bluuebunny/crossref_metadata_2025 huggingface dataset:
Multiple Levels of Stimulus Representation in Visual Working Memory
Authors: Shin Eunsam , Fabiani Monica , Gratton Gabriele
Published: May 2006

Object recognition presumably involves activation of multiple levels of representation. Here we use the encoding-related lateralization (ERL) method [Gratton, G. The contralateral organization of visual memory: A theoretical concept and a research tool. Psychophysiology, 35, 638–647, 1998] to describe the sequential activation of several of these levels. The ERL uses divided-field encoding to generate contralaterally biased representations in the brain. The presence and nature of these representations can be demonstrated by examining the event-related potentials (ERPs) elicited by centrally presented test probes for lateralized activity corresponding to the encoding side. We recorded ERPs during a memory-search task. Memory sets were composed of two or four uppercase letters displayed half to the left and half to the right of fixation. Probe stimuli were composed of one letter presented foveally in either upper- or lowercase. Letter case was manipulated to differentiate the time course of physical and symbolic levels of letter representation. Memory set size was manipulated to examine a relational level of letter representation. We found multiple ERLs in response to the probes: (1) An early (peak = 170 msec) case-dependent (but set size independent) ERL, most evident at P7/P8, indexing the availability of a physical level of letter representation; (2) a later (200–400 msec) more diffusedly distributed ERL, independent of both letter case and set size, indexing a symbolic level of letter representation; (3) a long-latency (400–600 msec) ERL occurring at posterior sites, larger for the case match, Set Size 2 condition, indexing competition for neural representation across multiple letters. By assuming that these ERL activities track the progression of letter representation over time, we propose a model of letter processing in the context of visual working memory.
Protein Sculptures: Life's Building Blocks Inspire Art
Authors: Voss-Andreae Julian
Published: May 2006

The author takes a literal look at the foundation of our physical existence by creating sculptures of proteins, the universal parts of the machinery of life. For him, it is less important to copy a molecule accurately in all its details than to find a guiding principle and follow it to see whether it yields artistically interesting results. The main idea underlying these sculptures is the analogy between the technique of mitered cuts and protein folding. The sculptures offer a sensual experience of a world that is usually accessible only through the intellect.
Interacting with an Intelligent Dancing Figure: Artistic Experiments at the Crossroads between Art and Cognitive Science
Authors: Bret Michel , Tramus Marie-Hélène , Berthoz Alain
Published: May 2006

The authors (a neurophysiologist and two computer artists) give an account of a collaboration that took place within the framework of a study—cum— artistic experiment on virtual interactive figures at the boundary of art and cognitive science. This study, called “‘Intelligent’ Interactivity (Connectionism, Evolutionary Science and Artificial Life) in Digital Arts in Relation with the Physiology of the Perception of Action and Movement,” was supported by the Cognitique 2000 Program on Art and Cognition, an initiative of the French Ministry of Research.
Welcome to the Neighbourhood: Belonging to the Universe
Authors: Nieman Adam
Published: May 2006

Space travel could be an experience available to everyone. This paper describes Welcome to the Neighbourhood, a combination of sculpture and multimedia designed to help people inhabit the solar system (without leaving the earth). The project aims to empower astronomers and nonastronomers alike to form an authentic conception of their place in the cosmos. The author discusses the sculptures that inspired the idea for the project, including the largest known kinetic sculpture ever built (60 light-years across), and then outlines Welcome to the Neigh-bourhood in the context of a broader discussion of public engagement with science and the role of space art in transforming people's experience of “being in the universe.”
Windows to the World, Doors to Space: The Psychology of Space Architecture
Authors: Vogler Andreas , Jørgensen Jesper
Published: May 2006

Living in a confined environment with minimal external stimuli available, such as a space habitat, is a strain on normal human life and puts great pressure on groups and individuals. Designers working on a space habitat not only must work on its functional role, but also must integrate functionality with mental representation and symbolic meaning. Space-connection interfaces such as doors and windows act as “sensory organs” of a building. They allow inside-out communication, but also allow the user to control the flow of light and air, which in a direct or indirect way are communication mediums. In this paper the authors advocate a closer connection among architecture, anthropology and psychology in designing space habitats as part of a new concept of environmental design strategy in space architecture.
Liberation or Control: Disobedient Connections in Contemporary Works
Authors: Kunst Bojana
Published: May 2006

The concept of connection has assumed a very ambivalent status today. Being connected exposes the liberating potential of connected public participation, which has changed our understanding of political and intimate life. At the same time there is a strong fear at work that this very potential could result in a more rigid form of contemporary life. Connection, as understood in this article, is something procedural that can at the same time be disobedient to its own procedure. This disobedience can be concretely observed in certain contemporary artistic works, for example in the project ωPack from Intima Virtual Base.
Psychological Implications of the History of Realistic Depiction: Ancient Greece, Renaissance Italy and CGI
Authors: Kozbelt Aaron
Published: May 2006

