Abstract：
:We propose a scalable semiparametric Bayesian model to capture dependencies among multiple neurons by detecting their cofiring (possibly with some lag time) patterns over time. After discretizing time so there is at most one spike at each interval, the resulting sequence of 1s (spike) and 0s (silence) for each neuron is modeled using the logistic function of a continuous latent variable with a gaussian process prior. For multiple neurons, the corresponding marginal distributions are coupled to their joint probability distribution using a parametric copula model. The advantages of our approach are as follows. The nonparametric component (i.e., the gaussian process model) provides a flexible framework for modeling the underlying firing rates, and the parametric component (i.e., the copula model) allows us to make inferences regarding both contemporaneous and lagged relationships among neurons. Using the copula model, we construct multivariate probabilistic models by separating the modeling of univariate marginal distributions from the modeling of a dependence structure among variables. Our method is easy to implement using a computationally efficient sampling algorithm that can be easily extended to high-dimensional problems. Using simulated data, we show that our approach could correctly capture temporal dependencies in firing rates and identify synchronous neurons. We also apply our model to spike train data obtained from prefrontal cortical areas.
journal_name
Neural Computjournal_title
Neural computationauthors
Shahbaba B,Zhou B,Lan S,Ombao H,Moorman D,Behseta Sdoi
10.1162/NECO_a_00631subject
Has Abstractpub_date
2014-09-01 00:00:00pages
2025-51issue
9eissn
0899-7667issn
1530-888Xjournal_volume
26pub_type
杂志文章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
pub_type： 杂志文章
doi：10.1162/089976602320264079
更新日期：2002-09-01 00:00:00
abstract：:Recently there has been great interest in sparse representations of signals under the assumption that signals (data sets) can be well approximated by a linear combination of few elements of a known basis (dictionary). Many algorithms have been developed to find such representations for one-dimensional signals (vectors...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/NECO_a_00385
更新日期：2013-01-01 00:00:00
abstract：:Many different types of integrate-and-fire models have been designed in order to explain how it is possible for a cortical neuron to integrate over many independent inputs while still producing highly variable spike trains. Within this context, the variability of spike trains has been almost exclusively measured using...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/0899766041732413
更新日期：2004-10-01 00:00:00
abstract：:This article presents new procedures for multisite spatiotemporal neuronal data analysis. A new statistical model - the diffusion model - is considered, whose parameters can be estimated from experimental data thanks to mean-field approximations. This work has been applied to optical recording of the guinea pig's audi...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/089976600300015150
更新日期：2000-08-01 00:00:00
abstract：:Neuroscience is progressing vigorously, and knowledge at different levels of description is rapidly accumulating. To establish relationships between results found at these different levels is one of the central challenges. In this simulation study, we demonstrate how microscopic cellular properties, taking the example...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/089976699300016377
更新日期：1999-07-01 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
pub_type： 信件
doi：10.1162/neco.2007.12-06-414
更新日期：2008-03-01 00:00:00
abstract：:Decision trees and neural networks are widely used tools for pattern classification. Decision trees provide highly localized representation, whereas neural networks provide a distributed but compact representation of the decision space. Decision trees cannot be induced in the online mode, and they are not adaptive to ...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/0899766041336396
更新日期：2004-09-01 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
pub_type： 杂志文章
doi：10.1162/NECO_a_00818
更新日期：2016-04-01 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：:Independent component analysis (ICA) aims at separating a multivariate signal into independent nongaussian signals by optimizing a contrast function with no knowledge on the mixing mechanism. Despite the availability of a constellation of contrast functions, a Hartley-entropy-based ICA contrast endowed with the discri...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/NECO_a_00700
更新日期：2015-03-01 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
pub_type： 杂志文章
doi：10.1162/NECO_a_00683
更新日期：2015-01-01 00:00:00
abstract：:We present a new supervised learning procedure for ensemble machines, in which outputs of predictors, trained on different distributions, are combined by a dynamic classifier combination model. This procedure may be viewed as either a version of mixture of experts (Jacobs, Jordan, Nowlan, & Hintnon, 1991), applied to ...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/089976699300016737
