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 makes explicit how recognition performance is affected by the number of objects that must be distinguished, the number of features included in the representation, the complexity of individual objects, and the clutter load, that is, the amount of visual material in the field of view in which multiple objects must be simultaneously recognized, independent of pose, and without explicit segmentation. Using the domain of text to model object recognition in cluttered scenes, we show that with corrections for the nonuniform probability and nonindependence of text features, the analytical model achieves good fits to measured recognition rates in simulations involving a wide range of clutter loads, word size, and feature counts. We then introduce a greedy algorithm for feature learning, derived from the analytical model, which grows a representation by choosing those conjunctive features that are most likely to distinguish objects from the cluttered backgrounds in which they are embedded. We show that the representations produced by this algorithm are compact, decorrelated, and heavily weighted toward features of low conjunctive order. Our results provide a more quantitative basis for understanding when spatially invariant conjunctive features can support unambiguous perception in multiobject scenes, and lead to several insights regarding the properties of visual representations optimized for specific recognition tasks.
journal_name
Neural Computjournal_title
Neural computationauthors
Mel BW,Fiser Jdoi
10.1162/089976600300015574subject
Has Abstractpub_date
2000-04-01 00:00:00pages
731-62issue
4eissn
0899-7667issn
1530-888Xjournal_volume
12pub_type
杂志文章abstract::Synchronized firings in the networks of class 1 excitable neurons with excitatory and inhibitory connections are investigated, and their dependences on the forms of interactions are analyzed. As the forms of interactions, we treat the double exponential coupling and the interactions derived from it: pulse coupling, ex...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/0899766053630387
更新日期:2005-06-01 00:00:00
abstract::The relationship between a neuron's complex inputs and its spiking output defines the neuron's coding strategy. This is frequently and effectively modeled phenomenologically by one or more linear filters that extract the components of the stimulus that are relevant for triggering spikes and a nonlinear function that r...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2009.02-09-956
更新日期:2010-03-01 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::Izhikevich (2003) proposed a new canonical neuron model of spike generation. The model was surprisingly simple yet able to accurately replicate the firing patterns of different types of cortical cell. Here, we derive a solution method that allows efficient simulation of the model. ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2007.19.12.3216
更新日期:2007-12-01 00:00:00
abstract::This article presents a reinforcement learning framework for continuous-time dynamical systems without a priori discretization of time, state, and action. Based on the Hamilton-Jacobi-Bellman (HJB) equation for infinite-horizon, discounted reward problems, we derive algorithms for estimating value functions and improv...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976600300015961
更新日期:2000-01-01 00:00:00
abstract::A variant of spiking neural P systems with positive or negative weights on synapses is introduced, where the rules of a neuron fire when the potential of that neuron equals a given value. The involved values-weights, firing thresholds, potential consumed by each rule-can be real (computable) numbers, rational numbers,...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00022
更新日期:2010-10-01 00:00:00
abstract::Generalized discriminant analysis (GDA) is an extension of the classical linear discriminant analysis (LDA) from linear domain to a nonlinear domain via the kernel trick. However, in the previous algorithm of GDA, the solutions may suffer from the degenerate eigenvalue problem (i.e., several eigenvectors with the same...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976604773717612
更新日期:2004-06-01 00:00:00
abstract::Neurons perform computations, and convey the results of those computations through the statistical structure of their output spike trains. Here we present a practical method, grounded in the information-theoretic analysis of prediction, for inferring a minimal representation of that structure and for characterizing it...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2009.12-07-678
更新日期:2010-01-01 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
pub_type: 杂志文章
doi:10.1162/NECO_a_00072
更新日期:2011-02-01 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
pub_type: 杂志文章
doi:10.1162/NECO_a_00031
更新日期:2010-11-01 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::A key problem in computational neuroscience is to find simple, tractable models that are nevertheless flexible enough to capture the response properties of real neurons. Here we examine the capabilities of recurrent point process models known as Poisson generalized linear models (GLMs). These models are defined by a s...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01021
更新日期:2017-12-01 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 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
pub_type: 杂志文章,评审
doi:10.1162/neco.1997.9.4.701
更新日期:1997-05-15 00:00:00
abstract::We argue that when faced with big data sets, learning and inference algorithms should compute updates using only subsets of data items. We introduce algorithms that use sequential hypothesis tests to adaptively select such a subset of data points. The statistical properties of this subsampling process can be used to c...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00796
更新日期:2016-01-01 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::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::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
pub_type: 杂志文章
doi:10.1162/NECO_a_00493
更新日期:2013-11-01 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::One standard interpretation of networks of cortical neurons is that they form dynamical attractors. Computations such as stimulus estimation are performed by mapping inputs to points on the networks' attractive manifolds. These points represent population codes for the stimulus values. However, this standard interpret...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00051
更新日期:2010-12-01 00:00:00
abstract::The hypothesis of invariant maximization of interaction (IMI) is formulated within the setting of random fields. According to this hypothesis, learning processes maximize the stochastic interaction of the neurons subject to constraints. We consider the extrinsic constraint in terms of a fixed input distribution on the...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976602760805368
更新日期:2002-12-01 00:00:00
abstract::We discuss robustness against mislabeling in multiclass labels for classification problems and propose two algorithms of boosting, the normalized Eta-Boost.M and Eta-Boost.M, based on the Eta-divergence. Those two boosting algorithms are closely related to models of mislabeling in which the label is erroneously exchan...
journal_title:Neural computation
pub_type: 信件
doi:10.1162/neco.2007.11-06-400
更新日期:2008-06-01 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::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::We study a model of the cortical macrocolumn consisting of a collection of inhibitorily coupled minicolumns. The proposed system overcomes several severe deficits of systems based on single neurons as cerebral functional units, notably limited robustness to damage and unrealistically large computation time. Motivated ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976604772744893
更新日期:2004-03-01 00:00:00
abstract::Visual navigation requires the estimation of self-motion as well as the segmentation of objects from the background. We suggest a definition of local velocity gradients to compute types of self-motion, segment objects, and compute local properties of optical flow fields, such as divergence, curl, and shear. Such veloc...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00479
更新日期:2013-09-01 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::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::Large-scale data collection efforts to map the brain are underway at multiple spatial and temporal scales, but all face fundamental problems posed by high-dimensional data and intersubject variability. Even seemingly simple problems, such as identifying a neuron/brain region across animals/subjects, become exponential...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00852
更新日期:2016-08-01 00:00:00
abstract::Particular levels of partial fault tolerance (PFT) in feedforward artificial neural networks of a given size can be obtained by redundancy (replicating a smaller normally trained network), by design (training specifically to increase PFT), and by a combination of the two (replicating a smaller PFT-trained network). Th...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/0899766053723096
更新日期:2005-07-01 00:00:00