Art historian Ernst Gombrich argued that learning to create convincing realistic depictions is a difficult, incremental process requiring the invention of numerous specific techniques to solve its many problems. Gombrich's argument is elaborated here in a historical review of the evolution of realistic depiction in ancient Greek vase painting, Italian Renaissance painting and contemporary computer-generated imagery (CGI) in video games. The order in which many problems of realism were solved in the three trajectories is strikingly similar, suggesting a common psychological explanation.
From Video Replay to the Relational Circuit to Threeing
Authors: Ryan Paul
Published: May 2006

This article traces the invention of the relational circuit, which makes possible an art of relationships called Threeing. This process of invention grew out of extensive video replay. Contrapposto made it possible to depict motion in stone. The relational circuit likewise makes possible a formal art of relationships for three people. This art form can be viewed in the light of relational aesthetics, a theory that judges artwork based on how it prompts inter-human activity and engagement with the world.
The Helium Stockpile: A Collaboration in Mathematical Folding Sculpture
Authors: Demaine Eric D. , Demaine Martin L. , Palmer A. Laurie
Published: May 2006

The Helium Stockpile is a manipulable folding structure of hundreds of wooden blocks, representing the transformation between surface and solid through a foldable one-dimensional chain. The sculpture grew out of an unexpected collaboration between a sculptor and two mathematicians, giving the structure a mathematical basis through which it is guaranteed to be foldable into essentially any three-dimensional shape.
Rembrandt's Portraits: Approach or Avoid?
Authors: Schirillo James A. , Fox Melissa A.
Published: May 2006

In 74% of Rembrandt's female portraits, the subject's left cheek faces the viewer. However, this occurs in only 26% of his male portraits. This asymmetry is consistent with viewers' assessment of Rembrandt's left-cheeked male portraits as preferably avoided, which may indicate that aggressive dominance is governed by the contralateral right hemisphere of the brain, while the rating of left-cheeked female faces as preferably approached may indicate sexual attractiveness. Rembrandt's exposed-cheek gender difference suggests that both sexual selection and dominance are governed by the more emotionally oriented right cerebral hemisphere.
The Syntax of Appositive Relativization: On Specifying Coordination, False Free Relatives, and Promotion
Authors: Vries Mark de
Published: May 2006

Appositive relative clauses differ in some essential respects from restrictive relative clauses. I argue that appositive relatives and appositions can be put together as a third class of coordination denoting specification. Thus, an appositive relative is a specifying conjunct to the visible antecedent. It is a semifree relative with a pronominal head that is normally empty. Therefore, its internal syntax is equivalent to that of restrictive relatives; hence, there is one syntax for both types of relative clauses. In essence, it is the context of specifying coordination that accounts for the different behavior of appositive relatives. In the light of this analysis, the properties of appositive relatives (as opposed to restrictive relatives) are systematically reviewed.
Voice Networks: The Human Voice as a Creative Medium for Musical Collaboration
Authors: Weinberg Gil
Published: May 2006

The author describes a musical installation that allows players to record, transform and share their voices in a group. A central computer system facilitates the interaction as participants interdependently collaborate in developing their “voice motifs” into a coherent musical composition. Observations of subjects interacting with two different applications that were developed for the installation lead to a discussion regarding the use of abstract sounds as opposed to spoken words, the effect of group interdependency on individual contribution by players and the tension between maintaining autonomy and individuality versus sharing and collaborative group playing.
Sound Body: The Ghost of a Program
Authors: Toop David
Published: May 2006

The author considers the importance of the voice as a transformative instrument in 20th-century art, particularly in relation to the tape recorder and digital audio technology. He examines his collaborative work with sound poet Bob Cobbing in the 1970s and compares this with a recent gallery installation created with artist John Latham. Research from the 1970s into acoustic voice masking and resonance is contrasted with the use of analog tape process-ing and the sonic potential of computer audio software programs both in studio work and in improvised performance. Finally, the author discusses the implications of these con-frontations between body and machine.
Technology Is Culture: Two Paradigms
Authors: Zimmermann Basile
Published: May 2006

The author discusses the relationship of technology to culture in the People's Republic of China. Basing his discussion on his experiences in Beijing between 2001 and 2004, the author suggests that the two paradigms of accumulation of decisions and struggle against difference can be used to describe technology in its relation to culture, including—but not limited to—popular electronic music in Beijing.
Artificial Phonology: Disembodied Humanoid Voice for Composing Music with Surreal Languages
Authors: Miranda Eduardo Reck
Published: May 2006

