Methods for Assessment of Memory Reactivation.

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

authors： Millidge B,Tschantz A,Buckley CL

更新日期：2021-01-05 00:00:00

abstract：:The hypothesis of invariant maximization of interaction (IMI) is formulated within the setting of random fields. According to this hypothesis, learning processes maximize the stochastic interaction of the neurons subject to constraints. We consider the extrinsic constraint in terms of a fixed input distribution on the...

journal_title：Neural computation

authors： Ay N

更新日期：2002-12-01 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

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

更新日期：2017-12-01 00:00:00

abstract：:We describe a model of short-term synaptic depression that is derived from a circuit implementation. The dynamics of this circuit model is similar to the dynamics of some theoretical models of short-term depression except that the recovery dynamics of the variable describing the depression is nonlinear and it also dep...

journal_title：Neural computation

authors： Boegerhausen M,Suter P,Liu SC

更新日期：2003-02-01 00:00:00

abstract：:This article presents a reinforcement learning framework for continuous-time dynamical systems without a priori discretization of time, state, and action. Based on the Hamilton-Jacobi-Bellman (HJB) equation for infinite-horizon, discounted reward problems, we derive algorithms for estimating value functions and improv...

journal_title：Neural computation

authors： Doya K

更新日期：2000-01-01 00:00:00

abstract：:We present formal specification and verification of a robot moving in a complex network, using temporal sequence learning to avoid obstacles. Our aim is to demonstrate the benefit of using a formal approach to analyze such a system as a complementary approach to simulation. We first describe a classical closed-loop si...

journal_title：Neural computation

authors： Kirwan R,Miller A,Porr B,Di Prodi P

更新日期：2013-11-01 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

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

更新日期：2019-11-01 00:00:00

abstract：:The problem of designing input signals for optimal generalization is called active learning. In this article, we give a two-stage sampling scheme for reducing both the bias and variance, and based on this scheme, we propose two active learning methods. One is the multipoint search method applicable to arbitrary models...

journal_title：Neural computation

authors： Sugiyama M,Ogawa H

更新日期：2000-12-01 00:00:00

abstract：:We derive solutions for the problem of missing and noisy data in nonlinear time&hyphenseries prediction from a probabilistic point of view. We discuss different approximations to the solutions &hyphen in particular, approximations that require either stochastic simulation or the substitution of a single estimate for t...

journal_title：Neural computation

authors： Tresp V V,Hofmann R

更新日期：1998-03-23 00:00:00

abstract：:To understand the interspike interval (ISI) variability displayed by visual cortical neurons (Softky & Koch, 1993), it is critical to examine the dynamics of their neuronal integration, as well as the variability in their synaptic input current. Most previous models have focused on the latter factor. We match a simple...

journal_title：Neural computation

authors： Troyer TW,Miller KD

更新日期：1997-07-01 00:00:00

abstract：:This article addresses the topic of extracting logical rules from data by means of artificial neural networks. The approach based on piecewise linear neural networks is revisited, which has already been used for the extraction of Boolean rules in the past, and it is shown that this approach can be important also for t...

journal_title：Neural computation

authors： Holena M

更新日期：2006-11-01 00:00:00

abstract：:Event-driven simulation strategies were proposed recently to simulate integrate-and-fire (IF) type neuronal models. These strategies can lead to computationally efficient algorithms for simulating large-scale networks of neurons; most important, such approaches are more precise than traditional clock-driven numerical ...

journal_title：Neural computation

authors： Rudolph M,Destexhe A

更新日期：2006-09-01 00:00:00

abstract：:Previous studies have combined analytical models of stochastic neural responses with signal detection theory (SDT) to predict psychophysical performance limits; however, these studies have typically been limited to simple models and simple psychophysical tasks. A companion article in this issue ("Evaluating Auditory P...

journal_title：Neural computation

authors： Heinz MG,Colburn HS,Carney LH

更新日期：2001-10-01 00:00:00

abstract：:We present a model of visual computation based on tightly inter-connected cliques of pyramidal cells. It leads to a formal theory of cell assemblies, a specific relationship between correlated firing patterns and abstract functionality, and a direct calculation relating estimates of cortical cell counts to orientation...

journal_title：Neural computation

authors： Miller DA,Zucker SW

更新日期：1999-01-01 00:00:00

abstract：:Current research on discrete and rhythmic movements differs in both experimental procedures and theory, despite the ubiquitous overlap between discrete and rhythmic components in everyday behaviors. Models of rhythmic movements usually use oscillatory systems mimicking central pattern generators (CPGs). In contrast, m...

journal_title：Neural computation

authors： Ronsse R,Sternad D,Lefèvre P

更新日期：2009-05-01 00:00:00

abstract：:In this letter, we propose a noisy nonlinear version of independent component analysis (ICA). Assuming that the probability density function (p. d. f.) of sources is known, a learning rule is derived based on maximum likelihood estimation (MLE). Our model involves some algorithms of noisy linear ICA (e. g., Bermond & ...

