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 performance on prespecified tasks. Here we develop a biologically plausible decoding model for classification tasks, that we refer to as neural quadratic discriminant analysis (nQDA). Specifically, we reformulate an optimal quadratic classifier as an LN-LN computation, analogous to "subunit" encoding models that have been used to describe responses in retina and primary visual cortex. We propose a physiological mechanism by which the parameters of the nQDA classifier could be optimized, using a supervised variant of a Hebbian learning rule. As an example of its applicability, we show that nQDA provides a better account than many comparable alternatives for the transformation between neural representations in two high-level brain areas recorded as monkeys performed a visual delayed-match-to-sample task.
journal_name
Neural Computjournal_title
Neural computationauthors
Pagan M,Simoncelli EP,Rust NCdoi
10.1162/NECO_a_00890subject
Has Abstractpub_date
2016-11-01 00:00:00pages
2291-2319issue
11eissn
0899-7667issn
1530-888Xjournal_volume
28pub_type
杂志文章abstract::Recent work suggests that synchronization of neuronal activity could serve to define functionally relevant relationships between spatially distributed cortical neurons. At present, it is not known to what extent this hypothesis is compatible with the widely supported notion of coarse coding, which assumes that feature...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.1995.7.3.469
更新日期:1995-05-01 00:00:00
abstract::As neural activity is transmitted through the nervous system, neuronal noise degrades the encoded information and limits performance. It is therefore important to know how information loss can be prevented. We study this question in the context of neural population codes. Using Fisher information, we show how informat...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00227
更新日期:2012-02-01 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::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
pub_type: 杂志文章
doi:10.1162/neco.1991.3.1.110
更新日期:1991-04-01 00:00:00
abstract::We analyze convergence of the expectation maximization (EM) and variational Bayes EM (VBEM) schemes for parameter estimation in noisy linear models. The analysis shows that both schemes are inefficient in the low-noise limit. The linear model with additive noise includes as special cases independent component analysis...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/0899766054322991
更新日期:2005-09-01 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::Field models provide an elegant mathematical framework to analyze large-scale patterns of neural activity. On the microscopic level, these models are usually based on either a firing-rate picture or integrate-and-fire dynamics. This article shows that in spite of the large conceptual differences between the two types ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/08997660260028656
更新日期:2002-07-01 00:00:00
abstract::In this letter, a standard postnonlinear blind source separation algorithm is proposed, based on the MISEP method, which is widely used in linear and nonlinear independent component analysis. To best suit a wide class of postnonlinear mixtures, we adapt the MISEP method to incorporate a priori information of the mixtu...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2007.19.9.2557
更新日期:2007-09-01 00:00:00
abstract::In the past decade the importance of synchronized dynamics in the brain has emerged from both empirical and theoretical perspectives. Fast dynamic synchronous interactions of an oscillatory or nonoscillatory nature may constitute a form of temporal coding that underlies feature binding and perceptual synthesis. The re...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976699300016287
更新日期:1999-08-15 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
pub_type: 杂志文章
doi:10.1162/0899766053491904
更新日期:2005-05-01 00:00:00
abstract::Pairwise correlations among spike trains recorded in vivo have been frequently reported. It has been argued that correlated activity could play an important role in the brain, because it efficiently modulates the response of a postsynaptic neuron. We show here that a neuron's output firing rate critically depends on t...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976603321043702
更新日期:2003-01-01 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
pub_type: 杂志文章,评审
doi:10.1162/089976698300017836
更新日期:1998-02-15 00:00:00
abstract::In this review, we compare methods for temporal sequence learning (TSL) across the disciplines machine-control, classical conditioning, neuronal models for TSL as well as spike-timing-dependent plasticity (STDP). This review introduces the most influential models and focuses on two questions: To what degree are reward...
journal_title:Neural computation
pub_type: 杂志文章,评审
doi:10.1162/0899766053011555
更新日期:2005-02-01 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
pub_type: 杂志文章
doi:10.1162/089976603321192103
更新日期:2003-03-01 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
abstract::Integrate-and-fire neurons are time encoding machines that convert the amplitude of an analog signal into a nonuniform, strictly increasing sequence of spike times. Under certain conditions, the encoded signals can be reconstructed from the nonuniform spike time sequences using a time decoding machine. Time encoding a...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00764
更新日期:2015-09-01 00:00:00
abstract::We present a first-order nonhomogeneous Markov model for the interspike-interval density of a continuously stimulated spiking neuron. The model allows the conditional interspike-interval density and the stationary interspike-interval density to be expressed as products of two separate functions, one of which describes...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2009.06-07-548
更新日期:2009-06-01 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
pub_type: 杂志文章
doi:10.1162/089976602753712972
更新日期:2002-06-01 00:00:00
abstract::Cortical neurons of behaving animals generate irregular spike sequences. Recently, there has been a heated discussion about the origin of this irregularity. Softky and Koch (1993) pointed out the inability of standard single-neuron models to reproduce the irregularity of the observed spike sequences when the model par...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976699300016511
更新日期:1999-05-15 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::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 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
pub_type: 杂志文章
doi:10.1162/neco.2007.19.10.2739
更新日期:2007-10-01 00:00:00
abstract::We formulate an equivalence between machine learning and the formulation of statistical data assimilation as used widely in physical and biological sciences. The correspondence is that layer number in a feedforward artificial network setting is the analog of time in the data assimilation setting. This connection has b...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01094
更新日期:2018-08-01 00:00:00
abstract::The bias/variance decomposition of mean-squared error is well understood and relatively straightforward. In this note, a similar simple decomposition is derived, valid for any kind of error measure that, when using the appropriate probability model, can be derived from a Kullback-Leibler divergence or log-likelihood. ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976698300017232
更新日期:1998-07-28 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
pub_type: 杂志文章
doi:10.1162/NECO_a_00290
更新日期:2012-07-01 00:00:00
abstract::This article presents a new theoretical framework to consider the dynamics of a stochastic spiking neuron model with general membrane response to input spike. We assume that the input spikes obey an inhomogeneous Poisson process. The stochastic process of the membrane potential then becomes a gaussian process. When a ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976601317098529
更新日期:2001-12-01 00:00:00
abstract::We present a system for the automatic interpretation of cluttered scenes containing multiple partly occluded objects in front of unknown, complex backgrounds. The system is based on an extended elastic graph matching algorithm that allows the explicit modeling of partial occlusions. Our approach extends an earlier sys...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2006.18.6.1441
更新日期:2006-06-01 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
pub_type: 杂志文章
doi:10.1162/08997660260293292
更新日期:2002-10-01 00:00:00
abstract::Real classification problems involve structured data that can be essentially grouped into a relatively small number of clusters. It is shown that, under a local clustering condition, a set of points of a given class, embedded in binary space by a set of randomly parameterized surfaces, is linearly separable from other...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976601753196012
更新日期:2001-11-01 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
pub_type: 杂志文章
doi:10.1162/NECO_a_00519
更新日期:2013-12-01 00:00:00