This paper examines some of the core techniques used to create an artificial phonologi-cal system for Sacra Conver-sazione, a short opera in five acts featuring human singers, artificially synthesized voices and complementary electro-acoustic sounds. It introduces some of the most significant techniques for computer simula-tion and manipulation of voice used to produce materials for the piece, namely physical modeling, additive re-synthesis, PROSE and PSOLA. The author concludes with a discussion of lessons learned throughout the process of composing with such techniques.
A Simple Neural Network Exhibiting Selective Activation of Neuronal Ensembles: From Winner-Take-All to Winners-Share-All
Authors: Fukai Tomoki , Tanaka Shigeru
Published: May 2006

A neuroecological equation of the Lotka-Volterra type for mean firing rate is derived from the conventional membrane dynamics of a neural network with lateral inhibition and self-inhibition. Neural selection mechanisms employed by the competitive neural network receiving external input sare studied with analytic and numerical calculations. A remarkable finding is that the strength of lateral inhibition relative to that of self-inhibition is crucial for determining the steady states of the network among three qualitatively different types of behavior. Equal strength of both types of inhibitory connections leads the network to the well-known winner-take-all behavior. If, however, the lateral inhibition is weaker than the self-inhibition, a certain number of neurons are activated in the steady states or the number of winners is in general more than one (the winners-share-all behavior). On the other hand, if the self-inhibition is weaker than the lateral one, only one neuron is activated, but the winner is not necessarily the neuron receiving the largest input. It is suggested that our simple network model provides a mathematical basis for understanding neural selection mechanisms.
Probabilistic Independence Networks for Hidden Markov Probability Models
Authors: Smyth Padhraic , Heckerman David , Jordan Michael I.
Published: May 2006

Graphical techniques for modeling the dependencies of random variables have been explored in a variety of different areas, including statistics, statistical physics, artificial intelligence, speech recognition, image processing, and genetics. Formalisms for manipulating these models have been developed relatively independently in these research communities. In this paper we explore hidden Markov models (HMMs) and related structures within the general framework of probabilistic independence networks (PINs). The paper presents a self-contained review of the basic principles of PINs. It is shown that the well-known forward-backward (F-B) and Viterbi algorithms for HMMs are special cases of more general inference algorithms for arbitrary PINs. Furthermore, the existence of inference and estimation algorithms for more general graphical models provides a set of analysis tools for HMM practitioners who wish to explore a richer class of HMM structures. Examples of relatively complex models to handle sensor fusion and coarticulation in speech recognition are introduced and treated within the graphical model framework to illustrate the advantages of the general approach.
Computing with the Leaky Integrate-and-Fire Neuron: Logarithmic Computation and Multiplication
Authors: Tal Doron , Schwartz Eric L.
Published: May 2006

The leaky integrate-and-fire (LIF) model of neuronal spiking (Stein 1967) provides an analytically tractable formalism of neuronal firing rate in terms of a neuron's membrane time constant, threshold, and refractory period. LIF neurons have mainly been used to model physiologically realistic spike trains, but little application of the LIF model appears to have been made in explicitly computational contexts. In this article, we show that the transfer function of a LIF neuron provides, over a wide parameter range, a compressive nonlinearity sufficiently close to that of the logarithm so that LIF neurons can be used to multiply neural signals by mere addition of their outputs yielding the logarithm of the product. A simulation of the LIF multiplier shows that under a wide choice of parameters, a LIF neuron can log-multiply its inputs to within a 5% relative error.
Shattering All Sets of ‘k’ Points in “General Position” Requires (k — 1)/2 Parameters
Authors: Sontag Eduardo D.
Published: May 2006

For classes of concepts defined by certain classes of analytic functions depending on n parameters, there are nonempty open sets of samples of length 2n + 2 that cannot be shattered. A slighly weaker result is also proved for piecewise-analytic functions. The special case of neural networks is discussed.
Bias/Variance Analyses of Mixtures-of-Experts Architectures
Authors: Jacobs Robert A.
Published: May 2006

This article investigates the bias and variance of mixtures-of-experts (ME) architectures. The variance of an ME architecture can be expressed as the sum of two terms: the first term is related to the variances of the expert networks that comprise the architecture and the second term is related to the expert networks' covariances. One goal of this article is to study and quantify a number of properties of ME architectures via the metrics of bias and variance. A second goal is to clarify the relationships between this class of systems and other systems that have recently been proposed. It is shown that in contrast to systems that produce unbiased experts whose estimation errors are uncorrelated, ME architectures produce biased experts whose estimates are negatively correlated.
Adaptive Encoding Strongly Improves Function Approximation with CMAC
Authors: Eldracher Martin , Staller Alexander , Pompl René
Published: May 2006

