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

abstract：:A firing rate map, also known as a tuning curve, describes the nonlinear relationship between a neuron's spike rate and a low-dimensional stimulus (e.g., orientation, head direction, contrast, color). Here we investigate Bayesian active learning methods for estimating firing rate maps in closed-loop neurophysiology ex...

journal_title：Neural computation

authors： Park M,Weller JP,Horwitz GD,Pillow JW

更新日期：2014-08-01 00:00:00

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：:The new time-organized map (TOM) is presented for a better understanding of the self-organization and geometric structure of cortical signal representations. The algorithm extends the common self-organizing map (SOM) from the processing of purely spatial signals to the processing of spatiotemporal signals. The main ad...

journal_title：Neural computation

authors： Wiemer JC

更新日期：2003-05-01 00:00:00

abstract：:A hierarchical dynamical map is proposed as the basic framework for sensory cortical mapping. To show how the hierarchical dynamical map works in cognitive processes, we applied it to a typical cognitive task known as priming, in which cognitive performance is facilitated as a consequence of prior experience. Prior to...

journal_title：Neural computation

authors： Hoshino O,Inoue S,Kashimori Y,Kambara T

更新日期：2001-08-01 00:00:00

abstract：:Although the commonly used quadratic Hebbian-anti-Hebbian rules lead to successful models of plasticity and learning, they are inconsistent with neurophysiology. Other rules, more physiologically plausible, fail to specify the biological mechanism of bidirectionality and the biological mechanism that prevents synapses...

journal_title：Neural computation

authors： Grzywacz NM,Burgi PY

更新日期：1998-04-01 00:00:00

abstract：:We have created a network that allocates a new computational unit whenever an unusual pattern is presented to the network. This network forms compact representations, yet learns easily and rapidly. The network can be used at any time in the learning process and the learning patterns do not have to be repeated. The uni...

journal_title：Neural computation

authors： Platt J

更新日期：1991-07-01 00:00:00

abstract：:We study the effect of competition between short-term synaptic depression and facilitation on the dynamic properties of attractor neural networks, using Monte Carlo simulation and a mean-field analysis. Depending on the balance of depression, facilitation, and the underlying noise, the network displays different behav...

journal_title：Neural computation

authors： Torres JJ,Cortes JM,Marro J,Kappen HJ

更新日期：2007-10-01 00:00:00

abstract：:Based on the dopamine hypotheses of cocaine addiction and the assumption of decrement of brain reward system sensitivity after long-term drug exposure, we propose a computational model for cocaine addiction. Utilizing average reward temporal difference reinforcement learning, we incorporate the elevation of basal rewa...

journal_title：Neural computation

authors： Dezfouli A,Piray P,Keramati MM,Ekhtiari H,Lucas C,Mokri A

更新日期：2009-10-01 00:00:00

abstract：:One standard interpretation of networks of cortical neurons is that they form dynamical attractors. Computations such as stimulus estimation are performed by mapping inputs to points on the networks' attractive manifolds. These points represent population codes for the stimulus values. However, this standard interpret...

journal_title：Neural computation

authors： Moazzezi R,Dayan P

更新日期：2010-12-01 00:00:00

abstract：:Temporal slowness is a learning principle that allows learning of invariant representations by extracting slowly varying features from quickly varying input signals. Slow feature analysis (SFA) is an efficient algorithm based on this principle and has been applied to the learning of translation, scale, and other invar...

journal_title：Neural computation

authors： Wiskott L

更新日期：2003-09-01 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：: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：:Synaptically generated subthreshold membrane potential (Vm) fluctuations can be characterized within the framework of stochastic calculus. It is possible to obtain analytic expressions for the steady-state Vm distribution, even in the case of conductance-based synaptic currents. However, as we show here, the analytic ...

journal_title：Neural computation

authors： Rudolph M,Destexhe A

更新日期：2005-11-01 00:00:00

abstract：:Physiological signals such as neural spikes and heartbeats are discrete events in time, driven by continuous underlying systems. A recently introduced data-driven model to analyze such a system is a state-space model with point process observations, parameters of which and the underlying state sequence are simultaneou...

journal_title：Neural computation

authors： Yuan K,Niranjan M

更新日期：2010-08-01 00:00:00

abstract：:We considered a gamma distribution of interspike intervals as a statistical model for neuronal spike generation. A gamma distribution is a natural extension of the Poisson process taking the effect of a refractory period into account. The model is specified by two parameters: a time-dependent firing rate and a shape p...

journal_title：Neural computation

authors： Miura K,Okada M,Amari S

更新日期：2006-10-01 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：: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

authors： Takase T,Oyama S,Kurihara M

更新日期：2018-07-01 00:00:00

abstract：:The visual systems of many mammals, including humans, are able to integrate the geometric information of visual stimuli and perform cognitive tasks at the first stages of the cortical processing. This is thought to be the result of a combination of mechanisms, which include feature extraction at the single cell level ...

journal_title：Neural computation

authors： Cocci G,Barbieri D,Citti G,Sarti A

更新日期：2015-06-01 00:00:00

abstract：:A general method is presented to classify temporal patterns generated by rhythmic biological networks when synaptic connections and cellular properties are known. The method is discrete in nature and relies on algebraic properties of state transitions and graph theory. Elements of the set of rhythms generated by a net...

journal_title：Neural computation

authors： Roberts PD

更新日期：1998-10-01 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：:The richness and complexity of recurrent cortical circuits is an inexhaustible source of inspiration for thinking about high-level biological computation. In past theoretical studies, constraints on the synaptic connection patterns of threshold-linear networks were found that guaranteed bounded network dynamics, conve...

journal_title：Neural computation

authors： Hahnloser RH,Seung HS,Slotine JJ

更新日期：2003-03-01 00:00:00

abstract：:In traditional event-driven strategies, spike timings are analytically given or calculated with arbitrary precision (up to machine precision). Exact computation is possible only for simplified neuron models, mainly the leaky integrate-and-fire model. In a recent paper, Zheng, Tonnelier, and Martinez (2009) introduced ...

journal_title：Neural computation

authors： Kaabi MG,Tonnelier A,Martinez D

更新日期：2011-05-01 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：:An iterative reweighted least squares (IRWLS) procedure recently proposed is shown to converge to the support vector machine solution. The convergence to a stationary point is ensured by modifying the original IRWLS procedure. ...

journal_title：Neural computation

authors： Pérez-Cruz F,Bousoño-Calzón C,Artés-Rodríguez A

更新日期：2005-01-01 00:00:00

abstract：:The emergence of synchrony in the activity of large, heterogeneous networks of spiking neurons is investigated. We define the robustness of synchrony by the critical disorder at which the asynchronous state becomes linearly unstable. We show that at low firing rates, synchrony is more robust in excitatory networks tha...

journal_title：Neural computation

authors： Neltner L,Hansel D,Mato G,Meunier C

更新日期：2000-07-01 00:00:00

abstract：:A stochastic model of spike-timing-dependent plasticity (STDP) postulates that single synapses presented with a single spike pair exhibit all-or-none quantal jumps in synaptic strength. The amplitudes of the jumps are independent of spiking timing, but their probabilities do depend on spiking timing. By making the amp...

journal_title：Neural computation

authors： Elliott T

更新日期：2010-01-01 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

authors： Golden RM

更新日期：2018-10-01 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：: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

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

更新日期：2019-03-01 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