Formal modeling of robot behavior with learning.

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

authors： Troyer TW,Miller KD

更新日期：1997-07-01 00:00:00

abstract：:In this letter, we propose a noisy nonlinear version of independent component analysis (ICA). Assuming that the probability density function (p. d. f.) of sources is known, a learning rule is derived based on maximum likelihood estimation (MLE). Our model involves some algorithms of noisy linear ICA (e. g., Bermond & ...

journal_title：Neural computation

authors： Maeda S,Song WJ,Ishii S

更新日期：2005-01-01 00:00:00

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：: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：: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

authors： Lücke J,von der Malsburg C

更新日期：2004-03-01 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

authors： Shaw SB,Dhindsa K,Reilly JP,Becker S

更新日期：2019-11-01 00:00:00

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

authors： König P,Engel AK,Roelfsema PR,Singer W

更新日期：1995-05-01 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

authors： Puggioni P,Jelitai M,Duguid I,van Rossum MCW

更新日期：2017-07-01 00:00:00

abstract：:Mild traumatic brain injury (mTBI) presents a significant health concern with potential persisting deficits that can last decades. Although a growing body of literature improves our understanding of the brain network response and corresponding underlying cellular alterations after injury, the effects of cellular disru...

journal_title：Neural computation

authors： Gabrieli D,Schumm SN,Vigilante NF,Meaney DF

更新日期：2021-01-01 00:00:00

abstract：:The role of correlations between neuronal responses is crucial to understanding the neural code. A framework used to study this role comprises a breakdown of the mutual information between stimuli and responses into terms that aim to account for different coding modalities and the distinction between different notions...

journal_title：Neural computation

authors： Chicharro D

更新日期：2014-06-01 00:00:00

abstract：:The new time-organized map (TOM) is presented for a better understanding of the self-organization and geometric structure of cortical signal representations. The algorithm extends the common self-organizing map (SOM) from the processing of purely spatial signals to the processing of spatiotemporal signals. The main ad...

journal_title：Neural computation

authors： Wiemer JC

更新日期：2003-05-01 00:00:00

abstract：:We extend the neural Turing machine (NTM) model into a dynamic neural Turing machine (D-NTM) by introducing trainable address vectors. This addressing scheme maintains for each memory cell two separate vectors, content and address vectors. This allows the D-NTM to learn a wide variety of location-based addressing stra...

journal_title：Neural computation

authors： Gulcehre C,Chandar S,Cho K,Bengio Y

更新日期：2018-04-01 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

authors： Miller DA,Zucker SW

更新日期：1999-01-01 00:00:00

abstract：:Humans learn categories of complex objects quickly and from a few examples. Random projection has been suggested as a means to learn and categorize efficiently. We investigate how random projection affects categorization by humans and by very simple neural networks on the same stimuli and categorization tasks, and how...

journal_title：Neural computation

authors： Arriaga RI,Rutter D,Cakmak M,Vempala SS

更新日期：2015-10-01 00:00:00

abstract：:Physiological signals such as neural spikes and heartbeats are discrete events in time, driven by continuous underlying systems. A recently introduced data-driven model to analyze such a system is a state-space model with point process observations, parameters of which and the underlying state sequence are simultaneou...

journal_title：Neural computation

authors： Yuan K,Niranjan M

更新日期：2010-08-01 00:00:00

abstract：:The motion of an object (such as a wheel rotating) is seen as consistent independent of its position and size on the retina. Neurons in higher cortical visual areas respond to these global motion stimuli invariantly, but neurons in early cortical areas with small receptive fields cannot represent this motion, not only...

journal_title：Neural computation

authors： Rolls ET,Stringer SM

更新日期：2007-01-01 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

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：:Complexity of one-hidden-layer networks is studied using tools from nonlinear approximation and integration theory. For functions with suitable integral representations in the form of networks with infinitely many hidden units, upper bounds are derived on the speed of decrease of approximation error as the number of n...

journal_title：Neural computation

authors： Kainen PC,Kůrková V

更新日期：2009-10-01 00:00:00

abstract：:Previous studies have combined analytical models of stochastic neural responses with signal detection theory (SDT) to predict psychophysical performance limits; however, these studies have typically been limited to simple models and simple psychophysical tasks. A companion article in this issue ("Evaluating Auditory P...

journal_title：Neural computation

authors： Heinz MG,Colburn HS,Carney LH

更新日期：2001-10-01 00:00:00

abstract：:How do multiple feature maps that coexist in the same region of cerebral cortex align with each other? We hypothesize that such alignment is governed by temporal correlations: features in one map that are temporally correlated with those in another come to occupy the same spatial locations in cortex over time. To exam...

journal_title：Neural computation

authors： Chen Y,Reggia JA

更新日期：1996-05-15 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：:A stochastic model of spike-timing-dependent plasticity (STDP) postulates that single synapses presented with a single spike pair exhibit all-or-none quantal jumps in synaptic strength. The amplitudes of the jumps are independent of spiking timing, but their probabilities do depend on spiking timing. By making the amp...

journal_title：Neural computation

authors： Elliott T

更新日期：2010-01-01 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

authors： Hoshino O

更新日期：2013-12-01 00:00:00

abstract：:Neural networks are often employed as tools in classification tasks. The use of large networks increases the likelihood of the task's being learned, although it may also lead to increased complexity. Pruning is an effective way of reducing the complexity of large networks. We present discriminant components pruning (D...

journal_title：Neural computation

authors： Koene RA,Takane Y

更新日期：1999-04-01 00:00:00

abstract：:Under the goal-driven paradigm, Yamins et al. ( 2014 ; Yamins & DiCarlo, 2016 ) have shown that by optimizing only the final eight-way categorization performance of a four-layer hierarchical network, not only can its top output layer quantitatively predict IT neuron responses but its penultimate layer can also automat...

journal_title：Neural computation

authors： Dong Q,Wang H,Hu Z

更新日期：2018-02-01 00:00:00

abstract：:Perceiving and identifying an object is improved by prior exposure to the object. This perceptual priming phenomenon is accompanied by reduced neural activity. But whether suppression of neuronal activity with priming is responsible for the improvement in perception is unclear. To address this problem, we developed a ...

journal_title：Neural computation

authors： Moldakarimov S,Bazhenov M,Sejnowski TJ

更新日期：2010-05-01 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：: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：: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

authors： Helson P

更新日期：2020-07-01 00:00:00