The Cerebellar Model Arithmetic Computer (CMAC) (Albus 1981) is well known as a good function approximator with local generalization abilities. Depending on the smoothness of the function to be approximated, the resolution as the smallest distinguishable part of the input domain plays a crucial role. If the binary quantizing functions in CMAC are dropped in favor of more general, continuous-valued functions, much better results in function approximation for smooth functions are obtained in shorter training time with less memory consumption. For functions with discontinuities, we obtain a further improvement by adapting the continuous encoding proposed in Eldracher and Geiger (1994) for difficult-to-approximate areas. Based on the already far better function approximation capability on continuous functions with a fixed topologically distributed encoding scheme in CMAC (Eldracher et al. 1994), we present the better results in learning a two-valued function with discontinuity using this adaptive topologically distributed encoding scheme in CMAC.
Average-Case Learning Curves for Radial Basis Function Networks
Authors: Holden Sean B. , Niranjan Mahesan
Published: May 2006

The application of statistical physics to the study of the learning curves of feedforward connectionist networks has to date been concerned mostly with perceptron-like networks. Recent work has extended the theory to networks such as committee machines and parity machines, and an important direction for current and future research is the extension of this body of theory to further connectionist networks. In this article, we use this formalism to investigate the learning curves of gaussian radial basis function networks (RBFNs) having fixed basis functions. (These networks have also been called generalized linear regression models.) We address the problem of learning linear and nonlinear, realizable and unrealizable, target rules from noise-free training examples using a stochastic training algorithm. Expressions for the generalization error, defined as the expected error for a network with a given set of parameters, are derived for general gaussian RBFNs, for which all parameters, including centers and spread parameters, are adaptable. Specializing to the case of RBFNs with fixed basis functions (basis functions having parameters chosen without reference to the training examples), we then study the learning curves for these networks in the limit of high temperature.
A Neuro-Mimetic Dynamic Scheduling Algorithm for Control: Analysis and Applications
Authors: Kwatra Harpreet S. , Doyle Francis J. , Rybak Ilya A. , Schwaber James S.
Published: May 2006

A simple neuronal network model of the baroreceptor reflex is analyzed. From a control perspective, the analysis suggests a dynamic scheduled control mechanism by which the baroreflex may perform regulation of the blood pressure. The main objectives of this work are to investigate the static and dynamic response characteristics of the single neurons and the network, to analyze the neuromimetic dynamic scheduled control function of the model, and to apply the algorithm to nonlinear process control problems. The dynamic scheduling activity of the network is exploited in two control architectures. Control structure I is drawn directly from the present model of the baroreceptor reflex. An application of this structure for level control in a conical tank is described. Control structure II employs an explicit set point to determine the feedback error. The performance of this control structure is illustrated on a nonlinear continuous stirred tank reactor with van de Vusse kinetics. The two case studies validate the dynamic scheduled control approach for nonlinear process control applications.
SEEMORE: Combining Color, Shape, and Texture Histogramming in a Neurally Inspired Approach to Visual Object Recognition
Authors: Mel Bartlett W.
Published: May 2006

Severe architectural and timing constraints within the primate visual system support the conjecture that the early phase of object recognition in the brain is based on a feedforward feature-extraction hierarchy. To assess the plausibility of this conjecture in an engineering context, a difficult three-dimensional object recognition domain was developed to challenge a pure feedforward, receptive-field based recognition model called SEEMORE. SEEMORE is based on 102 viewpoint-invariant nonlinear filters that as a group are sensitive to contour, texture, and color cues. The visual domain consists of 100 real objects of many different types, including rigid (shovel), nonrigid (telephone cord), and statistical (maple leaf cluster) objects and photographs of complex scenes. Objects were in dividually presented in color video images under normal room lighting conditions. Based on 12 to 36 training views, SEEMORE was required to recognize unnormalized test views of objects that could vary in position, orientation in the image plane and in depth, and scale (factor of 2); for non rigid objects, recognition was also tested under gross shape deformations. Correct classification performance on a test set consisting of 600 novel object views was 97 percent (chance was 1 percent) and was comparable for the subset of 15 nonrigid objects. Performance was also measured under a variety of image degradation conditions, including partial occlusion, limited clutter, color shift, and additive noise. Generalization behavior and classification errors illustrate the emergence of several striking natural shape categories that are not explicitly encoded in the dimensions of the feature space. It is concluded that in the light of the vast hardware resources available in the ventral stream of the primate visual system relative to those exercised here, the appealingly simple feature-space conjecture remains worthy of serious consideration as a neurobiological model.
Optimal, Unsupervised Learning in Invariant Object Recognition
Authors: Wallis Guy , Baddeley Roland
Published: May 2006