journal_title：Neural computation

authors： Maeda S,Song WJ,Ishii S

更新日期：2005-01-01 00:00:00

abstract：:Recurrent neural networks (RNNs) can learn to perform finite state computations. It is shown that an RNN performing a finite state computation must organize its state space to mimic the states in the minimal deterministic finite state machine that can perform that computation, and a precise description of the attracto...

journal_title：Neural computation

authors： Casey M

更新日期：1996-08-15 00:00:00

abstract：:Recent studies have employed simple linear dynamical systems to model trial-by-trial dynamics in various sensorimotor learning tasks. Here we explore the theoretical and practical considerations that arise when employing the general class of linear dynamical systems (LDS) as a model for sensorimotor learning. In this ...

journal_title：Neural computation

authors： Cheng S,Sabes PN

更新日期：2006-04-01 00:00:00

abstract：:Pairwise correlations among spike trains recorded in vivo have been frequently reported. It has been argued that correlated activity could play an important role in the brain, because it efficiently modulates the response of a postsynaptic neuron. We show here that a neuron's output firing rate critically depends on t...

journal_title：Neural computation

authors： Kuhn A,Aertsen A,Rotter S

更新日期：2003-01-01 00:00:00

abstract：:We present a graphical model framework for decoding in the visual ERP-based speller system. The proposed framework allows researchers to build generative models from which the decoding rules are obtained in a straightforward manner. We suggest two models for generating brain signals conditioned on the stimulus events....

journal_title：Neural computation

authors： Martens SM,Mooij JM,Hill NJ,Farquhar J,Schölkopf B

更新日期：2011-01-01 00:00:00

abstract：:We consider the effect of the effective timing of a delayed feedback on the excitatory neuron in a recurrent inhibitory loop, when biological realities of firing and absolute refractory period are incorporated into a phenomenological spiking linear or quadratic integrate-and-fire neuron model. We show that such models...

journal_title：Neural computation

authors： Ma J,Wu J

更新日期：2007-08-01 00:00:00

abstract：:A necessary ingredient for a quantitative theory of neural coding is appropriate "spike kinematics": a precise description of spike trains. While summarizing experiments by complete spike time collections is clearly inefficient and probably unnecessary, the most common probabilistic model used in neurophysiology, the ...

journal_title：Neural computation

authors： Wójcik DK,Mochol G,Jakuczun W,Wypych M,Waleszczyk WJ

更新日期：2009-08-01 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

authors： Khoei MA,Ieng SH,Benosman R

更新日期：2019-06-01 00:00:00

abstract：:A minimal model is presented to explain changes in frequency, shape, and amplitude of Ca2+ oscillations in the neuroendocrine melanotrope cell of Xenopus Laevis. It describes the cell as a plasma membrane oscillator with influx of extracellular Ca2+ via voltage-gated Ca2+ channels in the plasma membrane. The Ca2+ osci...

journal_title：Neural computation

authors： Cornelisse LN,Scheenen WJ,Koopman WJ,Roubos EW,Gielen SC

更新日期：2001-01-01 00:00:00

abstract：:Humans have the ability to learn novel motor tasks while manipulating the environment. Several models of motor learning have been proposed in the literature, but few of them address the problem of retention and interference of motor memory. The modular selection and identification for control (MOSAIC) model, originall...

journal_title：Neural computation

authors： Lonini L,Dipietro L,Zollo L,Guglielmelli E,Krebs HI

更新日期：2009-07-01 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

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

更新日期：2020-12-01 00:00:00

abstract：:The successor representation was introduced into reinforcement learning by Dayan ( 1993 ) as a means of facilitating generalization between states with similar successors. Although reinforcement learning in general has been used extensively as a model of psychological and neural processes, the psychological validity o...

journal_title：Neural computation

authors： Gershman SJ,Moore CD,Todd MT,Norman KA,Sederberg PB

更新日期：2012-06-01 00:00:00

abstract：:We address the problem of detecting the presence of a recurring stimulus by monitoring the voltage on a multiunit electrode located in a brain region densely populated by stimulus reactive neurons. Published experimental results suggest that under these conditions, when a stimulus is present, the measurements are gaus...

journal_title：Neural computation

authors： Nossenson N,Messer H

更新日期：2012-04-01 00:00:00

abstract：:A fast and accurate computational scheme for simulating nonlinear dynamic systems is presented. The scheme assumes that the system can be represented by a combination of components of only two different types: first-order low-pass filters and static nonlinearities. The parameters of these filters and nonlinearities ma...

journal_title：Neural computation

authors： van Hateren JH

更新日期：2008-07-01 00:00:00

abstract：:Common representation learning (CRL), wherein different descriptions (or views) of the data are embedded in a common subspace, has been receiving a lot of attention recently. Two popular paradigms here are canonical correlation analysis (CCA)-based approaches and autoencoder (AE)-based approaches. CCA-based approaches...

journal_title：Neural computation

authors： Chandar S,Khapra MM,Larochelle H,Ravindran B

更新日期：2016-02-01 00:00:00