Information recall using relative spike timing in a spiking neural network.

abstract：:A modular, recurrent connectionist network is taught to incrementally parse complex sentences. From input presented one word at a time, the network learns to do semantic role assignment, noun phrase attachment, and clause structure recognition, for sentences with both active and passive constructions and center-embedd...

journal_title：Neural computation

authors： Jain AN

更新日期：1991-04-01 00:00:00

abstract：:Place cells in the rat hippocampus play a key role in creating the animal's internal representation of the world. During active navigation, these cells spike only in discrete locations, together encoding a map of the environment. Electrophysiological recordings have shown that the animal can revisit this map mentally ...

journal_title：Neural computation

authors： Dabaghian Y

更新日期：2016-06-01 00:00:00

abstract：:We study both analytically and numerically the effect of presynaptic noise on the transmission of information in attractor neural networks. The noise occurs on a very short timescale compared to that for the neuron dynamics and it produces short-time synaptic depression. This is inspired in recent neurobiological find...

journal_title：Neural computation

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

更新日期：2006-03-01 00:00:00

abstract：:We study the expressive power of positive neural networks. The model uses positive connection weights and multiple input neurons. Different behaviors can be expressed by varying the connection weights. We show that in discrete time and in the absence of noise, the class of positive neural networks captures the so-call...

journal_title：Neural computation

authors： Ameloot TJ,Van den Bussche J

更新日期：2015-12-01 00:00:00

abstract：:Theories of learning and generalization hold that the generalization bias, defined as the difference between the training error and the generalization error, increases on average with the number of adaptive parameters. This article, however, shows that this general tendency is violated for a gaussian mixture model. Fo...

journal_title：Neural computation

authors： Akaho S,Kappen HJ

更新日期：2000-06-01 00:00:00

abstract：:We derive a synaptic weight update rule for learning temporally precise spike train-to-spike train transformations in multilayer feedforward networks of spiking neurons. The framework, aimed at seamlessly generalizing error backpropagation to the deterministic spiking neuron setting, is based strictly on spike timing ...

journal_title：Neural computation

authors： Banerjee A

更新日期：2016-05-01 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：:In this letter, we investigate the fundamental limits on how the interspike time of a neuron oscillator can be perturbed by the application of a bounded external control input (a current stimulus) with zero net electric charge accumulation. We use phase models to study the dynamics of neurons and derive charge-balance...

journal_title：Neural computation

authors： Dasanayake IS,Li JS

更新日期：2014-10-01 00:00:00

abstract：:Much experimental evidence suggests that during decision making, neural circuits accumulate evidence supporting alternative options. A computational model well describing this accumulation for choices between two options assumes that the brain integrates the log ratios of the likelihoods of the sensory inputs given th...

journal_title：Neural computation

authors： Kurzawa N,Summerfield C,Bogacz R

更新日期：2017-02-01 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：:We have studied some of the design trade-offs governing visual representations based on spatially invariant conjunctive feature detectors, with an emphasis on the susceptibility of such systems to false-positive recognition errors-Malsburg's classical binding problem. We begin by deriving an analytical model that make...

journal_title：Neural computation

authors： Mel BW,Fiser J

更新日期：2000-04-01 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：:Linear-nonlinear (LN) models and their extensions have proven successful in describing transformations from stimuli to spiking responses of neurons in early stages of sensory hierarchies. Neural responses at later stages are highly nonlinear and have generally been better characterized in terms of their decoding perfo...

journal_title：Neural computation

authors： Pagan M,Simoncelli EP,Rust NC

更新日期：2016-11-01 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：: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 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：: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 study the learning of an external signal by a neural network and the time to forget it when this network is submitted to noise. The presentation of an external stimulus to the recurrent network of binary neurons may change the state of the synapses. Multiple presentations of a unique signal lead to its learning. Th...

journal_title：Neural computation

authors： Helson P

更新日期：2020-07-01 00:00:00

abstract：:We study active learning (AL) based on gaussian processes (GPs) for efficiently enumerating all of the local minimum solutions of a black-box function. This problem is challenging because local solutions are characterized by their zero gradient and positive-definite Hessian properties, but those derivatives cannot be ...

journal_title：Neural computation

authors： Inatsu Y,Sugita D,Toyoura K,Takeuchi I

更新日期：2020-10-01 00:00:00

abstract：:A neurocomputational model based on emergent massively overlapping neural cell assemblies (CAs) for resolving prepositional phrase (PP) attachment ambiguity is described. PP attachment ambiguity is a well-studied task in natural language processing and is a case where semantics is used to determine the syntactic struc...

journal_title：Neural computation

authors： Nadh K,Huyck C

更新日期：2012-07-01 00:00:00

abstract：:Multiple adjacent, roughly mirror-image topographic maps are commonly observed in the sensory neocortex of many species. The cortical regions occupied by these maps are generally believed to be determined initially by genetically controlled chemical markers during development, with thalamocortical afferent activity su...

journal_title：Neural computation

authors： Schulz R,Reggia JA

更新日期：2005-05-01 00:00:00

abstract：:In this letter, we examine a general method of approximation, known as the Kikuchi approximation method, for finding the marginals of a product distribution, as well as the corresponding partition function. The Kikuchi approximation method defines a certain constrained optimization problem, called the Kikuchi problem,...

journal_title：Neural computation

authors： Pakzad P,Anantharam V

更新日期：2005-08-01 00:00:00

abstract：:We derive analytically the solution for the output rate of the ideal coincidence detector. The solution is for an arbitrary number of input spike trains with identical binomial count distributions (which includes Poisson statistics as a special case) and identical arbitrary pairwise cross-correlations, from zero corre...

journal_title：Neural computation

authors： Mikula S,Niebur E

更新日期：2003-03-01 00:00:00

abstract：:In this letter, we perform a complete and in-depth analysis of Lorentzian noises, such as those arising from [Formula: see text] and [Formula: see text] channel kinetics, in order to identify the source of [Formula: see text]-type noise in neurological membranes. We prove that the autocovariance of Lorentzian noise de...

journal_title：Neural computation

authors： Paris A,Vosoughi A,Berman SA,Atia G

更新日期：2018-07-01 00:00:00

abstract：:We investigated a model for the neural integrator based on hysteretic units connected by positive feedback. Hysteresis is assumed to emerge from the intrinsic properties of the cells. We consider the recurrent networks containing either bistable or multistable neurons. We apply our analysis to the oculomotor velocity-...

journal_title：Neural computation

authors： Nikitchenko M,Koulakov A

更新日期：2008-10-01 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：: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 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：: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：:Ordinal classification (also known as ordinal regression) is a supervised learning task that consists of estimating the rating of a data item on a fixed, discrete rating scale. This problem is receiving increased attention from the sentiment analysis and opinion mining community due to the importance of automatically ...

journal_title：Neural computation

authors： Baccianella S,Esuli A,Sebastiani F

更新日期：2014-03-01 00:00:00