A means for establishing transformation-invariant representations of objects is proposed and analyzed, in which different views are associated on the basis of the temporal order of the presentation of these views, as well as their spatial similarity. Assuming knowledge of the distribution of presentation times, an optimal linear learning rule is derived. Simulations of a competitive network trained on a character recognition task are then used to highlight the success of this learning rule in relation to simple Hebbian learning and to show that the theory can give accurate quantitative predictions for the optimal parameters for such networks.
Paradigmatic Working Memory (Attractor) Cell in IT Cortex
Authors: Amit Daniel J. , Fusi Stefano , Yakovlev Volodya
Published: May 2006

We discuss paradigmatic properties of the activity of single cells comprising an attractor—a developed stable delay activity distribution. To demonstrate these properties and a methodology for measuring their values, we present a detailed account of the spike activity recorded from a single cell in the inferotemporal cortex of a monkey performing a delayed match-to-sample (DMS) task of visual images. In particular, we discuss and exemplify (1) the relation between spontaneous activity and activity immediately preceding the first stimulus in each trial during a series of DMS trials, (2) the effect on the visual response (i.e., activity during stimulation) of stimulus degradation (moving in the space of IT afferents), (3) the behavior of the delay activity (i.e., activity following visual stimulation) under stimulus degradation (attractor dynamics and the basin of attraction), and (4) the propagation of information between trials—the vehicle for the formation of (contextual) correlations by learning a fixed stimulus sequence (Miyashita, 1988). In the process of the discussion and demonstration, we expose effective tools for the identification and characterization of attractor dynamics. 1

1 A color version of this article is found on the Web at: http://www.fiz.huji.ac.il/staff/acc/faculty/damita
The Joint Development of Orientation and Ocular Dominance: Role of Constraints
Authors: Piepenbrock Christian , Ritter Helge , Obermayer Klaus
Published: May 2006

Correlation-based learning (CBL) has been suggested as the mechanism that underlies the development of simple-cell receptive fields in the primary visual cortex of cats, including orientation preference (OR) and ocular dominance (OD) (Linsker, 1986; Miller, Keller, & Stryker, 1989). CBL has been applied successfully to the development of OR and OD individually (Miller, Keller, & Stryker, 1989; Miller, 1994; Miyashita & Tanaka, 1991; Erwin, Obermayer, & Schulten, 1995), but the conditions for their joint development have not been studied (but see Erwin & Miller, 1995, for independent work on the same question) in contrast to competitive Hebbian models (Obermayer, Blasdel, & Schulten, 1992). In this article, we provide insight into why this has been the case: OR and OD decouple in symmetric CBL models, and a joint development of OR and OD is possible only in a parameter regime that depends on nonlinear mechanisms.
Self-Organizing Map with Dynamical Node Splitting: Application to Handwritten Digit Recognition
Authors: Cho Sung-Bae
Published: May 2006

This article presents a simple yet elegant pattern recognizer based on a dynamic node-splitting scheme for the self-organizing map that can adapt its structure as well as its weights. The scheme makes use of a structure adaptation capability to place the nodes of prototype vectors into the pattern space accurately so as to make the decision boundaries as close to the class boundaries as possible. In order to show the performance of the proposed scheme, experiments with the unconstrained handwritten digit database of Concordia University in Canada were conducted. The proposed method for an incremental formation of feature maps is 96.05 percent of the recognition rate. In view of the elegant simplicity of the approach, the reported performance is remarkable and can stand up to one of the best results reported in the literature with the same database.
Controlling Hidden Layer Capacity Through Lateral Connections
Authors: Agyepong Kwabena , Kothari Ravi
Published: May 2006

We investigate the effects of including selected lateral interconnections in a feedforward neural network. In a network with one hidden layer consisting of m hidden neurons labeled 1,2… m, hidden neuron j is connected fully to the inputs, the outputs, and hidden neuron j + 1. As a consequence of the lateral connections, each hidden neuron receives two error signals: one from the output layer and one through the lateral interconnection. We show that the use of these lateral interconnections among the hidden-layer neurons facilitates controlled assignment of role and specialization of the hidden-layer neurons. In particular, we show that as training progresses, hidden neurons become progressively specialized—starting from the fringes (i.e., lower and higher numbered hidden neurons, e.g., 1, 2, m — 1 m) and leaving the neurons in the center of the hidden layer (i.e., hidden-layer neurons numbered close to m/2) unspecialized or functionally identical. Consequently, the network behaves like network growing algorithms without the explicit need to add hidden units, and like soft weight sharing due to functionally identical neurons in the center of the hidden layer. Experimental results from one classification and one function approximation problems are presented to illustrate selective specialization of the hidden-layer neurons. In addition, the improved generalization that results from a decrease in the effective number of free parameters is illustrated through a simple function approximation example and with a real-world data set. Besides the reduction in the number of free parameters, the localization of weight sharing may also allow for a method that allows procedural determination for the number of hidden-layer neurons required for a given learning task.
Mean-Field Theory for Batched TD(λ)
Authors: Pineda Fernando J.
Published: May 2006