更新日期：1999-02-15 00:00:00
abstract：:In this note, we demonstrate that the high firing irregularity produced by the leaky integrate-and-fire neuron with the partial somatic reset mechanism, which has been shown to be the most likely candidate to reflect the mechanism used in the brain for reproducing the highly irregular cortical neuron firing at high ra...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/NECO_a_00090
更新日期：2011-03-01 00:00:00
abstract：:We present an integrative formalism of mutual information expansion, the general Poisson exact breakdown, which explicitly evaluates the informational contribution of correlations in the spike counts both between and within neurons. The formalism was validated on simulated data and applied to real neurons recorded fro...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/neco.2010.04-09-989
更新日期：2010-06-01 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 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：:Spiking neural P systems (SN P systems) are a class of distributed parallel computing devices inspired by spiking neurons, where the spiking rules are usually used in a sequential way (an applicable rule is applied one time at a step) or an exhaustive way (an applicable rule is applied as many times as possible at a s...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/NECO_a_00665
更新日期：2014-12-01 00:00:00
abstract：:Attractor networks are widely believed to underlie the memory systems of animals across different species. Existing models have succeeded in qualitatively modeling properties of attractor dynamics, but their computational abilities often suffer from poor representations for realistic complex patterns, spurious attract...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/neco.2010.02-09-957
更新日期：2010-05-01 00:00:00
abstract：:We present a reduction of a Hodgkin-Huxley (HH)--style bursting model to a hybridized integrate-and-fire (IF) formalism based on a thorough bifurcation analysis of the neuron's dynamics. The model incorporates HH--style equations to evolve the subthreshold currents and includes IF mechanisms to characterize spike even...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/089976603322518768
更新日期：2003-12-01 00:00:00
abstract：:We investigate a recently proposed model for decision learning in a population of spiking neurons where synaptic plasticity is modulated by a population signal in addition to reward feedback. For the basic model, binary population decision making based on spike/no-spike coding, a detailed computational analysis is giv...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/neco.2010.05-09-1010
更新日期：2010-07-01 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
pub_type： 杂志文章
doi：10.1162/NECO_a_00643
更新日期：2014-10-01 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
pub_type： 杂志文章
doi：10.1162/NECO_a_00722
更新日期：2015-05-01 00:00:00
abstract：:In a biological nervous system, astrocytes play an important role in the functioning and interaction of neurons, and astrocytes have excitatory and inhibitory influence on synapses. In this work, with this biological inspiration, a class of computation devices that consist of neurons and astrocytes is introduced, call...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/NECO_a_00238
更新日期：2012-03-01 00:00:00
abstract：:This letter deals with neural networks as dynamical systems governed by finite difference equations. It shows that the introduction of
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/neco_a_01165
更新日期：2019-03-01 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：:Humans have the ability to learn novel motor tasks while manipulating the environment. Several models of motor learning have been proposed in the literature, but few of them address the problem of retention and interference of motor memory. The modular selection and identification for control (MOSAIC) model, originall...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/neco.2009.03-08-721
更新日期：2009-07-01 00:00:00
abstract：:Calculation of the total conductance change induced by multiple synapses at a given membrane compartment remains one of the most time-consuming processes in biophysically realistic neural network simulations. Here we show that this calculation can be achieved in a highly efficient way even for multiply converging syna...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/089976698300017061
更新日期：1998-10-01 00:00:00
abstract：:In this work, we propose a two-layered descriptive model for motion processing from retina to the cortex, with an event-based input from the asynchronous time-based image sensor (ATIS) camera. Spatial and spatiotemporal filtering of visual scenes by motion energy detectors has been implemented in two steps in a simple...
journal_title：Neural computation
pub_type： 杂志文章
doi：10.1162/neco_a_01191
更新日期：2019-06-01 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
pub_type： 杂志文章
doi：10.1162/NECO_a_00615
更新日期：2014-08-01 00:00:00
