Active Learning for Enumerating Local Minima Based on Gaussian Process Derivatives.

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：: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

authors： Cremers D,Herz AV

更新日期：2002-07-01 00:00:00

abstract：:The emergence of synchrony in the activity of large, heterogeneous networks of spiking neurons is investigated. We define the robustness of synchrony by the critical disorder at which the asynchronous state becomes linearly unstable. We show that at low firing rates, synchrony is more robust in excitatory networks tha...

journal_title：Neural computation

authors： Neltner L,Hansel D,Mato G,Meunier C

更新日期：2000-07-01 00:00:00

abstract：:Natural gradient learning is known to be efficient in escaping plateau, which is a main cause of the slow learning speed of neural networks. The adaptive natural gradient learning method for practical implementation also has been developed, and its advantage in real-world problems has been confirmed. In this letter, w...

journal_title：Neural computation

authors： Park H,Murata N,Amari S

更新日期：2004-02-01 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

authors： Sprekeler H,Wiskott L

更新日期：2011-02-01 00:00:00

abstract：:Energy-efficient information transmission may be relevant to biological sensory signal processing as well as to low-power electronic devices. We explore its consequences in two different regimes. In an "immediate" regime, we argue that the information rate should be maximized subject to a power constraint, and in an "...

journal_title：Neural computation

authors： Balasubramanian V,Kimber D,Berry MJ 2nd

更新日期：2001-04-01 00:00:00

abstract：:GABAergic synapse reversal potential is controlled by the concentration of chloride. This concentration can change significantly during development and as a function of neuronal activity. Thus, GABA inhibition can be hyperpolarizing, shunting, or partially depolarizing. Previous results pinpointed the conditions under...

journal_title：Neural computation

authors： Jeong HY,Gutkin B

更新日期：2007-03-01 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

authors： Tangkaratt V,Xie N,Sugiyama M

更新日期：2015-01-01 00:00:00

abstract：:A fast and accurate computational scheme for simulating nonlinear dynamic systems is presented. The scheme assumes that the system can be represented by a combination of components of only two different types: first-order low-pass filters and static nonlinearities. The parameters of these filters and nonlinearities ma...

journal_title：Neural computation

authors： van Hateren JH

更新日期：2008-07-01 00:00:00

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 ...

journal_title：Neural computation

authors： Shahbaba B,Zhou B,Lan S,Ombao H,Moorman D,Behseta S

更新日期：2014-09-01 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

authors： Verschure PF,König P

更新日期：1999-07-01 00:00:00

abstract：:Different analytical expressions for the membrane potential distribution of membranes subject to synaptic noise have been proposed and can be very helpful in analyzing experimental data. However, all of these expressions are either approximations or limit cases, and it is not clear how they compare and which expressio...

journal_title：Neural computation

authors： Rudolph M,Destexhe A

更新日期：2006-12-01 00:00:00

abstract：:In a recent paper, Poggio and Girosi (1990) proposed a class of neural networks obtained from the theory of regularization. Regularized networks are capable of approximating arbitrarily well any continuous function on a compactum. In this paper we consider in detail the learning problem for the one-dimensional case. W...

journal_title：Neural computation

authors： Corradi V,White H

更新日期：1995-11-01 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

authors： Frady EP,Kapoor A,Horvitz E,Kristan WB Jr

更新日期：2016-08-01 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

authors： Abarbanel HDI,Rozdeba PJ,Shirman S

更新日期：2018-08-01 00:00:00

abstract：:We show how a Hopfield network with modifiable recurrent connections undergoing slow Hebbian learning can extract the underlying geometry of an input space. First, we use a slow and fast analysis to derive an averaged system whose dynamics derives from an energy function and therefore always converges to equilibrium p...

journal_title：Neural computation

authors： Galtier MN,Faugeras OD,Bressloff PC

更新日期：2012-09-01 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：: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

authors： Sprekeler H

更新日期：2011-12-01 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

authors： Scaglione A,Moxon KA,Foffani G

更新日期：2010-06-01 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

authors： Edelman S,Duvdevani-Bar S

更新日期：1997-05-15 00:00:00

abstract：:We develop several kernel methods for classification of longitudinal data and apply them to detect cognitive decline in the elderly. We first develop mixed-effects models, a type of hierarchical empirical Bayes generative models, for the time series. After demonstrating their utility in likelihood ratio classifiers (a...

journal_title：Neural computation

authors： Lu Z,Leen TK,Kaye J

更新日期：2011-09-01 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 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

authors： Eckes C,Triesch J,von der Malsburg C

更新日期：2006-06-01 00:00:00

abstract：:A significant threat to the recent, wide deployment of machine learning-based systems, including deep neural networks (DNNs), is adversarial learning attacks. The main focus here is on evasion attacks against DNN-based classifiers at test time. While much work has focused on devising attacks that make small perturbati...

journal_title：Neural computation

authors： Miller D,Wang Y,Kesidis G

更新日期：2019-08-01 00:00:00

abstract：:Topographic maps such as the self-organizing map (SOM) or neural gas (NG) constitute powerful data mining techniques that allow simultaneously clustering data and inferring their topological structure, such that additional features, for example, browsing, become available. Both methods have been introduced for vectori...

journal_title：Neural computation

authors： Hammer B,Hasenfuss A

更新日期：2010-09-01 00:00:00

abstract：:Controlling for multiple hypothesis tests using standard spike resampling techniques often requires prohibitive amounts of computation. Importance sampling techniques can be used to accelerate the computation. The general theory is presented, along with specific examples for testing differences across conditions using...

journal_title：Neural computation

authors： Harrison MT

更新日期：2013-02-01 00:00:00

abstract：:Recurrent neural architectures having oscillatory dynamics use rhythmic network activity to represent patterns stored in short-term memory. Multiple stored patterns can be retained in memory over the same neural substrate because the network's state persistently switches between them. Here we present a simple oscillat...

journal_title：Neural computation

authors： Winder RK,Reggia JA,Weems SA,Bunting MF

更新日期：2009-03-01 00:00:00

abstract：:Reservoir computing is a biologically inspired class of learning algorithms in which the intrinsic dynamics of a recurrent neural network are mined to produce target time series. Most existing reservoir computing algorithms rely on fully supervised learning rules, which require access to an exact copy of the target re...

journal_title：Neural computation

authors： Pyle R,Rosenbaum R

更新日期：2019-07-01 00:00:00

abstract：:In pattern recognition, data integration is an important issue, and when properly done, it can lead to improved performance. Also, data integration can be used to help model and understand multimodal processing in the brain. Amari proposed α-integration as a principled way of blending multiple positive measures (e.g.,...

journal_title：Neural computation

authors： Choi H,Choi S,Choe Y

更新日期：2013-06-01 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

authors： Doya K

更新日期：2000-01-01 00:00:00