A representation-independent mean-field dynamics is presented for batched TD(λ). The task is learning to predict the outcome of an indirectly observed absorbing Markov process. In the case of linear representations, the discrete-time deterministic iteration is an affine map whose fixed point can be expressed in closed form without the assumption of linearly independent observation vectors. Batched linear TD(λ) is proved to converge with probability 1 for all λ. Theory and simulation agree on a random walk example.
Adaptive Online Learning Algorithms for Blind Separation: Maximum Entropy and Minimum Mutual Information
Authors: Yang Howard Hua , Amari Shun-ichi
Published: May 2006

There are two major approaches for blind separation: maximum entropy (ME) and minimum mutual information (MMI). Both can be implemented by the stochastic gradient descent method for obtaining the demixing matrix. The MI is the contrast function for blind separation; the entropy is not. To justify the ME, the relation between ME and MMI is first elucidated by calculating the first derivative of the entropy and proving that the mean subtraction is necessary in applying the ME and at the solution points determined by the MI, the ME will not update the demixing matrix in the directions of increasing the cross-talking.

Second, the natural gradient instead of the ordinary gradient is introduced to obtain efficient algorithms, because the parameter space is a Riemannian space consisting of matrices. The mutual information is calculated by applying the Gram-Charlier expansion to approximate probability density functions of the outputs.

Finally, we propose an efficient learning algorithm that incorporates with an adaptive method of estimating the unknown cumulants. It is shown by computer simulation that the convergence of the stochastic descent algorithms is improved by using the natural gradient and the adaptively estimated cumulants.
Adaptive Mixtures of Probabilistic Transducers
Authors: Singer Yoram
Published: May 2006

We describe and analyze a mixture model for supervised learning of probabilistic transducers. We devise an online learning algorithm that efficiently infers the structure and estimates the parameters of each probabilistic transducer in the mixture. Theoretical analysis and comparative simulations indicate that the learning algorithm tracks the best transducer from an arbitrarily large (possibly infinite) pool of models. We also present an application of the model for inducing a noun phrase recognizer.
Data Clustering Using a Model Granular Magnet
Authors: Blatt Marcelo , Wiseman Shai , Domany Eytan
Published: May 2006

We present a new approach to clustering, based on the physical properties of an inhomogeneous ferromagnet. No assumption is made regarding the underlying distribution of the data. We assign a Potts spin to each data point and introduce an interaction between neighboring points, whose strength is a decreasing function of the distance between the neighbors. This magnetic system exhibits three phases. At very low temperatures, it is completely ordered; all spins are aligned. At very high temperatures, the system does not exhibit any ordering, and in an intermediate regime, clusters of relatively strongly coupled spins become ordered, whereas different clusters remain uncorrelated. This intermediate phase is identified by a jump in the order parameters. The spin-spin correlation function is used to partition the spins and the corresponding data points into clusters. We demonstrate on three synthetic and three real data sets how the method works. Detailed comparison to the performance of other techniques clearly indicates the relative success of our method.
A Neural Model of the Scintillating Grid Illusion: Disinhibition and Self-Inhibition in Early Vision
Authors: Yu Yingwei , Choe Yoonsuck
Published: May 2006

A stationary display of white discs positioned on intersecting gray bars on a dark background gives rise to a striking scintillating effect—the scintillating grid illusion. The spatial and temporal properties of the illusion are well known, but a neuronal-level explanation of the mechanism has not been fully investigated. Motivated by the neurophysiology of the Limulus retina, we propose disinhibition and self-inhibition as possible neural mechanisms that may give rise to the illusion. In this letter, a spatiotemporal model of the early visual pathway is derived that explicitly accounts for these two mechanisms. The model successfully predicted the change of strength in the illusion under various stimulus conditions, indicating that low-level mechanisms may well explain the scintillating effect in the illusion.
A Theoretical Analysis of the Influence of Fixational Instability on the Development of Thalamocortical Connectivity
Authors: Casile Antonino , Rucci Michele
Published: May 2006

Under natural viewing conditions, the physiological instability of visual fixation keeps the projection of the stimulus on the retina in constant motion. After eye opening, chronic exposure to a constantly moving retinal image might influence the experience-dependent refinement of cell response characteristics. The results of previous modeling studies have suggested a contribution of fixational instability to the Hebbian maturation of the receptive fields of V1 simple cells (Rucci, Edelman, & Wray, 2000; Rucci & Casile, 2004). This letter examines the origins of such a contribution. Using quasilinear models of lateral geniculate nucleus units and V1 simple cells, we derive analytical expressions for the second- order statistics of thalamocortical activity before and after eye opening. We show that in the presence of natural stimulation, fixational instability introduces a spatially uncorrelated signal in the retinal input, which strongly influences the structure of correlated activity in the model. This input signal produces a regime of thalamocortical activity similar to that present before eye opening and compatible with the Hebbian maturation of cortical receptive fields.
Learning Beyond Finite Memory in Recurrent Networks of Spiking Neurons
Authors: Tiňo Peter , Mills Ashely J. S.
Published: May 2006

