Parameter learning for alpha integration.

abstract：:Tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during egomotion. In this study, we examine whether a simplified linear model based on the organization principles in tangential neurons can be used to estimate egomotion from the optic flow. We present a theory for the cons...

journal_title：Neural computation

authors： Franz MO,Chahl JS,Krapp HG

更新日期：2004-11-01 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

authors： Bose T,Reina A,Marshall JAR

更新日期：2019-05-01 00:00:00

abstract：:We propose a modular reinforcement learning architecture for nonlinear, nonstationary control tasks, which we call multiple model-based reinforcement learning (MMRL). The basic idea is to decompose a complex task into multiple domains in space and time based on the predictability of the environmental dynamics. The sys...

journal_title：Neural computation

authors： Doya K,Samejima K,Katagiri K,Kawato M

更新日期：2002-06-01 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：:Recent experimental and computational evidence suggests that several dynamical properties may characterize the operating point of functioning neural networks: critical branching, neutral stability, and production of a wide range of firing patterns. We seek the simplest setting in which these properties emerge, clarify...

journal_title：Neural computation

authors： Cayco-Gajic NA,Shea-Brown E

更新日期：2013-07-01 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：:Pharmacologically isolated GABAergic irregular spiking and stuttering interneurons in the mouse visual cortex display highly irregular spike times, with high coefficients of variation approximately 0.9-3, in response to a depolarizing, constant current input. This is in marked contrast to cortical pyramidal cells, whi...

journal_title：Neural computation

authors： Englitz B,Stiefel KM,Sejnowski TJ

更新日期：2008-01-01 00:00:00

abstract：:Perceiving and identifying an object is improved by prior exposure to the object. This perceptual priming phenomenon is accompanied by reduced neural activity. But whether suppression of neuronal activity with priming is responsible for the improvement in perception is unclear. To address this problem, we developed a ...

journal_title：Neural computation

authors： Moldakarimov S,Bazhenov M,Sejnowski TJ

更新日期：2010-05-01 00:00:00

abstract：:We present a neural network that is capable of completing and correcting a spiking pattern given only a partial, noisy version. It operates in continuous time and represents information using the relative timing of individual spikes. The network is capable of correcting and recalling multiple patterns simultaneously. ...

journal_title：Neural computation

authors： Sterne P

更新日期：2012-08-01 00:00:00

abstract：:A variant of spiking neural P systems with positive or negative weights on synapses is introduced, where the rules of a neuron fire when the potential of that neuron equals a given value. The involved values-weights, firing thresholds, potential consumed by each rule-can be real (computable) numbers, rational numbers,...

journal_title：Neural computation

authors： Wang J,Hoogeboom HJ,Pan L,Păun G,Pérez-Jiménez MJ

更新日期：2010-10-01 00:00:00

abstract：:We analyze convergence of the expectation maximization (EM) and variational Bayes EM (VBEM) schemes for parameter estimation in noisy linear models. The analysis shows that both schemes are inefficient in the low-noise limit. The linear model with additive noise includes as special cases independent component analysis...

journal_title：Neural computation

authors： Petersen KB,Winther O,Hansen LK

更新日期：2005-09-01 00:00:00

abstract：:The mutual information between a set of stimuli and the elicited neural responses is compared to the corresponding decoded information. The decoding procedure is presented as an artificial distortion of the joint probabilities between stimuli and responses. The information loss is quantified. Whenever the probabilitie...

journal_title：Neural computation

authors： Samengo I

更新日期：2002-04-01 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：:Recurrent neural architectures having oscillatory dynamics use rhythmic network activity to represent patterns stored in short-term memory. Multiple stored patterns can be retained in memory over the same neural substrate because the network's state persistently switches between them. Here we present a simple oscillat...

journal_title：Neural computation

authors： Winder RK,Reggia JA,Weems SA,Bunting MF

更新日期：2009-03-01 00:00:00

abstract：:Spiking neural P systems (SN P systems) are a class of distributed parallel computing devices inspired by spiking neurons, where the spiking rules are usually used in a sequential way (an applicable rule is applied one time at a step) or an exhaustive way (an applicable rule is applied as many times as possible at a s...

journal_title：Neural computation

authors： Zhang X,Wang B,Pan L

更新日期：2014-12-01 00:00:00

abstract：:We propose a novel paradigm for spike train decoding, which avoids entirely spike sorting based on waveform measurements. This paradigm directly uses the spike train collected at recording electrodes from thresholding the bandpassed voltage signal. Our approach is a paradigm, not an algorithm, since it can be used wit...

