Changes in GABAB modulation during a theta cycle may be analogous to the fall of temperature during annealing.

abstract：:Disparity tuning of visual cells in the brain depends on the structure of their binocular receptive fields (RFs). Freeman and coworkers have found that binocular RFs of a typical simple cell can be quantitatively described by two Gabor functions with the same gaussian envelope but different phase parameters in the sin...

journal_title：Neural computation

authors： Zhu YD,Qian N

更新日期：1996-11-15 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：: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

authors： Elliott T

更新日期：2017-06-01 00:00:00

abstract：:Modeling stereo transparency with physiologically plausible mechanisms is challenging because in such frameworks, large receptive fields mix up overlapping disparities, whereas small receptive fields can reliably compute only small disparities. It seems necessary to combine information across scales. A coarse-to-fine ...

journal_title：Neural computation

authors： Li Z,Qian N

更新日期：2015-05-01 00:00:00

abstract：:We consider the problem of training a linear feedforward neural network by using a gradient descent-like LMS learning algorithm. The objective is to find a weight matrix for the network, by repeatedly presenting to it a finite set of examples, so that the sum of the squares of the errors is minimized. Kohonen showed t...

journal_title：Neural computation

authors： Luo ZQ

更新日期：1991-07-01 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

authors： Pyle R,Rosenbaum R

更新日期：2019-07-01 00:00:00

abstract：:We explicitly analyze the trajectories of learning near singularities in hierarchical networks, such as multilayer perceptrons and radial basis function networks, which include permutation symmetry of hidden nodes, and show their general properties. Such symmetry induces singularities in their parameter space, where t...

journal_title：Neural computation

authors： Wei H,Zhang J,Cousseau F,Ozeki T,Amari S

更新日期：2008-03-01 00:00:00

abstract：:The bias/variance decomposition of mean-squared error is well understood and relatively straightforward. In this note, a similar simple decomposition is derived, valid for any kind of error measure that, when using the appropriate probability model, can be derived from a Kullback-Leibler divergence or log-likelihood. ...

journal_title：Neural computation

authors： Heskes T

更新日期：1998-07-28 00:00:00

abstract：:A mathematical model, of general character for the dynamic description of coupled neural oscillators is presented. The population approach that is employed applies equally to coupled cells as to populations of such coupled cells. The formulation includes stochasticity and preserves details of precisely firing neurons....

journal_title：Neural computation

authors： Sirovich L

更新日期：2008-05-01 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：:Previous work on analog VLSI implementation of multilayer perceptrons with on-chip learning has mainly targeted the implementation of algorithms like backpropagation. Although backpropagation is efficient, its implementation in analog VLSI requires excessive computational hardware. In this paper we show that, for anal...

journal_title：Neural computation

authors： Jabri M,Flower B

更新日期：1991-01-01 00:00:00

abstract：:A simple associationist neural network learns to factor abstract rules (i.e., grammars) from sequences of arbitrary input symbols by inventing abstract representations that accommodate unseen symbol sets as well as unseen but similar grammars. The neural network is shown to have the ability to transfer grammatical kno...

journal_title：Neural computation

authors： Hanson SJ,Negishi M

更新日期：2002-09-01 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

abstract：:We examined how the depression of intracortical inhibition due to a reduction in ambient GABA concentration impairs perceptual information processing in schizophrenia. A neural network model with a gliotransmission-mediated ambient GABA regulatory mechanism was simulated. In the network, interneuron-to-glial-cell and ...

journal_title：Neural computation

authors： Hoshino O

更新日期：2013-12-01 00:00:00

abstract：:Recently we presented a stochastic, ensemble-based model of spike-timing-dependent plasticity. In this model, single synapses do not exhibit plasticity depending on the exact timing of pre- and postsynaptic spikes, but spike-timing-dependent plasticity emerges only at the temporal or synaptic ensemble level. We showed...