We investigate possibilities of inducing temporal structures without fading memory in recurrent networks of spiking neurons strictly operating in the pulse-coding regime. We extend the existing gradient-based algorithm for training feedforward spiking neuron networks, SpikeProp (Bohte, Kok, & La Poutré, 2002), to recurrent network topologies, so that temporal dependencies in the input stream are taken into account. It is shown that temporal structures with unbounded input memory specified by simple Moore machines (MM) can be induced by recurrent spiking neuron networks (RSNN). The networks are able to discover pulse-coded representations of abstract information processing states coding potentially unbounded histories of processed inputs. We show that it is often possible to extract from trained RSNN the target MM by grouping together similar spike trains appearing in the recurrent layer. Even when the target MM was not perfectly induced in a RSNN, the extraction procedure was able to reveal weaknesses of the induced mechanism and the extent to which the target machine had been learned.
Response Variability in Balanced Cortical Networks
Authors: Lerchner Alexander , Ursta Cristina , Hertz John , Ahmadi Mandana , Ruffiot Pauline , Enemark Søren
Published: May 2006

We study the spike statistics of neurons in a network with dynamically balanced excitation and inhibition. Our model, intended to represent a generic cortical column, comprises randomly connected excitatory and inhibitory leaky integrate-and-fire neurons, driven by excitatory input from an external population. The high connectivity permits a mean field description in which synaptic currents can be treated as gaussian noise, the mean and autocorrelation function of which are calculated self-consistently from the firing statistics of single model neurons. Within this description, a wide range of Fano factors is possible. We find that the irregularity of spike trains is controlled mainly by the strength of the synapses relative to the difference between the firing threshold and the postfiring reset level of the membrane potential. For moderately strong synapses, we find spike statistics very similar to those observed in primary visual cortex.
Multiperiodicity and Exponential Attractivity Evoked by Periodic External Inputs in Delayed Cellular Neural Networks
Authors: Zeng Zhigang , Wang Jun
Published: May 2006

We show that an n-neuron cellular neural network with time-varying delay can have 2n periodic orbits located in saturation regions and these periodic orbits are locally exponentially attractive. In addition, we give some conditions for ascertaining periodic orbits to be locally or globally exponentially attractive and allow them to locate in any designated region. As a special case of exponential periodicity, exponential stability of delayed cellular neural networks is also characterized. These conditions improve and extend the existing results in the literature. To illustrate and compare the results, simulation results are discussed in three numerical examples.
Class-Incremental Generalized Discriminant Analysis
Authors: Zheng Wenming
Published: May 2006

Generalized discriminant analysis (GDA) is the nonlinear extension of the classical linear discriminant analysis (LDA) via the kernel trick. Mathematically, GDA aims to solve a generalized eigenequation problem, which is always implemented by the use of singular value decomposition (SVD) in the previously proposed GDA algorithms. A major drawback of SVD, however, is the difficulty of designing an incremental solution for the eigenvalue problem. Moreover, there are still numerical problems of computing the eigenvalue problem of large matrices. In this article, we propose another algorithm for solving GDA as for the case of small sample size problem, which applies QR decomposition rather than SVD. A major contribution of the proposed algorithm is that it can incrementally update the discriminant vectors when new classes are inserted into the training set. The other major contribution of this article is the presentation of the modified kernel Gram-Schmidt (MKGS) orthogonalization algorithm for implementing the QR decomposition in the feature space, which is more numerically stable than the kernel Gram-Schmidt (KGS) algorithm. We conduct experiments on both simulated and real data to demonstrate the better performance of the proposed methods.
Singularities Affect Dynamics of Learning in Neuromanifolds
Authors: Amari Shun-ichi , Park Hyeyoung , Ozeki Tomoko
Published: May 2006

The parameter spaces of hierarchical systems such as multilayer perceptrons include singularities due to the symmetry and degeneration of hidden units. A parameter space forms a geometrical manifold, called the neuromanifold in the case of neural networks. Such a model is identified with a statistical model, and a Riemannian metric is given by the Fisher information matrix. However, the matrix degenerates at singularities. Such a singular structure is ubiquitous not only in multilayer perceptrons but also in the gaussian mixture probability densities, ARMA time-series model, and many other cases. The standard statistical paradigm of the Cramér-Rao theorem does not hold, and the singularity gives rise to strange behaviors in parameter estimation, hypothesis testing, Bayesian inference, model selection, and in particular, the dynamics of learning from examples. Prevailing theories so far have not paid much attention to the problem caused by singularity, relying only on ordinary statistical theories developed for regular (nonsingular) models. Only recently have researchers remarked on the effects of singularity, and theories are now being developed.

