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. Based on the generally accepted view of cortical wiring, this formulation is applied to the retinal ganglion cell (RGC)/lateral geniculate nucleus (LGN) relay cell system, of the early mammalian visual system. The smallness of quantal voltage jumps at the retinal level permits a Fokker-Planck approximation for the RGC contribution; however, the LGN description requires the use of finite jumps, which for fast synaptic dynamics appears as finite jumps in the membrane potential. Analyses of equilibrium spiking behavior for both the deterministic and stochastic cases are presented. Green's function methods form the basis for the asymptotic and exact results that are presented. This determines the spiking ratio (i.e., the number of RGC arrivals per LGN spike), which is the reciprocal of the transfer ratio, under wide circumstances. Criteria for spiking regimes, in terms of the relatively few parameters of the model, are presented. Under reasonable hypotheses, it is shown that the transfer ratio is journal_name journal_title authors doi subject pub_date pages issue eissn issn journal_volume pub_type
abstract：:The Kalman filter provides a simple and efficient algorithm to compute the posterior distribution for state-space models where both the latent state and measurement models are linear and gaussian. Extensions to the Kalman filter, including the extended and unscented Kalman filters, incorporate linearizations for model...
journal_title：Neural computation
pub_type： 信件
doi：10.1162/neco_a_01275
更新日期：2020-05-01 00:00:00
abstract：:Observable operator models (OOMs) are a class of models for stochastic processes that properly subsumes the class that can be modeled by finite-dimensional hidden Markov models (HMMs). One of the main advantages of OOMs over HMMs is that they admit asymptotically correct learning algorithms. A series of learning algor...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/neco.2009.10-08-878
更新日期：2009-12-01 00:00:00
abstract：:Ohshiro, Hussain, and Weliky (2011) recently showed that ferrets reared with exposure to flickering spot stimuli, in the absence of oriented visual experience, develop oriented receptive fields. They interpreted this as refutation of efficient coding models, which require oriented input in order to develop oriented re...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/NECO_a_00333
更新日期：2012-09-01 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
pub_type： 杂志文章
doi：10.1162/NECO_a_00697
更新日期：2015-02-01 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
pub_type： 杂志文章
doi：10.1162/neco.2008.02-07-478
更新日期：2008-04-01 00:00:00
abstract：:Control in the natural environment is difficult in part because of uncertainty in the effect of actions. Uncertainty can be due to added motor or sensory noise, unmodeled dynamics, or quantization of sensory feedback. Biological systems are faced with further difficulties, since control must be performed by networks o...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/NECO_a_00151
更新日期：2011-08-01 00:00:00
abstract：:We show that Langevin Markov chain Monte Carlo inference in an energy-based model with latent variables has the property that the early steps of inference, starting from a stationary point, correspond to propagating error gradients into internal layers, similar to backpropagation. The backpropagated error is with resp...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/NECO_a_00934
更新日期：2017-03-01 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
pub_type： 杂志文章
doi：10.1162/NECO_a_00956
更新日期：2017-06-01 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
pub_type： 杂志文章
doi：10.1162/neco_a_01286
更新日期：2020-07-01 00:00:00
abstract：:The past decade has seen a rise of interest in Laplacian eigenmaps (LEMs) for nonlinear dimensionality reduction. LEMs have been used in spectral clustering, in semisupervised learning, and for providing efficient state representations for reinforcement learning. Here, we show that LEMs are closely related to slow fea...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/NECO_a_00214
更新日期：2011-12-01 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
pub_type： 杂志文章
doi：10.1162/NECO_a_00889
更新日期：2016-11-01 00:00:00
abstract：:The dynamic formation of groups of neurons--neuronal assemblies--is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis suggests that synchronized oscillations underpin their formation and functioning, with a focus...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/NECO_a_00502
更新日期：2013-11-01 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
pub_type： 杂志文章
doi：10.1162/089976600300015574
更新日期：2000-04-01 00:00:00