journal_title：Neural computation

authors： Appleby PA,Elliott T

更新日期：2007-05-01 00:00:00

abstract：:A study of a general central pattern generator (CPG) is carried out by means of a measure of the gain of information between the number of available topology configurations and the output rhythmic activity. The neurons of the CPG are chaotic Hindmarsh-Rose models that cooperate dynamically to generate either chaotic o...

journal_title：Neural computation

authors： Stiesberg GR,Reyes MB,Varona P,Pinto RD,Huerta R

更新日期：2007-04-01 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

authors： Weber AI,Pillow JW

更新日期：2017-12-01 00:00:00

abstract：:Synaptic runaway denotes the formation of erroneous synapses and premature functional decline accompanying activity-dependent learning in neural networks. This work studies synaptic runaway both analytically and numerically in binary-firing associative memory networks. It turns out that synaptic runaway is of fairly m...

journal_title：Neural computation

authors： Greenstein-Messica A,Ruppin E

更新日期：1998-02-15 00:00:00

abstract：:In learning theory, the training and test sets are assumed to be drawn from the same probability distribution. This assumption is also followed in practical situations, where matching the training and test distributions is considered desirable. Contrary to conventional wisdom, we show that mismatched training and test...

journal_title：Neural computation

authors： González CR,Abu-Mostafa YS

更新日期：2015-02-01 00:00:00

abstract：:Recent experiments indicate that the calcium store (e.g., endoplasmic reticulum) is involved in electrical bursting and [Ca2+]i oscillation in bursting neuronal cells. In this paper, we formulate a mathematical model for bursting neurons, which includes Ca2+ in the intracellular Ca2+ stores and a voltage-independent c...

journal_title：Neural computation

authors： Chay TR

更新日期：1996-07-01 00:00:00

abstract：:Many neurons that initially respond to a stimulus stop responding if the stimulus is presented repeatedly but recover their response if a different stimulus is presented. This phenomenon is referred to as stimulus-specific adaptation (SSA). SSA has been investigated extensively using oddball experiments, which measure...

journal_title：Neural computation

authors： Mill R,Coath M,Wennekers T,Denham SL

更新日期：2011-02-01 00:00:00

abstract：:Energy-efficient information transmission may be relevant to biological sensory signal processing as well as to low-power electronic devices. We explore its consequences in two different regimes. In an "immediate" regime, we argue that the information rate should be maximized subject to a power constraint, and in an "...

journal_title：Neural computation

authors： Balasubramanian V,Kimber D,Berry MJ 2nd

更新日期：2001-04-01 00:00:00

abstract：:The ability to achieve high swimming speed and efficiency is very important to both the real lamprey and its robotic implementation. In previous studies, we used evolutionary algorithms to evolve biologically plausible connectionist swimming controllers for a simulated lamprey. This letter investigates the robustness ...

journal_title：Neural computation

authors： Or J

更新日期：2007-06-01 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：:Natural gradient learning is known to be efficient in escaping plateau, which is a main cause of the slow learning speed of neural networks. The adaptive natural gradient learning method for practical implementation also has been developed, and its advantage in real-world problems has been confirmed. In this letter, w...

journal_title：Neural computation

authors： Park H,Murata N,Amari S

更新日期：2004-02-01 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

authors： Elliott T

更新日期：2016-11-01 00:00:00

abstract：:The pyloric network of the stomatogastric ganglion in crustacea is a central pattern generator that can produce the same basic rhythm over a wide frequency range. Three electrically coupled neurons, the anterior burster (AB) neuron and two pyloric dilator (PD) neurons, act as a pacemaker unit for the pyloric network. ...

journal_title：Neural computation

authors： Abbott LF,Marder E,Hooper SL

更新日期：1991-01-01 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：: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：:In signal restoration by Bayesian inference, one typically uses a parametric model of the prior distribution of the signal. Here, we consider how the parameters of a prior model should be estimated from observations of uncorrupted signals. A lot of recent work has implicitly assumed that maximum likelihood estimation ...

journal_title：Neural computation

authors： Hyvärinen A

更新日期：2008-12-01 00:00:00