This article gives an overview of the phenomena caused by the singularities of statistical manifolds related to multilayer perceptrons and gaussian mixtures. We demonstrate our recent results on these problems. Simple toy models are also used to show explicit solutions. We explain that the maximum likelihood estimator is no longer subject to the gaussian distribution even asymptotically, because the Fisher information matrix degenerates, that the model selection criteria such as AIC, BIC, and MDL fail to hold in these models, that a smooth Bayesian prior becomes singular in such models, and that the trajectories of dynamics of learning are strongly affected by the singularity, causing plateaus or slow manifolds in the parameter space. The natural gradient method is shown to perform well because it takes the singular geometrical structure into account. The generalization error and the training error are studied in some examples.
How Noise Affects the Synchronization Properties of Recurrent Networks of Inhibitory Neurons
Authors: Brunel Nicolas , Hansel David
Published: May 2006

GABAergic interneurons play a major role in the emergence of various types of synchronous oscillatory patterns of activity in the central nervous system. Motivated by these experimental facts, modeling studies have investigated mechanisms for the emergence of coherent activity in networks of inhibitory neurons. However, most of these studies have focused either when the noise in the network is absent or weak or in the opposite situation when it is strong. Hence, a full picture of how noise affects the dynamics of such systems is still lacking. The aim of this letter is to provide a more comprehensive understanding of the mechanisms by which the asynchronous states in large, fully connected networks of inhibitory neurons are destabilized as a function of the noise level. Three types of single neuron models are considered: the leaky integrate-and-fire (LIF) model, the exponential integrate-and-fire (EIF), model and conductance-based models involving sodium and potassium Hodgkin-Huxley (HH) currents. We show that in all models, the instabilities of the asynchronous state can be classified in two classes. The first one consists of clustering instabilities, which exist in a restricted range of noise. These instabilities lead to synchronous patterns in which the population of neurons is broken into clusters of synchronously firing neurons. The irregularity of the firing patterns of the neurons is weak. The second class of instabilities, termed oscillatory firing rate instabilities, exists at any value of noise. They lead to cluster state at low noise. As the noise is increased, the instability occurs at larger coupling, and the pattern of firing that emerges becomes more irregular. In the regime of high noise and strong coupling, these instabilities lead to stochastic oscillations in which neurons fire in an approximately Poisson way with a common instantaneous probability of firing that oscillates in time.
Analysis of Synchronization Between Two Modules of Pulse Neural Networks with Excitatory and Inhibitory Connections
Authors: Kanamaru Takashi
Published: May 2006

To study the synchronized oscillations among distant neurons in the visual cortex, we analyzed the synchronization between two modules of pulse neural networks using the phase response function. It was found that the intermodule connections from excitatory to excitatory ensembles tend to stabilize the antiphase synchronization and that the intermodule connections from excitatory to inhibitory ensembles tend to stabilize the in-phase synchronization. It was also found that the intermodule synchronization was more noticeable when the inner-module synchronization was weak.
A Sensorimotor Map: Modulating Lateral Interactions for Anticipation and Planning
Authors: Toussaint Marc
Published: May 2006

Experimental studies of reasoning and planned behavior have provided evidence that nervous systems use internal models to perform predictive motor control, imagery, inference, and planning. Classical (model-free) reinforcement learning approaches omit such a model; standard sensorimotor models account for forward and backward functions of sensorimotor dependencies but do not provide a proper neural representation on which to realize planning. We propose a sensorimotor map to represent such an internal model. The map learns a state representation similar to self-organizing maps but is inherently coupled to sensor and motor signals. Motor activations modulate the lateral connection strengths and thereby induce anticipatory shifts of the activity peak on the sensorimotor map. This mechanism encodes a model of the change of stimuli depending on the current motor activities. The activation dynamics on the map are derived from neural field models. An additional dynamic process on the sensorimotor map (derived from dynamic programming) realizes planning and emits corresponding goal-directed motor sequences, for instance, to navigate through a maze.
Correlation and Independence in the Neural Code
Authors: Amari Shun-ichi , Nakahara Hiroyuki
Published: May 2006

The decoding scheme of a stimulus can be different from the stochastic encoding scheme in the neural population coding. The stochastic fluctuations are not independent in general, but an independent version could be used for the ease of decoding. How much information is lost by using this unfaithful model for decoding? There are discussions concerning loss of information (Nirenberg & Latham, 2003; Schneidman, Bialek, & Berry, 2003). We elucidate the Nirenberg-Latham loss from the point of view of information geometry.