Rust NC

    更新日期:2016-11-01 00:00:00

  • Variations on the Theme of Synaptic Filtering: A Comparison of Integrate-and-Express Models of Synaptic Plasticity for Memory Lifetimes.

    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

    authors: Elliott T

    更新日期:2016-11-01 00:00:00

  • Energy-Efficient Neuromorphic Classifiers.

    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

    authors: Martí D,Rigotti M,Seok M,Fusi S

    更新日期:2016-10-01 00:00:00

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