On the performance of voltage stepping for the simulation of adaptive, nonlinear integrate-and-fire neuronal networks.

abstract：:Learning in a neuronal network is often thought of as a linear superposition of synaptic modifications induced by individual stimuli. However, since biological synapses are naturally bounded, a linear superposition would cause fast forgetting of previously acquired memories. Here we show that this forgetting can be av...

journal_title：Neural computation

authors： Senn W,Fusi S

更新日期：2005-10-01 00:00:00

abstract：:The free-energy principle is a candidate unified theory for learning and memory in the brain that predicts that neurons, synapses, and neuromodulators work in a manner that minimizes free energy. However, electrophysiological data elucidating the neural and synaptic bases for this theory are lacking. Here, we propose ...

journal_title：Neural computation

authors： Isomura T,Sakai K,Kotani K,Jimbo Y

更新日期：2016-09-01 00:00:00

abstract：:Recent advances in the technology of multiunit recordings make it possible to test Hebb's hypothesis that neurons do not function in isolation but are organized in assemblies. This has created the need for statistical approaches to detecting the presence of spatiotemporal patterns of more than two neurons in neuron sp...

journal_title：Neural computation

authors： Martignon L,Deco G,Laskey K,Diamond M,Freiwald W,Vaadia E

更新日期：2000-11-01 00:00:00

abstract：:This article addresses the relationship between long-term reward predictions and slow-timescale neural activity in temporal difference (TD) models of the dopamine system. Such models attempt to explain how the activity of dopamine (DA) neurons relates to errors in the prediction of future rewards. Previous models have...

journal_title：Neural computation

authors： Daw ND,Touretzky DS

更新日期：2002-11-01 00:00:00

abstract：:In this letter, we investigate the impact of choosing different loss functions from the viewpoint of statistical learning theory. We introduce a convexity assumption, which is met by all loss functions commonly used in the literature, and study how the bound on the estimation error changes with the loss. We also deriv...

journal_title：Neural computation

authors： Rosasco L,De Vito E,Caponnetto A,Piana M,Verri A

更新日期：2004-05-01 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：: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：: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：:Neural associative memories are perceptron-like single-layer networks with fast synaptic learning typically storing discrete associations between pairs of neural activity patterns. Previous work optimized the memory capacity for various models of synaptic learning: linear Hopfield-type rules, the Willshaw model employ...

journal_title：Neural computation

authors： Knoblauch A

更新日期：2011-06-01 00:00:00

abstract：:We develop a group-theoretical analysis of slow feature analysis for the case where the input data are generated by applying a set of continuous transformations to static templates. As an application of the theory, we analytically derive nonlinear visual receptive fields and show that their optimal stimuli, as well as...

journal_title：Neural computation

authors： Sprekeler H,Wiskott L

更新日期：2011-02-01 00:00:00

abstract：:Characterizing neural spiking activity as a function of intrinsic and extrinsic factors is important in neuroscience. Point process models are valuable for capturing such information; however, the process of fully applying these models is not always obvious. A complete model application has four broad steps: specifica...

journal_title：Neural computation

authors： Sarma SV,Nguyen DP,Czanner G,Wirth S,Wilson MA,Suzuki W,Brown EN

更新日期：2011-11-01 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：: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 propose that replication (with mutation) of patterns of neuronal activity can occur within the brain using known neurophysiological processes. Thereby evolutionary algorithms implemented by neuro- nal circuits can play a role in cognition. Replication of structured neuronal representations is assumed in several cog...

journal_title：Neural computation

authors： Fernando C,Goldstein R,Szathmáry E

更新日期：2010-11-01 00:00:00

abstract：:Uncertainty coming from the noise in its neurons and the ill-posed nature of many tasks plagues neural computations. Maybe surprisingly, many studies show that the brain manipulates these forms of uncertainty in a probabilistically consistent and normative manner, and there is now a rich theoretical literature on the ...

journal_title：Neural computation

authors： Huys QJ,Zemel RS,Natarajan R,Dayan P

更新日期：2007-02-01 00:00:00

abstract：:In this work, we discuss practical methods for the assessment, comparison, and selection of complex hierarchical Bayesian models. A natural way to assess the goodness of the model is to estimate its future predictive capability by estimating expected utilities. Instead of just making a point estimate, it is important ...

journal_title：Neural computation

authors： Vehtari A,Lampinen J

更新日期：2002-10-01 00:00:00

abstract：:The prevalence of coherent oscillations in various frequency ranges in the central nervous system raises the question of the mechanisms that synchronize large populations of neurons. We study synchronization in models of large networks of spiking neurons with random sparse connectivity. Synchrony occurs only when the ...

journal_title：Neural computation

authors： Golomb D,Hansel D

更新日期：2000-05-01 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：: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：:Different analytical expressions for the membrane potential distribution of membranes subject to synaptic noise have been proposed and can be very helpful in analyzing experimental data. However, all of these expressions are either approximations or limit cases, and it is not clear how they compare and which expressio...

journal_title：Neural computation

authors： Rudolph M,Destexhe A

更新日期：2006-12-01 00:00:00

abstract：:The Bayesian evidence framework has been successfully applied to the design of multilayer perceptrons (MLPs) in the work of MacKay. Nevertheless, the training of MLPs suffers from drawbacks like the nonconvex optimization problem and the choice of the number of hidden units. In support vector machines (SVMs) for class...

journal_title：Neural computation

authors： Van Gestel T,Suykens JA,Lanckriet G,Lambrechts A,De Moor B,Vandewalle J

更新日期：2002-05-01 00:00:00

abstract：:The Nyström method is a well-known sampling-based technique for approximating the eigensystem of large kernel matrices. However, the chosen samples in the Nyström method are all assumed to be of equal importance, which deviates from the integral equation that defines the kernel eigenfunctions. Motivated by this observ...

journal_title：Neural computation

authors： Zhang K,Kwok JT

更新日期：2009-01-01 00:00:00

abstract：:Regression aims at estimating the conditional mean of output given input. However, regression is not informative enough if the conditional density is multimodal, heteroskedastic, and asymmetric. In such a case, estimating the conditional density itself is preferable, but conditional density estimation (CDE) is challen...

journal_title：Neural computation

authors： Tangkaratt V,Xie N,Sugiyama M

更新日期：2015-01-01 00:00:00

abstract：:In considering a statistical model selection of neural networks and radial basis functions under an overrealizable case, the problem of unidentifiability emerges. Because the model selection criterion is an unbiased estimator of the generalization error based on the training error, this article analyzes the expected t...

journal_title：Neural computation

authors： Hagiwara K

更新日期：2002-08-01 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 representational scheme under which the ranking between represented similarities is isomorphic to the ranking between the corresponding shape similarities can support perfectly correct shape classification because it preserves the clustering of shapes according to the natural kinds prevailing in the external world. ...

journal_title：Neural computation

authors： Edelman S,Duvdevani-Bar S

更新日期：1997-05-15 00:00:00

abstract：:The motion of an object (such as a wheel rotating) is seen as consistent independent of its position and size on the retina. Neurons in higher cortical visual areas respond to these global motion stimuli invariantly, but neurons in early cortical areas with small receptive fields cannot represent this motion, not only...

journal_title：Neural computation

authors： Rolls ET,Stringer SM

更新日期：2007-01-01 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：: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：: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