abstract：:Bursting plays an important role in neural communication. At the population level, macroscopic bursting has been identified in populations of neurons that do not express intrinsic bursting mechanisms. For the analysis of phase transitions between bursting and non-bursting states, mean-field descriptions of macroscopic...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/neco_a_01300
更新日期：2020-09-01 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
pub_type： 杂志文章
doi：10.1162/NECO_a_00112
更新日期：2011-05-01 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
pub_type： 杂志文章
doi：10.1162/NECO_a_00789
更新日期：2015-12-01 00:00:00
abstract：:To date, Hebbian learning combined with some form of constraint on synaptic inputs has been demonstrated to describe well the development of neural networks. The previous models revealed mathematically the importance of synaptic constraints to reproduce orientation selectivity in the visual cortical neurons, but biolo...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/neco.2009.04-08-752
更新日期：2009-09-01 00:00:00
abstract：:We simulate the inhibition of Ia-glutamatergic excitatory postsynaptic potential (EPSP) by preceding it with glycinergic recurrent (REN) and reciprocal (REC) inhibitory postsynaptic potentials (IPSPs). The inhibition is evaluated in the presence of voltage-dependent conductances of sodium, delayed rectifier potassium,...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/NECO_a_00375
更新日期：2013-01-01 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
pub_type： 杂志文章,评审
doi：10.1162/089976699300016782
更新日期：1999-01-01 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
pub_type： 杂志文章
doi：10.1162/NECO_a_01067
更新日期：2018-07-01 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
pub_type： 杂志文章
doi：10.1162/NECO_a_00127
更新日期：2011-06-01 00:00:00
abstract：:Neuromorphic engineering combines the architectural and computational principles of systems neuroscience with semiconductor electronics, with the aim of building efficient and compact devices that mimic the synaptic and neural machinery of the brain. The energy consumptions promised by neuromorphic engineering are ext...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/NECO_a_00882
更新日期：2016-10-01 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
pub_type： 杂志文章
doi：10.1162/NECO_a_00558
更新日期：2014-03-01 00:00:00
abstract：:We perform a detailed fixed-point analysis of two-unit recurrent neural networks with sigmoid-shaped transfer functions. Using geometrical arguments in the space of transfer function derivatives, we partition the network state-space into distinct regions corresponding to stability types of the fixed points. Unlike in ...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/08997660152002898
更新日期：2001-06-01 00:00:00
abstract：:Knowledge of synaptic input is crucial for understanding synaptic integration and ultimately neural function. However, in vivo, the rates at which synaptic inputs arrive are high, so that it is typically impossible to detect single events. We show here that it is nevertheless possible to extract the properties of the ...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/NECO_a_00975
更新日期：2017-07-01 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
pub_type： 杂志文章
doi：10.1162/neco.1997.9.5.971
更新日期：1997-07-01 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
pub_type： 杂志文章
doi：10.1162/0899766054796932
更新日期：2005-11-01 00:00:00
abstract：:The brain is known to be active even when not performing any overt cognitive tasks, and often it engages in involuntary mind wandering. This resting state has been extensively characterized in terms of fMRI-derived brain networks. However, an alternate method has recently gained popularity: EEG microstate analysis. Pr...
journal_title：Neural computation
pub_type： 信件
doi：10.1162/neco_a_01229
更新日期：2019-11-01 00:00:00
abstract：:Activities of sensory-specific cortices are known to be suppressed when presented with a different sensory modality stimulus. This is referred to as cross-modal inhibition, for which the conventional synaptic mechanism is unlikely to work. Interestingly, the cross-modal inhibition could be eliminated when presented wi...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/NECO_a_00356
更新日期：2012-11-01 00:00:00
abstract：:We generalize recent theoretical work on the minimal number of layers of narrow deep belief networks that can approximate any probability distribution on the states of their visible units arbitrarily well. We relax the setting of binary units (Sutskever & Hinton, 2008 ; Le Roux & Bengio, 2008 , 2010 ; Montúfar & Ay, 2...
journal_title：Neural computation
pub_type： 信件
doi：10.1162/NECO_a_00601
更新日期：2014-07-01 00:00:00