journal_title：Neural computation

authors： Ventura V

更新日期：2008-04-01 00:00:00

abstract：:Particular levels of partial fault tolerance (PFT) in feedforward artificial neural networks of a given size can be obtained by redundancy (replicating a smaller normally trained network), by design (training specifically to increase PFT), and by a combination of the two (replicating a smaller PFT-trained network). Th...

journal_title：Neural computation

authors： Tchernev EB,Mulvaney RG,Phatak DS

更新日期：2005-07-01 00:00:00

abstract：:We present a new supervised learning procedure for ensemble machines, in which outputs of predictors, trained on different distributions, are combined by a dynamic classifier combination model. This procedure may be viewed as either a version of mixture of experts (Jacobs, Jordan, Nowlan, & Hintnon, 1991), applied to ...

journal_title：Neural computation

authors： Avnimelech R,Intrator N

更新日期：1999-02-15 00:00:00

abstract：:In this article, a biologically plausible and efficient object recognition system (called ORASSYLL) is introduced, based on a set of a priori constraints motivated by findings of developmental psychology and neurophysiology. These constraints are concerned with the organization of the input in local and corresponding ...

journal_title：Neural computation

authors： Krüger N

更新日期：2001-02-01 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：:For the paradigmatic case of bimanual coordination, we review levels of organization of behavioral dynamics and present a description in terms of modes of behavior. We briefly review a recently developed model of spatiotemporal brain activity that is based on short- and long-range connectivity of neural ensembles. Thi...

journal_title：Neural computation

authors： Jirsa VK,Fuchs A,Kelso JA

更新日期：1998-11-15 00:00:00

abstract：:For gradient descent learning to yield connectivity consistent with real biological networks, the simulated neurons would have to include more realistic intrinsic properties such as frequency adaptation. However, gradient descent learning cannot be used straightforwardly with adapting rate-model neurons because the de...

journal_title：Neural computation

authors： Fortier PA,Guigon E,Burnod Y

更新日期：2005-09-01 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

authors： Abarbanel HDI,Rozdeba PJ,Shirman S

更新日期：2018-08-01 00:00:00

abstract：:This article presents new procedures for multisite spatiotemporal neuronal data analysis. A new statistical model - the diffusion model - is considered, whose parameters can be estimated from experimental data thanks to mean-field approximations. This work has been applied to optical recording of the guinea pig's audi...

journal_title：Neural computation

authors： François O,Abdallahi LM,Horikawa J,Taniguchi I,Hervé T

更新日期：2000-08-01 00:00:00

abstract：:In "Isotropic Sequence Order Learning" (pp. 831-864 in this issue), we introduced a novel algorithm for temporal sequence learning (ISO learning). Here, we embed this algorithm into a formal nonevaluating (teacher free) environment, which establishes a sensor-motor feedback. The system is initially guided by a fixed r...

journal_title：Neural computation

authors： Porr B,von Ferber C,Wörgötter F

更新日期：2003-04-01 00:00:00

abstract：:Robust coding has been proposed as a solution to the problem of minimizing decoding error in the presence of neural noise. Many real-world problems, however, have degradation in the input signal, not just in neural representations. This generalized problem is more relevant to biological sensory coding where internal n...

journal_title：Neural computation

authors： Doi E,Lewicki MS

更新日期：2011-10-01 00:00:00

abstract：:Attractor networks are widely believed to underlie the memory systems of animals across different species. Existing models have succeeded in qualitatively modeling properties of attractor dynamics, but their computational abilities often suffer from poor representations for realistic complex patterns, spurious attract...

journal_title：Neural computation

authors： Hu X,Zhang B

更新日期：2010-05-01 00:00:00

abstract：:Volterra and Wiener series are perhaps the best-understood nonlinear system representations in signal processing. Although both approaches have enjoyed a certain popularity in the past, their application has been limited to rather low-dimensional and weakly nonlinear systems due to the exponential growth of the number...

journal_title：Neural computation

authors： Franz MO,Schölkopf B

更新日期：2006-12-01 00:00:00

abstract：:The ability to encode and transmit a signal is an essential property that must demonstrate many neuronal circuits in sensory areas in addition to any processing they may provide. It is known that an appropriate level of lateral inhibition, as observed in these areas, can significantly improve the encoding ability of a...

journal_title：Neural computation

authors： Coulon A,Beslon G,Soula HA

更新日期：2011-04-01 00:00:00

abstract：:Ramping neuronal activity refers to spiking activity with a rate that increases quasi-linearly over time. It has been observed in multiple cortical areas and is correlated with evidence accumulation processes or timing. In this work, we investigated the downstream effect of ramping neuronal activity through synapses t...

journal_title：Neural computation

authors： Wei W,Wang XJ

更新日期：2016-04-01 00:00:00